Steam Storage for Flexible Biomass Power Generation
Matthias Quirin Johannes Stark
Faculty of Computing, Engineering and Media
Submitted: January 2021
A thesis submitted in partial fulfilment of the requirements of De Montfort University for degree of Doctor of Philosophy (PhD)
Institute of Energy and Sustainable Development, De Montfort University Leicester Institute of new Energy Systems, Technische Hochschule Ingolstadt
Steam storage for flexible biomass CHP plants II
Declaration
I declare that the content of this submission is my own work. The contents of the work have not been submitted for any other academic or professional award. I acknowledge that this thesis is submitted according to the conditions laid down in the regulations. Furthermore, I declare that the work was carried out as part of the course for which I was registered at De Montfort University, United Kingdom from October 2015 until January 2021. I draw attention to any relevant considerations of rights of third parties.
Steam storage for flexible biomass CHP plants III
Steam storage for flexible biomass CHP plants IV
Abstract
Due to the widespread installation of renewable energy plants, with the aim of a decarbonised electricity supply, the proportion of this generation has increased significantly in recent years. However, these plants increase the variability of electricity generation. This variable power generation leads to challenges in the operation of the power grid, e.g. to local grid bottlenecks and grid overloads. To counteract this variable, and to a certain extent, unpredictable power generation, storage devices or flexible power plants are necessary. Several biogas plants have already been modified in such a way that demand-driven and flexible power plant operation is possible.
In response to the need for flexibility, the presented research aims at flexible power generation for solid biomass-fuelled CHP plants. By integrating a steam storage device into the biomass CHP plant, the steam turbines should be enabled to operate with a flexible, demand-dependent steam mass flow in order to adapt their power output to the grid demand. The main research questions are, which storage system is most suitable, what the key parameters of the flexible plant are and what impact it has on the grid and markets. The storage system as well as utilization in biomass CHP plants are novel concepts. Especially the operation parameter for the specific boundaries of these plant technologies have not been investigated before.
A combination of two or more technologies to separately store the latent and sensible energy of superheated steam was identified as necessary. A utility analysis, supported by a Delphi study with experts, was carried out. In this case, a steam accumulator (SA) combined with a solid thermal store (STS) has proven to be the most suitable storage device for the given requirements. A MATLAB/SIMULINK model for the flexible biomass CHP plant was developed and validated to investigate the proposed system.
Parameter studies were conducted to determine the key values of the flexible plant, such as energy capacity, charge/discharge time and efficiency. A storage configuration consisting of a 100 m³ SA and 12.3 m³ STS is capable of reducing the electricity production by 3.5–3.7 MWh during charging. During discharge, an additional amount of 1.8–1.9 MWh is generated. A system efficiency of 76–92 % was achieved. Flexible operation depending on prices in the short-term electricity markets causes a reduction of between 0.5% and 2% of the total revenue of the plant of due to process losses related to the operating the storage facility. The feed-in tariff structure has had a significant impact on this revenue shortfall. The flexible operation allows a temporary peak reduction of 28% (from 19.2 MW to 13.7 MW). Compared to competing technologies such as pumped hydro, batteries or hydrogen storage systems, the proposed flexible biomass CHP plant system is competitive. It was shown that an operation
Steam storage for flexible biomass CHP plants V
similar to flexible biogas plants is possible. The operation of already existing flexible biogas plants can also be improved by using the proposed solution.
Steam storage for flexible biomass CHP plants VI
Acknowledgements
I would deeply thank Prof Rick Greenough and Prof Wilfried Zörner. Rick has guided me through all of this work. His constructive feedback, as well as his detailed knowledge about academic research, gave me support at all times. Wilfried provided me with important comments and guidance throughout the research to significantly improve my research findings. Without their support, this work would have not been possible.
I am also very grateful to my colleagues at the Institute for New Energy Systems, especially Dr Christoph Trinkl, Dr Hermann Riess, Dr Christoph Reiter, Mathias Ehrenwirth, Norbert Grösch and Abdessamad Saidi, who helped me in so many places and with so many questions. I would like to express special thanks to my long-time office colleague, Katharina Bär. We have been on this journey together through so many ups and downs and I am so grateful for the motivation we have given each other on so many occasions.
I would also like to thank my former colleagues Andreas Reichel and Dr Martin Deckner, who mentored me during my first job as an engineer. They gave me an understanding of the mindset of an engineer and laid the foundation for my future career.
The motivation and support from my mother, friends and family helped me to continue my work and made everything easier. I would also like to thank my father who to taught me never to give up. His support and guidance throughout most time of my life was a major motivation to achieve my goals.
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Publications by the Author in Connection with this Research
Stark, M., Sonnleitner, M., Zörner, W. & Greenough, R., 2016. Approaches for Dispatchable Biomass Plants with Particular Focus on Steam Storage Devices. Chemical Engineering & Technology, Issue 40(2), 227-237.
Stark, M., Trinkl, C., Zörner, W. & Greenough, R., 2018. Methodological Evaluation of Storage Systems for Flexible Power Generation from Solid Biomass. Chemical Engineering & Technology, Issue 41(11), 2168-2176.
Stark, M., Conti, F., Saidi, A. & Zörner, W., 2019. Steam Storage systems for Flexible biomass CHP Plants - Evaluation and inital model based calculation. Biomass and Bioenergy, Issue 128, pp. 1-9.
Stark, M., Philipp, M., Saidi, A., Trinkl, C., Zörner, W., Greenough, R., 2018. Steam Accumulator Integration for Increasing Energy Utilisation of Solid Biomass-Fuelled CHP Plants in Industrial Applications. Chemical Engineering Transactions, Issue 70, pp. 2137-2142.
Stark, M., Philipp, M., Saidi, A., Trinkl, C., Zörner, W., Greenough, R., 2019. Design Parameters of Steam Accumulators for the Utilization in Industrial Solid Biomass-Fuelled CHP Plants. Chemical Engineering Transactions, Issue 76, pp. 817-822.
Hechelmann, R., Seevers, J., Otte, A., Sponer, J. & Stark, M. 2020, Renewable Energy Integration for Steam Supply of Industrial Processes—A Food Processing Case Study. Energies, Issue 13(1), pp. 2532.
Steam storage for flexible biomass CHP plants IX
Table of Contents
Declaration...... III
Abstract ...... V
Acknowledgements ...... VII
Publications by the Author in Connection with this Research ...... IX
Table of Contents...... X
List of Figures ...... XIV
List of Tables ...... XIX
Abbreviations ...... XXII
Symbols ...... XXIII
Subscripts ...... XXIV
Glossary ...... XXV
1 Introduction ...... 1
1.1 Background ...... 2
1.2 Research Approach ...... 3
1.3 Methodology and Identified Research Gaps ...... 4
2 Literature Review ...... 7
2.1 Demand for Flexible Power Generation ...... 7
2.1.1 Electricity Markets ...... 7
2.1.2 Demand-driven Operation ...... 10
2.2 Flexible Biomass CHP Plants ...... 11
2.2.1 Flexibility measures can be implemented at any level...... 11
2.2.2 Flexibilisation on the Power Generation Unit ...... 12
2.2.3 Flexibilisation on IEC Level ...... 15
2.3 High-Temperature Storage for Biomass CHP Plants ...... 17
2.3.1 Technical and functional requirements ...... 18
2.3.2 Storage Technologies ...... 22
Steam storage for flexible biomass CHP plants X
2.3.3 Storage Systems ...... 26
2.4 Research Gaps and Chapter Summary ...... 29
3 Evaluation of Suitable Storage Systems ...... 32
3.1 Definition of the Problem...... 33
3.2 Prioritisation of the Criteria ...... 33
3.2.1 Selection of Criteria ...... 33
3.2.2 Delphi Survey ...... 36
3.2.3 Survey Analysis ...... 40
3.3 Evaluation of the Most Suitable Concept ...... 41
3.3.1 Assignment of Parameters ...... 41
3.3.2 Assessment Scheme ...... 46
3.3.3 Evaluation of the Concepts ...... 47
3.4 Chapter Summary...... 47
4 Modelling the Flexible Plant ...... 49
4.1 Modell Structure ...... 51
4.1.1 Model Components...... 53
4.1.2 Flow-Vector Concept ...... 54
4.2 Turbine Model ...... 54
4.2.1 Selection of the Turbine Model ...... 56
4.2.2 Turbine Model of Lou et al...... 60
4.2.3 Validation of the Turbine Model ...... 61
4.3 Steam Accumulator Model ...... 66
4.3.1 Selection of Steam Accumulator Model ...... 67
4.3.2 Stevanovic Steam Accumulator Model...... 69
4.3.3 Validation ...... 71
4.4 Solid Thermal Store Model ...... 72
4.4.1 Available Models for STS...... 73
Steam storage for flexible biomass CHP plants XI
4.4.2 Implementation of the Solid Thermal Store Model ...... 75
4.4.3 ANSYS Model Development ...... 77
4.4.4 Validation of the STS Model...... 80
4.4.5 Development of the Parameter Model ...... 82
4.4.6 Simplified Model Development ...... 85
4.5 System Model ...... 86
4.5.1 Additional Components for the System Model ...... 89
4.5.2 Sensitivity Study ...... 91
4.5.3 Model Assumptions and Simplifications ...... 93
4.6 Chapter Summary ...... 93
5 Simulation Results ...... 95
5.1 Charging and Discharging Behaviour ...... 96
5.1.1 Charging the Storage System ...... 97
5.1.2 Discharging the Storage System ...... 99
5.2 Parameter Study ...... 101
5.2.1 Power Output ...... 104
5.2.2 Charging/Discharging Time...... 107
5.2.3 Capacity ...... 109
5.2.4 System Efficiency ...... 113
5.2.5 Summary of the Parameter Study ...... 115
5.3 Operation of the Flexible Plant Within the Energy Markets and the Grid ...... 116
5.3.1 Operation According to the Day-Ahead Market Price ...... 118
5.3.2 Operation According to the Intraday Market Price ...... 126
5.3.3 Balancing the Grid at the Distribution Network Level ...... 134
5.4 Chapter Summary ...... 143
6 Comparison of Storage System with Alternative Technologies...... 145
6.1 Investment Costs of the Flexible Biomass CHP Plant ...... 145
Steam storage for flexible biomass CHP plants XII
6.2 Proposed Energy Storage Concept Compared with Competitor Systems ...... 151
6.2.1 Proposed Concept Compared with PHS and CAES ...... 153
6.2.2 Proposed Concept Compared with Battery Storage Systems ...... 154
6.2.3 Proposed Concept Compared with Hydrogen storage ...... 155
6.3 Proposed System Compared with Flexible Biogas Power Plants ...... 156
6.4 Chapter Summary...... 158
7 Conclusion ...... 159
7.1 Evaluation of the Most Suitable Storage Device ...... 160
7.2 Operation of the Proposed Steam Storage Concept ...... 161
7.3 Operation to Relieve the Energy System ...... 162
7.4 Comparison with Other Technologies ...... 162
7.5 Contribution to Knowledge ...... 164
7.6 Avenues for Future Research ...... 165
References ...... 167
Appendix I – Delphi Survey ...... 174
Appendix II – Simulation results (Parameter Study) ...... 186
Appendix III – Calculation of the AI ...... 189
Appendix IV – ANSYS/CFX Modell Settings ...... 190
Steam storage for flexible biomass CHP plants XIII
List of Figures
Figure 1-1: Influence of fluctuating power generation (Faulstich, 2011) ...... 1
Figure 1-2: Simplified scheme of the research approach...... 3
Figure 1-3: Main research topics...... 4
Figure 1-4: Research methodology...... 5
Figure 1-5: Gaps in research...... 6
Figure 2-1: Chronological sequence of submarkets in Germany (cf. BMWi, 2015)...... 9
Figure 2-2: Dispatch via hot water storage...... 13
Figure 2-3: Power output as a function of steam extraction and pressure (Stark, et al., 2017)...... 15
Figure 2-4: Dispatch via high-temperature storage...... 16
Figure 2-5: Classification of HTS according to Gil et al. (2010) ...... 18
Figure 2-6: Functional structure of the steam storage system...... 19
Figure 2-7: Substructure of the steam storage system...... 20
Figure 2-8: Simplified T-s diagrams for various storage media (Steinmann, 2006)...... 21
Figure 2-9: Steam accumulator (Steinmann, 2006)...... 23
Figure 2-10: PCM storage (Laing, et al., 2011)...... 24
Figure 2-11: Steam accumulator with solid superheater (Seitz, et al., 2013)...... 26
Figure 2-12: Concepts for PCM storage systems (Seitz, et al., 2013)...... 27
Figure 2-13: Storage systems for flexible power generation (Stark, et al., 2018)...... 29
Figure 3-1: Summary of the criteria...... 34
Figure 3-2: Simplified flexible plant operation according to Stark et al. (2018)...... 35
Figure 3-3: Participants separated according to their sector...... 39
Figure 3-4: Operation steps of the calculated plant (Stark, et al., 2018)...... 41
Figure 4-1: Modelling and simulation...... 49
Figure 4-2: Schematic of the storage system...... 51
Figure 4-3: Schematic representation of the flexible biomass CHP plant process...... 52
Steam storage for flexible biomass CHP plants XIV
Figure 4-4: Structure of the flexible plant model, including model boundaries...... 53
Figure 4-5: Flow-vector creator block...... 54
Figure 4-6: Willans line according to Mavromatis & Kokossi (1998)...... 57
Figure 4-7: Complex multistage turbine equivalents to multiple single-stage turbines (Sun & Smith, 2015)...... 58
Figure 4-8: Design efficiency depending on turbine dimension ...... 59
Figure 4-9: Two-stage turbine model...... 61
Figure 4-10: Turbine validation data...... 62
Figure 4-11: Power generation of the turbine model compared with the measurement values ...... 64
Figure 4-12: Outliers caused by the soot blower...... 65
Figure 4-13: Power generation depending on the extraction...... 66
Figure 4-14: Steam accumulator layout (Stevanovic, et al., 2012)...... 70
Figure 4-15: Non-equilibrium and equilibrium model (Stevanovic, et al., 2014)...... 71
Figure 4-16: Validation of the steam accumulator model...... 72
Figure 4-17: Solid thermal Store (Jian, et al., 2015) ...... 73
Figure 4-18: Crosssectional interface of the solid thermal storage (Jian, et al., 2015)...... 74
Figure 4-19: Interconnection of the StorageTechThermo model (Tamme, et al., 2006)...... 75
Figure 4-20: Two-step modelling process...... 76
Figure 4-21: Input and output parameters of the STS model ...... 78
Figure 4-22: Charging the STS Model...... 78
Figure 4-23: Discharging the STS Model...... 79
Figure 4-24: Temperature development of the idle storage...... 80
Figure 4-25: Discharge validation according to data from Laing (2008)...... 80
Figure 4-26: Charge validation according to the data of Bai et al. (2011)...... 81
Figure 4-27: Temperature profiles for STS model development...... 84
Figure 4-28: Charge and discharge model...... 85
Steam storage for flexible biomass CHP plants XV
Figure 4-29: Simplified schematic (top) and screenshot (bottom) of the flexible CHP plant model...... 87
Figure 4-30: Measured live-steam data...... 87
Figure 4-31: Sensitivity study – charge...... 92
Figure 4-32: Sensitivity study – discharge...... 92
Figure 5-1: Simulation model with parameters...... 95
Figure 5-2: Power generation during charging – LSmean...... 98
Figure 5-3: Power generation during charging – LSprof...... 98
Figure 5-4: STS Operation during charging – LSmean...... 99
Figure 5-5: Power generation during discharging – LSmean...... 100
Figure 5-6: Power generation during discharging – LSprofile...... 100
Figure 5-7: STS operation during discharge – LSmean...... 101
Figure 5-8: Flexible plant power output depending on the charge mass flow rate...... 104
Figure 5-9: Influence of a lower mass flow rate on power output of the plant turbine...... 105
Figure 5-10: Flexible plant power output for the variation of the discharge mass flow rate. 105
Figure 5-11: Steam accumulator pressure pSA during discharge at different mass flow rates...... 106
Figure 5-12: Power output of the storage turbine PST during discharge at different mass flow rates...... 106
Figure 5-13: Power output of the storage turbine PST during discharge with different STS sizes
/ ṁdischarge = 2 kg/s...... 106
Figure 5-14: Power output of the storage turbine PST during discharge with different STS sizes
/ ṁdischarge = 4 kg/s...... 106
Figure 5-15: Charging time of the storage system for varying VSA and mass flow rate...... 107
Figure 5-16: Charging time by varying the STS volume...... 108
Figure 5-17: Discharging time as a function of SA volume and discharge mass flow rate. . 108
Figure 5-18: Steam mass stored in the storage system as a function of VSA...... 109
Figure 5-19: Flexible energy capacity of the plant as a function of VSA...... 110
Steam storage for flexible biomass CHP plants XVI
Figure 5-20: Influence of ṁcharge and VSTS on CFlex,charge...... 111
Figure 5-21: Influence of ṁdischarge and VSTS on CFlex,discharge...... 111
Figure 5-22: Energy capacity of the flexible plant by incrementally increasing the VSTS. .... 111
Figure 5-23: Specific energy capacity of the flexible plant by incrementally increasing the VSTS...... 111
Figure 5-24: Discharge as a function of TSTS,ini...... 112
Figure 5-25: System efficiency during charging...... 114
Figure 5-26: System efficiency during discharging...... 114
Figure 5-27: System efficiency as a function of charge and discharge mass flow rate...... 114
Figure 5-28: Day-ahead spot market (EPEX, 2019)...... 117
Figure 5-29: Indices on the intraday market (EPEX, 2019)...... 117
Figure 5-30: Flexible operation according to day-ahead with DA...... 119
Figure 5-31: Flexible operation according to the day-ahead market with DL...... 120
Figure 5-32: Flexible operation according to the day-ahead market with DH...... 120
Figure 5-33: Flexible operation according to the day-ahead DH with different storage volumes...... 121
Figure 5-34: Flexible operation according to day-ahead DA with measured data...... 122
Figure 5-35: Income from day-ahead operation with a FIT of 100 €/MWh...... 124
Figure 5-36: ΔR depending on FIT for the day-ahead market...... 126
Figure 5-37: Flexible operation according to intraday IH...... 127
Figure 5-38: Flexible operation according to the intraday IA with storage S1...... 128
Figure 5-39: Flexible operation according to the typical intraday trend (IA) with storage S2...... 130
Figure 5-40: Revenues from intraday operations with a FIT of 100 €/MWh and the maximum price trend (IM) ...... 131
Figure 5-41: Revenue from intraday operations with a FIT of 100 €/MWh and average price behaviour (IA)...... 132
Steam storage for flexible biomass CHP plants XVII
Figure 5-42: Difference between baseload and flexible operation depending on the FIT for intraday market...... 133
Figure 5-43: Case scenario 1: High generation peak...... 136
Figure 5-44: Case scenario 1: Reduction of the total generation by utilizing the flexible plant...... 137
Figure 5-45: Impact of a flexible plant with VSA = 100 m³, VSTS = 12.4 m³. ṁcharge = 1.5 kg/s and mdischarge = 2 kg/s...... 138
Figure 5-46: Impact of a flexible plant with VSA = 150 m³, VSTS = 18.4 m³. ṁcharge = 1.5 kg/s and mdischarge = 2 kg/s...... 139
Figure 5-47: Case scenario 2: Volatile generation...... 140
Figure 5-48: Case scenario 2 with different mass flow rates and a 100 / 12.3 m³ storage system...... 140
Figure 5-49: Case scenario 2 with different mass flow rates and a 150 / 18.4 m³ storage system, ...... 141
Figure 5-50: Impact of a flexible plant with VSA = 150 m³, VSTS = 18.4 m³. ṁcharge = 1.5 kg/s and mdischarge = 2 kg/s...... 142
Figure 6-1: Specific cost of the storage system depending on VSA...... 147
Figure 6-2: Specific cost of the storage system by increasing the VSTS for VSA = 100 m³. ... 148
Figure 6-3: Increase of the specific costs for various turbine sizes (mean values)...... 149
Figure 6-4: Overview of different energy storages and their indicated application fields adapted from Baumann et al. (2019)...... 151
Steam storage for flexible biomass CHP plants XVIII
List of Tables
Table 2-1: Markets in the German grid according to Dotzauer (2015)...... 9
Table 2-2: Average biomass CHP parameters (Scheftelowitz, et al., 2014), (Savola, 2007). 18
Table 2-3: PCM materials with potential for biomass CHP plants (Laing, et al., 2009)...... 25
Table 2-4: Summary of steam storage systems (Stark, et al., 2017)...... 28
Table 3-1: Suitable storage system for the utilization in flexible biomass CHP Plants...... 32
Table 3-2: Blank decision matrix for the criteria performance ...... 37
Table 3-3: Overview of the survey participants...... 38
Table 3-4: Survey results – Criterion matrix...... 40
Table 3-5: Plant properties for the case scenario...... 42
Table 3-6: Properties of the selected storage media...... 42
Table 3-7: Provision losses rating categories...... 43
Table 3-8: Application area rating categories...... 44
Table 3-9: Assessment matrix...... 45
Table 3-10: Chart correlating parameter magnitudes with value scales...... 46
Table 3-11: Evaluation results...... 47
Table 4-1: Comparison of the available turbine models...... 58
Table 4-2: Regression coefficients for the simple turbine model (Lou et al., 2015)...... 60
Table 4-3: Measured values...... 62
Table 4-4: Simulation parameters...... 63
Table 4-5: Steam accumulator model comparison...... 69
Table 4-6: Geometric characteristics of the STS...... 83
Table 4-7: Mass flow rates, charge study...... 83
Table 4-8: Initial volume-average temperature, charge study...... 83
Table 4-9: Mass flow rates, discharge study...... 84
Table 4-10: Initial volume-averaged temperature, discharge study...... 84
Table 4-11: Recorded simulation results...... 90
Steam storage for flexible biomass CHP plants XIX
Table 4-12: Error parameters...... 90
Table 4-13: Error sources and their amplitude...... 91
Table 5-1: Boundary parameters of the biomass CHP plant used for the simulations...... 96
Table 5-2: Simulation parameters – Charge and discharge study...... 97
Table 5-3: STS charge mass flow rates chosen for the parameter study...... 102
Table 5-4: STS discharge mass flow rates chosen for the parameter study...... 103
Table 5-5: Interdependencies of parameters (by increasing values)...... 115
Table 5-6: Overview on simulation results...... 116
Table 5-7: Effects of increasing the storage volume...... 121
Table 5-8: Revenue from day-ahead operation with a FIT of 100 €/MWh on a simulated day...... 125
Table 5-9: Flexible plant capacity according to the intraday high price trend (IH)...... 128
Table 5-10: Flexible plant capacity according to the typical intraday trend (IA) with S1...... 129
Table 5-11: Flexible plant capacity corresponding to the typical intraday trend (IA) with S2...... 130
Table 5-12: Revenue from intraday IM operation with a FIT of 100 €/MWh...... 131
Table 5-13: Revenue from intraday IA operation with a FIT of 100 €/MWh...... 132
Table 5-14: Power plants in the investigated grid section...... 135
Table 5-15: Results from case scenario 1...... 137
Table 5-16: Simulation results from case scenario 2...... 142
Table 6-1: Specific investment cost for the storage components...... 146
Table 6-2: Approximate investment cost for the storage system...... 146
Table 6-3: Additional costs for the storage turbine ...... 148
Table 6-4: Specific cost of the proposed system compared with alternative technologies (Baumann, et al., 2019)...... 150
Table 6-5: Overview of different energy storage technologies ...... 152
Table 6-6: Strengths and weaknesses of the proposed system compared with PHS and CAES...... 153
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Table 6-7: Strengths and weaknesses of the proposed system compared with battery storage systems...... 155
Table 6-8: Strengths and weaknesses of the proposed system compared with hydrogen. 155
Table 6-9: Strengths and weaknesses of the proposed system compared with flexible biogas plants...... 157
Steam storage for flexible biomass CHP plants XXI
Abbreviations
AHP Analytic hierarchy process
BE Balance energy
CHP Combined heat and power
CFD Computational fluid dynamics
CSP Concentrated solar power plants
DNL Distribution network level
EM Equilibrium model (steam accumulator)
FEM Finite element method
FIT Feed-in tariff
HTS High-temperature storage
HTF Heat-transfer fluid
IEC Intermediate energy carrier
NEM Non-equilibrium model (steam accumulator)
PCM Phase change material
PDE Partial differential equation
SM Storage media
SA Steam accumulator
STS Solid thermal store
TRL Technology readiness level
Steam storage for flexible biomass CHP plants XXII
Symbols
C Energy capacity kWh
FF Flex-Factor -
h Specific enthalpy kJ/kg
H Enthalpy kJ
ṁ Mass flow rate kg/s
M Mass kg
LR Load range -
P Pressure MPa
P Electric capacity kW
Q Energy kWh
t Time s
T Temperature °C
v Velocity m/s
V Volume m³
x Steam quality -
z Turbine stage -
β Water level of the steam accumulator -
ηis Isentropic efficiency (turbine) -
ηmech Mechanical efficiency (turbine) -
ηel Electrical efficiency (turbine) -
ηsystem System efficiency -
ηstorage Storage efficiency -
Steam storage for flexible biomass CHP plants XXIII
Subscripts
baseload Baseload
c Control
charge Charge
CS Charge steam
d Design
discharge Discharge
DS Discharge steam
ex Extraction
Flex Flexible
ini Initial
in Inlet
LS Live-steam
mean Average value
max Maximum
min Minimal
PT Plant turbine
out Outlet
red Reduced
SA Steam accumulator
ST Storage turbine
STS Solid thermal store
total Total
Steam storage for flexible biomass CHP plants XXIV
Glossary
Biomass CHP Combustion plants, fuelled with solid biomass and operated in combined heat and power mode. Unless otherwise stated, these plants are referred to as biomass CHP.
Baseload operation Conventional operation of a thermal power plant with constant power output.
Flexible power generation Electricity generation adapted to the needs of a grid or market.
Flexibilisation Conversion of a base-load plant into a flexible power plant through structural, technical and/or regulatory measures
Live-steam Steam generation from the furnace/boiler of the plant
Extraction steam Steam extracted from the turbine of the plant to provide steam or heat.
Steam storage for flexible biomass CHP plants XXV
1 Introduction
Given the rising costs of fossil fuels and the German decision to phase out coal and nuclear power generation, it is not surprising that the importance of renewable energy technologies is increasing. However, in recent years, the extensive installation of renewable energy systems – particularly wind and photovoltaic systems – has led to various difficulties arising from their fluctuating power generation characteristics. This less predictable, and naturally fast-changing power generation poses a major challenge to the current grid infrastructure. Indeed, the increasing difference between electricity demand and supply at certain points in time leads to the risks of both grid congestion and overcapacity (Castillo & Dennice, 2014), as can be seen in Figure 1-1 below.
Figure 1-1: Influence of fluctuating power generation (Faulstich, 2011)
To ensure the stable operation of the power grid, power input and output must be the same at all times. Similarly, the unpredictable profiles of volatile generation, as well as of demand, make balancing devices necessary. One approach to overcome this problem is the use of energy storage technologies such as pumped hydro, batteries or compressed air energy storage. Other ways to balance the power system include demand-side management and smart grid technologies. A third option is to increase the flexibility of power plants. Flexible power plants can adjust the power output to the demand of the grid. Although some plants, such as biogas or natural gas, already supply electricity on demand, most thermal power plants continue to be used for baseload operation.
Steam Storage for Flexible Biomass CHP Plants Page 1
If biomass CHP plants can be converted to generate electricity more flexibly, then they can meet both climate protection targets and the demand for grid relief. Due to their ability to operate independently of weather and solar radiation, biomass plants are the ideal basis for a highly flexible power plant. In addition, the decentralised distribution of these plants is advantageous for grid relief in the lower grid levels. Szarka (2013), for example, represents the view that bioenergy can meet most of the requirements for a flexible energy supply, but that research must look at the entire energy system. While the basic technologies are available, further research is needed regarding the complex interplay within the power plant and with the power system (Thrän, 2015).
1.1 Background
In Germany, the total installed capacity of solid biomass-fuelled combustion plants is 1.56 GW. More than 90% of this capacity is provided by plants with a steam cycle. The remaining capacity is generated from technologies using an organic Rankine cycle (ORC) or biomass gasification (wood gasifier). The generation capacity of biomass steam plants in Germany is in a range between 0.5–20 MW. Solid biomass-fuelled plants with an installed capacity of more than 3 MW are exclusively steam plants, these plants have an average capacity of 6 MW (Scheftelowitz, et al., 2014; Eltrop, et al., 2014). In Finland and Sweden, countries with a historically high share of solid biomass-fuelled combustion plants, the plants have an installed capacity between 3–20 MW (Savola, 2007)
Most biomass combustion plants are operated in combined heat and power (CHP) mode. They supply heat for district heating, process steam and/or process heat applications. The power output varies depending on the heat extraction (Hoffstede, et al., 2016). Unless otherwise stated, solid biomass-fuelled plants are referred to as biomass CHP plants in this study.
Even though some biogas plants are already operated flexibly, the use of biomass CHP plants has not yet been realised on a significant scale due to the lack of an efficient and economic concept (Ortwein & Lenz, 2015). For this reason, new approaches for flexible biomass CHP plants need to be investigated.
Steam Storage for Flexible Biomass CHP Plants Page 2
1.2 Research Approach
The objective of this research is to acquire knowledge of flexible power generation from solid fuelled biomass plants. In particular, the approach of this research is to biomass CHP plants with steam storage systems to enable a demand-oriented operation. The decoupling of steam generation (combustion and steam generator) and energetic steam utilization (turbine) enables an adjustable power production by managing the charge of the storage system (Figure 1-2).
Figure 1-2: Simplified scheme of the research approach.
The inflexible part of the plant continues to produce a constant amount of steam. This live-steam mass flow from the steam generator is divided between the more flexible plant turbine and the storage system. A reduction of the power output during charging, as well as an additional generation during discharging of the storage is achieved. This concept is inspired by flexible biogas plants (Liebetrau, et al., 2015). Due to the easy storability of biogas, this flexible operation is obvious for biogas but not yet well investigated for biomass CHP plants.
The core of the research will be the development of a simulation model with which different operating solutions can be investigated and compared. For this purpose, models for a biomass CHP plant with the corresponding components and a steam storage device have to be built and validated. Both are combined to simulate a flexible biomass power plant for specific case scenarios.
The research will focus on the influence of a steam storage device on the biomass CHP process. The feasibility of this concept depends on cost and benefits. This requires a comparison between flexible plant operation and conventional base-load operation. Also, the optimisation potential should be determined.
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1.3 Methodology and Identified Research Gaps
As mentioned in the title of the thesis, three major research areas: ‘steam storage’, ‘flexible’ and ‘power generation from biomass’ is of interest throughout the research (as shown in Figure 1-3).
Figure 1-3: Main research topics.
The central aim of this research is to increase the penetration of renewable energies by making existing biomass CHP plants more flexible. Available technologies, system approaches and their specific properties are of interest.
‘Flexible’ power generation is the provision of electricity according to demand. The demands of the markets and the grid require investigation. This is the basis for the development of control strategies for flexible plants.
The third issue, that of ‘power generation from biomass’, must also be considered. The system of the plant and the process are influenced by the integration of the storage devices. The interaction of the storage with the components of the plant is of particular interest.
These three topics are central to this study. With this in mind, the methodology described below was developed to achieve these research objectives (see Figure 1-4). In order to achieve the desired results, it is necessary to define four work packages, namely those of literature review, system evaluation, modelling and simulation.
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Figure 1-4: Research methodology.
This thesis consists of the following chapters, which are aligned with this methodology.
• Literature review with a focus on the research areas: flexible power generation, biomass plants and steam storage, to identify research gaps and justify research needs. • Evaluation of the most suitable storage system for biomass CHP depending on the requirements of the biomass CHP plant and the energy system • Development of a simulation model of the storage system and the biomass CHP plant and their validation. • Simulation study to determine the basic operation and key parameters as well as an investigation of the operation to reduce the load on the energy system • Comparison of the proposed storage system with competing technologies • Summary of the generated knowledge on flexible biomass CHP plants with steam storage devices in the conclusions.
In this work, research gaps regarding flexible biomass CHP plants are identified. This research addresses these gaps and findings. A number of research gaps were identified during the literature review. This research therefore addresses these gaps and the results in order to contribute to knowledge. The following research gaps are identified and summarized in Figure 1-5.
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Figure 1-5: Gaps in research.
The literature review and identification of research gaps led to four research questions that are addressed in this thesis:
Which storage technology is suitable for the proposed operation?
This topic is discussed in Chapter 3, where requirements for the proposed system are defined and the available systems are rated against each other to identify the most suitable storage system
What is the basic operation of this concept and what are the main parameters?
The basic operation of the storage concept is explained in Chapter 3. All important elements and sub-components are analysed in Chapter 4, and a model for a computational study is developed. In Chapter 5, simulations are carried out to determine the basic behaviour of the developed model and important parameters such as energy capacity, load range and efficiency to gain knowledge about this novel system
What are the implications for the power grid?
In Chapter 5, operation with different control strategies is simulated to determine the impact on the power system. The flexible operation is examined under realistic conditions.
What is the scope of the concept?
To weigh the strengths and weaknesses of the proposed systems against competing technologies, a comparison is made in Chapter 6.
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2 Literature Review
This chapter provides an overview of the relevant literature on flexible electricity generation from biomass CHP plants. In order to cover all important boundaries, the chapter is subdivided into three topics:
• Demand for flexible power generation (Section 2.1) • Flexible biomass CHP plants (Section 2.2) • High-temperature storage technologies (Section 2.3)
In this work, the ability to manage electricity production according to grid or market demand is referred to as ‘flexible power generation’. The term ‘flexibilisation’ is defined as the conversion of a conventional base-load plant into a flexible power plant.
2.1 Demand for Flexible Power Generation
In the course of the German Federal Government’s energy concept, a long-term schedule for the expansion of renewable energies was drawn up (BMWi, 2015). The concept for the development of the energy supply is based on the studies of the Federal Ministry of Economics (BMWi, 2010). This study forecasts that the fluctuating part of the power supply in relation to gross electricity consumption will increase from 8% in 2011 to 55% in 2050. This means that the installed capacity and thus the possible peak loads of fluctuation power plants will be subject to a considerable increase. At peak, loads of 80 GW and installed renewable power generation capacity of 117 GW are expected by 2020. This requires a very large number of devices to balance the grid (BMWi, 2015). Due to the Energiewende,1 the described problems occurred earlier in Germany than in other countries, but at the same time, it is natural to expect these challenges to occur in any other country that uses an increasing amount of variable renewable energy (Castillo & Dennice, 2014).
2.1.1 Electricity Markets
Several electrical energy markets are designed to balance fluctuating power consumption and supply. These markets feature the opportunity to offer electricity depending on the demand of the grid. The market structures as described in this section represent the Central European markets, but other countries have similar arrangements.
1 Energiewende: The transition from a fossil based energy system into a more sustainable, renewable energy system in Germany. Steam Storage for Flexible Biomass CHP Plants Page 7
These can be divided into energy-only and control-reserve markets (Dotzauer, et al., 2015). Electricity is sold and bought on these markets according to various demand and supply forecasts.
In the energy-only markets, a certain amount of electricity is traded and delivered in defined time slices (a few hours at a certain time and date). They are divided into future and spot markets. The future markets are based on long-term transactions and are therefore not relevant in the context of flexible power generation.
The spot markets are divided into day-ahead and intraday markets. Electricity is traded in the day-ahead market, 24 hours before the delivery. Prices depend on supply and demand, based on the forecasts for the next day. According to the continuously refined forecasts, trades are made on the intraday markets up to 45 minutes before delivery. Thus, the difference between demand and supply can be reduced more and more due to the forecast error.
To some extent, the demand of the electricity grid is represented by the prices on these markets. High prices indicate high expected demand, while low prices indicate low expected demand in most situations (Scheftelowitz, et al., 2014).
In order to ensure the stability of the grid, the control reserve (or balancing power) markets exist. Forecast errors and corrections after the spot markets have closed are unavoidable. In order to ensure the stability of the grid in these cases, the control reserve can be called up. Control-reserve capacities for defined time slices are contracted in advance (weeks or days in advance). During these time slices, the capacity must be kept on standby. Both negative (reduction of output power) and positive capacity (increase of output power) can be traded. The provider receives remuneration for holding the control- reserve capacity available, irrespective of whether the capacity is called or not. In the case of demand, the provider obtains an additional amount (€/kWh). According to the response time (time from call to delivery), the control-reserve markets are divided into the primary, secondary and minute reserve (Thrän, 2015).
Figure 2-1 below shows the chronological sequence of the trading processes and electricity supply in Germany. The upper part of the figure shows the processes of energy producers and consumers in the electricity market. The lower part shows the processes of the European Network of Transmission System Operators (ENTSO), which are responsible for operating the grid. The process can be divided into two important areas. One is before the gate closes, where trading takes place in the electricity markets.
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Another is after the gate has been closed, measures are taken to stabilize the grid (e.g. net frequency).
Figure 2-1: Chronological sequence of submarkets in Germany (cf. BMWi, 2015).
Table 2-1 summarises the different markets according to their response time and scope.
Table 2-1: Markets in the German grid according to Dotzauer (2015).
Reaction Market subtype To balance Type of market Time slices (NEXT time Kraftwerke, 2016)
Up to 30 sec Primary control-reserve Net frequency Control-reserve 1 week
Up to 5 min Secondary-control- Net frequency Control-reserve 12 h reserve
5–15 min Minute-reserve Net frequency Control-reserve 4 h
15 min–6 h Intraday continuous Forecast error Energy-only 15 min (spot)
6–24 h Intraday and day-ahead Residual load Energy-only 15/60 min (spot)
1–31 day Future markets Macro weather Energy-only diverse situations/ seasonal demand
To balance the net frequency in the reserve markets, the capacity of the device is kept on standby for certain time slices. During this time, the grid balancer device could increase or decrease the power output as needed. In spot markets, electricity is traded for 15 minutes to 60 minutes (NEXT Kraftwerke, 2016).
Energy-only and control-reserve markets are also available in other countries such as the UK and the USA, for example. The main difference between the individual markets
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is the response time and the detailed structure of the markets. Of course, the transferability of the results from the present study to other countries and markets should be investigated.
In addition to the energy-only and control-reserve markets, there are other ways to relieve the energy systems. The control-reserve and energy-only markets described are general markets for large grid areas. The disadvantage of these existing markets is their inability to meet the needs of the individual, local lower grid levels.
For example, on cloudy days (low PV power production) with low wind load (low wind power generation), there will be a shortage of capacity in the market. However, it is possible that at the same time local parts of the grid may suddenly experience high wind loads. Due to the limited transmission capacity of the grid, intervention is necessary in this region with its high wind load. The electricity markets cannot react to this regional effect, as they can only try to balance the entire market area. In this case, emergency shutdowns of renewable energy plants in the grid area concerned must be carried out. These cause a number of additional costs, as remuneration has to be paid to the operators of the renewable power plant (Messe, et al., 2015). Balancing at the distribution network level (DNL) can make an additional contribution to relieving the burden on the energy system. As these situations cannot be avoided by the existing market mechanism, local flexible markets have started to develop at lower grid levels in recent years (Messe, et al., 2015).
2.1.2 Demand-driven Operation
If an economic operation of the flexible biomass CHP plant is to be achieved, control strategies have to be determined according to the existing markets. Thus, several biogas plants are already able to operate on the secondary control-reserve and the minute control-reserve markets as well as on the spot markets (BMWi, 2015). The three different market types (spot, control energy and local flexible markets) require different control mechanisms.
For operation on the spot markets, the market price can be used as a control signal. The power output of a plant should be reduced in low price phases and increased in high price phases. Some research has been conducted on the market-driven operation of biogas (Häring, et al., 2016) and solid biomass-fuelled plants (Muche, et al., 2016; Hoffstede et al., 2016;), and plant-specific and universal control strategies have been developed.
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For operation on the control-reserve markets, the plant must be operated according to the demand for balancing energy. During the corresponding time slice, the capacity must be kept on standby. After the energy has been called up, the capacity must be delivered within the reaction time. The demand signals of the grid operators are used as control signals. This type of operation for biomass CHP plants was discussed by Hoffstede et al. (2016) and Muche et al. (2016).
For grid balancing at the distribution network level, the local load of the grid, as well as the current level of generation, power export or import can be used for control. In some cases, grid capacity is limited, requiring all generating units to coordinate their output (Bär, et al., 2020).
Unfortunately, no market data is currently available for the envisaged ‘local flexible markets’. However, these markets are expected to become increasingly important in the near future (BMWI, 2015). Messe et al. (2015) predicted that the approved forecast mechanism for existing markets would not be suitable for these new ones. Local grid and weather forecast could be a steering signal for the markets in development.
In addition to the electricity markets, heat supply must also be taken into account in the flexible operation of CHP plants. The control strategies, which are primarily focused on economic operation, must include the heat demand for the optimum efficiency of the plant. Several authors have studied the combination of heat and electricity demand- driven operation for biomass and biogas CHP plants (Pirouti, et al., 2011; Muche, et al., 2016; Hoffstede, et al., 2016). In all cases, the supply heat was set at a higher priority than flexible power supply to ensure the assured heat delivery. However, there is a lack of intelligent control strategies that combine heat- and electricity-demand-led operation.
2.2 Flexible Biomass CHP Plants
The available options to achieve flexible energy supply of solid-fuelled biomass CHP plants have been investigated by both Ortwein & Lenz (2015) and Hoffstede (2016). The former authors separated the concepts into three levels:
• Thermochemical conversion process, • Intermediate energy carrier (IEC), • Power generation unit.
Flexibility measures can be implemented at any level.
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In the plants considered with steam as an intermediate energy carrier, thermochemical conversion takes place by combustion; the power generation unit is a steam turbine (Ortwein & Lenz, 2015).
2.2.1 Flexibilisation of the Thermochemical Conversion
To influence the electricity generation, the power of the combustion can be varied by changing the fuel input. The amount of steam produced can be reduced and a load change to 30–110% from the nominal furnace capacity (depending on furnace technology) is achievable. A disadvantage of this mode of operation is the start-stop behaviour and the slow load ramps, which are below 10% per minute. The combustion chamber is designed to stabilise heterogeneous combustion of solid biomass fuel. This design causes reduced efficiency and slow load-change speed in flexible operation. Part- load operation has some other disadvantages such as increased pollutant emissions and wear (Thrän, 2015). In general, it is the slow ramping capability that is the limiting factor in operating a plant according to the grid demand. Also, the power output can only be reduced. In times of high electricity demand, a small excess capacity of up to 110% of the nominal furnace capacity limits the achievable increase in generation (Thrän, 2015).
Another promising option for flexibilization is the direct storage of the combustion heat. By storing the energy of the high-temperature flue gas, the steam output can be varied to a higher degree than usual. However, this concept requires significant changes in the CHP control and the boiler, e.g. new heat exchangers, a modified control system and a modification of the internal piping. As this research is focused on concepts for upgrading existing biomass CHP plants without fundamently changing the existing system, it is not considered in this work anymore. However, for new build biomass CHP plants this concept is a promising option.
2.2.2 Flexibilisation on the Power Generation Unit
Steam turbines are considered to be very flexible due to their fast ramping ability and good efficiency in a part load operation (Herrmann & Kearney, 2002). In biomass CHP plants, the turbines are usually designed as extraction turbines.
Extraction turbines are operated with a constant mass flow of live-steam. A controllable amount of steam can be taken from one or more extraction points to supply steam or heat applications. The amount of steam extracted also has an impact on power generation. As a result, these turbines are able to change their power-to-heat ratio.
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The maximum electrical power is generated when no steam is extracted. The more steam is extracted, the lower the amount of electricity generated.
Thus, the extraction can be used for flexible operation by adjusting the amount of steam extracted according to the power demand. In this operating mode, however, heat extraction and heat demand are no longer the same. Heat storage is therefore necessary for the buffering of energy. Such a storage system decouples the heat extraction from the heat supply and guarantees a reliable heat supply to the customers. The interconnection of hot water storage and the turbine is shown in Figure 2-2 below.
Figure 2-2: Dispatch via hot water storage.
This type of flexible operation can be described in more detail:
Conventional operation (without usage of the storage)
The energy of the extraction steam is equal to the heat demand. The amount of the extracted steam is adjusted to the demand. Therefore, in the case of flexible operation, the generated power is not directly controllable, and the storage is neither charged nor discharged.
Reducing the Power Output
To reduce the power output, the amount of extraction steam is increased. In this case, the energy of the extracted steam is higher than the demand of the heat supply. The surplus energy is stored in hot water storage. Thus, the power reduction is limited by the storage capacity. A fully loaded storage has no capacity for additional surplus energy, in this case, no power reduction can be realised.
Increasing the Power Output
To increase the power output, the extraction steam quantity can be reduced or switched off completely. Therefore, the energy of the extracted steam is less than the required
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heat demand. The remaining heat demand is covered by the energy of the storage. This operation is limited by the energy content of the storage. If the hot water storage is empty, the power increase is not possible, as the heat demand can be covered in this case.
Pirouti et al. (2011) investigated this mode of operation for the UK market on specific winter, summer and spring/autumn days. The possibility to sell to the electricity market is strongly limited by the heat demand and the available heat in the storage.
A constant high heat demand limits the possibility to increase the power output. A high proportion of the energy extracted is used for supply and thus less energy is stored. In times of high heat demand, the consistently low storage level limits the possibility of increasing the power output, as there is no energy available in the storage to substitute the extraction steam.
On the other hand, low heat demand limits the ability to reduce power since a fully loaded storage cannot be used for power reduction. Increasing storage size can reduce this effect, but oversized hot water storages are also limited to the total heat demand.
For a biomass ORC-plant, Muche et al. (2016) investigated this mode of operation for day-ahead markets. He concludes that the amount of electricity generated during periods of high demand and the resulting revenues are too low for economical operation. However, he claims that if a way could be found to supply more electricity during periods of high demand, it would be very valuable.
Thus, the main disadvantage of the flexibilisation of the power generation unit is the limited load range. The maximum power output remains at 100% of the nominal load due to the constant live-steam mass flow. Even if a turbine overload for short periods is possible, a reliable, longterm increase of the power output is not feasible. A surplus of power generation can only be achieved by the substitution of extraction steam. The minimum power output is related to the pressure level of the extraction system (Krautkremer & Hoffstede, 2015). As shown in Figure 2-3 below, the lower the extraction pressure level, the lower the load range of the turbine. The figure shows a simplified (fixed turbine efficiency) energetic calculation of a 5 MW turbine operating with 8 MPa superheated steam. Different extraction pressure levels are compared. A typical extraction level for district heat applications is 0.2 MPa. For example, if 60% of the amount of live-steam is extracted, the nominal power is reduced to 87% when steam is extracted at 0.2 MPa (2 bar).
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Figure 2-3: Power output as a function of steam extraction and pressure (Stark, et al., 2017).
This option for flexible power generation can only be realised if the power plant supplies hot water. If the power plant supplies heat in the form of steam, then hot water storage cannot be used as a buffer. This option for flexible power generation is therefore characterised by high efficiency and fast response time. However, several constraints such as heat demand and extraction pressure limit the amount of energy that can be flexibly generated.
2.2.3 Flexibilisation on IEC Level
One option for dispatch at the intermediate energy carrier (IEC) level is to bypass the turbine. The steam or a part of the steam is relaxed and condensed instead of being fed into the turbine. This allows a quick reduction of the power supply. Despite the economic advantage, the steam is depressurized and cooled in an air condenser. Therefore, the gross efficiency of the plant decreases. A few plants in Germany are already operated in this mode to supply balancing power (Thrän, 2015; Krautkremer & Hoffstede, 2015).
Another option is to buffer the live-steam. A high-temperature storage system for storing steam decouples the steam generator from the turbine. This allows the live-steam to be distributed between the storage system and the steam turbine depending on the current grid demand. The combustion and the steam generator can be operated at their design operation point and produce a constant live-steam quantity. In times of low demand, only the minimum amount of steam is fed into the turbine. Excess steam is stored in the storage system. This makes it possible to reduce the plant’s power output to a minimum.
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In times of high demand, the entire live-steam volume is fed into the turbine. In addition, the storage system can be discharged, and the resulting discharge steam can then be injected into an additional turbine or into a lower pressure level of the main turbine, thus increasing the total amount of power generated. A simplified diagram is shown in Figure 2-4.
This concept has been implemented in the past for conventional peak load plants. In Berlin, Germany, the first plant was equipped with low-pressure steam storage (~ 1.4 MPa) in 1926. In the 1930s, coal plants in Vienna, Austria, were equipped with high pressure (~12 MPa) steam accumulators (Beckmann & Gilli, 1984). This concept did not gain widespread acceptance due to the low demand for peak-load plants at that time (Goldstern, 1970). Later, concepts for peak-load turbines in nuclear power plants were developed and the economic benefits investigated (Gilli & Fritz, 1970).
Figure 2-4: Dispatch via high-temperature storage.
Storing steam in its vapour form is neither economically nor technically feasible (Goldstern, 1970). Therefore, the so-called steam accumulator is the dominant technology for storing steam. This storage device is charged with steam, which is converted into a saturated liquid. During discharging, the pressurised liquid is expanded and vaporised to saturated steam. Characteristic features of the steam accumulator are high pressure drops, exergetic losses and a sliding pressure output (Goldstern, 1970; Beckmann & Gilli, 1984). Recently, studies have been conducted on the integration of steam accumulators in steam cycles of conventional power plants for reasons of flexible power generation (Brauner, 2011). For example, Siemens has filed a patent for the integration of a steam accumulator for flexible power generation in gas-fuelled power plants (Vortmeyer, 2012). Stevanovic developed a concept to improve the flexibility of thermal power plants by using steam accumulators (Stevanovic, et al., 2020).
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Ortwein & Lenz (2015) describe this concept for biomass as the option with the lowest efficiency. Although these statements are true in some cases, they are not substantiated for plant designs that combine steam accumulators with other HTS technologies. There are many alternative and improved technologies that offer the possibility of steam storage and have shown better characteristics than the steam accumulator. Hoffstede et al. (2016) stated that there is a need for research in the development of HTS technologies as far as higher energy storage capacities are concerned.
In the area of concentrated solar power (CSP) plants, several options for steam storage have been investigated. Due to the uncontrollable solar radiation, storage systems are essential for this type of power plant. The main objective in this case is to enable stable plant operation during periods of low or no solar radiation.
Eck et al. (2003) and Zarza et al. (2004) found that direct steam generation can reduce the power generation costs of CSP plants, so various CSP plants with steam cycles were developed. Several prototypes and commercially used plants with integrated steam storage devices have been built. Due to the disadvantages, especially the exergetic losses and the limited capacity of the conventional steam accumulator, new solutions for the storage of steam have been introduced.
Regardless of the energy source and the method of steam generation, the basic designs of steam cycles for solar electric and biomass power plants are very similar. Therefore, the technical feasibility of integrating HTS from solar to other power plants is assumed. However, the boundary conditions of the respective technology must be taken into account.
Richter et al. (2016) concluded that the integration of thermal energy storage is a promising concept for making steam power plants more flexible. They identified a need for further studies on the integration of storage in steam plant processes.
2.3 High-Temperature Storage for Biomass CHP Plants
The following section summarizes published research on steam storage. There are several different categorizations of storage devices. One categorization of HTS refers to the storage material, as shown in Figure 2-5.
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Figure 2-5: Classification of HTS according to Gil et al. (2010)
A further distinction is made between direct and indirect systems. In direct storage, the heat transfer fluid (HTF) is also the storage medium. In contrast, in indirect storage systems, the HTF is separated from the storage medium via a heat exchanger.
A third categorization is made between active and passive storage systems. In active storage systems, the storage medium circulates, while in passive storage systems, the medium does not circulate (Gil, et al., 2010).
2.3.1 Technical and functional requirements
According to the engineering design process methodology (Pahl, et al., 2007), requirements and functional structures should be identified. For the integration of HTS systems in biomass CHP plants, typical steam cycle parameters are summarised in Table 2-2.
Table 2-2: Average biomass CHP parameters (Scheftelowitz, et al., 2014), (Savola, 2007).
Parameter Value
Generation capacity 3–20 MW
Live-Steam temperature 400–500 °C
Live-Steam pressure 6–9 MPa
Saturation temperature (live-steam pressure) 275–300°C
The parameters of the live-steam are given by the plant design. Therefore, the pressure and temperature of the charge steam are fixed. However, the parameters of the discharge steam are defined by the storage system. For power plant applications, it is neither the stored mass nor the heat but the storage of available energy (i.e. the exergy)
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that is relevant (Beckmann & Gilli, 1984). The parameters (pressure and temperature) and the amount of discharge steam define the possible amount of electricity generated. In order to develop a highly efficient storage system, the exergetic losses must be minimised. Therefore, the temperature and pressure difference between charging and discharging steam is an important value for comparing different systems.
In order to define the task of the storage concepts, the basic operating principles of steam storage applications must be considered. In accordance with the conceptual design process, a functional analysis is performed to examine the system.
As shown in Figure 2-6, the basic tasks of the storage system are thermal storage and conversion of the HTF. The storage has three different modes of operation: charging, discharging and stagnation/storage (Gil, et al., 2010). During the charging process (Figure 2-6a), the superheated steam must be converted into subcooled condensate, and the thermal energy has to be stored in the system. During discharge (Figure 2-6b), the stored energy is used to heat and evaporate the HTF.
Figure 2-6: Functional structure of the steam storage system.
The conversion part of the system is of particular interest here. A sub-function on a lower level shows the three steps of the conversion. During discharge (Figure 2-7b), the condensate is preheated to saturation temperature, evaporated and finally superheated to the required steam temperature. During charging (Figure 2-7a), the superheated
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steam is cooled down to condensation temperature, condensed and subcooled to the temperature of the condensate line.
Two parts of the transformation are characterized by sensible heat exchange — precooling/superheating and preheating/subcooling — and one part by latent heat exchange — evaporation/condensing. Steinmann describes the advantages of selecting appropriate technologies for each conversion step. He described the pinch-point problem that arises when only sensible storage technologies are used for the entire conversion process.
Figure 2-7: Substructure of the steam storage system.
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Figure 2-8 shows the simplified heat exchange of a sensible storage system (Steinmann, 2006).
Figure 2-8: Simplified T-s diagrams for various storage media (Steinmann, 2006).
As shown, the charging steam enters the storage system and is cooled to condensing temperature. The HTF is then condensed at a constant temperature. Finally, the condensate is subcooled. During this charging process, the energy of the HTF is transferred to the sensible storage medium, which is thereby heated. The temperature of the storage medium also increases during the isothermal condensation of the HTF. Therefore, the lowest temperature difference (pinch point) between the charging steam and the storage medium is approximately the condensation temperature of the steam, on the right-hand side of the diagram.
During discharge, the evaporating temperature of the HTF must be set below the temperature of the storage media. Thus, the pinch point between HTF and storage medium is on the left-hand side of the diagram. Since the achievable evaporating temperature determines the steam pressure, there is a significant difference between the charging and discharging steam pressure.
Figure 2-8b shows a combination of latent storage media for the latent parts and sensible storage media for the sensible parts. Due to the isothermal heat exchange of the HTF and latent material, the evaporation temperature and, respectively, the pressure can be set significantly higher than in Figure 2-8a (Steinmann, 2006).
To identify the most appropriate storage technology for storing the superheated steam from a biomass CHP, a storage system consisting of several technologies may be advantageous. In addition to the solution of the pinch-point problem, storage media and
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technologies can be optimised for each conversion step, such as the best medium for the respective temperature level can be selected.
2.3.2 Storage Technologies
Several HTS technologies are available and are summarised in this section.
Solid Thermal Store: The solid thermal store is a regenerative storage concept. Hot and cold HTF is passed through alternately. During charging, the hot HTF heats the material. During discharge, the cold HTF is heated by the stored energy. The most common storage medium for passive storage devices is concrete. The thermal stability of concrete can be guaranteed up to 400 °C (Laing, et al., 2009). Studies with concrete cubes support the assumption that high-temperature concrete can store sensible heat up to 500 °C (Laing, et al., 2011). This system has good scalability to higher heat storage capacities. Increasing the storage performance leads to decrease of the specific costs, especially for storage units < 10 MW. Concrete storage systems offer specific heat storage capacities between 60–160 kWh/m³ (Gil, et al., 2010).
Concrete is a material characterized by high thermal capacity and a low material price (Kuravi, et al., 2013). Instead of concrete, cast iron and ceramics are also used for solid storage systems, whereby the storage capacity can be increased up to 600 kWh/m³. A disadvantage of this passive storage technology is the poor heat transfer compared to active storage concepts. Passive heat transfer causes the temperature of the storage to change during the charging/discharging process. Therefore, the efficiency of heat transfer decreases from the beginning to the end of the charging/discharging process. Due to the pinch-point effect (Section 2.3.1), solid storage can only be used for the sensible storage parts.
Two-Tank System: in two-tank systems, salt or oil is normally used as the storage medium. The main feature of the two-tank system is a hot tank and a cold tank. In two- tank systems, the HTS flows through a heat exchanger. During the charging process, the cold medium is pumped from the cold tank to the hot tank via the heat exchanger. In the heat exchanger, the storage medium absorbs the energy from the HTS. During the discharging process, the HTS medium is pumped from the hot tank to the cold tank and releases its energy to the HTS via the heat exchanger.
The volumetric heat capacity of the material is between 50–250 kWh/m³. Common storage media for two-tank systems are molten nitrite and nitrate salts with operation temperatures in the range of 250–265 °C on the cold side and 450–565 °C on the hot
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side. These materials offer a very good heat capacity (150–250 kWh/m³). The main disadvantages of these storage systems are the high cost of material and the risk of freezing (Gil, et al., 2010). Two-tank molten salt energy storage systems are well- established in the field of CSP plants. Several applications of up to 110 MW have been installed and are now in commercial use, but exclusively on plants without a steam cycle.
Steam Accumulator: The steam accumulator, also called the ‘Ruths storage’, is an established storage technology. Steam accumulators (Figure 2-9) store sensible heat in the form of liquid water. Even when the storage is completely discharged, there is still a liquid phase inside. The steam accumulator is charged with saturated or superheated steam, which is injected directly into the liquid phase and condenses immediately. By opening a valve in the discharge line, saturated steam is generated immediately. During the discharging process, the pressure decreases continuously (Steinmann, 2006).
Figure 2-9: Steam accumulator (Steinmann, 2006).
The subcooling/preheating part is not necessary with this system, as the storage media remains preheated. The steam accumulator can operate at pressures of up to 10 MPa, and the volumetric storage capacity of the steam accumulator is about 20–30 kWh/m³ (Steinmann & Eck, 2006). This capacity is strictly dependent on the charging and discharging pressure and can be increased if a higher-pressure difference can be achieved.
Advantages of the steam accumulator are the immediate availability of saturated steam, its simple construction and the direct connection to the steam cycle. Another advantage is the practical experience gained in years of use in industry and also in fossil-fuel plants. Disadvantages include the large vessel sizes, sliding pressure and the inability to
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produce superheated steam (Beckmann & Gilli, 1984). Another disadvantage of the steam accumulator is the cost intensive scalability. Upscaling to a higher storage capacity requires a larger vessel. Due to the high storage pressure, the thickness of the vessel walls would have to be increased in addition to the increase in volume, which in turn would lead to a significant increase in costs.
Phase Change Material (PCM) Storage: Latent storage devices use phase- change energy. A PCM storage device for steam is shown in Figure 2-10 below. The HTF can flow through the storage unit in both directions. During charging, the steam enters the PCM storage (C), is condensed and leaves the storage (B). The energy is transferred to the PCM and melts the material. During discharge, the liquid HTF enters the PCM storage (B), the PCM freezes and the released phase-change energy evaporates the HTF. A steam drum is required for discharging. This storage concept was tested in a pilot plant for natural and forced circulation (Steinmann, 2006; Laing, et al., 2011; Laing, et al., 2009).
Figure 2-10: PCM storage (Laing, et al., 2011).
Considering the cost factor as well as the thermal properties, Laing evaluated NaNO2 and KNO3-NaNO3 as the most suitable materials for steam storage in CSP plants (Laing, et al., 2009). Their melting temperature is in the range of 220–320 °C. Therefore, these materials are also suitable for the evaporation temperatures in a biomass CHP plant. Since each material has a defined melting temperature, the charge and discharge parameters are limited by the number of materials available. Low pressure drops are achieved by melting (see the pinch-point problem, Section 2.3.1). Table 2-3 summarises
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phase-change materials with suitable melting temperatures from Gil et al. (2010) and Laing (2012).
Table 2-3: PCM materials with potential for biomass CHP plants (Laing, et al., 2009).
PCM material Melting temperature Heat of fusion
KNO3 333–336 °C 116–266 kJ/kg
NaNO3 305–310 °C 172–199 kJ/kg
NaNO2 280 °C 200 kJ/kg
LiNO3 254 °C 380 kJ/kg
KNO3–NaNO3 220 °C ~ 100 kJ/kg
LiNO3–NaNO3 195 °C 252 k//kg
A major problem of latent-storage systems in the past was the change of the melting temperature due to ageing. However, charging tests with multiple charging and discharging cycles showed that the long-term stability of the PCM (KNO3-NaNO3) is given (Osuna, et al., 2006).
A reaction time of 5–10 minutes from the start of the discharging process to the provision of steam was measured on a prototype storage unit (Laing, et al., 2013). The isothermal heat transfer fits very well with the isothermal phase change of the HTF, as mentioned in Section 3.1. PCM storage provides a high volumetric storage capacity of around 100 kWh/m³ (Medrano, et al., 2010). Despite all its advantages, PCM storage technology has the highest complexity in design and construction compared to other HTS technologies. The low thermal conductivity leads to an increased effort in the construction of heat transfer structures (Laing, et al., 2009).
Thermochemical Storage: Thermochemical storage systems store energy via reversible chemical reactions. These storage systems have a high potential for long-term storage. However, as far as short-term devices are concerned, the dynamics do not match the requirements of steam cycles (Herrmann & Kearney, 2002). These technologies are the least studied and applied HTS technologies. Scaling up from prototypes to a multi-megawatt system would be a major challenge. In the future, this technology could have a high potential for integration into power plants. The focus of this work is on the transfer and improvement of established thermal energy storage systems
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from the solar field to thermal power plants. For this reason, such thermochemical concepts are not considered in the present study.
2.3.3 Storage Systems
As described in Section 2.3, efficient storage of superheated steam requires appropriate storage devices for the various conversion steps (Laing, et al., 2011). To achieve optimal results, adapted storage systems are required for each step of the conversion. Several system concepts described in the literature have already been implemented.
Steam Accumulator (SA): In the CSP plant PS10 in Andalusia, a single steam accumulator is installed for storing steam. Only saturated steam can be generated, and therefore, a special saturated steam turbine is required. Superheated steam cannot be generated, so a special saturated steam turbine was needed (Medrano, et al., 2010).
Steam accumulator with concrete storage (SA+C): To provide superheated steam, a solid thermal store is connected to the charge/discharge line of the steam accumulator (see Figure 2-11 below). The solid storage is used for the pre-cooling of the charging steam and superheating of the discharge steam. This system has been studied and simulated by Bai (Bai & Xu, 2011).
Figure 2-11: Steam accumulator with solid superheater (Seitz, et al., 2013).
Steam Accumulator with a Two Tank System (SA+2T): instead of solid storage, a two-tank system can be used for superheating. In contrast to passive concrete storage, an active two-tank system can provide constant efficiency in the heat
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exchanger. This concept has already been implemented in a CSP pilot plant in Beijing (Xu, et al., 2011).
Steam accumulator with encapsulated PCM (P+SA): Steinmann (2006) describes the integration of PCM into a steam accumulator. The PCM is designed to keep the temperature in the steam accumulator at an almost constant level. This can significantly reduce the pressure drop. No further information was available on this concept.
PCM with two Concrete Blocks (P+2C): Due to the pinch-point-problem (Section 2.3.1), Feldhoff (2012) describes a combination of a PCM storage for evaporation and two thermal solid storage for sensible heat transfer parts. The design can be operated in reverse for charging and discharging. This concept is illustrated in Figure 2-12a. A pilot- scale storage has been built and tested in Andalusia, Spain (Laing, et al., 2011).
Figure 2-12: Concepts for PCM storage systems (Seitz, et al., 2013).
PCM with Three Tank System (P+3T): Feldhoff (2012) also describes a concept with three tanks (Figure 2-12b). The salt melting tanks fulfil the tasks of superheating
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and preheating. The buffer tank allows for different mass flows of the storage media in the preheater and superheater, further reducing the pinch-point-problem (Feldhoff, et al., 2012).
PCM with Two-Tank Superheater (P+2T): This concept (Figure 2-12c) is similar to the P+3T concept. There, only the superheating part is performed by a two-tank system. The preheating, as well as the evaporation, is fulfilled by the PCM. Feldhoff (2012) concludes that this option is the most promising option at the current stage of development for CSP plants.
Other Technologies: Some other storage systems for use in solar power plants are also worth mentioning, such as thermocline, (Kuravi, et al., 2013), double-steam accumulator with indirect superheating (Gilli & Fritz, 1970) and packed-bed systems (Gil, et al., 2010). These systems have several significant disadvantages when used in steam cycles — particularly with regard to the pinch-point problem — and are therefore not considered further.
The capabilities of the above storage devices are summarised in Table 2-4
Table 2-4: Summary of steam storage systems (Stark, et al., 2017).
System Superheated Sliding Latent heat Sensible heat TRL Speed of steam pressure capacity capacity reaction
SA no yes 20–30 kWh/m³ x 9 <30 s
SA+C yes yes 20–30 kWh/m³ 60–120 kWh/m³ 7–9 <30 s
SA+2T yes yes 20–30 kWh/m³ 150–250 kWh/m³ 5–7 <30 s
SA+PCM no yes 30–100 kWh/m³ x 1–3 n.a.
P+2C yes no 100–150 kWh/m³ 60–10 kWh/m³ 5–7 5-10 min
P+2T yes no 100–150 kWh/m³ 150–250 kWh/m³ 3–4 5-10 min
P+2T yes no 100–150 kWh/m³ 150–250 kWh/m³ 3–4 5-10 min
The ability to deliver superheated steam at a constant pressure affects the efficiency of the systems and the output of the downstream power generator. The heat capacity of the storage material and also the pressure level of the storage affect the size and thus the cost of the storage system. The speed of reaction is the time required from the start of the discharge process until steam is available at the desired parameters. The technology readiness level (TRL) shows the status of research and development.
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An overview of the storage systems investigated is shown in Figure 2-13. The structure of the systems that can produce superheated steam is shown.
Figure 2-13: Storage systems for flexible power generation (Stark, et al., 2018).
Each of these storage systems has the technical possibilities for integration into the steam cycle as well as advantages and disadvantages.
2.4 Research Gaps and Chapter Summary
Summarising the state-of-the-art, there are several possibilities for the use of biomass CHP plants:
• Flexibilisation at the chemical conversion level (Section 2.2.1) suffers from a number of disadvantages and restrictions. • Flexibilisation via hot water storage (Section 2.2.2) has some advantages, but the small load range and the limitations in terms of heat demand have a negative impact on economic feasibility. • The concept of steam storage devices (Section 2.2.3) can be seen as an improved version of dispatch via hot water storage.
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Decoupling the inflexible parts (combustion and boiler) from the highly flexible parts (turbine and steam cycle) of the plant allows each of the components to operate closer to design operating point with the best efficiency. The stored energy can be used to generate electricity, so there is no longer any limitation due to the heat demand. Also, the highest possible load range can be achieved as the steam flow in the turbine can be reduced to its minimum partial load point (Herrmann & Kearney, 2002).
Unfortunately, there is a lack of knowledge here as the flexibilisation of biomass power plants has not been the focus of scientific research in recent years. The summarized work describes the steam accumulator as not very efficient, but a detailed investigation is missing. In the ever-changing markets and grid situations, grid-relevant operation may also be achievable with less efficient technologies. New steam storage concepts and also improved concepts for the steam accumulator have not yet been investigated with regard to their integration in power plants; this applies in particular to biomass CHP plants.
The integration of high-temperature storage units for flexible power generation from biomass CHP plants is therefore a novel approach. Referring to Ortwein (2015) and Thrän (2015), new technologies need to be developed and improved. The basic components are available at high technology readiness levels, but system integration needs further investigation.
Due to their operation on the steam cycle (IEC level) of the plant, the integration of HTS in different types of steam-cycle power plants is similar but not identical. Regardless of whether the research is focused on coal, solar or biomass, the results are transferable to some degree but with limitations.
According to the described research results in the field of concentrated solar power (CSP) plants, a solution for the pinch-point problem is necessary. To overcome this pinch-point problem (Section 2.3.1), a combination of more than one technology seems to be advantageous. This would allow the optimal technology to be used for both sensible and latent parts.
The main gap of knowledge in biomass CHP concerns the impact on the power generation process associated with the integration of a storage system. Identifying the costs and benefits of these approaches are the main objectives of this research. Of additional interest are the possibilities for the technical integration of a storage system into the plant and its interaction with the turbine.
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Given the large number of different storage devices available, the most suitable system must be defined. Therefore, the specific requirements of the market, storage and plant must be considered to identify the important capabilities of the systems.
As far as markets are concerned, a considerable amount of research has been carried out to identify the relevant markets and to develop control strategies for flexible power generation. An upcoming task would now be the modification of the existing control strategies with regard to the specific performance of a biomass CHP plant with steam storage devices.
Another knowledge gap concerns the interaction of storage operation with heat and steam supply. Existing research on HTS for power plants focuses on buffering steam, exclusively for power generation. In biomass CHP plants, but also in all other steam- driven CHP plants, the concept of steam storage offers some additional advantages. The discharge steam can also be used for steam and heat instead of generating electricity. This results in additional degrees of freedom, which offer a further improvement of the overall benefit.
The combination of control strategies for heat-led and power-led modes of operation can also be identified as a research gap. Although some work has been done in this area, the interaction and especially the combination of several control strategies for a flexible power supply has not been investigated at all.
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3 Evaluation of Suitable Storage Systems
This chapter focuses on the development of a comprehensive evaluation method to identify the most suitable storage system. In accordance with section 2.3.3, the considered storage concepts are summarized in Table 3-1. Five storage concepts were identified as basically suitable for the required task. For the PCM storage, two materials are available for the pressure range of a biomass CHP plant: LiNO3 and KNO3+NaOH3. So, in total 8 different storage systems are basically suitable for the required task.
Table 3-1: Suitable storage system for the utilization in flexible biomass CHP Plants.
Acronym Latent part Sensible part(s)
SA+2T Steam accumulator 2-tank molten salt storage
SA+C Steam accumulator Solid thermal store
PL+2T PCM (LiNO3) 2-tank molten salt storage
PL+3T PCM (LiNO3) 3-tank molten salt storage
PL+2C PCM (LiNO3) 2 solid thermal stores
PK+2T PCM (KNO3+NaOH3) 2-tank molten salt storage
PK+3T PCM (KNO3+NaOH3) 3-tank molten salt storage
PK+2C PCM (KNO3+NaOH3) 2 solid thermal stores
Choosing the best solution from a selection of alternatives is a common problem in research, and it is therefore unsurprising that a number of decision methodologies are available. Many of these concepts are based on a multi-criteria comparison.
Billig & Thrän (2016) compared several of these decision methodologies. The authors evaluated wood gasification units from a selection of available technologies and identified both the Analytic Hierarch Process (AHP) (Saaty, 2008) and the Utility-Value- Analysis from the Engineering Design Process (Pahl, et al., 2007) as suitable options.
The authors then went on to develop the adapted AHP by including several procedures from the Utility-Value-Analysis into the AHP. This procedure attaches significant importance to the selection and the prioritisation of the criteria. This will be also of particular importance in the present research, which is why the adapted AHP was chosen for the research. Billig & Thrän (2016) envisaged the following procedure:
• First, the problem of the comparison has to be described • Depending on the nature of the decision problem, criteria need to be defined
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• The criteria have to be prioritised against each other. In the present research, this was performed by experts using a Delphi survey. A criteria matrix has to be created. • Assignment of criteria with parameters or statements • Setting up an assessment scheme • Evaluation of the concepts according to the assessment scheme and multiplication with the criteria matrix. • A critical analysis of the results is performed to identify the most suitable concept, which in this case is the best storage system for further research.
3.1 Definition of the Problem
The aim of this evaluation is to identify the most suitable storage system for the integration of biomass CHP plants. Due to the importance of the three main topics ‘flexibility’, ‘biomass plant’ and ‘storage system’ (see section 1.3) the unit that might appear to be the best storage unit may also not necessarily lead to the best system performance. Consequently, the storage system should not only be evaluated as a stand-alone component but as part of the system.
This therefore entails defining the problem as a technic-economic comparison of concepts for flexible biomass CHP plants. This necessitates comparison of not only the storage systems but also the entire plant systems. Hence, each of the storage systems, summarised in Table 2 4 below, is handled as a biomass CHP plant equipped with the system.
This approach allows for a holistic view of the problem. Additionally, a comparison of the novel steam storage concepts with the existing (Section 2.2) or any other future flexibilisation concepts can be undertaken.
3.2 Prioritisation of the Criteria
3.2.1 Selection of Criteria
The problem has been broken down into assessable criteria. Pahl (2007) suggests drawing up objectives that cover the relevant requirements and constraints, as well as the properties of the system. From these objectives, it is possible to derive criteria. These criteria are needed for evaluation in the next parts.
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Currently, flexible biomass plants with steam storage only exist at the concept stage. This means that there are no exact datasheets or measurements for the flexible plants and their operation currently available. Nevertheless, it is important to ensure that sufficient data are available for the assessment of the criteria. Another challenge for the definition of the criteria relates to their comprehensibility for experts. In the recently emerging research field of flexible power generation, there are no exact definitions for all of the specific points of interest, which means that defining the criteria will clearly be key.
Considering the available data and the relevant boundaries, criteria and sub-criteria were defined. In total 5 main-criteria and 12 sub-criteria were developed (Figure 3-1). The definition of the criteria was done with a particular focus on the specific aim of this evaluation and the research at hand:
• The performance of the plant in flexible operation • The efficiency of the flexible operation compared to a base-load operation • Ability to act on the available markets or the grid (Application areas) • The costs of the flexibilisation concept • Process synergies are creating some additional advantages-
Figure 3-1: Summary of the criteria.
The criteria Performance, Efficiency and Application Area are related to a wide range of individual abilities. Therefore, these criteria have been divided into sub-criteria.
Performance
In general, the aim of flexible power generation is the shift of capacity from low demand periods into high demand periods. During conventional operation, the plant is operated
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at its baseload capacity PBaseload. This available capacity must be shifted into periods where the electricity is needed. An overview of this process is provided in Figure 3-2.
In flexible generation mode, the power generation of the plant is reduced to Pmin while charging the storage. During discharge, the additional storage turbine is operated. The sum of both, PBaseload and PST is the maximum power generation Pmax. The charge tcharge and discharge tdischarge periods are depending on the energy capacity of the storage as well as on charge or discharge load.
Figure 3-2: Simplified flexible plant operation according to Stark et al. (2018).
The criterion performance is subdivided into load range, period, minimal load and part- load flexibility.
Load Range (LR): The load-range is the difference between the minimum and the maximum load of the power generation
Period (t): This criterion describes how long the flexible load can be supplied. In other words, the length of the period, the maximum or minimum load that can be generated is assessed in this criterion. The period is limited by the storage capacity and the charge/discharge mass flow. For this evaluation tcharge and tdischarge are adjusted to the same value.
Minima Load (Pmin): This criterion describes how far the power generation of the plant can be reduced. In periods of oversupply the plant has to reduce the load to a minimum.
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Part-load Flexibility: This criterion describes the ability to generate power in discrete load intervals between the minimal and the maximum load. In the best case, each value between minimal and maximum load can be supplied with infinitely variable control.
Compared to the baseload operation a reduction of the plant’s efficiency during flexible power generation is expected. The criterion efficiency is subdivided into flex factor and provision losses.
Flex Factor (FF): The plant is operated away from its designed operation point. Due to this part-load or overload operation, losses will occur. Compared with the baseload operation of the plant, less electricity will be generated (see Section 2.3). These resulting losses are evaluated in the criteria flex factor.
Provision Losses: Given the provision of capacity to increase or decrease the power output, additional losses are expected. These losses occur due to the additional losses of the loaded storage system. These losses are rated within the sub-criteria provision losses.
Application Areas/Market: This criterion rates the ability to act on the available markets or areas described in section 2.1.1. This ability is mainly defined by the achievable reaction time of the increase or, rather, decrease in power generation.
• Operation on control-reserve or balance energy (BE) markets (primary, secondary, minute-balance control-reserve) • The energy-only markets (intraday and day-ahead-market) • Balancing the grid on the distribution network level (DNL).
Cost: The investment costs of the flexibilisation are rated in these criteria.
Process Synergies: The criterion process synergy relates to the interaction of the flexibilisation concept with the power plants process and the heat supply. Some concepts can create some additional benefits: For example, a steam accumulator is mainly used for flexible power generation. In addition, this storage can also increase the coverage ratio and the efficiency of the plant’s external and internal heat supply. Also, the process stability or the process efficiency can be increased. The advantages are described in more detail in the further sections.
3.2.2 Delphi Survey
In the next step, each of the criteria must be prioritised, for which the present research used a Delphi survey. The aim of such a survey is to generate an objective evaluation Steam Storage for Flexible Biomass CHP Plants Page 36
by involving a group of experts and thus eliminate any subjective opinions of the author. Ultimately, the average of all the expert judgements is regarded as being objective, even if the single ratings are also subjective (Billig & Thrän, 2016).
A Delphi survey can be carried out with a single survey or with an iteration of surveys where the experts can refine their decision regarding the judgements of the other experts. In their study, Billig & Thrän (2016) identified that the response rate of a second survey round is relatively low. In light of these findings and also to avoid influencing the experts, only one survey round was carried out.
To achieve reliable results, the following points have to be considered:
• Coherent description of the questions and the criteria, • A carefully chosen selection of relevant experts, • Careful processing of the results.
A survey was designed to give the experts an overview of the problem, an explanation of the criteria and the opportunity to make some comments. In line with the AHP, decision matrices were created to enable the experts with a pair-wise comparison of the criteria. A decision matrix for the criteria performance is shown in Table 3-2 as an example. The full survey can be found in Appendix I.
Table 3-2: Blank decision matrix for the criteria performance.
Description Load range Period Minimal load Part-load flexibility
Load range 1 - - -
Period 1 - -
Minimal load 1 -
Part-load flexibility 1
Given the recently increasing interest in flexible power generation, a number of different actors have begun working in this specific field of science. Experts from following sectors were identified as relevant for this survey:
• Experts from research and development for flexible power generation from biomass and other decentralised CHP units.
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• Energy distributors acting between the plant operators and the markets. Of particular interest in this instance are the so-called ‘direct marketers’ who trade electricity for the plant operators. • Plant operators who deliver flexible power • Planners for biomass CHP plants considering the implementation of new technologies for flexible power supply in planned plants.
It was anticipated that experts who have recently begun to work in this field would have a detailed idea about the markets and the technology. As a result, only these experts were asked to participate in this survey. Because the criteria were designed to evaluate the whole plant's system, the specific storage technology has no direct influence on the prioritisation of the criteria. Thus, no detailed knowledge in this field was necessary.
Flexible power generation is developing rapidly. The experts were asked to make their judgements not only on the current state of development but also to predict future development.
In total 40 experts were selected and contacted (see Table 3-3). A relatively high return of the surveys was achieved (58%). Two surveys had to be removed due to an incomplete prioritisation. The sectors of the experts are shown in Figure 3-3 below. In total there are 25 participants because some experts are active in more than one sector.
Table 3-3: Overview of the survey participants.
Description Number Share
Selected experts 40 100%
Replied 24 58%
Incomplete survey 2 5%
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Figure 3-3: Number of participants separated according to their sector.
The AHP procedure requires that the consistency of the decision matrices be checked. If the criteria A, B and C receive the ranking A > B > C, it should also be A > C. If this is not the case, the matrix is considered as inconsistent. In practice, experts can hardly achieve this, especially when using large criteria matrices and complex issues. Saaty (2008) defined the Criterion Ratio (CR) for the consistency of multi-criteria matrices- If the CR is smaller than 0.1 the matrix can be considered as inconsistent.
Inconsistency causes errors and points to the lack of certainty to obtain logical and true results. Davoodi (2009), for example, remarks that (especially) complex questions can reach a high share of inconsistent results. Saaty (2008) recommended that decision- makers should review their ratings in a second survey round. However, this led to an increased risk of errors. Large matrices require a significant number of reviews necessary to obtain a consistent matrix. Due to the low response rates during additional rounds, the amount of usable data becomes increasingly less. Independent of the consistency, the first judgements of the participants are valuable information. Davoodi (2009) remarks that even if the matrix is inconsistent, the single rows are consistent in any case. The author thus developed an algorithm to convert the judgements of an inconsistent matrix into a consistent matrix based on the single row judgements. With the Davoodi algorithm, inconsistent matrices can be converted into consistent matrices with a slight deviation of the results.
Each matrix from the survey is then checked for consistency and, where these are found to be inconsistent, these matrices are converted, which guarantees that all the important information of the carefully chosen experts can be used.
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3.2.3 Survey Analysis
The results from the Delphi survey are concluded in a criterion matrix in Table 3-4. Given the variety of different branches, the individual priorities of the experts, statistical outliers were expected. A median is a common tool for this type of survey, because of its resistance against extreme values. So, the median was formed taking all experts’ results into account. These results are normalised, and they receive the priority of every criterion against each other. The sum of the sub-criteria priorities results in the priority of the main criteria.
Table 3-4: Survey results – Criterion matrix.
Criterion Sub-Criterion Priority Priority MD median min max Performance 16% 5% 59% 10% Load-Range 4.4% 0.6% 19.5% 3.6% Period 5.7% 0.6% 26.9% 5.1% Minimal-Load 3.7% 0.2% 19.6% 2.5% Part-load-ability 2.4% 0.2% 7.4% 1.3%
Efficency 11% 4% 31% 6% Flex factor 7.8% 0.9% 21.4% 4.0% Provision losses 3.2% 0.4% 26.2% 4.3%
Application areas / markets 20% 5% 30% 7% Primary balancing energy 2.0% 0.1% 12.7% 1.9% Secondary balancing energy 1.9% 0.4% 4.0% 0.7% Minute balancing energy 1.7% 0.2% 3.5% 0.6% Intraday-Marktes 5.8% 0.8% 11.0% 2.7% Day-Ahead-Markets 4.1% 0.3% 10.7% 2.0% Balancing DNL 4.0% 0.6% 11.6% 1.9%
Costs 31% 5% 65% 15%
Process Synergies 22% 3% 49% 14%
Total 100%
The costs were prioritised as the most important criteria (31%). Performance (16%), application area (20%) and process synergies (22%) were weighed similarly. Efficiency was rated with the lowest value (11%). The mean deviation from the median (MD) is around 6–15% from the main and with a maximum of 5% from the sub-criteria.
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3.3 Evaluation of the Most Suitable Concept
3.3.1 Assignment of Parameters
In the next step of the decision process, criteria have to be assigned to known (or analytically determine) parameters. Values may be expressed by statements where this is not possible (Pahl, et al., 2007). Because present research is still at the concept stage, there are no measurement data available yet. The determination of the detailed behaviour of these plants will be the main task of this research. Hence at this stage, no simulation results are available. For comparison purposes, a case scenario was defined as a simplified calculation for each of the storage concepts is done.
The plant setting of this case scenario is shown in Figure 3-4, where turbine 1 is the original plant turbine and turbine 2 is the storage turbine. Turbine 2 is a part of the storage system in order to temporally increase the maximum power generation of the plant. A constant live-steam flow is assumed, so the turbine is the only relevant component for the determination of the power generation.
Figure 3-4: Operation steps of the calculated plant (Stark, et al., 2018).
With this scenario, one full charge and discharge cycle in flexible operation (cf. Figure 3-4b) is compared with the same plant at baseload operation (Figure 3-4a).
The total storage volume is divided between the storage components according to the required capacity. So, each storage system has the same total volume of the storage media, but different properties. The plant’s live-steam is directly used for charging;
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therefore the live-steam properties (LS) are the same as the charge steam (CS) parameters. The total volume of the storage systems was set at 150 m³ for all concepts. The parameters of the calculation are concluded in Table 3-5.
Table 3-5: Plant properties for the case scenario.
PBaseload 5 MW Plant turbine: T1
Pmin 1.5 MW ηis 70% (PBaselod)…52%(Pmin)
TLS=TCS 450°C pout 0.01 MPa
pLS=pCS 7.5 MPa Storage turbine: T2
ṁLS 5.69 kg/s ηis 70%–65%
Vtotal 150 m³ pout 0.05 MPa
The storage media were selected according to the required steam properties. The focus was set on materials that have proved their suitability in similar storage concepts (see Section 2.3.2). The selected storage media are described in Table 3-6.
Table 3-6: Properties of the selected storage media.
Storage Type Specific heat capacitya,b Specific costsb,c
Solid thermal store (concrete) 100 kWh/m³ 15–20 €/kWh b
Molten (nitrate salt) salt storage 250 kWh/m³ 30–40 €/kWh b
Steam accumulator calculated (Stevanovic, et al., 2014) 7,000–8,200 €/m3 c
b PCM storage (NaOH3+KNO3) 55 kWh/m³ 100–200 €/kWh
b PCM storage (LiNO3) 249 kWh/m³ 100–200 €/kWh
a (Gil, et al., 2010) b (Laing, et al., 2009) c (Stark, et al., 2018)
Due to its complex operation, the steam accumulator is modelled using the equilibrium model formulation (Stevanovic, et al., 2014). A pressure drop from 6 MPa to 1 MPa and a useable volume of 80% are assumed. As the storage systems consist of n components, the total volume is subdivided into these components. It was assumed that 100% of the sensible energy was stored in the sensible storage components and 100% of the latent energy in the latent components. Thermal losses and detailed storage behaviour were neglected.
The criteria load range, min load, period as well as flex factor and costs are calculated with the given values and equations. In Equation (3-1) the load-range is normalized as a share of the installed plant’s capacity.
Steam Storage for Flexible Biomass CHP Plants Page 42
푃 + 푃 퐿푅 = 푚푎푥 푚𝑖푛 (3-1) 푃푏푎푠푒푙표푎푑
The flex factor FF is defined as a share of electricity that is generated in a flexible mode compared to the base-load operation (Equation 3-2).
푄 + 푄 푡푑𝑖푠푐ℎ푎푟𝑔푒 ∗ 푃푚푎푥 + 푡푐ℎ푎푟𝑔푒 ∗ 푃푚𝑖푛 퐹퐹 = 푚푎푥 푚𝑖푛 = (3-2) 푄퐵퐿 (푡푑𝑖푠푐ℎ푎푟𝑔푒 + 푡푐ℎ푎푟𝑔푒) ∗ 푃퐵퐿
A detailed description of the calculation is given by Stark et al. (2018). The remaining criteria cannot be calculated with sufficient accuracy at this stage. For this reason, they were rated using qualitative statements.
Part-load flexibility: Due to the use of a flexible steam turbine in each concept, the criterion part-load flexibility is rated as infinitely variable.
Provision Losses: The calculation of the provision efficiency needs a specific investigation. The provision losses are primarily dependent on the temperature losses of the storage and therefore the size of the storage system. Three statements: high, average and low losses are set (see Table 3-7).
Table 3-7: Provision losses rating categories.
+ low losses No storage
o average losses PCM, solid and molten salt storage
- high losses steam accumulator
Application Area: The ability to act on the markets depends on the required and the achievable reaction time. This ability can be divided into three categories (see Table 3-8). According to the information gained in the literature review, especially Table 2-1 and Table 2-4 the values are set. If there are system components in addition to the storage with a relevant start-up speed (pumps, indirect heat exchanger), the rating achievable with effort are given.
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Table 3-8: Application area rating categories.
+ Achievable Response time suitable to act on the concerned area
o Achievable with effort Additional effort is necessary to guarantee the response time of the system
- Not Achievable Response time is too slow to act in the concerned market
Process Synergies: The exact influence on the system cannot be estimated at this stage. This will be a part of this research. Five different process tasks were identified that can be supplied from the storage system. Each task offers an additional degree of freedom for the operation strategy of the plant. The best combination of flexible power generation and the addition process synergies may lead to the most profitable operation case.
• External steam supply: The coverage ratio of steam supply to external consumers (e.g. industry) can be increased. This can substitute extraction steam and increase the revenues. • External heat supply: The coverage ratio of heat supply to external consumers. This can substitute extraction steam and increase the revenues. • Internal steam supply: Supply of the plant’s internal steam demand like soot blowers, motive steam. Fast reaction is required; this can substitute extraction steam. • Internal heat demand: Supply of the plant's internal heat demand like preheating to substitute extraction steam. • Smoothing of steam extraction: Buffering of steam extraction. Decoupling of the extraction point and the storage. A rapid reaction in response to changes in steam demand can increase the total efficiency and avoid turbine failures.
The total amount of tasks which can be performed by the storage system is the rating value for the criterion process synergies.
The results of the assessment are concluded in the assessment matrix Table 3-9.
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Table 3-9: Assessment matrix.
-
3
o
o
o
+
+
i.v.
82%
104%
1.06 h 1.06
PK+2C
average
1000 T€ 1000
1.5 1.5 MW
-
3
o
o
o
+
+
i.v.
82%
105%
1.23 h 1.23
PK+3T
average
1200 T€ 1200
1.5 1.5 MW
-
3
o
o
o
+
+
i.v.
82%
105%
1.02 h 1.02
810 T€ 810
PK+2T
average
1.5 1.5 MW
-
3
o
o
o
+
+
i.v.
84%
109%
2.46 h 2.46
PL+2C
average
2250 T€ 2250
1.5 1.5 MW
-
3
o
o
o
+
+
i.v.
85%
3.9 3.9 h
109%
PL+3T
average
2700 T€ 2700
1.5 1.5 MW
-
3
o
o
o
+
+
i.v.
85%
109%
3.93 h 3.93
PL+2T
average
3600 T€ 3600
1.5 1.5 MW
4
+
+
+
+
+
+
i.v.
high
80%
1.3 1.3 h
100%
SA+C
1160 T€ 1160
1.5 1.5 MW
4
o
o
+
+
+
+
i.v.
high
80%
1.4 1.4 h
100%
SA+2T
1300 T€ 1300
1.5 1.5 MW
Costs
Balancing Balancing DNL
Day-ahead markets Day-ahead
Intraday markets Intraday
Minute Minute BE
Secondary Secondary BE
Primary BE
Provision loss Provision
Flex factor
Part-load flexibility Part-load
Minimal load
Period
Load range Load
Process synergies
Process synergies Process
Costs
Application area Efficency Performance Steam Storage for Flexible Biomass CHP Plants Page 45
Due to the definition of the case scenario, part-load flexibility and minimal load criteria are rated with the same value for each storage concept. Although the criteria are not helpful in this scenario, they were not neglected for a comparative assessment of different concepts for flexible power plants.
3.3.2 Assessment Scheme
It is necessary to set a value scale for the assessment of the concepts. Pahl (2007) proposes a small-scale where the characteristics of the concepts are inadequately known. Therefore, a scale between 0 and 4 is set. According to the determined parameters, the scheme is set in correlation with the parameters. Table 3-10 correlates the parameters with value scales.
Table 3-10: Chart correlating parameter magnitudes with value scales.
Points 0 1 2 3 4
Load range < 70% 80–105% 105–120% 120–125% > 125%
Period < 0.5 h 0.5–1 h 1–2 h 2–3 h > 3 h
Minimal load >75% 50–75% 25–50% 0–25% 0%
Part-load 1 stage >4 stages i.V. flexibility
Flex-Factor < 50% 50–60% 60–65 65–70 >70%
Provision losses - o +
Application area - o +
Process 0 1 2 3 4–5 synergies
Costs > €2,500 T €1,500–2,500 T €1,000–1,500 T €500–1,000 T < €500 T
According to this value scales, the assessment matrix can be created.
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3.3.3 Evaluation of the Concepts
Finally, the assessment matrix is multiplied with the criterion matrix. Now each concept receives a utility value. The utility value is the share of available points the concepts have achieved. Table 3-11 provides a ranking of the compared concepts.
Table 3-11: Evaluation results.
Ranking Utility Value Concept
1 79% SA+C
2 71% PK+2C
3 68% SA+2T
4 64% PK+3T
5 63% PK+2T
6 56% PL+2T
7 56% PL+3T
8 54% PL+2C
With a utility value of 79%, the storage system consisting of a steam accumulator combined with a solid thermal store is the best fitting concept. The advantages of a short reaction time, which make the system highly applicable to several markets; and low investment costs dominate overall despite the better performance and higher efficiency of other concepts. For further research, the steam accumulator combined with solid thermal store storage will be the system of choice.
3.4 Chapter Summary
An evaluation was carried out for a number of steam storage concepts. After experts rated the criteria it was possible to perform the evaluation.
A drawback of this decision process is the unavailability of measurement data. This means that there can only be a ‘rough’ calculation. However, the evaluation can be said to be suitable at this stage of the process. The concept of the steam accumulator combined with a solid thermal store has proven as the most fitting concept with a utility value of 79%.
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Due to this result, the thesis will focus on the steam accumulator with a concrete system. Nevertheless, a detailed investigation of the other systems, especially for their usage in thermal power plants, would be a future task. Currently, the reaction speed and the costs of the PCM system are very unfavourable. Maybe future development can improve the suitability of the PCM storage system. Also, the development of new PCM materials, more suitable for the biomass CHP plant parameters, could lead to better utility values.
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4 Modelling the Flexible Plant
In the previous chapter, the combination of a steam accumulator and a solid storage thermal storage was identified as the most promising storage concept. By integrating this storage system into a biomass CHP plant, this baseload plant is modified into a flexible power generator. The overall objective of this research is to investigate the performance of this flexible biomass CHP system.
In this chapter, a MATLAB Simulink model is developed. The steam storage system, as well as the relevant components of the biomass CHP plant, are modelled. This model forms the basis for the subsequent simulation studies. Figure 4-1 shows the flow of modelling and simulation.
Figure 4-1: Modelling and simulation.
The plant and the storage model are used for the simulation study. Based on the input parameters such as case scenarios and dimensioning parameters, the model is used to estimate the behaviour of the biomass plant and storage system and the impact on the energy system. Parameter studies are carried out with different sizes of a steam accumulator, a storage turbine and a solid thermal store in order to check the dependencies and the influence among each other.
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Due to the novelty of the approach, no biomass CHP plants with such storage systems have been implemented yet. Therefore, no measurement data are available for the validation of the overall system. To verify the results of the system model, the single- component and subsystem models are validated against measured data before they are combined into a system.
The following tasks are worked through one after the other in this chapter:
• Development of the model layout, • Modelling of the required sub-models/components, • Validation of the submodels, • Merging of the partial models into a flexible plant model, • Error analysis.
The most important sub-models are the steam turbine (4.2), the steam accumulator (4.3) and the solid thermal store (4.4). In separate sections, suitable model formulations are selected, implemented and validated against measurement data.
For the calculation of steam processes, it is necessary to consider the data of the steam table (e.g. enthalpy, entropy, specific volume), which depend on pressure, temperature and the vapour quality. The MATLAB add-in XSteam is used for this purpose. XSteam is a material library that has the function of calculating steam properties such as enthalpy, entropy, density, etc. depending on pressure and temperature. Backward functions for calculating pressure and temperature are also integrated. XSteam uses the industry standard IAPWS-IF97 for the interpolation of the steam properties.
For this research, the unit of analysis is the energy system, so it is not considered necessary to model the detailed physical behaviour of each single system component. It is important to calculate the performance over a wide range of different storage and turbine dimensions, operation parameters and control strategies. Model formulations used to calculate the exact physical behaviour require detailed information about the geometry (e.g. angles of the turbine blades) of the relevant components. Such information is not readily available to the researcher, especially for a wide range of different component sizes. Therefore, models with the ability to handle a wide range of dimensions are preferred, even at the expense of model accuracy.
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4.1 Modell Structure
The hydraulic diagram of the storage system is shown in Figure 4-2. The system consists of the solid thermal store, the steam accumulator and a storage turbine. In addition, two control valves are implemented for the charging and discharging process. The storage system has two connections to the plant to receive the superheated steam and remove the exhaust steam. The control system is fed with signals from the market or the grid to initiate and control the charging and discharging process.
Figure 4-2: Schematic of the storage system.
Figure 4-3 shows the interconnection of the storage system with the CHP process. The charge line is located between the steam boiler and the plant turbine so that part of the live-steam can be injected into the storage system to charge the storage and reduce the power output of the plant turbine. During discharge, the exhaust steam from the storage turbine is relaxed to condensation temperature and flows into the condenser. In addition, the feedwater tank is connected to the steam accumulator to allow a bidirectional fluid exchange.
An alternative connection point for the discharge steam is the external steam or heat supply. Here, depending on the extractions pressure level, the discharge steam can be used as a substitute for extraction steam. Direct supply from the steam accumulator without using the storage turbine is also possible. In a few cases, the existing turbine system could be used to avoid the storage turbine. Here, the discharge steam is reintroduced into the plant turbine.
For this research, the concept shown in Figure 4-3 is considered, as this option will be applicable to the majority of the existing biomass CHP plants.
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Figure 4-3: Schematic representation of the flexible biomass CHP plant process.
This hydraulic integration is chosen to avoid any expense of redesigning the biomass CHP plant system. This concept is applicable as an upgrade to the most existing biomass CHP plants. Apart from the storage system, no additional redesign is necessary in the plant’s process. It is an important task of the concept design to minimize the influence on the main process of the biomass CHP plant. Therefore, a retrofit concept that does not affect the baseload operation of the plant is most advantageous.
Since most parts of the processes are not affected by the storage, respectively, by the flexible operation, the plant model covers only part of the entire plant system. Figure 4-4 shows the structure of the flexible plant model developed in this research as well as its limitations.
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Figure 4-4: Structure of the flexible plant model, including model boundaries.
Three different input signals are fed to the model. The live-steam is fed into the plant turbine or into the storage system. The control system is responsible for charging and discharging the storage system, control signals, e.g. market or grid signals, are fed into the model to trigger the control system. Since the charging and discharging steam have individual energy contents, the water level βSA of the steam accumulator decreases with time. A connection to the feedwater system is used to balance the water level of the steam accumulator.
The extraction steam, as well as the exhaust steam, are outlet signals of the model and not processed further.
4.1.1 Model Components
The flexible plant model consists of the main parts, storage system and plant system. The following modules are the required parts of the model:
• Steam accumulator, • Solid thermal store, • Storage turbine (single-stage), • Plant turbine (multi-extraction turbine).
In addition, the system controller, consisting of control valves and a control unit, must be developed.
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In this system model, the fluid mechanics and thermal influence of pipes, fittings and measuring devices are not modelled in detail. In this system view, the behaviour of the main components is more important and has the greatest influence on the system.
The individual components of the storage system model are developed as standalone Simulink blocks. This modular design enables various advantages. Simulation runs can be carried out to investigate the performance of sub-system only. Also, the steam flow can be rearranged for specific studies. For future studies, researchers can optimize individual parts of the models or add additional components without having to redesign the entire system model.
4.1.2 Flow-Vector Concept
Simulink blocks are connected with individual signals that can be grouped. For this research, a flow-vector system is developed to combine all the fluid properties of the steam and condensate flows within a bus system. Any necessary steam or liquid property (e.g. pressure, vapour quality, entropy and enthalpy) is summarised in the flow vector. The flow-vector creator block is shown in Figure 4-5. This block collects different steam properties and creates a flow-vector.
Figure 4-5: Flow-vector creator block.
The task of the flow vector is to transfer all fluid properties between the components. The connection of the flow vector to the system components is similar to that of pipes of the storage system. Therefore, all other components such as valves and fittings can be easily added, removed or replaced, which speeds system development.
4.2 Turbine Model
A turbine model is needed to calculate the power generation of the storage turbine as a function of the mass flow rate and the properties of the input steam. Furthermore, this
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model is used to simulate the multi-stage turbine of the biomass CHP plant. Power generation (P) is the value of most interest. The design input (ṁin,d) and extraction
(ṁex_n,d) mass flow rates as well as the input pressure (pin), output pressure (pout) and the pressure at n-extraction steps (pn,ex) are parameters that are determined during the design of the turbine. Equation 4-1 is the fundamental basis for the shaft power of a turbine. ṁ and ΔHis are given by the operation of the turbine and the steam properties. The turbine dimension, partial loads and the design parameter influence the isentropic efficiency ηis. The design mass-flow rate is the optimum operation of the turbine in its maximum efficiency. Thus, the design parameters are often referred to as maximum parameters (ṁin,d = ṁin,max, ηis,d = ηis,max), but in this research, the index d is used for design.
(4-1) 푃푠 = 휂𝑖푠 ∗ 푚̇ ∗ 훥퐻𝑖푠
Overall, the turbine model must perform the following tasks:
Part-load Behaviour: The storage system model shall simulate grid or market-driven operation. The power output of the storage turbine must therefore be very flexible. In most cases, the storage turbines are operated in the partial load range. In addition, the multi-stage turbine is confronted with partial load operation during storage charge or extraction. Steam turbines exhibit significant changes in efficiency and their outlet steam properties during part-load operation. Therefore, it is important that the model can predict the partial load behaviour and thus the efficiency changes.
Universal Model: During the simulation runs, a wide range of different turbine and storage sizes are to be investigated in the simulation runs, so a universal turbine model is needed that can be scaled to different dimensions.
Small-scale Turbines: Compared to typical steam turbines (e.g. those used in coal- fired power plants), biomass CHP plants use the smallest turbines available on the market. They are often referred to as small or micro-turbines. It is required that the selected model be valid for turbines with power outputs below 20 MW. The storage turbine will operate in the range of 1–5 MW. As indicated in Chapter 1, the average nominal performance of turbines in biomass CHP plants in Germany is 6.9 MW.
Multi-stage Turbine: It will be necessary to simulate the multi-stage extraction turbine of the plant. For the selection of the turbine model, a model capable of calculating the performance of multi-stage extraction turbines is required.
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4.2.1 Selection of the Turbine Model
There are several models for steam turbines prediction. The thermodynamic parameter models studied are based on either the continuity equation, Stodola’s cone law or the Willan's line.
Dulau & Bica (2014) developed a model based on the continuity equation. By using a first-order transfer function, they presented a simple turbine model. This model can predict the start-up behaviour of a steam turbine; unfortunately, partloads are not considered.
Chaibakhsh & Ghaffari (2008) developed a turbine model based on Stodola’s cone law. Using generic algorithms, they were able to parameterise the model via experimental data. Both part-loads (down to 50%) and the dynamic behaviour of the turbines can be predicted. Luo et al. (2011) pointed out that these models oversimplify the turbine performance because the turbine operation depends not only on design size and operation load but also significantly on input and output steam properties.
For the design of steam turbines, Mavromatis & Kokossis (1998) developed the Turbine Hardware Model (THM) based on the Willans line. This approach assumes a linear interpolation of power generation with respect to the design and part-load mass flow. The Willans line approach is shown in Figure 4-6. Based on the steam mass flow ṁ and the turbines shaft power Ps, a linear expression part-load behaviour of the turbine is constructed. The design mass flow (ṁd) and design turbine shaft power (Ps,d), as well as the minimum flow rate, are used to fit the Willans line to a given turbine. The regression parameters are extracted from experimental data to parameterise the model for specific turbine sizes. The aim of the THM is the prediction of efficiency and power output for turbines of different sizes, at design and part-load operation.
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Figure 4-6: Willans line according to Mavromatis & Kokossi (1998).
Medina-Flores & Picón-Núñez (2010) adopted and improved the THM to achieve better accuracy by adjusting the regression parameters using commercially available steam turbines. A disadvantage of both THM models is the simplification of using a single weighted average efficiency for each turbine size (Luo, et al., 2011). To improve the existing THM model, Luo et al. (2011) combined THM concepts with the Stodola cone law approach to calculate the regression parameter by including the inlet pressure of each turbine step. In this model the design efficiency ηis,d is calculated as a function of the design parameters. Sun & Smith (2015) improved this model by refining these parameters and developed a separate parameter for backpressure and condensing turbines to improve accuracy.
A comparison of the models studied is shown in Table 4-1. The models of Luo et al.
(2011) as well as Sun & Smith (2015), scale the design efficiency ηis,d as a function of the design parameter. In the next section, the models are referred to as the Sun or Lou model. Both models meet the defined model criteria. At first glance, the Sun model seems to be the best fitting model, as it has better accuracy in the respective sources.
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Table 4-1: Comparison of the available turbine models.
Continuity Willans Stodola THM < 100 >100 Scaling Part- equation line cone- MW MW ηis,d loads law
Dulau & Bica x x (2014)
Chaibakhsh & x x Ghaffari (2008)
Mavromatis & x x x x Kokossis (1998)
Medina-Flores & x x x x Picón-Núñez (2010)
Luo et al. (2011) x x x x x
Sun & Smith x x x x x (2015)
Both are model formulations described as suitable for small-scale turbines. The THM models are used for the simulation of single turbine stages. Complex multi-stage turbines can be modelled by the connection of several individual turbines (Figure 4-7).
Figure 4-7: Complex multistage turbine equivalents to multiple single-stage turbines (Sun & Smith, 2015).
This decomposition method, shown in Figure 4-7, is a common approach for simulating a multi-stage turbine. Chou & Yen-Shiang (1987) developed this method, which is adopted by subsequent model developers. They noted that the decomposing method may cause slight deviations compared to a single turbine calculation. They use a turbine model with a fixed design efficiency ηis,d for arbitrary turbine dimensions. Breaking down a multi-stage turbine into several smaller turbines resulted in a smaller design size for each individual turbine. Since Chou & Yen-Shiang (1987) used the same design efficiencies for all turbine dimensions, no additional error was generated. However, by
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using a model with a size-dependent efficiency (e.g. Lou or Sun), the overall efficiency of the decomposed turbines becomes smaller and smaller. This effect introduces an additional error caused by the decomposition method.
Therefore, a closer look at the model errors must be taken to identify the most appropriate turbine model. Both model formulations are implemented, and validation simulations are performed. A three-stage turbine with input steam properties of 6 MPa and 420 °C is simulated. The extraction levels are set to 1.7 MPa, 0.3 MPa and 5x10-3 MPa.
The design steam mass flow rate ṁd is set to the same value as the recent mass flow ṁ. Thus, operation at maximum efficiency is investigated. The steam mass flow rate is gradually increased to simulate different turbine dimensions. A different behaviour of the design efficiency is observed for both models (cf. Figure 4-8).
The basic behaviour of both models is similar. However, the overall efficiency shows some differences. The Sun model shows a higher efficiency than the Lou model. The deviation between both models increases with increasing turbine size.
Figure 4-8: Design efficiency depending on turbine dimension
Validation runs (cf. Section 4.2.3 for turbine specifications and procedure) were performed for both models to identify the model error. The deviation between simulated and measured power generation is 20% for the Sun model and 3% for the Lou model. Therefore, the Lou model was chosen in this research.
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4.2.2 Turbine Model of Lou et al.
The model formulation of Lou et al. (2011) is based on the calculation of the isentropic efficiency ηis. Since both the input steam properties and the output pressure are known, the isentropic enthalpy difference Δhis can be calculated. Using the isentropic efficiency, the real enthalpy difference Δhreal can be determined (Equation 4-2).
훥ℎ푟푒푎푙 ℎ𝑖푛,푟푒푎푙 − ℎ표푢푡,푟푒푎푙 (4-2) 휂𝑖푠 = = 훥ℎ𝑖푠 ℎ𝑖푛,푟푒푎푙 − ℎ표푢푡,𝑖푠
The isentropic efficiency is influenced by the turbine dimension as well as by part-load operation. Lou et al. (2011) developed an approach to calculate the isentropic efficiency of a single turbine stage z as a function of the design mass flow ṁd,z, the input mass flow
ṁin,z as well as the parameter A and B (Equation 4-3). Since the relationship between
ṁd,z and ṁin,z is considered, the partial load operation is depicted.
6 퐴 ṁ푑,푧 (4-3) 휂𝑖푠,푧 = ∗ (1 − ) (1 − ) 5퐵 훥ℎ𝑖푠,푧 ∙ ṁ푑,푧 6ṁ𝑖푛,푧
The isentropic efficiency at the design operation ηis,z,d is calculated using the simplified
Equation 4-4. At design conditions, ṁd,z is equal to ṁin,z.
1 퐴 (4-4) 휂𝑖푠,푧,푑 = ∗ (1 − ) 퐵 훥ℎ𝑖푠,푧 ∙ ṁ푑,푧
Model parameters A (Equation 4-5) and B (Equation 4-6) include regression coefficients (α, β, γ, λ) as well as the input pressure. These coefficients (cf. Table 4-2 ) are derived from different turbine hardware specifications (Medina-Flores & Picón-Núñez, 2010).
(4-5) 퐴 = 훼 + 훽 ∙ 푝𝑖푛,푧
(4-6) 퐵 = 훾 + λ ∙ 푝𝑖푛,푧
Table 4-2: Regression coefficients for the simple turbine model (Lou et al., 2015).
Symbol Value Unit
α 0.1854 MW
β 0.0433 MW/Pa
γ 1.2057 -
λ 0.0075 MPa-1
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Since the model is used to calculate shaft power, the turbine’s power generation is the value of interest. As mentioned by Sun & Smith (2015), mechanical efficiency, as well as the generator efficiency, is considered. In Equation 4-7, these efficiency values are incorporated into the turbine’s baseline calculation (cf. Equation 4-1).
(4-7) 푃푒푙 = 휂푒푙 ∗ 휂푚푒푐ℎ ∗ 휂𝑖푠 ∗ 푚̇ ∗ 훥퐻𝑖푠 = 푃푠 ∗ 휂푒푙 ∗ 휂푚푒푐ℎ
Typically, the mechanical efficiency ηmech is in the range of 97–99%, while the generator efficiency ηel is around 95–99% (Sun & Smith, 2015). Since the influence of partial load operation is very small, this behaviour is neglected by Sun & Smith (2015).
The model is designed as a Simulink block. This block is shown in Figure 4-9. Each turbine block has an input and output flow-vector connection. This model enables a calculation of the efficiency, output steam flow and power generation. In addition, the parameters pout and ṁd are used in the turbine model to define the turbine size. Figure 4-9 shows the connection of two individual THMs to a multi-stage turbine.
Figure 4-9: Two-stage turbine model.
4.2.3 Validation of the Turbine Model
Measurement data from an existing biomass CHP plant were used to validate the turbine model. The rated thermal capacity of the plant is 21.6 MW, which corresponds to a plant size typical in Germany. A total of 26,700 t of dry solid biomass (forestry and landscape conservation residues) are used per year. A condensation-extraction steam turbine with three stages and two extraction levels is used. The maximum output of the turbines is 4.5 MW.
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Figure 4-10: Turbine validation data.
Process steam for an industrial facility is extracted at the mid-pressure level (MP), while steam for the supply of a heating water circuit is extracted at the low-pressure level (LP). Additionally, several streams of MD and LP steam are extracted for the plant’s internal processes such as motive steam and seal steam for the turbine operation, the air preheater and the deaerator. Figure 4-10 shows the input and output mass flow required for the turbine calculation. The mass flow rates (green) are available as measurement data with a time resolution of 60 s. Measurement data for the smaller mass flow rates (orange) are not available but are given as fixed values by the plant’s control system.
Table 4-3: Measured values.
Symbol Unit Value range Average value
Tin °C 452–491 479
pin,1 MPa 6.1–6.3 6.2
-3 -3 pex = pout,3 MPa (3.7–18.2) x 10 6.6 x 10
ṁin kg/s 4.54–6.51 5.77
ṁprocess steam kg/s 0.25–2.99 2.37
ṁheat extraction kg/s 0.10–0.78 0.52
ṁdearator kg/s 0.13–0.77 0.41
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Table 4-4: Simulation parameters.
Symbol Unit Value
ṁseal steam, MP kg/s 0.041
ṁmotive steam kg/s 0.159
ṁseal steam, LP kg/s 0.042
ṁair-preheater, LP kg/s 0.2
pout,1= pin,2 MPa 1.75
pout,2= pin,3 MPa 0.3
ṁd,1 kg/s 6.110
ṁd,2 kg/s 2.778
ṁd,3 kg/s 2.778
A list of the validation parameters is given in Table 4-3 and Table 4-4. According to the datasheet of the turbine, the design mass flows of Stages 1, 2 and 3 are also parametrised. For ηmech and ηel, 95% is selected. The design mass flow rates ṁd,z are also taken from the datasheet.
A period of 1.07*107 s (12.5 days) is simulated. The results of the simulation are compared with the measurement data of the turbine power generation. When comparing the power generation of the measurement data (938 MWh) and the simulation results (959 MWh), a deviation of 2.29% is found. There was a mean squared error of 0.018 MW during the period studied. Figure 4-11 shows a section of the simulation.
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Figure 4-11: Power generation of the turbine model compared with the measurement values
The simulation results align well with the measurement data. Over the entire period, the simulated power generation is slightly higher than the measured data. This behaviour occurs almost over the entire simulation period. However, outliers occur in some time periods. An exemplary outlier is shown in Figure 4-12. Similar deviations occur periodically, a total of 12 times over 1.5–2 hours each time.
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Figure 4-12: Outliers caused by the soot blower.
The source of this error is the soot blower of the CHP plant. The soot blower uses steam to clean soot from the heat exchanger tubes in the boiler. Every day, the soot blower is activated manually by opening a valve. The required steam is taken from the mid- pressure steam line and increases the total extraction of mass flow. This behaviour explains the significantly lower power generation of the measurement data (cf. Figure 4-12) since the soot blower extraction is not taken into account in the simulation. Due to the manual valve, it is not possible to quantify the soot blower mass flow without additional measuring devices. Nevertheless, the outliers are assigned to this behaviour. If you remove these time periods from the database, the number of data points is reduced by 7.7%.
The deviation between measurement data and simulation results is reduced to 1.53%, the mean quadratic difference to 0.004 MW. This accuracy is judged to be sufficient for the task of this research.
Since partial load operation will be an important factor in this research, a closer look will be taken. Since the process of steam extraction has the greatest influence on part-load operation, the influence of the extraction of mass flow on the power generation is examined. Since the values shown in Table 4-3 have many interdependencies, outliers cannot be avoided in this investigation. In order to reduce the influence of outliers on the input mass flow, only data points are used for which the other simulation parameters are
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in the range of their average value. The behaviour of the measured and simulated power generation at part-loads is shown in a scatter diagram (Figure 4-13). Even during part- loads, the behaviour of the simulation model is close to the measured data.
Figure 4-13: Power generation depending on the extraction.
After implementing and validating the model from Lou et al. (2011), the model has proven its applicability for the research task and will be used for further research.
4.3 Steam Accumulator Model
A steam accumulator model is required in order to calculate the dynamic behaviour of the charging and discharging processes. The amount of charge and discharge steam, as well as its properties, determines the performance of the flexible plant. The following parameters influence the steam capacity:
• Steam accumulator volume VSA (design parameter),
• Maximum design pressure pSA,d (design parameter),
• Charging steam properties pLS and TLS,
• The mass flow rates of charge/discharge steam ṁcharge, and ṁdischarge,
• Water level of the steam accumulator βSA,
• Pressure levels for full pSA,max,c or empty state pSA,min,c of the storage (operating mode).
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As part of this research, various values of these parameters must be examined. In addition, the SA model must fulfil the following tasks.
Dynamic Behaviour: The dynamic behaviour of fast charging and discharging processes must be taken into account in the model, so a dynamic model is required.
High-pressure Steam Accumulator: To use the live-steam from the biomass CHP plant, the steam accumulator must be operated at high-pressure (5-8 MPa). This pressure range is not common for typical industrial steam accumulators and must be taken into account when selecting a suitable model.
Scalable Model: Various VSA are examined during the simulation runs. Therefore, the model should be parametrizable on different dimensions.
4.3.1 Selection of Steam Accumulator Model
In the past, researchers have developed various methods for analysing steam accumulators. Dynamic models can be divided into ‘equilibrium’ and ‘non-equilibrium’ models. A liquid and a vapour phase exist within the steam accumulator. Dynamic models represent changes in both phases during and after each change in the storage state (by charging or discharging).
The equilibrium models always assume a thermodynamic equilibrium between the two phases. It is assumed that the vapour and liquid phases have the same pressure and temperature at all times. The condensation and evaporation processes within the storage are treated as if they were instantaneous (Biglia, et al., 2017).
The non-equilibrium models consider the liquid and the vapour phases separately. An exchange of heat and mass between the phases, which is necessary to achieve a thermodynamic equilibrium, is taken into account. In contrast to the equilibrium models, evaporation and condensation are not treated as if they were instantaneous but rather delayed. This delay is specified by condensation and evaporation relaxation times (Biglia, et al., 2017).
Steinmann & Eck (2006) developed a model for calculating storage capacity and discharge energy. Their approach only uses the beginning and ending states of the discharge procedure. This method is suitable for the approximate dimensioning of steam accumulator concepts in earlier planning phases of industrial project development. Dynamic behaviour is not taken into account in this model (Biglia, et al., 2017).
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Another model formulation is based on the ideal gas laws (Shnaider, et al., 2010). This model takes into account the dynamic behaviour of the steam accumulator. The use of the ideal gas laws for the steam calculation has negligible errors at pressures below 10 kPa. However, accuracy degrades significantly at pressure levels greater than 10 kPa.
An equilibrium model based on a volume balance and a pressure control formulation (Bai & Xu, 2011) was used for dynamic simulation. However, this model was only developed to calculate discharge behaviour.
Fabrizio et al. (2011) used a series of differential equations based on the mass and energy balance in the accumulator. In this model, the effects of the kinetic and potential energy are neglected, resulting in an estimate of the average capacity. This model also includes the calculation of the thermal energy losses of the storage vessel and can be scaled to various vessel sizes.
Stevanovic et al. (2014) used a similar model to Fabrizio, which is also based on mass and energy balance. In this model, the kinetic and potential energy are taken into account, which increases the accuracy. In contrast to the previous model, both the steam mass flow and liquid mass flow into and out of the steam accumulator can be calculated.
Stevanovic et al. (2014) developed a non-equilibrium model of a steam accumulator. They extended the equilibrium model to include the mass and energy balance for each phase. An evaporation/condensation mass flow was defined for the exchange between liquid and vapour phase. To describe this mass flow, empirical parameters for the condensation and evaporation relaxation are used. Stevanovic et al. (2014) concluded that the non-equilibrium model has better accuracy than the equilibrium model. However, the empirical parameter used to calculate the model is only available for a certain vessel size. The available models are compared in contrast to their required abilities (Table 4-5).
Both Stevanovic’s equilibrium and non-equilibrium models have been used in practical studies. Equilibrium models are used in several cases for the investigation and planning of steam accumulators for buffering batch processes in the food or pharmaceutical industry (Fabrizio et al (2011), Biglia et al. (2017), Stark et al. (2018) and Hechelmann et al. (2020)The non-equilibrium model has been used in simulation studies of the high- speed discharge of steam catapults on aircraft carriers (Sun, et al., 2015).
The equilibrium model offers better scalability due to its independence from experimental data. Furthermore, its simple structure reduces the computation time and computational stability, but at the expense of a lower accuracy compared to the non-equilibrium model.
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The non-equilibrium model requires a set of experimental data (relaxation times for condensation and evaporation). These data are only available for the size of the storage vessel in the research in question.
Table 4-5: Steam accumulator model comparison.
Non- High- Equilibrium Accuracy Model Scalability Dynamic equilibrium pressure approach of results approach
Steinmann & X x x low Eck (2006)
Shnaider et al. x x very low (2010)
Bai & Xu x x x x high (discharge) (2011)
Fabrizio et al. x x x x average (2011)
Stevanovic et al. (2014) x x x x high equilibrium Stevanovic et al. (2014) non- x x x very high equilibrium
Biglia (2017) claimed that for a complex system in which the steam accumulator is only one component, among others, the EM is actually more suitable than the NEM due to its scalability. It is for these reasons that the EM is used for this research. For further investigation, the NEM can be used for a more detailed investigation of a specific case.
4.3.2 Stevanovic Steam Accumulator Model
In the following section, the equilibrium model (EM) is implemented. The calculation of the EM model is based on the variables shown in Figure 4-14. Liquid mass flows are given the suffix n = 1, and vapour mass flows are given the suffix n = 2. In both phases, input mass flow rate ṁn,in and enthalpy hn,in, as well as the output mass flow rate ṁn,out, are given to the model as limits. Both models differ in the way they view the content of the SA. The EM takes into account only a mass (M), a pressure (p) and an enthalpy (h) within the storage. In every situation, a thermal equilibrium between liquid and vapour phase is assumed. The proportion of steam and liquid is determined by the steam quality x. Both liquid and vapour phases are taken into account in the NEM. Within the accumulator, the mass Mn and the enthalpy hn are calculated separately for both phases.
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In the NEM, the exchange between the two phases through evaporation or condensation is also taken into account.
Figure 4-14: Steam accumulator layout (Stevanovic, et al., 2012).
As shown in Figure 4-15, the main difference between the equilibrium and non- equilibrium model is the change in pressure after charging or discharging. Pressure peaks after charging/discharging processes can be seen in the illustration. In the non- equilibrium model, a balancing process between the liquid and vapour phases is assumed after charging/discharging. At the end of the charging/discharging process, the liquid and vapour phases are in a state of non-equilibrium. Both phases strive for a state of equilibrium. This effect is intensified by a higher charge or discharge mass flow rate. The deviation of the results between non-equilibrium and equilibrium models increases with the speed of discharge.
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Figure 4-15: Non-equilibrium and equilibrium model (Stevanovic, et al., 2014).
The formulation of the EM is shown below (Equation (4-8)-(4-10)). In contrast to the NEM, a mass and energy balance are calculated for the entire storage vessel, liquid and vapour phases together (Stevanovic, et al., 2014).
푑푀 (4-8) = 푚̇ + 푚̇ 푑푡 1퐵 2퐵
푑퐻 푑푝 (4-9) = (푚̇ ℎ) + (푚̇ ℎ) + 푉 푑푡 1퐵 2퐵 푑푡
푟푉 (푚̇ ℎ) + (푚̇ ℎ) + ( 푀 − ℎ) (푚̇ + 푚̇ ) 1퐵 2퐵 휗´´ − 휗´ 1퐵 2퐵 푑푝 (4-10) = 푑푡 푉 푉 푑ℎ´ − 휗´ 푑푟 푟 푑휗´ − 휗´ 푑(휗´´−) 푀 ( + 푀 − − 푟 푀 ) − 푉 푑푝 휗´´ − 휗´ 푑푝 휗´´ − 휗´ 푑푝 (휗´´ − 휗´)2 푑푝
4.3.3 Validation
For validation, the simulation model was run to compare the EM with the NEM using the parameter from Stevanovic et al. (2014). Figure 4-16 shows the results with measured values and the results from Stevanovic et al (2014) compared.
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Figure 4-16: Validation of the steam accumulator model.
A mean quadratic deviation of 0.15 MPa and a mean deviation between the measured values and the EM was calculated to be 3.69%. This model suggests that the EM is sufficient for the purposes of this research. For future research, the NEM model offers the perspective of higher accuracy, especially for starting up and shutting down the storage process.
4.4 Solid Thermal Store Model
The main task of the solid thermal store (STS) is superheating the discharge steam to avoid turbine damage. The heat transfer fluid is fed into the solid storage, and thermal energy is transferred between the heat transfer fluid and the concrete (see Figure 4-17).
During charging, energy is transferred from the live-steam to the solid material. This reduces the temperature of the steam supplied to the steam accumulator. Due to the reduced internal energy of the charging steam, more steam can be stored in the steam accumulator. The main task of the solid thermal store (STS), however, is superheating the saturated steam from steam accumulator during discharge.
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Figure 4-17: Solid thermal Store (Jian, et al., 2015)
A model is required to calculate the temperature change of concrete and steam. In addition, the heat distribution and standby behaviour are of particular interest. The following features should be supported by the STS model.
Dynamic Behaviour: The dynamic behaviour of fast charging and discharging processes must be represented by the model. Dynamically changing steam properties such as temperature and pressure should be taken into account in the model calculation.
Heat Distribution: A major disadvantage of solid – especially concrete – storage systems is their low thermal conductivity. A very slow heat conduction compared to fluids is expected. In order to take these effects into account, the heat distribution within the STS should be examined.
Simple Model: To include the model in the Simulink steam storage system, a simple model is required. Since a large number of different parametric studies are envisaged, the computation time should be considered.
Scalable Model: A parameter study with different storage sizes is carried out. Therefore, a model is required that is scalable to different storage sizes.
4.4.1 Available Models for STS
Due to the increasing research interest on STS over the past decade, driven by the CSP plants, there are several models of solid thermal storage systems available. Most of these models assume the use of liquid thermal oil as HTF.
Bai & Xu (2011) developed a model based on partial differential equations for an STS charged with thermal oil storage. The model formulation is based on the 2D-temperature
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distribution in the transverse surface of the concrete. A fully developed velocity profile is taken into account.
Jian et al. (2015) used a similar model for their calculations. Using a Laplace transform, they also developed a model formulation based on partial differential equations (PDE) that takes into account a constant steam mass flow rate. The distribution of the pipes in the STS was set triangular to achieve uniform hexagonal storage units. The hexagonal units were simplified to cylindrical units with the same cross-sectional area (Figure 4-18).
Figure 4-18: Crosssectional interface of the solid thermal storage (Jian, et al., 2015).
This principle of placing the cylindrical units in a triangular structure is also used by Salomoni et al. (2014). Starting from an energy balance, they developed a system of equations based on an FEM method to model the storage module. Again, assumptions are made to estimate the turbulent heat exchange between the pipe and steam.
Laing et. al (2006) and Tamme et al. (2006) also developed a model for use in their simulation environment StorageTechThermo. They separated the storage into several segments that were connected in the axial direction (Figure 4-19). The storage material is discretized in the radial direction using a finite difference scheme (Tamme, et al., 2006).
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Figure 4-19: Interconnection of the StorageTechThermo model (Tamme, et al., 2006).
All of the above models neglect thermal losses by assuming well-insulated components. They do not take into account the thermal distribution in the axial direction. It is also assumed that the velocity profile is fully developed throughout the storage process. The researchers mentioned that the heat transfer through the thin steel pipes can be neglected due to low thermal resistance compared to the solid material. The concrete is considered as a homogeneous material with uniform (but temperature-depended) properties.
The models studied neglect the pressure drops inside the pipe. This simplification applies to constant and low-velocity flows in the pipe. In the case of a steam accumulator, the pressure of the steam slides over time, resulting in an increasing specific volume (m³/kg). Due to the increasing volume flow at the end of discharge processes, high velocities of up to 50 m/s can be expected in small STS pipes. For this reason, the pressure drop through the STS needs to be taken into account.
4.4.2 Implementation of the Solid Thermal Store Model
To calculate the above model formulations using MATLAB, special solution methods for the partial differential equations are required. It is intended to use a finite-element method within MATLAB to evaluate the partial differential equations. However, these methods are very resource-intensive. For testing purposes, the Bai model was implemented in MATLAB. A workstation with 3.30 GHz and 384 GB RAM is used for these test runs. A single simulation run for one charge process takes over 6 hours in real time (1 h simulation time).
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In addition, the modelling effort required to improve the meshing, the model itself and the thermal equations within MATLAB was considered too high given the likely benefits within this research.
Finally, regarding the implementation of the Bai model in MATLAB, the following observations can be made. Since the other models (Jian, Salomoni) require similar solution methods, these findings are also applicable to them:
• In MATLAB, the creation of meshing functions and the implementation of the heat transfer equations are additional sources of error that can be avoided by using functions already available in other tools such as computational fluid dynamics (CFD) software. • Even after extensive development and optimization, it was not practical to consider phenomena such as turbulent flow and start-up behaviour within these models. • The test run of the model was done using MATLAB, an implementation into Simulink would further increase the simulation time. These points support the decision to use a different tool to develop an STS model. For this type of modelling, MATLAB and especially Simulink are not the ideal tools. In order to achieve both a reliable and accurate physics-based simulation, as well as a simple and universal MATLAB/Simulink model, a two-step process was chosen for model development (Figure 4-20).
Figure 4-20: Two-step modelling process.
First, the STS is modelled in ANSYS-CFX, a computational fluid dynamics (CFD) tool. This ANSYS model is used for parameter studies with different steam temperatures, mass flow rates and pressures. Different charging and discharging processes are calculated. The results of the parameter studies are used to generate characteristic
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curves for the parameter model in MATLAB/Simulink. In this way, a simplified STS model is created for implementation in the system model.
4.4.3 ANSYS Model Development
For the development of the model in ANSYS, the STS is simplified to only one cylindrical heat storage module. Since the same conditions are assumed in each embedded tube, and due to the arrangement of tubes in a triangular grid as shown in Figure 4-18, a cylindrical heat storage module can be modelled and then multiplied by the number of tubes for the total storage capacity (Jian, et al., 2015). Although real concrete has a granular aggregate that is non-uniform in size and thermal properties, the concrete within the storage module is assumed to be homogeneous, continuous, and isotropic. It also assumes a uniform distribution of the steam flow through the various pipes. Since all the calculations assume homogenous concrete properties, the heat distribution will be axially symmetric, therefore, the cylindrical model is simplified to a quarter model.
Geometrical features such as length and diameter are set as scalable parameters. Two volumes, a liquid volume and a solid volume, are defined. Between the two, the heat transfer interface is defined as a thin steel wall. Standard meshing algorithms are used. The mesh has an increasing density at the interface between fluid and solid. In the axial direction, the mesh is divided into identical slices. In the fluid area, the ANSYS internal k-Epsilon turbulence model is used for the calculation.
Since the individual storage modules are considered to be identical, they will have the same conditions on the outer surface, such as temperature, so that there is no heat transfer between them, and it can safely be assumed that the outer surface is symmetrical. The models discussed in Section 4.4.1 neglect the pressure drop inside the pipes. Due to the increasing pressure and specific volume in the discharge steam, the pressure drop is an important value considered in this model.
The steam input mass-flow rates and properties are given as time series to account for the constant charge as well as the variable discharging parameters (Figure 4-21). The temperature of the concrete is defined in a 3D-temperature profile with 36,096 temperature points. The initial temperature is specified as a model boundary condition. The material properties are set as pressure and temperature-dependent characteristic curves. Depending on the input steam flow, the model calculates the properties of the output steam as well as the temperature distribution.
Parameter and formulations of the ANSYS model are attached in Appendix IV.
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Figure 4-21: Input and output parameters of the STS model
In order to verify the functionality of the ANSYS model, some exemplary charging and discharging simulation runs were performed. These simulation runs were used to investigate the mesh quality. Figure 4-22 shows the charging behaviour of the STS. The initial temperature of the concrete is set at 200 °C, and a constant mass flow rate of 0.03 kg/s with typical biomass CHP plant live-steam properties (480 °C, 6 MPa) is used.
Figure 4-22: Charging the STS Model.
During the simulation time, the uniform temperature of 200 °C increases at t = 0s. The heat transfer rate decreases in the axial direction as the steam cools. After 9520s, the left part of the STS, where the steam enters, is hotter than the right side.
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A discharge process (0.003 kg/s, initial temperature of 430 °C and a variable steam temperature and pressure profile) is also shown in Figure 4-23. The same effects are depicted. Due to the ΔT from input to output, the heat transfer rate is decreasing in the axial direction. So, after 9,870 s, the left side is cooler than the right side.
Figure 4-23: Discharging the STS Model.
Both simulations show the typical heat transfer and temperature profile as assumed for in stationary heat transfer. The test runs of Jian et al. (2015) shows similar results.
After a charging process, the simulation was continued. As shown in Figure 4-24, the temperature distribution over the diameter becomes uniform after a certain time. Here, the typical behaviour of heat transfer in solids is shown.
The behaviour of the temperature shown in Figure 4-22, Figure 4-23 and Figure 4-24 is typical for the heat transfer in solid components. Jian’s research also led to similar behaviour. These results show a sufficient quality of the mesh.
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Figure 4-24: Temperature development of the idle storage.
4.4.4 Validation of the STS Model
To validate the results of the simulations with the ANSYS model, two sets of published data are compared with the results of the developed model. Two datasets are available from Bai & Xu (2011) and Laing et al. (2008). For each dataset, the parameters (concrete and heat transfer fluid properties, geometry and charging/discharging steam profile) were adjusted in the ANSYS model.
Validation with the Measurement Data of Laing
Laing et al. (2008) tested the discharge behaviour of an STS unit. A total of 132 pipes were used in their test rig.
Figure 4-25: Discharge validation according to data from Laing (2008).
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The storage is operated with thermal oil as a high-temperature fluid. The temperature- dependent properties of the heat transfer fluid and the concrete (e.g. density, thermal conductivity, thermal capacity) are described with characteristic curves. The input parameters for the ANSYS model are a constant flow rate of 20 m³/h, and an input temperature time series (Thot) measured by Laing et al. is used. The temperature Thot is the input temperature of both the simulation and the measurement. Figure 4-25 compares the results of the ANSYS simulation by (Tsim) with the Laing (Tmeas) test results. The deviation between measured and simulated temperature is less than 1%. Thus, the results show that the model is quite accurate. The outlet temperature (Tmeas) is measured at the outlet of the entire storage unit, where the steam flows of all pipes are accumulated. Laing’s measurement considers an STS module with 132 pipes/modules. In this ANSYS simulation, a quarter of one module is calculated and scaled up to 132 modules. The small deviation between simulated outlet temperature and the measured data of Laing et al. (2008) supports the validity of simplifying the STS into individual quarterly modules.
Validation with the Simulation Data of Bai & Xu
A second validation run was performed to compare the simulated outlet temperature with the results of Bai & Xu (2011). Here, the simulation runs were performed for an STS with steam as a heat transfer fluid. In this research, the inlet steam temperature (Tin), mass flow rate, steam properties and geometry of the storage are adopted from the publication (Bai & Xu, 2011).
Figure 4-26: Charge validation according to the data of Bai et al. (2011).
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As shown in Figure 4-26, the first simulation (Simreal) has a deviation of 2.3% from the results of Bai & Xu (SimBai). This discrepancy is magnified by the simplifications of Bai & Xu (2011) described above. Unlike the ANSYS model, the Bai & Xu model assumes a fully developed flow profile for the entire simulation. This results in greater heat transfer between the fluid and concrete in the Bai & Xu model. For a better comparison, a second simulation (Simideal) was performed with a fully developed flow profile using the ANSYS model. Here, the model error is reduced to 1%. This shows that the increased model error of Simreal is caused by the simplifications of Bai & Xu.
Overall, both validation tests have an acceptable model error (~ 1%) in the context of this research.
4.4.5 Development of the Parameter Model
In order to create a simplified model in Simulink, a parameter study was carried out. Different steam temperatures and mass flow rates, as well as different concrete initial temperatures, are simulated. As defined above, the temperature difference between input and output steam, as well as the heat capacity in the STS, are the decisive parameters. The heat transfer between concrete and steam changes the temperature. The outlet temperature of the steam outlet is used to create characteristic curves representing the behaviour of the STS. To estimate the internal heat capacity, the average temperature TSTS,mean is calculated from the 36,096 temperature points of the concrete. Since the mesh consists of elements of different sizes, this average temperature is calculated using the volume fraction of the individual components.
By simplifying this complex, physics-based ANSYS model into a parameter model, an additional error is unavoidable. In order to reduce this error to a minimum, a narrow scope of application was defined.
Bai and Xu (2011) developed an STS adapted to a steam accumulator. After an extensive investigation of various designs and materials, they defined their optimum for pipe arrangement and geometry. Therefore, the geometric features used by Bai and Xu were chosen for this parameter study (Table 4-6). Although these properties are fixed in the parameter model, it remains scalable by varying the number of individual storage modules.
To obtain a more accurate parameter model, two separate parameter models are developed for the charging and discharging processes. In these studies, steam with different mass flow rates, temperatures and pressures is injected into the storage.
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Table 4-6: Geometric characteristics of the STS.
Pipe diameter 0.013 m
Pipe length 10 m
Concrete module diameter 0.064 m
Number of individual storage quarters 132x4
Parameter Study of the Charge Process
Since live-steam is used to charge the steam storage system, the input steam properties remain almost constant during the entire charging process. Three different mass flow rates ṁcharge (Table 4-7) are calculated.
Table 4-7: Mass flow rates, charge study.
Parameter variation C1 C2 C3
ṁcharge,m per module 0.004 kg/s 0.008 kg/s 0.012 kg/s
ṁcharge total 2.11 kg/s 4.22 kg/s 6.34 kg/s
Different initial temperatures TSTS,mean,ini are used to account for the different initial load levels of the STS (Table 4-8).
Table 4-8: Initial volume-average temperature, charge study.
Parameter variation CT1 CT2 CT3
TSTS,mean,ini (°C) 100 200 300
Thus, a total of nine (3 ṁcharge x 3 TSTS,mean,ini) parameter studies are processed. The simulation time was set to 14,400 s in each case.
Parameter Study of the Discharge Process
During discharge, the behaviour of the STS depends on the steam accumulator. The sliding pressure during discharge leads to a falling steam temperature as a function of the discharge mass flow rate ṁdischarge and the volume of the steam accumulator VSA.
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Table 4-9: Mass flow rates, discharge study.
Parameter variation D1 D2 D3
ṁdischarge,m per module 0.0035 kg/s 0.011 kg/s 0.02 kg/s
ṁdischarge total 1.85 kg/s 5.80 kg/s 10.56 kg/s
tdischarge for VSA = 50m³ 4,500 s 1,440 s 780 s
tdischarge for VSA = 150m³ 13,520 s 4,300 s 2,370 s
Three different mass flow rates and two SA volumes are used in the parameter study (Table 4-9). The operating pressure of the SA is set from 6 MPa to 0.5 MPa, the discharge starts at 6 MPa and ends at 0.5 MPa.
A total of six temperature time series were generated with the steam accumulator model for each combination of ṁdischarge and VSA (Figure 4-27). The discharge profile of the steam accumulator depends on VSA, tdischarge as well as the water level βSA and the pressure range Δp = pSA,max – pSA,min. However, all these values result in the temperature profile.
Figure 4-27: Temperature profiles for STS model development.
In charge simulations, the post-charge TSTS,mean was determined to be between 320–
430 °C. Therefore, TSTS,mean,ini was set for the discharge in the range of this parameter (Table 4-10).
Table 4-10: Initial volume-averaged temperature, discharge study.
Parameter variation DT1 DT2 DT3
TSTS,mean,ini (°C) 300 375 450
A total of 18 (3 x ṁdischarge, 3 x TSTS,mean,ini, x 2 VSA) discharge studies were conducted.
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4.4.6 Simplified Model Development
In the parameter studies, the outlet steam temperature and the mean temperature are recorded during each simulation run. These results are used to develop multi- dimensional lookup tables in MATLAB/Simulink.
Separate parameter models are built into MATLAB/Simulink for charging and discharging (Figure 4-28). However, both STS models represent the same storage in reality. After a charging or discharging process, the value of TSTS,mean and thus the charged heat is transferred to the other storage model. The charge model is fed with a time profile of the simulation, the charge mass flow rate ṁcharge and the initial mean temperature TSTS,mean,ini. The discharge model uses the ṁdischarge and TSTS,mean,ini but unlike the charging, the inlet steam temperature changes during the discharging process, so a time series for the input temperature is used instead of the simulation time profile.
Figure 4-28: Charge and discharge model.
Separate parameter models are built-in MATLAB/Simulink for charging and discharging (Figure 4-28). However, both STS models represent the same storage in reality. After a charging or discharging process, the value of TSTS,mean and thus the charged heat is transferred to the other storage model. The pressure loss over the length of storage depends on the velocity of the heat transfer fluid. A pressure drop function, based on the velocity in the pipe, is implemented in the models.
Simplifying the ANSYS into the Simulink model causes several errors:
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• For the calculation of the output values, a lookup-table model is used for the interpolation of characteristic curves, which results in interpolation errors. Thus, the output of additional operation points of the ANSYS model is compared with the results of another parameter model. • In addition, the parameter model only takes into account the mean temperature of the solid thermal storage. In any case, the distribution of the temperature in the storage is not taken into account. The temperature distribution of the 3D-model in the concrete is simplified to a mean temperature.
Comparing this simplified model with the ANSYS model, an additional error in the outlet steam temperature of 0.5–3.3% is obtained. The maximum error of 1.3% is increased to 4.3% by this simplification.
4.5 System Model
After modelling the three main components (steam turbine, steam accumulator and solid thermal storage), a system model was created. Based on the structural concept (Figure 4-4), a flexible CHP plant model is developed in MATLAB/Simulink (Figure 4-29). The components are connected to the flow vector (see Section 4.1.2), which transmits the steam mass flow rate and steam properties to each other. The flow vector is connected as the pipes would be connected in a real plant. To monitor the simulation runs, the steam properties (p, T, ṁ, x and h) are displayed after each operating step.
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Figure 4-29: Simplified schematic (top) and screenshot (bottom) of the flexible CHP plant model.
A set of measured data for pressure, temperature and mass flow rate is used for the live- steam input (1) of the model. In real operation, the plant is controlled in such a way that an almost constant live-steam mass flow rate, as well as constant steam properties, are achieved. Due to the inhomogeneous biomass fuel, there is a slight variation in the steam properties in the real operation (Figure 4-30).
Figure 4-30: Measured live-steam data.
The pressure and the temperature have small deviations of 1.8–2% and 2–5% from their mean value. In this data series, the largest deviation is found in the mass flow rate (6-8%). This behaviour is typical for live-steam generation in biomass CHP plants. To
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investigate the influence of these volatile steam properties, the simulation runs are processed with the data series but also with mean values of pLS, TLS and ṁLS as input values. The respective input values are labelled with LSmean and LSprofile.
In the next block (2), the live-steam is divided between the plant turbine and steam storage system. The steam distributor splits live-steam from the main pipe for the charging process. To account for the dynamic behaviour of the charging valves, a transfer function is used to model their step response during opening and closing.
The power generation is calculated in the plant turbine module (3). Two extractions points at different pressure levels are available to accommodate steam extraction for external heat or steam supply. Depending on the input and output steam flows, the total power output of the plant turbine is calculated (turbine model in Section 4.2).
The amount of charge and discharge steam is limited by the volume and the pressure range of the steam accumulator. A storage controller (4) stops the charging and the discharging process depending on the pressure of the steam accumulator.
In the simulation, the solid thermal store (5a, 5b) is divided into two model blocks. However, they are the same physical component. All model parameters are transferred after charging and discharging to set both blocks to the same initial state at any time. The temperature of the input steam is changed by heat transfer between steam and concrete. In addition, the pressure loss in this component is calculated (solid thermal storage model in Section 4.4)
The steam accumulator model (6) evaluates the change in pressure and load level corresponding to the input and output of steam, according to the steam accumulator model in Section 4.3.
In the storage turbine (7), the power generation from the discharge steam is calculated in a single-stage turbine. Since the storage turbine must start during discharge, a transfer function is implemented for the start-up behaviour.
In order to collect all results of the different components and to get an overview of the system model, the results are collected in a separate module (8).
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4.5.1 Additional Components for the System Model
In addition to the main modules, which are described in detail, some other components are added:
• Steam distributor and dynamic valve (2), • Start-up of the storage turbine (7), • Storage controller (4).
The steam distributor separates the charging steam from the live-steam. The start and stop signals of a charging process are given by the supervisory control. A steam valve in the charging line has the task of separating the charging steam flow from the live- steam. Here the dynamic behaviour during opening and closing is taken into account. For the considered range of steam pressure and steam mass flow rates (see Table 2-2), valve switching times of 1.5–2.5 s are specified by the manufacturers. By implementing a transfer function, a dynamic switching time of 2 s is assumed for opening and closing.
The start-up of the storage turbine is also represented with a transfer function. Fast start- up times are described by operators to be in the range of 50-250 s. A pre-heated turbine is assumed, designed for a fast start-up with a start-up time of 200 s. In contrast to the storage turbine (7), the plant turbine (3) is operated in a steady state over the entire simulated period. Therefore, no start-up behaviour is considered there. However, there are load changes due to the changing steam input and extraction flows. Stark et. al (2018) investigated the load behaviour of steam turbines in biomass CHP plants. Based on these findings, the load change of the dynamic valve is sufficient to represent the load change behaviour of the plant turbine.
The main limiting factor of the storage system is the capacity of the steam accumulator. When the steam accumulator is full, the charging process has to stop immediately. When the steam accumulator is empty, the discharge process must be stopped. A discharge controller (4) is developed. The pressure of the steam accumulator is the main control variable. When the maximum value of the steam pressure is achieved, the charging process is stopped. Due to the delay caused by the opening of valves and starting the storage turbine, it is important to ensure that maximum pressure is not exceeded. Also, the pressure of the accumulator should not fall below the minimum pressure. Therefore, four different limit values for the pressure of the steam accumulator are defined. For the design limits, pSA,max,d and pSA,min,d must be observed. If the pressure reaches the control limits pSA,max,c and pSA,min,c, the storage controller intervenes and stops the process. The
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control pressure level (c) is adjusted so that the design (p) pressure level is not reached, taking into account delays due to valves and turbine start-up. Considering the objectives of this research, several simulation results will be examined. In order to keep the model structure clear, the recorded results are concluded in a separate block. The values recorded are summarised in Table 4-11. Some parameters are exported as time series, for others, only the total value at the end of the simulation is of interest.
Table 4-11: Recorded simulation results.
Parameter Symbol Unit Time series Total value
Pressure SA pSA MPa x
Mass SA MSA Kg x
Specific enthalpy SA hSA kJ/kg x
Temperature SA TSA °C x
Pressure SA pSA MPa x
Charge steam total mass Mcharge Kg x
Charge steam mass flow rate ṁcharge kg/s x
Discharge steam total mass Mdischarge kg x
Discharge steam mass flow rate ṁdischarge kg/s x
Discharge steam pressure pdischarge = pSA MPa x
Discharge steam temperature Tdischarge = TSA °C x
Performance ST/PT/STS PST//PT/STS kW x
Electricity generation ST QST/PT MWh x
In addition to values required for further evaluation of the system, error parameters are also recorded (Table 4-12).
Table 4-12: Error parameters.
Error Parameter Symbol unit Upper limit Lower limit
Water level SA βSA - 0.95 0.45
Pressure drop STS ΔpSTS MPa 0.1 MPa x (charge/discharge)
Steam velocity STS vSTS m/s 60 m/s x
Pressure SA pSA MPa pSA,max,d pSA,min,d
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After each simulation run, these parameters are checked against an upper and a lower bound. In this way, the transgression of physical and design boundaries can be excluded. If a limit value is exceeded, the modeller can perform a detailed investigation.
4.5.2 Sensitivity Study
Each of the sub-models considered was validated individually using measured data. However, the interaction of all components results in a combined fault. For this reason, a sensitivity study is conducted to investigate the emergent behaviour of the overall system. In this study, two types of error sources are considered, the errors of the individual sub-models and the measurement errors of the input parameter (Table 4-13).
Table 4-13: Error sources and their amplitude.
Parameter Unit Error amplitude +/-
Measurement error: Life-steam mass flow rate kg/s 1.0%
Measurement error: Life-steam temperature °C 0.5%
Measurement error: Life-steam pressure MPa 0.5%
Measurement error: Charge/discharge steam mass flow rate kg/s 1.0%
Model error: STS (charge) outlet temperature °C 2.5%
Model error: STS (discharge) outlet temperature °C 3.5%
Model error: Steam accumulator pressure MPa 3.69%
Model error: Storage turbine performance kW 1.54%
Model error: Plant turbine performance kW 1.54%
The system model shall calculate the total electricity production (Qtotal = QST + QPT) of the plant and the storage turbine. In the sensitivity study, the total error for this value is calculated during both charge and discharge cycles. By setting each parameter to its maximum and minimum error value, the impact on power generation is calculated. In a tornado diagram (Figure 4-31), the generation with errors is set in relation to Qtotal without errors (100%).
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Figure 4-31: Sensitivity study – charge.
The most important influences on the total error during charge are the pressure of the steam accumulator and the accuracy of the live-steam mass flow rate. If you set each error to its maximum value, the theoretical maximum error is14.5%.
During discharge, the values with the most significant impact are the live-steam mass flow rate and plant turbine performance (see Figure 4-32). The theoretical maximum total error is less than 5% during discharge.
Figure 4-32: Sensitivity study – discharge.
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The sensitivity analysis shows how the various sources of error, caused by measurement or model errors, affect the total error. These errors are acceptable for recent research.
4.5.3 Model Assumptions and Simplifications
The task for this system model is the general identification of the suitability of steam storage devices in biomass CHP plants. In order to investigate a number of different component dimensions, a high scalability of the system model is required. Gaining knowledge about the general operating principle and the basic parameters of this type of steam storage system is the main task of this research. This focus on the system level makes some neglect acceptable.
Thermal losses and pressure drops are neglected. Except for the critical pressure drop within the solid thermal storage; no pressure drops are considered. In addition, each component is considered to be sufficiently insulated so that thermal losses are not taken into account.
Since they are not affected by the steam storage system, the downstream auxiliary operations of the plant (air condenser, feedwater and condensate system) are not considered. There is no influence assumed on the system as long as the capacity of the air condenser is sufficient. Especially on days with very high ambient temperature, the maximum discharge steam volume may be limited in some plants due to the air- condenser capacity. However, this effect is considered to be outside the scope of this research.
Even a well-insulated steam accumulator and solid thermal storage cannot avoid losses during prolonged stagnation. Since only short-term (several hours) storage of the energy is considered in the cases investigated, these loss effects are also not taken into account in this research.
4.6 Chapter Summary
In this chapter, a computer model for the calculation of a flexible biomass CHP plant is developed. For the three main components, steam accumulator, steam turbine and solid thermal store, suitable models are developed or taken from the literature. These models are validated against measured data. Based on these models, a system model for a flexible biomass CHP plant is created. The emergent behaviour of the overall system model is estimated in sensitivity studies.
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However, for this stage of research, the accuracy of the models used is considered sufficient to study the general behaviour of this type of storage system.
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5 Simulation Results
The purpose of the system model is to gain insight into the fundamental behaviour of the proposed system. In this chapter, related to the research methodology (Figure 1-4), the storage system itself, the flexible plant performance, as well as the influence on the grid and the markets, are investigated. The developed system model is used to investigate these issues in the proposed storage concept. Three stages of simulation studies are considered:
• Charge/discharge behaviour, • Parameter study and • Operation of the flexible plant on electricity markets and the grid
In the first stage, the basic charging and discharging behaviour of the storage system are investigated. The aim of this stage is to gain knowledge about the operation of the storage system and its components.
Figure 5-1: Simulation model with parameters.
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In the second step, several simulations are carried out with a number of parameters. Design parameters such as the volume of the steam accumulator (SA) or the solid thermal storage (STS), as well as the mass flow rates, are varied. The objective of this step is to determine the performance of the flexible plant in terms of storage capacity, efficiencies and power output. Also, the interdependencies of the model parameters and their influence on the performance are investigated. In the final stage, the operation of a flexible plant is investigated under realistic conditions. Based on electricity price development, the baseload operation of the biomass CHP is modified to become flexible and market-driven. The structure of the simulation model, as well as important parameters, are shown Figure 5-1. The subscripts charge and discharge are defined to indicate the state. The subscript ini is used for each initial parameter. The boundary parameters are adjusted to the values of the real biomass CHP plant (Table 5-1), which is described in Section 4.5.
Table 5-1: Boundary parameters of the biomass CHP plant used for the simulations.
Parameter Symbol Value
Rated thermal input Pr,th 28 MW
Nominal plant turbine capacity PPT,Baseload 6.93 MW
Live-steam pressure (average) pLS 6.27 MPa
Live-steam temperature (average) TLS 480 °C
Live-steam mass flow rate (average) ṁLS 6.73 kg/s
Maximum SA pressure pSA,max,d 6.0 MPa
Minimum SA pressure pSA,min,d 1.0 MPa
Backpressure of the plant turbine pend,PT 0.05 MPa
Backpressure of the storage turbine pend,ST 0.1 MPa
As described in Section 4.5.1, two alternative datasets for the live-steam properties pLS,
TLS, and ṁLS are used in these sections. Further, the time-series data of the plant (LSprof) and the average values of these time series (LSmean) are used. For a better illustration of system behaviour and the interdependencies between storage parameters, mainly fixed steam properties (LSmean) are used in the next sections.
5.1 Charging and Discharging Behaviour
The purpose of this study is to visualize the overall behaviour of the storage system. At this stage, characteristic values such as capacity, performance or efficiency are not the
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focus of the investigation. The basic operation of the main SA and STS should be visualised. The simulation parameters for this study are listed in Table 5-2. These parameters are the average values from the parameter study (see Subchapter 5.2).
Table 5-2: Simulation parameters – Charge and discharge study.
Parameter Symbol Value
Steam accumulator volume VSA 100 m³
Solid thermal store volume VSTS 18.5 m³
Solid thermal store modules - 150
Charge mass flow rate ṁcharge 3.5 kg/s
Discharge mass flow rate ṁdischarge 3.5 kg/s
Initial concrete temperature (charge) TSTS,ini,charge 280 °C
Initial concrete temperature (discharge) TSTS,ini,discharge 390 °C
Initial SA beta value (charge) βSA,ini,ch 55%
Initial SA beta value (discharge) βSA,ini,dh 80%
5.1.1 Charging the Storage System
The charging process is initiated by opening the charging valve. Within a start-up time of 2 seconds, the charging mass flow increases from 0 to 3.5 kg/s. This reduces the live- steam flow into the plant turbine to 3.23 kg/s. The reduction in the power output of the plant from 6.93 MW to 2.49 MW is shown in Figure 5-2. Since the response time of the charging valve is in the range of several seconds, the start-up behaviour is not shown in this figure.
As steam is injected into the SA, the pressure pSA increases. As soon as the upper storage limit pSA.max,c is reached, the storage process stops, and the charging valve is closed. After this charging process, the plant’s power output reverts to its baseload of 6.93 MW.
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Figure 5-2: Power generation during charging – LSmean.
Comparing the live-steam input values LSmean with LSprofile, their behaviour is similar
(Figure 5-3). The fluctuation of pLS, TLS, and especially ṁLS, lead to a more volatile performance of the plant turbine. However, due to the buffering effect of the steam accumulator, these fluctuations do not show any significant influence on the increase of pSA.
Figure 5-3: Power generation during charging – LSprof.
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In Figure 5-4, the operation of the STS during charge is shown. Live-steam with a constant temperature of 480 °C enters the STS and transfers heat to the storage. As the concrete temperature rises, the temperature difference between input and output decrease over time. Hence the output temperature of the STS, Tcharge increases. An average temperature difference of 101 °K is achieved. The saturation temperature of the incoming steam is 276 °C. As expected, only a share of the energy in the superheated steam is stored in the STS.
Figure 5-4: STS Operation during charging – LSmean.
The average temperature of the STS rises from 280 °C to 346° during this charging process. Due to the low heat transfer rate and the thermal mass of the STS, no significant difference can be observed between LSmean and LSprof.
5.1.2 Discharging the Storage System
During discharging, the plant turbine is operated at its baseload power output of 6.93 MW. The discharge steam of the storage system is fed into the storage turbine, and an additional amount of electricity is generated (Figure 5-5). After the start-up of the turbine, the total power output of the plant increases to 9.1 MW. As the pressure of the SA continuously decreases, the temperature and pressure of the discharge steam also decrease during the discharge. This also reduces the power output of the storage turbine. When the SA pressure reaches pSA,min,c, the storage turbine is switched off.
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Figure 5-5: Power generation during discharging – LSmean.
Since the storage system acts as a buffer between the live-steam input and the storage turbine, the power output of the storage turbine is not affected by the volatile live-steam input of LSprof. Due to the volatile generation of the plant turbine, the total output also fluctuates (Figure 5-6).
Figure 5-6: Power generation during discharging – LSprofile.
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In the STS, the saturated steam is superheated (Figure 5-7) because its temperature was increased by the stored heat of the concrete, but this temperature falls during discharging. The average temperature difference between the output temperature
Tdischarge and TSA is 47 K when the concrete temperature decreases from 350 °C to 285 °C. The main objective of the STS, the superheating of the saturated discharge steam, can be fulfilled at any time.
Figure 5-7: STS operation during discharge – LSmean.
Since the STS is decoupled from the live-steam of the plant, no difference in the STS, between LSmean and LSprof is observed.
5.2 Parameter Study
The combination of a steam accumulator (SA) and a solid thermal store (STS) is a novel and potentially advantageous concept. Due to the range of parameters that influence each other, key values such as energy capacity or efficiency must be derived. Therefore, a series of simulation studies are conducted with varying simulation parameters. The following parameters are varied in the parameter study:
• Steam accumulator volume VSA (m³),
• Solid thermal store volume VSTS (m³),
• Charge/discharge mass flow rate ṁcharge / ṁdischarge (kg/s),
• Initial mean temperature of the concrete TSTS,ini (°C),
• Initial beta value of the steam accumulator βSA (-).
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The volume of the SA is investigated in the range of 50 to 150 m³. These dimensions are chosen to maintain a balance between sufficient capacity and cost. As identified in preliminary studies, the STS is responsible for only a portion of the energy content (Stark, et al., 2019). Therefore, the STS size is selected between 6 m³ to 25 m³. The volume of the STS is defined by the number of STS modules (see Section 4.4.6). Only the effective storage volume is considered, i.e. additional masses at the edges are not taken into account.
For the selection of a suitable mass flow range, several aspects have to be considered. During charging, a minimum mass flow rate of the plant turbine has to be guaranteed. At least 1.55 kg/s of live-steam must be supplied to the plant turbine. A maximum of 5.28 kg/s can therefore be extracted from the live-steam mass flow of 6.93 kg/s.
In addition, the pipes inside the STS are the bottleneck of the entire storage system. As the diameter of the piping is reduced to divide the steam flow, high velocities should be avoided as they lead to high-pressure drops and additional energy losses. Therefore, the range of mass flow rates is set as boundary conditions for the parameter model (Table 4-7 andTable 4-9). The validity of the simplified STS model can only be assumed for steam velocities within a certain range. Thus, the range of the mass flow rate depends on the STS size and hence the number of STS modules. In Table 5-3 and Table 5-4, the mass flow rates corresponding to different volumes of STS are shown. These combinations depend on the number of STS modules, and they determine the minimum and maximum pipe velocity.
Table 5-3: STS charge mass flow rates chosen for the parameter study.
Charge mass flow rate ṁcharge STS volume STS modules 1 kg/s 2 kg/s 3.5 kg/s 5.38 kg/s
6.2 m³ 50 x x
12.3 m³ 100 x x
18.5 m³ 150 x x
24.6 m³ 200 x x
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Table 5-4: STS discharge mass flow rates chosen for the parameter study.
Discharge mass flow rate ṁcharge STS volume STS modules 1 kg/s 3 kg/s 7.5 kg/s 10 kg/s
6.2 m³ 50 x x
12.3 m³ 100 x x
18.5 m³ 150 x x x
24.6 m³ 200 x x x
There are two extraordinary parameters in the simulation study: the temperature of the STS and the beta value of the SA. Due to the slow heat transfer compared to the SA, the STS is only fully charged to the maximum temperature of 480°C in very rare cases. The temperature of the STS therefore defines its state of charge. The beta value defines the water level in the SA. Since the energy of the charge steam is affected by the STS, different amounts of steam can be charged into the SA, which affects the final beta value. Although the capacity of the steam accumulator is mainly defined by the volume and the pressure range, the beta value still has a small influence on the discharge capacity. Each of the TSTS and βSA parameters has an initial and a final state. The final state of charge is the initial state of discharge and vice versa. All parameter values during the charging process have an effect on these two values. In addition, both values are influenced by factors such as standby times, operation cycles and thermal losses, which are not considered in this research.
In this parameter study, the key values and their dependence on the main simulation parameter are of particular interest. In Sections 5.2.1 to 5.2.3, the charging and discharging operation should start with comparable initial values to exclude minor impacts of TSTS and βSA. Therefore, the initial values for both are set to the same value at the beginning of all simulations runs.
The intended application of the range system assumes a few charging and discharging operations in the course of a day. Since sufficient insulation is assumed, the initial and final values for TSTS and βSA are expected to settle to constant initial and final values after several charge and discharge cycles, which are simulated in succession for different storage configurations in order to determine average values for them. Based on these simulations, the initial temperature for charging is set to 280°C and for discharging to 390°C. The beta value varies between 55% (charging) and 80% (discharging).
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5.2.1 Power Output
While charging the storage system the power generation of the plant turbine is reduced (Figure 5-8). As more steam is extracted, the power output is reduced. The visualised results include several different values of VSA and VSTS. As they are arranged downstream of the extraction, these design parameters show no influence on the power output during charge.
Figure 5-8: Flexible plant power output depending on the charge mass flow rate.
Although low power output is expected during the charging process, the efficiency of the plant turbine must be taken into account. The efficiency of the plant turbines decreases with a lower mass flow rate into the turbine (see Figure 5-9). Therefore, the gross efficiency of the plant system suffers from high-charge mass flow rates.
When discharging, high mass flow rates result in higher power output (Figure 5-10). Note that the discharge mass flow rate is not the only parameter that affects the output power. Other parameters such as the volume of SA and STS have an influence that causes the scatter of results. Nevertheless, the mass flow rate is still the most significant influence on the power output. Due to the sliding pressure when discharging the SA, shown in
Figure 5-5, the power output decreases with time. The difference between Ptotal,discharge,max and Ptotal,discharge,mean increases with a higher mass flow rate.
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Figure 5-9: Influence of a lower mass flow rate on power output of the plant turbine.
Figure 5-10: Flexible plant power output for the variation of the discharge mass flow rate.
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Figure 5-11 and Figure 5-12 show the system behaviour at different discharge mass flow rates. A system with VSA = 100 m³ and VSTS =12.3 m³ is considered.
Figure 5-11: Steam accumulator pressure pSA Figure 5-12: Power output of the storage turbine during discharge at different mass flow rates. PST during discharge at different mass flow rates.
The pressure drop during the sliding pressure discharge of the SA is shown. It influences the power output of the storage turbine. The higher the discharge mass flow rate, the faster the drop in the power output. Particularly high mass flow rates cause uneven power generation. A low discharge mass flow rate (e.g. 2 kg/s) has the effect of making the power output more constant over time.
The distribution of the results shown in Figure 5-10 is caused by different STS sizes. For the SA volume of 100 m³, the discharge behaviour is shown for a ṁdischarge of 2 kg/s (Figure 5-13) and 4 kg/s (Figure 5-14).
Figure 5-13: Power output of the storage turbine Figure 5-14: Power output of the storage turbine PST during discharge with different STS sizes / PST during discharge with different STS sizes / ṁdischarge = 2 kg/s. ṁdischarge = 4 kg/s.
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The superheat energy of larger STS units can increase the overall power output of the plant turbine. However, this effect is limited and becomes less significant with increasing
STS size. For example, the mean value of PST increases by 3.9% from 6.2 m³ to 12.3 m³ but only by 1.2% from 6.2 m³ to 18.4 m³.
5.2.2 Charging/Discharging Time
In addition to the power output, the charging and discharging time is another important performance parameter of the storage system. The charging time mainly depends on the
SA volume VSA and the charging mass flow rate ṁcharge (Figure 5-15). As expected, higher
VSA lead to longer charging time. In addition, the smaller the value of the ṁcharge, the longer the tcharge.
Figure 5-15: Charging time of the storage system for varying VSA and mass flow rate.
In contrast to the influence of VSA and ṁcharge, the volume of the STS has little influence on the charge time. By lowering the charging steam temperature, the energy fed into the steam accumulator is reduced. The lower the energy content of the charge steam, the greater the total mass of steam stored in the SA. For this reason, Figure 5-15 shows a spread through the white areas.
The influence of the STS volume VSTS and ṁcharge is shown in Figure 5-16. Here the charging times for a 100 m³ steam accumulator are shown for different mass flow rates and STS volumes. Here the influence of increasing VSTS is visualised for several mass flow rates.
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Figure 5-16: Charging time by varying the STS volume.
In contrast to the charging operation, the discharging time is not directly influenced by the STS volume. During discharge, the STS is downstream of the SA and has no influence on the discharging times. Figure 5-17 visualizes the influence of VSA and
ṁdischarge on the charging times.
Figure 5-17: Discharging time as a function of SA volume and discharge mass flow rate.
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As mentioned in Section 2.1.2, the envisaged operation of the flexible biomass CHP plant will be a mid- and short-term operation, from 15 min to several hours. The simulated charging times are within this range.
5.2.3 Capacity
The steam and energy capacity of the flexible CHP plant is a key value for the study of this system. The charging/discharging time, as well as the modified power output, defines the capacity of the flexible plant. As shown in Figure 5-18, the total mass of discharge steam depends only on VSA. In contrast, the STS has an influence on the total charge mass during the charging process, while a cloud of data points is displayed. The amount of discharge steam is determined solely by the energy content of the SA. Independent of previous charging operations, the fully charged SA can discharge a defined amount of steam.
Figure 5-18: Steam mass stored in the storage system as a function of VSA.
Since the superheated live-steam has a higher energy content than the discharge steam, more steam can be generated during discharge. Using the liquid in the steam accumulator, a larger mass of steam is discharged (Mdischarge) than charged (Mcharge) (see Section 2.3.2).
The whole concept of this steam storage system implies the conversion of a baseload- capable biomass CHP plant into a flexible plant. This transition allows the plant to
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operate similar to an electric battery. The main objective of flexible operation is to shift electricity generation from periods of low demand to periods of high demand. To measure this amount of energy, the energy capacity CFlex of the flexible plant is defined as one of the key values of the plant. The flexible plant’s energy capacity for charge CFlex,charge (Equation 5-1) is the reduced amount of electricity compared to baseload operation. The flexible plant’s energy capacity for discharge CFlex,discharge (Equation 5-2) is defined as the energy generated by the storage turbine during discharge. Both capacities CFlex are defined for the respective charging or discharging period.
(5-1) 퐶퐹푙푒푥,푐ℎ푎푟𝑔푒 = 푄푏푎푠푒푙표푎푑 − 푄푐ℎ푎푟𝑔푒 = 푃푏푎푠푒푙표푎푑 ∗ 푡푐ℎ푎푟𝑔푒 − 푄푐ℎ푎푟𝑔푒
(5-2) 퐶퐹푙푒푥,푑𝑖푠푐ℎ푎푟𝑔푒 = 푄푑𝑖푠푐ℎ푎푟𝑔푒 = 푄푆푇
Figure 5-19 plots the CFlex,charge and CFlex,discharge values. The main influencing factor is the volume of the steam accumulator. In contrast to the steam mass, CFlex,charge is higher than
CFlex,discharge. As seen from the scattering of results, mass flow rates and STS volume also have an influence on CFlex.
Figure 5-19: Flexible energy capacity of the plant as a function of VSA.
To investigate this influence, another analysis of CFLEX for VSA =100 m³ is shown in Figure 5-20 and Figure 5-21. The flexible energy capacity of the flexible plant is shown as a function of the mass flow rate and the STS volume is shown.
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Figure 5-20: Influence of ṁcharge and VSTS on Figure 5-21: Influence of ṁdischarge and VSTS on CFlex,charge. CFlex,discharge.
A larger volume of the STS leads to an increased CFLEX. This can be explained by the greater heat capacity of the STS, which also benefits CFlex. In contrast, CFlex decreases at higher mass flow rates. This is due to two reasons, first, the velocity inside of the STS increases as the mass flow rate increases, which reduces the heat exchange, and second, a high mass flow rate reduces the charging and discharging times. Due to the low thermal conductivity, less heat can be transferred between steam and STS. From an energy perspective, slow charging and discharging are preferable. During charging
(Figure 5-20), VSTS has a greater impact on CFlex than during discharging. As described in Section 5.2.2, the volume of the STS tcharge also increases. From an energy perspective, the volume of the STS is therefore more important during charging than during discharging.
Figure 5-22: Energy capacity of the flexible plant Figure 5-23: Specific energy capacity of the by incrementally increasing the VSTS. flexible plant by incrementally increasing the VSTS.
In Figure 5-22 and Figure 5-23, it can be seen that each incremental increase of VSTS has a smaller impact. From a design perspective, it is important to weigh the benefits
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against the costs of increasing the VSTS. As assumed above, the specific increase in CFlex decreases with each increase in STS size. A doubling of the VSTS from 6.2 m³ to 12.3 m³ leads to an increase of 6% and 4% in CFLEX, respectively. By doubling the volume again, values of 1.5% and 0.8% are achieved.
Another value that has not been considered so far is the concrete mean temperature
TSTS and thus βSA. In the previous calculations, initial values are set as previously described. This requires a steady system operation with charge and discharge cycles.
The final temperature of the STS varies, depending on the charging behaviour. To investigate this influence, several initial temperatures are set. The investigations are carried out for VSA = 100 m³ and VSTS = 12.3 m³. For comparison, the minimum temperature of 280 °C (assuming no prior charging) and the maximum temperature of 390 °C (assuming steady operation) are considered. For this setup of SA and STS, both 2 kg/s and 4 kg/s charging are investigated. As shown in Figure 5-24, a slow charging mass flow rate has a small but beneficial impact on CFlex,discharge.
Figure 5-24: Discharge as a function of TSTS,ini.
For the calculation of efficiency, charging and discharging processes are calculated in pairs. Thus, for each efficiency calculation, a charging process is initiated, and the final temperature TSTS,end is used as TSTS,ini for the discharging process.
Regardless of TSTS, changing beta values, due to varying charge mass flow rate, have no discernible effect on the amount of discharge steam.
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5.2.4 System Efficiency
When comparing this flexible plant concept with competing technologies, energy efficiency is an important parameter. The round-trip efficiency (Equation 5-3) is defined by the ratio of discharge capacity to charge capacity of the flexible plant. In the simulation study, the range of this value is between 40% to 57%.
퐶퐹푙푒푥,푑𝑖푠푐ℎ푎푟𝑔푒 (5-3) 휂푠푡표푟푎𝑔푒 = 퐶퐹푙푒푥,푐ℎ푎푟𝑔푒
The round-trip efficiency of the storage system ηstorage defines the efficiency and losses of the storage system as part of the flexible plant. Usually, energy storage technologies store electricity and release it at a later time. For this operation, efficiency is a good value for technology comparison. However, the proposed steam storage concept is an upgrade of a plant. The charge does not use electricity that has already been generated but avoids generation from the plant. Therefore, the important question in this case is how much energy generation is avoided by using storage. Therefore, the system efficiency was defined to take this effect into account from the perspective of energy losses.
For all design and economic decisions, as well as for the development of control strategies, the reduction of electricity production caused by the flexible operation is crucial for the success of the system. Assessing this reduction will be key to quantifying the benefits of flexible operation. In accordance with the definition of the flex factor (see
Section 3.3), the system efficiency ηsystem was defined (Equation 5-4). This efficiency value combines the efficiency of the plant with the efficiency of the entire storage system.
푄푐ℎ푎푟𝑔푒+ 푄푃푇 + 푃퐵푎푠푒푙표푎푑 ∗푡푑𝑖푠푐ℎ푎푟𝑔푒 (5-4) 휂푠푦푠푡푒푚 = 푃퐵푎푠푒푙표푎푑 ∗ (푡푐ℎ푎푟𝑔푒+푡푑𝑖푠푐ℎ푎푟𝑔푒)
Figure 5-25 (charging) and Figure 5-26 (discharging) show the dependence of the system efficiency on the capacity of the flexible plant. The volume of the steam accumulator has no influence on the system efficiency. For each SA size, the system efficiency shows the same range. The flexible plant energy capacity CFlex also has no significant impact.
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Figure 5-25: System efficiency during charging. Figure 5-26: System efficiency during discharging.
Figure 5-27 examines the influence of charging and discharging mass flow rates. Here, the 100 m³ SA and the 12.3 m³ STS are considered. It can be seen, that both an increasing ṁcharge, as well as an increasing ṁdischarge, have a negative impact on the system efficiency. This can be explained by the lower heat transfer rate in the STS at higher mass flow rates.
Figure 5-27: System efficiency as a function of charge and discharge mass flow rate.
The effect of increasing STS sizes on the system efficiency is a very slight decrease the system efficiency. The larger the mass flows rates, the lower the overall efficiency. From this perspective, the system operation should be designed for the lowest possible mass flow rates. On the other hand, high mass flow rates lead to high Pmax and low Pmin values.
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Since the load range between Pmin and Pmax is important for flexible operation, low mass flow rates are not possible under any circumstances.
For future research, an optimum of the mass flow rates needs to be determined for specific cases. Mass flow rates should be not too high to reduce efficiency, nor too low to reduce flexibility. Therefore, the operating strategy of the flexible plant must be developed according to the area of application.
5.2.5 Summary of the Parameter Study
In this part of the work, the interdependencies between the parameters and the key values of the storage system in Table 5-5 are investigated. The influence of the parameters is shown here. The impact of increasing these values is presented separately as very positive impact (++), positive impact (+), insignificant impact (o), negative impact (-) and no impact (=).
Table 5-5: Interdependencies of parameters (by increasing values).
Parameter Symbol Pmax Pmin Tcharge/discharge CFLEX ηsystem
SA Volume VSA o o + ++ =
STS Volume VSTS o o ++ + =
Charge mass flow rate ṁcharge o ++ - - --
Discharge mass flow rate ṁdischarge ++ o O - -
The effect of increasing parameters is shown. A reduction has the opposite effect. For an overview, a selected range of simulation results is shown in Table 5-6. The parameters are on the left (grey), and the results for the reference system are on the right (white) side.
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Table 5-6: Overview on simulation results.
Simulation parameter Simulation results of the reference system
SA STS ṁcharge ṁdischarge tcharge tdischarge Pmax Pmin CFLEX,charge QFLEX,discharge ηstorage ηsystem m³ m³ kg/s kg/s s s MW MW MWh MWh - -
100 12.3 2 2 5,920 6,700 8.09 4.67 3.709 1.882 50.7% 92.5%
100 6.2 2 2 3,040 3,360 8.05 4.67 1.899 0.925 48.7% 92.1%
100 12.3 4 2 2,850 6,700 8.07 2.41 3.572 1.855 51.9% 90.7%
100 12.3 4 4 2,850 3,360 9.14 2.41 3.572 1.794 50.2% 85.1%
150 12.3 4 4 2,950 3,360 9.15 2.41 3.703 1.820 49.2% 84.5%
100 18,4 4 7.5 4,230 2,700 10.98 2.41 5.301 2.571 48.5% 79.5%
Several parameter variations are shown leading to the full range of ηsystem. All simulation results are presented in the Appendix. Since the combination of a 100 m³ SA and 12.3 m³ STS has an average efficiency and operating parameter, this case will be further investigated in the following study.
5.3 Operation of the Flexible Plant Within the Energy Markets and the Grid
The purpose of the flexible plant is to contribute to balancing the electricity supply system. This part of the study simulates a plant operating under realistic technical and economic conditions. An overall objective of this simulation chapter is to investigate the theoretical operation of the flexible CHP plant concept. All the proposed operations could be fulfilled by this flexible plant. The purpose of this simulation study is to investigate the energy capacity of the storage system and the yield situation during flexible operation in various realistic case scenarios. The flexible plant model is used to study three different types of flexible operation. These are the cases rated with the highest priority in the expert survey (see Section 3.2.3).
• Operation according to the day-ahead market price, • Operation according to the intraday market price and • Balancing the grid at the distribution level.
One way to support the energy system is to adjust plant output based on energy prices in short-term markets such as the day-ahead or intraday markets (see Subchapter 2.1). Power generation is reduced in low-price periods and increased in high-price periods.
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Figure 5-28: Day-ahead spot market (EPEX, 2019).
For the day-ahead market simulation, three different price developments are used as control signals for the operation (Figure 5-28). These historical price trends are data series from 2019. The days with the highest prices (DH) and the lowest prices (DL), as well as one day with an average price curve (DA), are selected. In addition, intraday price trends from 2017 were used. The day with the highest price (IM) and a day with a typical price curve with an average price behaviour (IA) are used (Figure 5-29).
Figure 5-29: Indices on the intraday market (EPEX, 2019).
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The day-ahead market gives fixed prices for 1h time slices, one day in advance. This way, the trades on this market and thus the mode of operation can be pre-planned. An operating schedule is established to set the place charging on the times with the lowest price and discharging on the times with the highest price.
In contrast, in the intraday market, energy is traded continuously in 15-minute time slices. Depending on demand and generation forecasts, trading takes place until the end of the market period (5–20 minutes before the call, depending on the country). Therefore, there is no fixed price for each time slice, as is the case on the day-ahead markets. The prices shown in Figure 5-29 are index values representing the average trading prices for the respective time slice. Because of this short-term trading, no mode of operation can be planned in advance. Minimum prices for discharge EPmin,discharge and maximum prices for charge EPmax,charge are defined here to control the operation of the flexible plant.
As discussed in the literature review (see Subchapter 2.1), electricity markets cover a nationwide or even larger area. The operation of the flexible plant in these energy-only markets is based on the amount of electrical energy and not on the output of the plant. Local supply shortages or grid overloads are not indicated by the market price. Therefore, the impact of flexible, market-driven operation on the grid is limited. For this reason, the balancing of the grid at the distribution level is investigated. The load on the grid in the vicinity of the flexible plant is considered as a control signal. Of particular interest is not the amount of electricity, but the power output. Therefore, the third part of this section examines the ability to reduce or avoid grid shortages or overload.
In all of the following simulations, the baseload operation of the plant (where it is operated with constant power) is compared with the flexible operation. In the previous simulations
(in Subchapter 5.2) the energy capacities of the flexible plant CFlex,charge and CFlex,discharge were calculated considering full-charge or discharge processes. This section presents the results of simulation runs where multiple full or partial charge/discharge cycles occur.
Therefore, instead of calculating the full energy capacity CFlex for a single cycle, the charged quantity of electricity Qel,charge and discharged quantity of electricity Qel,discharge are calculated. For Qel,charge, the same calculation method as previously used for CFlex,charge, is used to determine the reduction in electricity generation due to charging the storage.
5.3.1 Operation According to the Day-Ahead Market Price
For operation on the day-ahead market, an operating schedule is drawn up based on the price development. The storage is charged during low price periods and discharged
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during high price periods. To maximize storage capacities, full charge and discharge cycles are included in these operation schedules. Figure 5-30 simulates a day with the average electricity price (DA) and different discharge mass flow rates. The charge mass flow rate ṁcharge is set to 2 kg/s for all cases. A storage system with VSA = 100 m³ and
VSTS = 12.3 m³ is used. The pressure of the steam accumulator pSA represents the state of charge. In each of the simulations, two peaks and valleys of the electricity price are covered with two full charge and discharge cycles. At a discharge with 2 kg/s, tdischarge lasts longer than 1 hour, so discharge is not achieved only during a 1-hour time slice.
Figure 5-30: Flexible operation according to day-ahead with DA.
During the envisaged period, electricity production will be reduced by 7.2–7.4 MWh in low-price periods. The stored energy is used to generate Qel,discharge in the range of 3.4– 3.8 MWh during high-price periods. Due to the constant steam properties of the live- steam, the charging process takes place at a constant load level. During discharge, Pmax and tdischarge vary. The lowest discharge mass flow rate of 2 kg/s shows the most stable operation along with the largest amount of Qel,discharge (7.4 MWh). However, at 2 kg/s, a longer discharge time is required, and not all power can be sold during the maximum price level.
In the next simulations, the low-price (Figure 5-31) and the high-price (Figure 5-32) signals are examined. In both cases, operating schedules are adjusted to cover a low
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and a peak during the day. In the DL case, the lowest price even becomes negative. In both cases, ṁcharge = 2 kg/s, VSA = 100 m³ and VSTS is 12.3 m³.
Figure 5-31: Flexible operation according to the day-ahead market with DL.
Figure 5-32: Flexible operation according to the day-ahead market with DH.
Since the simulated operation (one full charge and one full discharge) is the same in both the high-price and low-price cases, Qel,charge and Qel,discharge are also the same in both cases. In both the high-price and low-price cases, the electricity generation is reduced
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by 3.6 MWh during charging and increased by 1.7–1.8 MWh during discharging. In contrast to the DA simulation, only one charging and discharging operation takes place here, and thus the amount of energy is also halved.
To investigate the impact of larger storage systems, VSTS and VSA are increased as shown in Figure 5-33. The mass flow rates are set at 2 kg/s for charging and 4 kg/s for discharging. Here, the amount of electricity generated increases even though part of this generation takes place outside the maximum price period.
Figure 5-33: Flexible operation according to the day-ahead DH with different storage volumes.
As shown in Table 5-7, the energy storage capacity is almost doubled by doubling the volumes. However, only the 100 m³ SA was able to discharge exclusively during the highest price period, while the greater amount of charged and discharged electricity in the two larger accumulators results in more generation during periods of lower prices. Thus, an increasing storage volume does not necessarily lead to a higher yield, which will be considered on the next pages.
Table 5-7: Effects of increasing the storage volume.
VSA / VSTS 100 m³/ 12.3 m³ 150 m³/ 18.4 m³ 200 m³/ 24.6 m³
Qel,charge 3.61 MWh 5.51 MWh 7.36 MWh
Qel,discharge 1.81 MWh 2.74 MWh 3.63 MWh
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Finally, a simulation is performed using the LSprofile data series. Figure 5-34 uses the average price trend and the same operating schedule as before. The charged electricity
Qel,charge is in the range of 7.0–7.4 MWh, while 3.4–3.6 MWh is discharged. These values have no significant difference to the calculation with LSmean. It can be seen that different power output occurs even beyond charging and discharging operations. Since these fluctuations can be handled, the sliding power discharge also seems to be valid. Therefore, no problems are assumed due to the sliding discharge, at least for discharge mass flow rates of 2 kg/s and 4 kg/s.
While the previous simulation was performed with the mean values of the measured live- steam data (LSmean), a simulation is now performed with the LSprofile data series. Figure 5-34 uses the average price trend and the same operating schedule as before. The generated electricity during the charge cycle Qel,charge varies between 7.0–7.4 MWh, while during the discharge cycle, it varies between 3.4–3.6 MWh. These values are not significantly different from those derived using the LSmean data series. It can be seen that even outside the charging and discharging operations, the output power varies due to the sliding pressure. Just as the difference between the results with LSmean and LSprofile is so small, we find that the difference between energy production during these sliding- pressure periods is also very small. The deviation between the power output of LSmean and LSprof is less than 1%.
Figure 5-34: Flexible operation according to day-ahead DA with measured data.
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A detailed, case-specific study will be required in future work for a comprehensive evaluation of the economic feasibility. In this research, only the yield of the flexible operation compared to the baseload operation is calculated, as this is a key value for the economic evaluation.
Shifting electricity generation from low-price into high-price periods generates additional income. However, due to storage losses, a lower total amount of electricity is generated compared to the conventional, baseload operation.
Most biomass plants are operated with a fixed feed-in-tariff (FIT). The FIT for bioenergy plants varies depending on various factors such as country, plant size, fuel type, heat consumption and even the year of commissioning. In 2020, the maximum German feed- in tariff for new solid biomass-fuelled plants was 148.8 €/MWh. In Germany, the theoretical range of the FIT is between 57 and 246 €/MWh, with values above 200 €/MWh mainly for small size plants such as biogas plant (BMWE, 2015) The FIT varies across European countries; for example, in Austria, the FIT for solid-fuelled biomass is in the range of 60–80 €/MWh (AEBIOM, 2012).
In the following investigations, a FIT of 100 €/MWh is assumed. In baseload operation, only the plant turbine is in operation and the amount of electricity generated by the plant turbine QPT is multiplied by the FIT (Equation 5-5). In flexible operation, both the plant turbine and the storage turbine are in operation. Trading electricity at high-price times generates additional income FI (Equation 5-6).
(5-5) 퐵푎푠푒푙표푎푑 𝑖푛푐표푚푒 = 퐹퐼푇 ∗ 푄푃푇
(5-6) 퐹푙푒푥𝑖푏푙푒 𝑖푛푐표푚푒 = 퐹퐼푇 ∗ (푄푃푇 + 푄푆푇) + 퐹I
Since the German FIT is relatively high compared to the rest of Europe, there are many days when even the maximum electricity price is lower than the FIT. In order to incentivise plant operators for flexible operation, a special additional income (AI) calculation mode is made available to markets to support flexible operation. For the amount of electricity sold in high price periods, the difference in income between the monthly average electricity price and the respective electricity price in the respective time slice is added to the total FIT income (Equation 5-7). For this reason, the AI is calculated for each time slice (n).
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푛 (5-7) 퐴퐼 = ∑[(푝푟𝑖푐푒푛 − 푎푣푒푟푎𝑔푒_푝푟𝑖푐푒푚표푛푡ℎ) ∗ 푄푃푇,푛]
To determine the revenue impact of comparing baseload and flexible operation, the revenue difference ΔR between the two is examined (Equation 5-8).
훥푅 = 퐹푙푒푥𝑖푏푙푒 𝑖푛푐표푚푒 − 퐵푎푠푒푙표푎푑 𝑖푛푐표푚푒 (5-8)
The total yields for the day-ahead markets are shown in Figure 5-35 with mean values from the simulation results. Since the output of the plant is constant at 6.93 MW during the baseload, the FIT revenue is the same in all cases studied. This baseload yield is compared with the yield during flexible operation. The total result is the difference between baseload income (FIT) and the flexible income (FIT + AI).
Figure 5-35: Income from day-ahead operation with a FIT of 100 €/MWh.
The numerical data of Figure 5-35 are presented in Table 5-8. The calculation of additional income (AI) is presented in Appendix III.
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Table 5-8: Revenue from day-ahead operation with a FIT of 100 €/MWh on a simulated day.
Difference between Additional Price Storage Baseload Flexible revenues from flexible income trend configuration income (FIT) income (FIT) baseload and flexible (markets) AI operation (ΔR)
DA 100/12 m³ €16,643 €16,242 €32 -€388
DL 100/12 m³ €16,643 €16,442 €0 (*327) €138
DH 100/12 m³ €16,643 €16,442 €98 -€88
DH 150/18 m³ €16,643 €16,350 €144 -€147
DH 200/24 m³ €16,643 €16,255 €195 -€192
In each case studied, the FIT income for flexible operation is lower than the baseload income due to the losses of the steam storage system. The additional income (AI) can generally only compensate for a part of the deficit caused by these losses. In the average price (DA) trend, only 8% (€32) of the total revenue loss can be covered by additional revenues on the markets (AI).
In the low-price trend (DL), the electricity price is always below the monthly average price. In this case, no additional income (AI) is generated. However, this is a special situation caused by negative prices. Since electricity generation is reduced by 3.6 MWh in negative price periods, costs of €327 could be avoided. In the portfolio of a virtual plant pool or a grid operator, the reduced power output can be used profitably to reduce the losses caused by other, not dispatchable plants.
In the highest price periods (DH), up to half of the deficit can be covered by the additional revenue. Interestingly, even larger storage volumes do not improve the situation. Indeed, they increase AI income, but the overall deficit increases even more. This can be explained by the fact that larger storage systems cannot discharge the competing capacity during the highest-price phase.
These calculations show that there is a significant influence of the price difference between the lowest and the highest price. In DA, the difference is 21.67 €/MWh, in the DH, 65.09 €/MWh and DL has 117.32 €/MWh. There is a correlation between this margin and the overall deficit. The larger the deficit, the more income can be generated in the market, thereby reducing the losses.
The income is highly dependent on avoided FIT income. The higher the FIT, the higher the deficit will be. Figure 5-36 shows the influence of the FIT on ΔR. In most cases, the
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losses are not compensated by the operation. However, even with the largest deficit shown (€550), 3.3% of the daily income is lost to flexible plant operation.
Figure 5-36: ΔR depending on FIT for the day-ahead market.
The examination of the day-ahead markets reveals several interesting points. A major goal of this research is to investigate the flexible operation of a biomass CHP plant, similar to the operation of biogas plants. The simulation results show that this operation is possible. However, a lower yield then in baseload operation was determined, which was caused by the lower amount of electricity generated in flexible operation. In most cases, this loss of income cannot be covered by the additional income (AI) in the markets.
If we conclude all calculations in the day-ahead markets by assuming a FIT of 100 €/MWh, we can see that the total income for flexible 24-hour operation is reduced by a maximum of 0.5% to 2% (1.2% on average) compared to baseload mode.
5.3.2 Operation According to the Intraday Market Price
In contrast to the day-ahead market, intraday prices are not known in advance. Instead of an operating schedule, a minimum price for discharge (EPmin,discharge) and a maximal price for a charge (EPmax,charge) are defined. Charging and discharging processes are initiated when the market price reaches these boundaries. This means that a fixed minimum price difference between charging and discharging can be guaranteed.
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For the initial simulations, the high price curve (IH) is used. The control prices are set at
EPmin,discharge = 200 €/MWh, and EPmax,charge = 35 €/MWh. Two different storage configurations (S1: VSA = 100 m³, VSTS = 12.3m³ and S2: VSA = 150 m³, VSTS = 18.4 m³) are examined. For all cases ṁcharge = 2 kg/s, where the discharge mass flow rate ṁdischarge is set to 4 kg/s or 7.5 kg/s. Figure 5-37 shows the flexible operation of the two storage configurations.
In the simulation with the day-ahead market (Section 5.3.1), the storage is fully charged or fully discharged. Since the maximum and minimum prices are next to each other at IH, full charge and full discharge processes also occur in most cases. Note that the larger storage (S2) is processed in two steps when discharged at 4 kg/s. Despite the operation on the intraday market, the charging or discharging processes can occur partially. In real operation, the daily prices are not known in advance, so EPmax,charge is estimated. Hence, the charging process is not carried out at the lowest price.
Figure 5-37: Flexible operation according to intraday IH.
Since the operation on this day is very similar to the day-ahead calculations, Qcharge and
Qdischarge are almost the same as the previous results (Table 5-9).
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Table 5-9: Flexible plant capacity according to the intraday high price trend (IH).
Storage / ṁdischarge S1 / 4 kg/s S1 / 7.5 kg/s S2 / 4 kg/s S2 / 7.5 kg/s
Qel,charge 3.61 MWh 3.61 MWh 5.51 MWh 5.51 MWh
Qel.discharge 1.79 MWh 1.71 MWh 2.72 MWh 2.63 MWh
Although the maximum price trend (IM) curve is very similar to the day-ahead curves, the average price trend (IA) shows more changes from high to low prices. This results in a distribution of many partial charging and discharging processes throughout the day.
EPmin,discharge was set at 110 €/MWh and EPmax,charge at 35 €/MWh. Different charging and discharging mass flow rates for storage S1 (Figure 5-38) and S2 (Figure 5-39) are investigated.
In Figure 5-38 the smaller storage S1 is used. By observing the pressure of the SA, the charge is divided into different time slices wherever the price is below EPcharge,max. At the end of the day, after a complete discharge, a low-price period is used to recharge the storage somewhat.
Figure 5-38: Flexible operation according to the intraday IA with storage S1.
Here, the advantage of a higher ṁcharge can be seen. Since there is only a 15-minute time slice below EPcharge,max, the recharge is significantly higher at 4 kg/s. Also, the discharge is distributed over several time slices. This is seen as a disadvantage of the intraday market. Since the prices are not known in advance over the day, the control prices (EP)
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cannot be optimized. In this example, no electricity is discharged at the daily maximum price because the storage is empty at this time.
The fast start-up of the system, especially during charge operation, enables very effective utilization of the high- and low-price periods. Even short time slices can be used to recharge the storage. However, there are additional due to the increased number of starts and stops.
In Table 5-10 summarises the stored energy and the storage efficiency. The efficiency is displayed to show an additional effect. In addition to the efficiency, the storage utilisation is of interest. The efficiency is slightly lower for S1c than for S1b, but Qel,charge and Qel,discharge are higher for S1c. Even with lower efficiency, a greater impact on the energy system can be achieved. In addition to the yields, the use of the storage system has an influence on the energy system. The higher the storage utilization, the higher the amount of electricity that is shifted from low into high prices periods.
Another relevant result is shown by the increase of ṁcharge from 2 kg/s to 4 kg/s (S1b and S1c). Recall that in Section 5.2.4, an increase in mass flow rate causes a decrease in overall efficiency. However, in the operating mode shown in Table 5-10, the efficiency increases with increasing mass flow. This can be explained by the partial-load operation of the components. Due to the fact that more steam can be used with a higher mass flow rate, for example, by using short recharging time slices, a better partial-load utilization of the storage turbine is achieved.
Table 5-10: Flexible plant capacity according to the typical intraday trend (IA) with S1.
S1: VSA = 100, VSTS = 12.3m³
Case S1b S1b S1c
ṁcharge/ ṁdischarge 2 / 4 kg/s 2 / 7.5 kg/s 4 / 7.5 kg/s
Qcharge 4.29 MWh 4.29 MWh 4.67 MWh
Qdischarge 2.01 MWh 1.88 MWh 2.10 MWh
ηStorage 46.8% 43.8% 44.9%
By using a larger storage (S2), more of the high-price time slices can be covered (Figure 5-39). Due to the increased storage capacity, it is possible with of S2a to discharge whenever the price is above EPmin,discharge.
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Figure 5-39: Flexible operation according to the typical intraday trend (IA) with storage S2.
The flexible plant capacities are shown in Table 5-11. Comparing S2b and S2c, the efficiency decreases even though more electricity is generated due to better utilization. S2a shows optimised efficiency but again with the lowest storage utilization.
Table 5-11: Flexible plant capacity corresponding to the typical intraday trend (IA) with S2.
S2: VSA = 150, VSTS = 18.4m³
Case S2a S2b S2c
ṁcharge/ ṁdischarge 4 / 2 kg/s 5.38 / 4 kg/s 5.38 / 7.5 kg/s
Qel,charge 6.61 MWh 6.97 MWh 6.97 MWh
Qel,discharge 2.79 MWh 3.10 MWh 3.19 MWh
ηstorage 54.2% 46.3% 45.0%
The revenue situation for the intraday market is examined in the same way as for the day-ahead market. In contrast to the day-ahead prices, the intraday prices are recorded in continues trading. The index values for this study are average values for each time slice so that in practice, even higher prices are obtained. In all cases considered, the baseload income is €16,643 as in the day-ahead simulation (FIT * Pbaseload * 24h). The total income of the simulated day shown in Figure 5-40. In contrast to the day-ahead market (see Figure 5-35), an economic advantage is achieved compared to the baseload case.
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Figure 5-40: Revenues from intraday operations with a FIT of 100 €/MWh and the maximum price trend (IM)
In the case of price development with the maximum price (IM), a positive result can be achieved (Table 5-12). With a larger storage size, better results are achieved. This is caused the fact that more electricity is generated and sold during high price periods. Due to the decreasing efficiency at the higher mass flow rates, the difference between baseload and flexible operation ΔR is worse here, even if the utilisation of the storage and thus the amount of electricity are higher. Looking at this operation, which is very similar to the day-ahead operation, one can see the economic benefit of trading on the intraday markets.
Table 5-12: Revenue from intraday IM operation with a FIT of 100 €/MWh.
Difference between Additional revenues from Market Storage ṁcharge/ FIT income flexible income baseload and trend configuration ṁdischarge flexible operation (markets) AI flexible operation (ΔR)
IM S1a 2 / 4 kg/s €16,451 €392 €200
IM S1b 2 / 7.5 kg/s €16,442 €348 €147
IM S2a 2 / 4 kg/s €16,353 €596 €306
IM S2b 2/ 7.5 kg/s €16,344 €584 €286
Since the price range between minimum and maximum price is 302 €/MWh, more positive results can be achieved than for day-head operation.
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Figure 5-41 shows the revenues during the day with the average intraday behaviour. Here, the total income in flexible operation is lower than the income in baseload operation. However, the additional income AI is higher than in the day-ahead market.
Figure 5-41: Revenue from intraday operations with a FIT of 100 €/MWh and average price behaviour (IA).
The price curve with the typical price signal (IA) has a significantly smaller difference between the minimum and maximum price (141 €/MWh). Therefore, the additional income is also lower. In all cases considered, the results are negative, but the results are better than in the day-ahead market (Table 5-13).
Table 5-13: Revenue from intraday IA operation with a FIT of 100 €/MWh.
Difference between FIT income Additional Market Storage ṁcharge/ revenues from flexible flexible income trend configuration ṁdischarge baseload and flexible operation (markets) AI operation (ΔR)
IA S1a 2 / 4 kg/s €16,404 €178 -€61
IA S1b 2 / 7.5 kg/s €16,392 €148 -€103
IA S1c 4 / 7.5 kg/s €16,372 €164 -€103
IA S2a 4 / 2 kg/s €16,239 €240 -€163
IA S2b 5.38 / 4 kg/s €16,245 €273 -€125
IA S2c 5.38 / 7.5 kg/s €16,245 €264 -€124
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For the smaller storage (S1), the total deficit is nearly equal when comparing S1b and S1c. Although the S1b has lower efficiency, more additional income is generated due to the higher storage utilisation. It is found that both the efficiency and the utilization of the storage affect the revenue optimisation.
The results for the larger storage (S2) show another important effect. The option with the best storage efficiency (S2a) has the largest deficit ΔR. Here, the low utilization of the storage generates a lower additional income. The deficit of the two cases S2b and S2c is almost the same. In summary, the results of the economic behaviour on the intraday market show a higher potential for the storage system. Due to the higher price spread and the possibility to optimise the use of storage, this market offers additional advantages.
The FIT also has an important impact on the overall results. Figure 5-42 shows the influence of individual FIT on ΔR. Compared to the day-ahead markets (see Figure 5-36), the revenues in flexible operation are higher. Due to the influence of the storage utilisation as well as the effect of partial-load operation, the overall results have a wider spread than in the day-ahead markets. Due to efficiency losses, the higher the FIT, the smaller the revenue difference between baseload and flexible operation.
Figure 5-42: Difference between baseload and flexible operation depending on the FIT for intraday market.
From a revenue perspective, the intraday market shows more advantages than the day- ahead operation. Nevertheless, a deficit is reached there as well. If we conclude all
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calculations in the intraday markets by assuming a FIT of 100 €/MWh, the total income in 24h operation is reduced by a maximum of 1% (0.62% on average) compared to baseload operation, in exchange for flexible operation.
Compared to the day-ahead market, the operation on the intraday market more difficult to manage due to the uncertainty of market prices. Even with forecasts and other market tools, optimisation for EPmax and EPmin is difficult and an optimised operation schedule similar to that for the day-ahead market cannot be achieved.
On the other hand, the income potential, as well as the number of storage cycles over one day, are higher than in the day-ahead market. The ability to act quickly enough to operate in the intraday market could be a strength of the proposed storage system.
Despite the advantages of trading on the day-ahead market, storage utilization is more important, as the shorter time slices and volatile prices mean that several charging and discharging cycles are possible within a day. The increasing number of start-ups procedures, as well as the less efficient partial-load operation, causes additional losses.
5.3.3 Balancing the Grid at the Distribution Network Level
Flexible operation on both intraday and day-ahead markets has a drawback. These markets cover large grid areas (e.g. Germany or central Europe). Hence, local grid shortages or overloads are not reflected in the market prices. Flexible operation on these spot markets is intended to balance the electricity grid at a national level. Here, it is primarily the amount of electrical energy that is important. The power output, which is relevant for grid overloads and shortages, is not directly related to the market prices. Hence, the ability of the storage system to relieve the local grid is not utilized by operating in these energy-only markets.
Therefore, two case scenarios are investigated in which the storage system can contribute to grid relief. Here, the current load level and not the electricity price is decisive. For these case simulations, the measured data from a specific distribution network in south Germany are used. Data series are available for the energy import and export as well as for the consumption and the power generation of the grid section under consideration. In this grid section, wind power, photovoltaic, biogas and natural gas CHP units are installed in addition to a biomass CHP plant Table 5-14. Due to this variety of different renewable energy sources, this grid section is suitable for investigating the influence of flexible CHP plants on future energy grids. The total generation from all plants is available as a data-series in 15-minute resolution:
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Table 5-14: Power plants in the investigated grid section.
Plants in the investigated Natural Biogas and Landfill Photovoltaic Hydro Wind grid section gas CHP solid biomass gas
Amount 17 5 1 932 6 2
Generation [MWh/a]] 1,314 47,871 126 16,842 630 11,308
Installed capacity [MW] 0.40 6.61 0.20 18.91 0.13 6.05
The biomass CHP plant is currently to provide baseload with constant power output and supply a local food facility with process-steam. By utilizing the steam storage system, the power output of the CHP plant and thus the total generation in the grid section may be varied. For the simulations, the CHP plant described in Table 5-1 is used. As the real plant supplies steam, a constant mass flow is extracted from the plant’s turbine.
In the previous operation on the markets, a constant mass flow rate was assumed during the charge and discharge processes. In these simulation runs, a slow start-up of the mass flow rate is required. The charge/discharge is initiated when the total generation in the grid reaches a specific value. Here, an additional control block is added, which gradually increases the mass flow rate up the configured parameter. When a specific load level PGrid,max is achieved, the charge of the storage system starts. To avoid a sharp decrease in the generation, the mass flow rate is slowly raised from ṁmin to ṁmax.
The minimal mass flow rate ṁmin,charge, is 0% of ṁcharge,max. The charging process can be started immediately with any desired mass flow rate. As the turbine minimum load has to be considered during discharge, ṁmin,discharge is 35% of ṁdischarge,max. As the STS model is not validated for varying mass flow rates, an additional error is expected. This error, based on the findings in Section 4.4, is not expected to be greater than -3%. The error causes a lower STS heat transfer rate than in reality.
In the first case scenario (Figure 5-43) a day with a high generation peak is envisaged. On this day, a peak load of 19.8 MW is achieved; this value is the annual maximum. Due to the behaviour of the curve, a sunny day with high solar radiation is assumed. Due to the high generation, a high amount of energy is also exported. Here, the maximum export is 8.1 MW. The peaks of generation and export are time-delayed, as the export depends on the difference between consumption and generation. The biomass CHP plant in the grid section is operated on the baseload mode. Hence, the load curves for conventional operation without a flexible plant are called baseload.
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Figure 5-43: Case scenario 1: High generation peak.
The average baseload generation of the biomass CHP plant is 5.34 MW; therefore, it generated 128 MWh of electrical energy on the day in question, which represents 49% of the total generation of all power plants in the grid section under consideration. On this day, a constant mass flow rate of 1.9 kg/s is extracted from the plant turbine to supply the food facility. To relieve the local grid, the flexible plant power output is adjusted to reduce this generation peak.
In Figure 5-44, the generation peak was decreased by deploying the storage system.
Different values for ṁcharge,max and ṁdischarge,max were investigated. A storage system with
VSA = 100 m² and VSTS = 12.3 m³ is utilized. If the storage system is charged with 1.5 kg/s, a significant share of the generation peak can be buffered by the flexible plant.
Here, the maximum load is reduced by 1.7 MW, a reduction of 9%. The larger ṁdischarge,max = 4 kg/s shows a temporarily decrease from 19.2 MW to 13.7 MW (28%). However, the energy storage capacity of the steam storage system is too low to hold this load level during the whole peak. As shown for ṁdischarge,max = 2 kg/s and 4 kg/s, the storage reaches its maximum pressure before the peak is over and the charging process is interrupted. Despite the limitations imposed by the lack of storage capacity, from the perspective of the grid operator, the flexible storage could still be useful to bridge the period until a slower plant can reduce its power output.
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Figure 5-44: Case scenario 1: Reduction of the total generation by utilizing the flexible plant.
Table 5-15 summarises the results of this simulation case are concluded. It can be seen that the ṁcharge,max = 1.5 kg/s causes the biggest reduction in the generation peak. The pressure did not reach its maximum level. Hence, the storage is not fully charged during the envisaged period. Even if ṁcharge,max at 2 kg/s and 4 kg/s can achieve a temporally lower power output, the total peak reduction is lower. In all cases, the stored energy can be discharged during a high consumption period in the evening.
Table 5-15: Results from case scenario 1.
ṁcharge,max/ ṁdischarge,max 1.5 / 2 kg/s 2 / 4 kg/s 4 / 4 kg/s
Qel,charge 3.32 MWh 3.71 MWh 3.61 MWh
Qel,discharge 1.56 MWh 1.69 MWh 1.66 MWh
Generation peak reduction 1.68 MW 0.83 MW 0.27 MW
Difference between baseload and flexible operation without AI € 175 € 201 € 195 (FIT = 100 €/MWh)
The difference caused by energy losses compared to the baseload operation is in the range of €175–€195, with the 1.5 / 2kg/s case also showing the lowest additional cost. Figure 5-45 shows the 1.5 / 2 kg/s case in comparison to the baseload generation. In
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addition to the reduced generation peak, the total generation curve approaches the consumption curve. The chance of a grid overload is reduced. However, in this case, the export peak is not reduced as it occurs a later date.
Figure 5-45: Impact of a flexible plant with VSA = 100 m³, VSTS = 12.4 m³. ṁcharge = 1.5 kg/s and mdischarge = 2 kg/s.
An additional simulation was done to investigate the impact of bigger storage (Figure 5-46) with the task of reducing the export peak.
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Figure 5-46: Impact of a flexible plant with VSA = 150 m³, VSTS = 18.4 m³. ṁcharge = 1.5 kg/s and mdischarge = 2 kg/s.
Here, the storage size was increased to VSA = 150 m³ and VSTS = 18.4 m³ and the control strategy adjusted to a wider range. Here, the maximum export is reduced from 8.1 MW to 7.4 MW, while the peak generation is reduced to 18.04 MW.
These simulations for case scenario 1 show that a significant peak reduction is possible with the proposed storage system. Both the export and generation peaks can be buffered, and the maximum load can be reduced. This can contribute to the avoidance of grid overload caused by the variability of renewable energy technologies.
In case scenario 2, a varying generation curve is investigated. Variable wind or photovoltaic generation are typical reasons for this behaviour. This means additional effort (e.g. costs for grid balancing) for the grid operators. Regardless of forecasts, wind gusts and sudden cloud cover affect the generation in an unpredictable way Figure 5-47 shows a day where the power generation is very volatile, resulting in high fluctuations in the export and import.
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Figure 5-47: Case scenario 2: Volatile generation.
In the next simulation runs, the flexible plant is deployed to buffer the generation. On the envisaged day, the average base load of the plant is 5 MW, while continuously 2.2 kg/s is extracted to supply the food facility. In the first set of simulations, storage with
VSA = 100 m³ and VSTS = 12.3 m³ is considered (Figure 5-48).
Figure 5-48: Case scenario 2 with different mass flow rates and a 100 / 12.3 m³ storage system.
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Here, a buffering of the peaks can be seen, but only to a relatively small degree. The charge with 1 kg/s leads to a very low utilization of the storage, which can be seen in the pressure diagram. The charge with 3 kg/s shows a smoothing of the curve, but the energy capacity is too low. As none of the simulation runs shows satisfying results, the storage size was increased to VSA = 150 m³ and VSTS = 18.4 m³ for additional simulation runs (Figure 5-49).
Figure 5-49: Case scenario 2 with different mass flow rates and a 150 / 18.4 m³ storage system,
In this case, all the peaks in the generation curve can be buffered. For the discharge, the 2 kg/s mass flow gives a smoother discharge. The results of the simulations in case 2 are shown in Table 5-16. Here, with the bigger storage, the volatile generation can be buffered more, and the peaks are reduced by up to 3.4 MW. The most significant peak reduction is achieved, with bigger storage.
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Table 5-16: Simulation results from case scenario 2.
Storage Volume 100/12.3 m³ 100/12.3 m 100/12.3 m 150/18.4 m³ 150/18.4 m³
ṁcharge / ṁdischarge 1 / 2 kg/s 2 / 4kg/s 3 / 4 kg/s 3 / 2 kg/s 3 / 4 kg/s
Qel,charge 3.2 MWh 5.1 MWh 5.0 MWh 7.1 MWh 7.1 MWh
Qel,discharge 0.7 MWh 1.7 MWh 1.7 MWh 2.4 MWh 2.6 MWh
Peak reduction 1.1 MW 2.3 MW 0.8 MW 3.4 MW 3.4 MW
Difference between baseload and flexible € 249 € 345 € 333 € 466 € 450 operation without AI (FIT = 100 €/MWh)
Figure 5-50 shows the impact of the 3 / 4 kg/s case. The peaks can be reduced, and the volatile generation curve is adjusted more to the consumption. In addition, most of the energy export is avoided.
Figure 5-50: Impact of a flexible plant with VSA = 150 m³, VSTS = 18.4 m³. ṁcharge = 1.5 kg/s and mdischarge = 2 kg/s.
The investigations of the case scenarios show that the flexible operation of the CHP plant can make a significant contribution to the local grid. A particular strength of this flexible plant system is the sudden reaction of the charging process and thus the potential for reduction of the power output.
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Several individual costs and drawback, for distribution and transmission grid operations, as well as for electricity traders, can be avoided or additional income can be generated. There is a wide range of options such as the reduction of grid usage costs, performance prices or additional income sources by balancing energy or capacity markets, as well as the developing smart energy markets. Each of these options has various influencing factors.
As the aim of this research is the technical investigation of the flexible plant and the storage system, the full scope of these options has not been investigated. For this reason, only the costs of avoiding FIT income are included.
5.4 Chapter Summary
In this chapter, the flexible steam storage system was investigated from various perspectives. First, a fundamental knowledge of flexible plant operation and the relevant components could be gained. The key values of this novel concept were determined. As expected, the steam accumulator is the core element of the investigated storage system. Its pressure level defines and limits the load level of the entire storage system. The very fast availability of the stored energy, especially the fast start-up of the charging process, is one of the strengths of this concept.
The most important parameters, such as the flexible plant’s energy capacity, the efficiencies and charge/discharge times could be identified, and their interdependencies were investigated. Here, a better understanding of this novel system and its interaction with the biomass CHP plant was achieved. The flexible plant energy capacity is of particular interest here. In addition to the expected importance of the volume of STS and SA, the mass flow rate also has a significant influence.
Flexible operation under realistic conditions was also investigated. The simulation of a market-driven operation shows advantages and drawbacks. From a technical perspective, the addition of the proposed storage system is able to convert a biomass CHP plant into a flexible plant, similar to a flexible biogas plant. A market-driven operation on both the day-ahead and in the intraday market is possible. However, due to the energy losses that occur, the revenue from a flexible operation is limited. In most cases, flexible operation causes additional costs. Operation on the intraday market has the potential for more income but less predictable operation. On this market, which is characterized by its smaller time-slices, the fast availability of the flexible load in the proposed system is advantageous. Especially for the operation in the intraday market,
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with several partial charges and discharges, the use of the storage system offers an additional degree of optimization.
Another strength of the flexible biomass CHP plant is the ability to balance the surrounding distribution grid. The investigation shows that the peak loads can be reduced, and volatile power generation can be smoothed. To validate economic feasibility, deeper investigations have to be done with a closer look at specific cases, income sources, investment and running costs of the flexible CHP plant system.
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6 Comparison of Storage System with Alternative Technologies
In the course of this research, detailed knowledge about the flexible biomass CHP plant and its components was generated. Operational behaviour and key values have been determined. In this chapter, these findings are used to classify the proposed storage system in the field of competing technologies. Even though this topic was not within the scope of the research question from the beginning, it has to be considered of important value for the thesis. Due to the complexity of the compared technologies, only rough characteristic values are used in this research. A case-specific, detailed technical- economic investigation will be still necessary for the evaluation of specific cases.
In the context of deploying a storage device to relieve the energy system, the following technologies and systems fulfil the same purpose:
• Large-scale grid balancing plants such as pumped hydro storage (PHS) or compressed air energy storage systems (CAES), • electric battery systems such as Li-ion, Ni-based or lead-acid batteries, • hydrogen storage with fuel cells, • and flexible biogas plants.
In this chapter, the proposed system is compared with these competing technologies. The knowledge gained is used to identify the strengths and weaknesses of other systems to estimate the most promising area of operation.
As most of the competing technologies are classical energy storage technologies, the flexible biogas plants take on a special role. These plants are, just as the proposed system, flexible plants that are operated as energy storage systems. For this reason, this topic is considered separately.
6.1 Investment Costs of the Flexible Biomass CHP Plant
For a technology comparison, the investment costs of the proposed system are a key value. In this work, a wide range of energy storage configurations and parameters were investigated. Based on the results from the parameter study (Section 5.2) the investment required to upgrade a biomass CHP plant with the proposed storage concept is calculated.
Table 6-1 shows specific costs for the main components. Usually for thermal storage units, cost per kWh are utilized for cost calculations. However, in this research the thermal capacity of the storage devices has a subordinated role. The flexible energy
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capacity (electricity) of the whole plant, depending on the storage volumes, was investigated. Hence, for the steam accumulator and for the solid thermal store, volume- based specific costs were taken from the literature.
The characteristic costs for typical CHP steam-turbines with generator are used to calculate the costs of the storage turbine. For the calculation of the turbine costs, the investment in particular depends on pressure level, dimension, extraction, backpressure levels and several other parameters. For this reason, the available cost information for the turbines only provides an indication and cannot replace a detailed, project-related cost calculation. Additional costs are incurred for piping, valves, installation and auxiliary equipment, hence 20% of the total investment is added.
Table 6-1: Specific investment cost for the storage components.
Component Specific investment cost
Solid thermal store (STS) 1,500–2,000 €/m³ (Hoffstede, et al., 2016)
Steam accumulator (SA) 7,000–8,200 €/m³ (Stark, et al., 2019)
Steam turbine / generator 570–970 €/MW (U.S. Department of Energy, 2016)
Additional costs 20% of total investment
The total investments for three different storage configurations are calculated in Table 6-2. To separate the costs of the storage unit and the generation unit the turbine costs are not taken into account yet, but only in the next step.
Table 6-2: Approximate investment cost for the storage system.
SA STS Costs SA Costs STS Additional costs Total investment
50 m³ 6.2 m³ € 350–410 k € 912 k € 71–84 k € 430–506 k
100 m³ 12.3 m³ € 700–820 k € 1824 k € 143–168 k € 861–1,012 k
150 m³ 18.4 m³ € 1,050-1,230 k € 27–36 k € 215253 k € 1,292–1,519 k
To generate a comparable value, the specific costs are calculated. Depending on the flexible plant’s energy capacity, the specific costs for the charge (CFlex,charge) and discharge (CFlex,discharge), are calculated for all three storage configurations.
Figure 6-1 shows the average value of the specific costs and the range of costs. The bigger the storage, the lower the specific prices will be. Due to the energy losses between Steam Storage for Flexible Biomass CHP Plants Page 146
charge and discharge, the specific parameter also has a significant difference. For a comparison with other storage systems, the value for discharge (CFlex,discharge) is of greater interest.
Figure 6-1: Specific cost of the storage system depending on VSA.
As investigated in the parameter study, increasing the STS has a positive effect on the stored energy capacity. The additional costs of a bigger STS storage system are correlated with the increasing energy content. Figure 6-2 shows the specific costs of a steam accumulator with 100 m³ and an incremental increase of the VSTS.
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Figure 6-2: Specific cost of the storage system by increasing the VSTS for VSA = 100 m³.
Here, the increase of 6.2 m³ to 12.3 m³ causes lower specific costs. The increased capacity has a bigger impact on the capacity than the additional investment. However, another increase to 18.4 m³ has just a very small impact. This correlates with the findings from Figure 5-22, where the benefits of increasing VSTS tends to decrease with each incremental increase in volume.
The storage turbine is a significant additional cost factor. The design mass flow rate
(which depends on the design power output PST,d = PST,max) of the storage turbine is the key value for cost estimation. The maximum discharge mass flow rate defines the dimension of the turbine (see Table 6-3). Here, the costs for the turbine including generator and ancillary costs (20%) are applied (see Table 6-1).
Table 6-3: Additional costs for the storage turbine.
ṁdischarge PST,d Total Investment (incl. additional costs.
2 kg/s 1.1–1.2 MW € 744–1,386 k
4 kg/s 2.1–2.3 MW € 1.452–2,693 k
7.5 kg/s 3.8–4.2 MW € 2.590–4,830 k
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The turbine cost calculations show that these components are two to three times as expensive as the rest of the storage system (Figure 6-3). Here, there is a significant increase in specific costs due to the addition of turbines.
Figure 6-3: Increase of the specific costs for various turbine sizes (mean values).
At this stage, a weakness of the method used to estimate the turbine investment costs is revealed. As mentioned before, the cost value for the turbines covers a range of typical plant turbines with operation hours > 8,000 h/a, extraction stages and various pressure and mass flow rates. There may be a high potential for a significant cost reduction. As the typical operation times of flexible storage turbines can be below 1,000 h/a, more cost- effective turbines could be used. The deployment of used (refurbished) turbines can also be considered.
Referring to the technical findings in Section 5.2, it was recommended to reduce the mass flow rate to increase efficiency. Besides affecting the efficiency, the discharge mass flow rate and hence the size of the plant turbine has an important influence on the total investment.
Table 6-4 compares the specific costs of the proposed system with the costs of competing technologies. System designs where an existing turbine system can be used instead of buying a new storage turbine are competitive in most cases. A 1 MW turbine will be more expensive than most competing technologies, whereas a 4 MW turbine will be more than twice as expensive as the other concepts.
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Table 6-4: Specific cost of the proposed system compared with alternative technologies (Baumann, et al., 2019).
Specific Investment Technology €/kWh
Proposed system (without additional turbine) 477–764 €/kWh
Proposed system (1 MW turbine) 900– 1,894 €/kWh
Proposed system (4 MW turbine) 1,860–2,574 €/kWh
Pumped hydro storage (PHS) 46–500 €/kWh
Compressed air energy storage (CAES) 3–300 €/kWh
Hydrogen fuel cell *per kW 10k +
Li-Ion batteries 376–696 €/kWh
Ni-based batteries 290–2,300 €/kWh
Lead-acid batteries 179–320 €/kWh
Comparing the investment costs, the proposed system has higher costs in most cases. However, this investment cost comparison is very general. The system described in this thesis is very complex, and the costs of each individual installation will depend strongly on the specific features of each unit. Here, only the costs of the storage unit are compared. One advantage of the proposed concept is that a biomass CHP plant can be upgraded with the storage.
Unlike many of the competing concepts for supplying the electricity system, part of the infrastructure is already in place at the plant. The connections to the grid (especially transformers), plant control system, staff and maintenance equipment are typically already in place at an existing biomass CHP plant. For competitor approaches, these system components need to be developed from the beginning. Several economic advantages over competing technologies are expected as part of a detailed cost analysis during project development.
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6.2 Proposed Energy Storage Concept Compared with Competitor Systems
The storage technologies considered have a wide range of applications. One way to represent and compare this application range is to specify the power rating and the storage time. Figure 6-4 shows the application areas for the technologies compared. The steam storage system is included, based on the knowledge gained through this research.
Figure 6-4: Overview of different energy storages and their indicated application fields adapted from Baumann et al. (2019).
The proposed system covers the range between short-term and long-term storage, for this the main competing technology is batteries. Compared to CAES and pumped hydro, the proposed concept can be viewed as an extension of these technologies into smaller power ratings. Table 6-5 summarises the key parameters of storage efficiency, specific energy capacity and specific costs. The specific energy capacity of the proposed concept is in the range of 13–18 kWh/m³, whereas the storage efficiency is in the range of 45– 57%. However, the storage efficiency is not completely comparable to other energy storage systems. As described in Section 5.2.4, storage efficiency is a factor that can be used to represent the energy storage losses. To represent a reliable value for the efficiency of the flexible plant, the system efficiency was introduced.
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Table 6-5: Overview of different energy storage technologies
Technology Storage efficiency Specific energy Investment capacity €/kWh
kWh/m³
Steam storage concept 40–57% (ηstorage) 13-18 kWh/m³ 480–2,885 €/kWh
75–93% (ηsystem)
Pumped hydro storage (PHS) 65–88%a 0.5–1.5 kWh/m³ b 46–500 €/kWh a
Compressed air energy 54–88%a 3–8 kWh/m³ b 3–300 €/kWh a storage (CAES)
Hydrogen fuel cell 20–35%a 270–380 kWh/m³ c *per kW 10k + a
Li-Ion batteries 81–98%a 200–250 kWh/m³ c 376–696 €/kWh a
Ni-based batteries 60–85%a 60-300 kWh/m³ c 290–2,300 €/kWh a
Lead Acid batteries 69–93%a 100 kWh/m³ d 179–320 €/kWh a
a (Baumann, et al., 2019) b (Hamidreza, et al., 2019) c (Wilberforce, et al., 2018) d (Thomas, 2009)
Due to their individual application areas and features, the storage technologies are compared with the proposed concept in groups.
For the hydrogen fuel cell, the system efficiency range for a whole conversion process (electrolysis, storage and power generation) is considered.
The battery efficiency (Ni, Li-Ion, Lead Acid) considers the range for single cells, without any auxiliaries. The higher values (up to 98%) are the considered to be new batteries under laboratory conditions. A real system configuration, is assumed to be in the lower to middle parts of the efficiency range.
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6.2.1 Proposed Concept Compared with PHS and CAES
Pumped hydro storage and compressed air energy storage are large-scale technologies with the main task of storing electricity in the medium and long term. PHS has been used for many years to balance the power system. Both competing technologies feature high energy capacities and high power ratings. Whereas PHS and CAES are built as very large-scale units, typically between 10–4,000 MW, the proposed concept can cover the demand for lower scale plants and power output (Barbour, et al., 2015). In this comparison, the costs for PHS and CAES are cheaper than for the proposed system. However, it must be considered that large-scale PHS and CAES are compared with the smaller flexible biomass CHP plant concepts.
As PHS and CAES are mechanical energy storage concepts, the standby losses are very low, whereas the proposed system has thermal losses over time. Hence, the proposed concept is less suitable for long-term storage.
A big drawback of PHS and CAES is the availability of suitable locations. The dependence on big storage reservoirs like high-altitude lakes or caverns limits the possible sites. In addition, the environmental impact of building these storage systems is a growing issue that needs to be considered (Táczi, 2016). The limitation of the suitable locations as well as the larger scale of PHS and CAES leads to the conclusion that the proposed concept is favourable for decentralised, small-scale storage concepts. Theoretically, any biomass CHP plant can be equipped with the proposed storage system.
This leads to an additional strength. While the suitable locations for PHS and CAES are often quite distant from the existing grid and transport infrastructure, the CHP plant sites are already fully developed in terms of grid access and transport infrastructure.
The strengths and weaknesses of the proposed system compared with PHS and CAES are summarised in Table 6-6.
Table 6-6: Strengths and weaknesses of the proposed system compared with PHS and CAES.
Strengths Weaknesses
Small size Long-term storages
Decentralised Investment costs Lower ecologic impact Available infrastructure
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6.2.2 Proposed Concept Compared with Battery Storage Systems
Electric batteries are used in a range of applications to relieve the burden on the energy system, from small-scale for buffering the photovoltaic generation in households to big- storage units for control reserve. Comparing the efficiency and especially the specific storage capacity with the proposed concept, battery systems generally have far better performance parameters.
However, the main drawback of electric batteries is their maximum number of cycles. Common battery systems are constructed for about 2,000 cycles and even the best modern Li-Ion systems are only capable of 8,000 cycles (Baumann, et al., 2019). Here, the long-term usability of the steam storage concept is more advantageous. All components are standard steam plant equipment designed for long-term operation (>15ya, > 8,000 h/a). No significant decrease in performance is expected during their whole lifetime, whereas degradation is observed for batteries in long-term operation.
Many kinds of battery systems require rare materials. These materials are limited, and some are mined in conflict regions. Also, the demand for Li-Ion batteries is very high, especially for electromobility. Second-life battery systems can be used, but there may be better applications for this technology, such as the support of fast charge devices. (Casals, et al., 2019).
When comparing the proposed system with batteries, an argument from section 6.2.1. is also relevant, which concerns the existing power plant infrastructure. Even if battery storage could be built on the location of a power plant and benefit from the existing infrastructure, not every part of the plant’s infrastructure could be utilized. In the case of the proposed system, specialised maintenance and operations staff and control systems for steam equipment are already in place. It is assumed since battery storage is quite specialised from a technical perspective, additional staff and infrastructure will be required in some cases. The strengths and weaknesses are summarised in Table 6-7.
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Table 6-7: Strengths and weaknesses of the proposed system compared with battery storage systems.
Strengths Weaknesses
Storage cycles Specific capacity
No rare materials necessary Cost
No capacity decreases over time
Available infrastructure
6.2.3 Proposed Concept Compared with Hydrogen storage
There are some similarities between hydrogen storage and the proposed concept. Compared to most other storage technologies, hydrogen storage uses two different process steps for charge and discharge.
However, this enables an alternative use of the stored energy. Whereas the charged energy only has an effect on the electricity system, the discharged energy could be used in an alternative way. Hydrogen can be used as fuel for mobility, while stored steam can be used only to supply steam or heat to a consumer. In some cases, the use of a fuel cell for the generation of steam in industrial plants is being considered (Hechelmann, et al., 2020).
Compared with a hydrogen fuel cell composition, the proposed concept has lower costs and better efficiency. In addition, the flexible biomass CHP plant uses proven technologies, whereas the fuel cell is still under development. In contrast to green hydrogen systems, the proposed steam storage system consists of established technology and is nearly fully developed.
The strengths and weaknesses are summarized in Table 6-8 here both technologies are only partially comparable, as they differ more in application.
Table 6-8: Strengths and weaknesses of the proposed system compared with hydrogen.
Strengths Weaknesses
Costs Specific capacity
Efficiency Mobility Steam utilisation Long-term storage Technical availability
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6.3 Proposed System Compared with Flexible Biogas Power Plants
The proposed concept of flexible biomass CHP plants is inspired by flexible biogas plants. For some years, gas storage has been utilized to enable flexible power generation (Liebetrau, et al., 2015). A similar concept is intended for solid biomass CHP, as well. Both plant types are upgraded with storage for the intermediate energy carrier (biogas or steam) as well as an additional power generation unit. However, this upgrade is far easier to realise for biogas plants, as the gas can be stored with less effort. In the proposed concept, the steam storage system and a turbine are used, while flexible biogas requires gas storage and additional CHP units. Similar to the proposed concept, the investment cost for additional CHP capacity for flexible biogas plants (350– 983 €/kW), are also very high (ASUE, 2014).
The concept of a flexible biogas plant is the constant production of biogas while the CHP plant is operated on demand. Biogas can be stored relatively easily and does not change its properties during storage. Hence, flexible biogas generation has very low losses compared with baseload generation.
For flexible operation, the CHP units are completely shut down during low-demand periods and operated at full-load during high-demand periods. The CHP units are constructed to operate at a fixed load-point. Part-load operation is not recommended due to increased abrasion and losses. In addition, the heat-to-power ratio is fixed. Independently from the current heat demand, a fixed amount of heat is generated during the operation of the CHP unit. In the proposed concept, both turbines can be controlled infinitely variable to any required output level between maximum and minimum. Also, the amount of heat extraction can be adjusted to the demand.
In the proposed concept, the reduction of generation can be done within seconds, whereas flexible biogas plants need more than 3 minutes to shut down (Bär, et al., 2020). The reaction speed of the flexible biomass CHP plant is a strength, compared to biogas. Due to the larger size of biomass CHP plants, the change in total power output is higher than for the biogas plants. As the speed and the power output is more important for balancing at the distribution network level (DNL), the proposed concept is more suitable for this operation. On the other hand, flexible biogas has an advantage in the energy- only markets. In these markets, the amount of flexible energy is more important than the power output. Hence, flexible biogas plants are more suitable due to their high efficiency.
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Usually, the biogas plant operators are farmers who operate the plants alongside their daily business. Their tolerance for additional effort caused by the flexible operation is limited, so usually, only a day-ahead operation is achieved, as this can be remotely controlled by electricity traders or operators of plant pools. Biomass CHP plants usually have staff for 24h service and maintenance. The average installed capacity of biogas plants in Germany was 663 kW, which is 1/10th of the average load of biomass CHP plants (Statista, 2019). Due to the larger size of biomass CHP plants, permanent staff and better grid infrastructure are available to realise flexible operation especially in markets that require more effort in operation.
This larger plant size in combination with the available staff and infrastructure justify the additional effort for flexible operation in more complex markets or in the grid. As shown in Section 5.3.3., the biomass CHP plant can use its high installed capacity to have a significant influence on local grids.
Unlike most renewable energy technologies like wind, solar or hydropower, the operation of biomass plants is independent of environmental influences. Both solid biomass and biogas plants have the ability to generate electricity on demand. Since the Renewable Energy Sources Act (EEG 2017), new biomass plants, but also existing plants, can take part in a tender to extend the period of their feed-in-tariff. Participation in this tender is linked to the ability to operate flexibly (BfJ, 2017). Hence, solutions for flexible power generation will be necessary for the future. The strength and weaknesses are summarised in Table 6-9.
Table 6-9: Strengths and weaknesses of the proposed system compared with flexible biogas plants.
Strength Weaknesses
Infinitely variable Efficiency Variable CHP factor Energy capacity Plant size Market-driven operation Balancing on the DNL
Although both concepts have strength and weaknesses, they are not competing technologies. Both biogas and biomass CHP plants already exist in large numbers. Also, suitable locations for both are too individual to speak of competition. In principle, both systems can contribute to relieving the energy supply system. The flexible biogas seems to be more suitable for energy-only markets where the amount of energy is important. For large load changes and balancing of the local grid, the proposed flexible biomass
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CHP plant is perceived as more beneficial. The deployment of both types of flexible plants in combination thus offers the chance for a holistic balance of the energy system.
6.4 Chapter Summary
In this chapter, the knowledge gained through the research is used to identify suitable application areas in the context of competing technologies. The investment costs of the proposed storage system strongly depend on the turbine size. Plant systems with the capability to use an existing turbine may be more beneficial.
The comparison of the proposed concepts shows several advantages and drawbacks compared to alternative energy storage technologies. One of the most promising application areas will be the task of pumped hydropower on a smaller scale. Another promising application is to derive an operation similar to flexible biogas plants. Situations in which the steam accumulator can also be utilized to supply steam for heating should be investigated in more detail.
There are similarities between flexible biogas and biomass CHP plants. Here, synergy effects can be applied.
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7 Conclusion
The overall aim of this work was to analyse the modification of a biomass CHP plant with a steam storage device to create a flexibly power producing plant. The research question was to select the most suitable storage device, identify its operational parameters and simulate the impact on the power supply system. This research focused on simulation studies to gain knowledge about this concept. In the multitude of available technologies for the steam storage, suitable technologies and system combinations are identified that meet the requirements of both biomass CHP plants and the electric supply system.
The most suitable storage system was identified by using the adapted analytic hierarchy process. By means of a Delphi survey, the knowledge of experts was used to weigh up the individual strengths and weaknesses of the available steam storage systems. These findings and the available data were used for a utility value analysis. The combination of a steam accumulator with a solid thermal store was identified as the most suitable storage system.
For a comprehensive investigation of the research questions, a simulation model with all relevant parts of the flexible biomass CHP plant was developed in MATLAB/SIMULINK. The principal components were implemented using model formulations from the literature (SA and turbine) or self-developed models by the author. For the development of the solid thermal store model, computational fluid dynamics software (ANSYS) was used. Each of the principal components was validated against measured data.
The simulation environment was used to investigate the basic behaviour as well as important operational parameters such as energy capacity, power output and charging/discharging times of the flexible biomass CHP plant. In the process, knowledge was gained about the interdependencies of the design and operational parameters. Then, a series of different scenarios were considered to study the operation of the flexible plant according to the demand of electricity markets or the electricity grid. Finally, the knowledge of the proposed concept was set into consideration against competing technologies.
The findings in this thesis are in line with the research questions stated in Subchapter 1.3. A detailed summary with conclusions and the contribution to knowledge is summarised in the following subchapters. Finally, future research questions that arose during this research are stated.
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7.1 Evaluation of the Most Suitable Storage Device
Several concepts of steam storage technologies and systems were considered from the literature reviewed. In the field of concentrated solar plants (CSP), phase change materials or thermochemical storage systems are often considered to have the highest potentials for storing steam. These technologies offer high storage capacities and efficiencies. However, most of the literature focuses on steam storage for large-scale plants. Due to the dependence on the availability of solar radiation, these CSP plants are operated with a slow charge once over the day and a slow discharge during the night. Therefore, no quick start charging and discharging is required.
In the expert’s survey (see Chapter 3), the criteria of the speed of response and cost were each given a high priority, whereas efficiency was assigned a low priority (Table 3-4). Cheaper storage with a fast response time is required, even if this comes with a disadvantage of low efficiency. As the biomass CHP plants investigated are small-scale plants, the investment costs for the storage device are more important than for large- scale plants due to the economies of scale.
A steam accumulator, combined with a solid thermal store, provides the desired benefits. The steam accumulator is responsible for storing the steam, while the solid thermal store is responsible for superheating the discharge steam. In the range of basically suitable storage systems, this system has the lowest investment cost. Also, the components are known and standard plant equipment, and the amount of additional equipment required, such as pumps, valves and tanks, is very small compared to the other technologies. A key advantage of this system combination is the fast availability of discharge steam and the even faster reaction time for charging operation. The solid thermal storage offers the possibility of superheating the steam at low costs and with little effort.
The literature for the flexible operation of biomass CHP plants with steam storage devices (Section 2.2.3) focuses exclusively on the steam accumulator. The comprehensive evaluation of suitable systems is a novel addition to existing research knowledge.
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7.2 Operation of the Proposed Steam Storage Concept
It has been demonstrated by simulations that a flexible operation of the biomass CHP plant can be realised by integrating the proposed steam storage system. Charging and discharging times in the range of 15 minutes to several hours are achieved, which are within the scope of the targeted markets (see Section 2.1.1). The required superheating of the discharge steam can be ensured.
In the parameter study, system efficiencies of 75–93% and storage efficiencies of up to 57% are demonstrated. Ortwein & Lenz (2015) assumed a storage efficiency of less than 50% for steam accumulator systems in a flexible biomass CHP plant. Through the more detailed investigations of the storage system, as well as the integration of the solid thermal storage, new knowledge was gained, and operating points with higher efficiencies were identified.
In addition to the characteristics of the storage system such as the energy capacity, the storage time and the efficiency, the dependencies between these properties were also investigated. The relationship between energy capacity and storage volume was obvious and had been studied by previous researchers. But also, the mass flow rate of charging and discharging has a significant influence on both the efficiency and the energy capacity of the flexible plant system. It was found that lower mass flow rates are associated with higher energy storage capacity, as the heat exchange to the solid thermal store is more beneficial.
The parameter study was able to identify comparable key parameters of the storage system. The specific electric discharge capacity of 13-18 kWh/m³ has proven to be competitive compared to other available storage technologies.
The main objective of the flexible biomass plant is to vary the power output. During the charging/discharging process, the power output of the plant can be changed significantly. During the charging of the storage, the power output of the plant turbine can be reduced to its minimum design output. The power output during discharge can be selected by the system designer by specifying the dimensions of the storage turbine and the discharge mass flow rate, which leads to a very important conclusion. Depending on the role of the storage, it can either be optimized to change the plant’s power output by a very large amount for a short period of time or by a small amount for longer period of time.
These simulation results are limited by the lack of a laboratory testing facility. The developed model has several limitations (heat loss, model errors and simplifications
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summarized in Section 4.5.3). Even though the individual components have been validated against measurement data, and an error analysis has been performed for the overall system, there is still a degree of uncertainty. Since the focus of the presented research was to study different storage configurations, it was necessary to use a universal model, which has a lower accuracy than detailed models for specific cases.
7.3 Operation to Relieve the Energy System
The most important inspiration for this research approach was the desire for flexible operation of biogas plants. It has been shown through simulations that similar operation can be achieved in biomass CHP plants with the integration of the proposed energy storage system.
Operation according to the market prices of the short-term electricity markets is possible. Even in the intraday market with small time slices and constantly changing patterns of charge and discharge, economically viable operation can be achieved. However, in many cases, the flexible operation does not bring an economic profit compared to baseload operation. Although some days have been identified where flexible operation leads to an additional income, it can be said that mostly the total revenue of the plants decreases in flexible operation. This can be explained by the high feed-in tariffs, which penalise any reduction in electricity production through flexible operation due to storage losses. Even if this market-driven operation benefits the electricity system, the costs are not recovered by the market under today’s price structures.
A strength of the system is shown in the simulation for supplying the local power grid. A significant contribution to the local grid can be achieved by a fast and high load reduction. Because of the system’s ability to respond very quickly to a change in demand, peak loads can be shaved, whether the forecast is accurate or not. The simulated cases showed that a generation peak in the considered grid section could be reduced by 28%. This is one of the main differences between the proposed solution and a flexible biogas plant. The proposed flexible CHP plant has its strength by increasing the grid load for short periods, whereas flexible biogas plants are stronger by supplying larger total amounts of electricity over longer periods.
7.4 Comparison with Other Technologies
The proposed concept shows several disadvantages and advantages compared to competing technologies (batteries, pumped hydro, compressed air, hydrogen and flexible biomass plants), which are highlighted in Chapter 6.
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The costs of the proposed system are competitive but relatively high compared to the other systems. Especially the size of the storage turbine has a very large influence on the investment costs. Due to the low efficiency at high mass flow rates, the costs also increase, as high mass flow rates require larger turbines. It can be concluded that a high discharge power output has disadvantages.
An operation similar to pumped hydro or flexible biogas plants seems to be the most promising option. Pumped hydro storage power plants are not built very often in smaller scales, here the proposed concept can cover the scale of 3–10 MW. In addition, new sites for pumped hydro storage plants are limited, while many biomass CHP plants are available.
The operation similar or in cooperation with flexible biogas plants offers the addition of features. While biogas plants are optimised for shifting higher amounts of electricity, the proposed concepts allow for a short but significant change in plant power output. Through decentralised distribution, both flexible biogas plants and biomass CHP plants can contribute to local supply at the distribution network level.
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7.5 Contribution to Knowledge
This subchapter summarises the author’s contributions to knowledge. Some of the existing research studies dealt with the general integration of a steam storage in power plants (Steinmann & Eck, 2006). Other studies have investigated the flexible operation of biomass CHP plants and have proposed concepts (Ortwein & Lenz, 2015). This research combines the findings from both (Chapter 2) of the different research areas and includes several new findings.
• The requirements of a flexibly operated biomass CHP plant, according to its properties and the demand of the energy systems, are analysed in detail. • Different storage technologies were investigated for their suitability for flexible power plants with a special focus on biomass CHP plants. Due to the superheated live-steam in biomass CHP plants, more than one technology is required to cover the latent and the sensible parts of the heat storage with different technologies. • The feedback from experts regarding flexible operation is evaluated. Here a clear path is found for the development of these and other flexible plant devices. • For the simulation of the small-scale, multi-extraction turbines in biomass CHP plants, the model of Luo et al. (2011) found higher accuracy than the improved model of Sun & Smith (2015). • Based on the results of the simulation studies, key parameters for the planning and design of the proposed storage system are extracted. Based on the knowledge gained in this research, a prototype storage system can be developed. • Previous studies often report that steam accumulators have low efficiency and energy storage capacity. These statements are usually based on data from industrial steam accumulators. In this research the purpose of the steam accumulator is carefully defined. Due to the low pressure differential in an industrial application, the energy capacity is much lower than the processes studied in this research. • The price difference between high and low prices on the spot markets is generally too small to derive any financial reward from relieving the burden on the energy system.
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7.6 Avenues for Future Research
During this work, various additional scientific questions arose in addition to the proposed research question. Depending on their relevance to the research questions, and due to the unavailability of test storage or the time available, some of them were considered as ‘out of scope’. A very important topic for future research will be the experimental investigation of the proposed system. Even if the models are validated against measured data, the emergent behaviour of a specific system cannot be fully investigated without using a physical test facility. Some aspects, such as the control of the discharge valve, as well as the start-up of the storage turbine, have the potential to cause challenges in a real application that cannot be predicted during this theoretical investigation.
Since the objective of this research was to holistically investigate the fundamental behaviour of a steam storage system, the simulation model was designed with a focus on scalability to cover a wide range of different storage configurations. There are more detailed modelling approaches that are better suited for a smaller area or a more detailed investigation. With access to measurement data from experimental studies, more detailed studies can be performed for specific cases.
The discharge steam does not necessarily have to be used in the storage turbine to generate electricity. It can also be used to supply heat or steam. Here, too, a substitution of extraction steam can be obtained (see Subchapter 2.2). These process synergies can improve the efficiency of the entire plant system. In this context, further detailed simulation should be carried out to evaluate the system performance when the discharge energy is also used for heat and steam services.
Finally, the economic viability of the system was only roughly considered. Due to a large number of different operating modes (e.g. market, grid balancing, etc.), stakeholders (plant operator, grid operator, electricity trader), economic benefits (revenues, avoided costs) and the storage parameters; a detailed economic study for all the cases envisaged has not been carried out. Here, a detailed economic study is needed to find business cases for the proposed storage system.
The final aspect highlighted in this work is government decision making. As shown in Subchapter 5.4, the revenue generated by flexible operation according to the markets is too low for economically viable operation. Here, we need to discuss who should pay for the grid benefits that this system provides. Other flexible plant concepts, such as flexible biogas plants, receive government subsidies to finance the effort required to convert a
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baseload plant into a flexible plant. As this work has demonstrated, flexible operation can also be achieved in biomass CHP plants. The arguments to justify funding for these types of plants are given. Thus, this work can be used to advance policy discussion processes about the electricity supply system.
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Laing, D. et al., 2013. Development of high-temperature phase-change-material storages. Applied Energy, 9 January, pp. 497-504.
Laing, D., Bauer, T., Steinmann, W. D. & Lehmann, D., 2009. Advanced high- temperature Latent Heat Storage System - Design and Test Results. Stockholm, s.n.
Laing, D., Lehmann, D. & Bahl, C., 2008. Concrete Storage for Solar Thermal Power Plants and Industrial Process Heat. Proceedings: Third International Renewable Energy Storage Conference, November.
Laing, D., Steinmann, W. D., Tamme, R. & Richter, C., 2006. Solid media thermal storage for parabolic trough power plants. Solar Energy, pp. 1283-1289.
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Steam Storage for Flexible Biomass CHP Plants Page 173
Appendix I – Delphi Survey
Delphi survey for the prioritisation of criteria
Evaluation of different concepts for controllable power generation from decentralized power plants
Dear participant,
Thank you very much taking part at this survey. During my doctoral studies, I have developed several concepts for the conversion of decentralized power plants from base- load into a controllable, market- or grid driven operation.
To identify the best concept, several evaluation criteria were defined. These criteria are subdivided into five groups and twelve sub-groups. The evaluation criteria are explained in detail, later in this document.
The aim of this survey is to weight the different criteria against each other. By capturing the knowledge of many experts, objective results are expected.
This questionnaire will take about 20 minutes. If you don’t want to answer all questions at a time you can save and continue later.
Steam Storage for Flexible Biomass CHP Plants Page 174
Your data will be saved on a secured server at the THI. Your data will be used only for my PhD thesis and will be handled confidentially.
Please send me the completed questionnaire via email before 20th December 2016.
The procedure of the survey is the pairwise comparison according to the ‘Analytic Hierarchy Process’. If you don`t know this methodology please find a manual attached (at page 8).
Steam Storage for Flexible Biomass CHP Plants Page 175
Expert-Survey
General information
Name:
Institution: This information will be used only for the assignment of the results in-between the two survey rounds and for statistic Country: reasons. No reverence between your data and the questionnaire will be Email: published. Sector:
O Research & Development
O Electricity trade
O Plant operator
O Plant developer
O Governmental organization
O Others
Evaluation
In this questionnaire the prioritisation of the criteria against each other should be done by the experts. Please consider the following notes:
• Give us your personal opinion
• Don`t be restricted by the recent state of the art in the field of bioenergy or flexible power generation. All of the developed concepts contain innovative ideas and technologies.
• Although, the focus of my work were set in the field of solid fueled biomass power plants, the criteria were created to evaluate all kinds of flexible, decentralized power generators.
• Especially your opinion about the future development of the energy system and the regarding markets should play a part in your decision.
Steam Storage for Flexible Biomass CHP Plants Page 176
Prioritisation – Sub-Criteria First the sub-criteria and afterwards the main-criteria are getting rated. Each criteria is described to in more detail. Please give your opinion in each empty cell.
Performance
For the evaluation of the performance, various criteria regarding flexible power generation were created. In general, a plant should be modified from a constant base- load generation to a controllable, demand driven generation.
Following criteria will be evaluated:
Load range: This criteria is used for the load-range between minimal and maximal electric power generation.
Period: This criteria describes how long the flexible load can be hold. That means, how long can the minimal or the maximal load be supplied.
Minimal-Load: This criteria describes how far the power generation of the plant can be reduced. In the best case 0% will be achieved.
Part loads: This criteria describes the ability of generating power in part-load areas between the minimal and the maximal load. In the best case, the power can be controlled infinitely variable.
Question (for each cell): Is the criteria in the regarding row more or less important than the criteria in the column?
Steam Storage for Flexible Biomass CHP Plants Page 177
Part-load- Performance Load range Period Minimal-Load ability
Load range 1 - - -
Period * 1 - -
Minimal-Load 1 -
Part-load-ability 1
*Example question: Is the length of the period more or less important, than the load range?
Steam Storage for Flexible Biomass CHP Plants Page 178
Efficiency
Compared to the standard operation mode, during flexible power generation additional losses are expected. The plant is operated beyond its design operation point. Due to this part-load or overload operation, losses will occur. This resulting losses are evaluated in the criteria ‘Flex-losses’.
In addition, due to the provision of capacity to increase or decrease the power output, additional losses are expected. For example storage or standing losses. These losses are rated with the criteria ‘provision losses’.
Question: Are the flex-losses more or less important than the provision loss?
Provision Efficiency Flex-losses losses Flex-losses 1 -
Provision losses 1
Steam Storage for Flexible Biomass CHP Plants Page 179
Application area
The area of application is mostly defined by the accessible markets. The ability to act on the following markets was set as criteria.
Balance energy markets primary balance energy (reaction-time within seconds) secondary balance energy (reaction-time within 5 minutes) minute balance energy (reaction-time within 15 minutes)
Energy markets intraday market (reaction-time within 15-45 minutes) day-ahead market (reaction-time within a day)
In addition to the existing markets, new applications for the future are expected. Especially, the balancing of fluctuation power generation on the distribution network level (DNL). So the ability of load balancing on the DNL is added as an additional criteria.
The market evaluation is strongly related to the German and the central European power markets. If there are similar markets in your country you can make your decision related to the reaction time or skip this question.
Question (each cell): Is the application area of the regarding row more or less important as the application area of the regarding column?
intraday day head balance on Application area primary BE secondary BE minute BE markets markets DNL
primary BE 1 - - - - -
secondary BE * 1 - - - -
minute BE 1 - - -
intraday markets 1 - -
day head markets 1 -
balance on DNL 1
*Example Question: Is the ability to act on the secondary balancing energy markets more or less important than the ability to act on the primary balancing energy market?
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Prioritisation – Main Criteria The following main criteria are used for the evaluation of flexibilisation concepts.
Efficiency:
Separated into two sub-criteria (page 4)
Performance:
Separated into four sub-criteria (page 3)
Application areas:
Separated into six sub-criteria (page 5)
Costs:
These criteria rates the various costs of the regarding concept.
Process Synergy:
The criteria process-synergy rates the interaction of the flexibilisation concept with the power plants process. Some concepts can create advantages in addition ot the ability of flexible power generation.
For example, a hot water storage can be used to vary the power output of a biomass CHP plant. In addition this storage can increase the coverage ratio and the efficiency of the plants heat supply. Also the process stability or the process efficiency can be increased by various concepts.
This effects are rated with the criteria process synergy.
Efficiency Performance Application areas Costs Process-Synergy
Efficiency 1 - - - -
Performance * 1 - - -
Application areas ** 1 - -
Costs 1 -
Process-Synergy 1
Example Questions
Steam Storage for Flexible Biomass CHP Plants Page 182
*Is the performance more or less important than the efficiency? ** Is range of accessible application areas more or less important than the efficiency?
Additional Criteria:
The selection of the criteria shows, that the effort for the prioritisation is increasing more and more with each additional criteria. In addition, it has to be considered that each criteria is independent from all the others. For this reason, the selection of defined criteria should be as small as possible.
If some criteria is missing in your opinion, you can remark this in the following list.
☐ Operation costs
☐ Consumption costs
☐ Investment costs
☐ Required space
☐ Technology readiness factor
☐ Reliability
☐ Safety
☐ Legal boundaries
Do you miss some additional criteria?
Do you have some remarks to the questionnaire?
I would like to thank you a lot for all your help!
Best regards Matthias Stark
Steam Storage for Flexible Biomass CHP Plants Page 183
Introduction to the procedure
The aim of this questionnaire is, to weight the importance of the different criteria among each other. Therefore, a pairwise comparison of the criteria will be done. For a better understanding, ask yourself this question.
For each row of the table, how important is the criterion in the regarding column compared to the criteria in the row?
For the evaluation you can use the following values according to the ‘Analytic Hierarchy Process (AHP; Saaty, 1980).
Value Definition Value Definition 9 extremely more important 1/9 extremely less important 7 strongly more important 1/7 very strongly less important 5 very strongly more important 1/5 strongly less important 3 moderately more important 1/3 moderately less important 1 equal importance *intermediate values 8, 4, 2, 1/2, 1/4, 1/8 are also allowed Example for the evaluation
Example – part 1: Example – part 2:
For the evaluation of a various cars, the In the first cell the price, compared to the exemplary criteria performance, price and performance is weighted. In this example, color were defined. During the evaluation the price is rated with ‘moderately more only the empty white cells has to be filled. importance (3)’ than the performance.
Example – part 3: Example – part 4:
Steam Storage for Flexible Biomass CHP Plants Page 184
In reverse, the performance has a Further the color was rated as ‘strongly ‘moderately less importance (1/3)’ than less important (1/5)’ as the performance, the price. This value results automatically. and ‘extremely less important (1/9)’ as the price. .
Example – part 5:
Again, the reciprocal values are set automatically. The performance was set as ‘strongly more important (5)’, than the color and the price was set as ‘extremely more important (9)’ than the color.
As you see in this example, you only need compare two criteria a time. The properties from the different concepts are not an issue here.
Steam Storage for Flexible Biomass CHP Plants Page 185
Appendix II – Simulation results (Parameter Study)
47%
47%
920 920
920 920
0.87 0.87
1.86 1.86
0.82 0.82
7.50 7.50
5.38 5.38
27
0.85 0.85
1.82 1.82
2.41 2.41
7.50 7.50
4.00 4.00
15
74.8%
78.9%
1,100 1,100
1,450 1,450
10.86 10.86
18.45 18.45
50.00 50.00
10.85 10.85
12.30 12.30
50.00 50.00
48%
49%
0.90 0.90
1.86 1.86
0.82 0.82
9.08 9.08
4.00 4.00
5.38 5.38
26
0.89 0.89
1.82 1.82
2.41 2.41
9.08 9.08
4.00 4.00
4.00 4.00
14
82.2%
84.6%
1,690 1,690
1,100 1,100
1,690 1,690
1,450 1,450
18.45 18.45
50.00 50.00
12.30 12.30
50.00 50.00
49%
50%
0.91 0.91
1.86 1.86
0.82 0.82
8.02 8.02
2.00 2.00
5.38 5.38
25
0.92 0.92
1.82 1.82
2.41 2.41
8.03 8.03
2.00 2.00
4.00 4.00
13
89.0%
90.3%
3,360 3,360
1,100 1,100
3,360 3,360
1,450 1,450
18.45 18.45
50.00 50.00
12.30 12.30
50.00 50.00
46%
45%
920 920
920 920
0.88 0.88
1.89 1.89
2.41 2.41
7.50 7.50
4.00 4.00
24
0.86 0.86
1.90 1.90
4.67 4.67
7.50 7.50
2.00 2.00
12
78.4%
86.3%
1,510 1,510
3,040 3,040
10.87 10.87
18.45 18.45
50.00 50.00
10.88 10.88
12.30 12.30
50.00 50.00
48%
47%
0.90 0.90
1.89 1.89
2.41 2.41
9.08 9.08
4.00 4.00
4.00 4.00
23
0.89 0.89
1.90 1.90
4.67 4.67
9.10 9.10
4.00 4.00
2.00 2.00
11
84.0%
89.0%
1,690 1,690
1,510 1,510
1,690 1,690
3,040 3,040
18.45 18.45
50.00 50.00
12.30 12.30
50.00 50.00
49%
49%
0.92 0.92
1.89 1.89
2.41 2.41
8.03 8.03
2.00 2.00
4.00 4.00
22
0.93 0.93
1.90 1.90
4.67 4.67
8.05 8.05
2.00 2.00
2.00 2.00
10
89.7%
92.1%
3,360 3,360
1,510 1,510
3,360 3,360
3,040 3,040
18.45 18.45
50.00 50.00
12.30 12.30
50.00 50.00
45%
53%
920 920
960 960
8
0.88 0.88
1.96 1.96
4.67 4.67
7.50 7.50
2.00 2.00
21
0.85 0.85
1.61 1.61
0.82 0.82
9.06 9.06
4.00 4.00
5.38 5.38
6.15 6.15
86.2%
85.0%
3,130 3,130
1,690 1,690
10.91 10.91
18.45 18.45
50.00 50.00
50.00 50.00
46%
56%
960 960
7
0.91 0.91
1.96 1.96
4.67 4.67
9.11 9.11
4.00 4.00
2.00 2.00
20
0.90 0.90
1.61 1.61
0.82 0.82
8.02 8.02
2.00 2.00
5.38 5.38
6.15 6.15
88.7%
91.4%
1,690 1,690
3,130 3,130
3,360 3,360
18.45 18.45
50.00 50.00
50.00 50.00
47%
51%
5
0.93 0.93
1.96 1.96
4.67 4.67
8.04 8.04
2.00 2.00
2.00 2.00
19
0.85 0.85
1.68 1.68
2.41 2.41
9.06 9.06
4.00 4.00
4.00 4.00
6.15 6.15
91.8%
85.8%
3,360 3,360
3,130 3,130
1,690 1,690
1,340 1,340
18.45 18.45
50.00 50.00
50.00 50.00
48%
54%
920 920
4
0.85 0.85
1.78 1.78
0.82 0.82
7.50 7.50
5.38 5.38
18
0.90 0.90
1.68 1.68
2.41 2.41
8.03 8.03
2.00 2.00
4.00 4.00
6.15 6.15
75.5%
91.4%
1,060 1,060
3,360 3,360
1,340 1,340
10.84 10.84
12.30 12.30
50.00 50.00
50.00 50.00
50%
48%
2
0.89 0.89
1.78 1.78
0.82 0.82
9.07 9.07
4.00 4.00
5.38 5.38
17
0.85 0.85
1.78 1.78
4.67 4.67
9.08 9.08
4.00 4.00
2.00 2.00
6.15 6.15
83.0%
89.4%
1,690 1,690
1,060 1,060
1,690 1,690
2,850 2,850
12.30 12.30
50.00 50.00
50.00 50.00
51%
51%
1
0.91 0.91
1.78 1.78
0.82 0.82
8.02 8.02
2.00 2.00
5.38 5.38
16
0.91 0.91
1.78 1.78
4.67 4.67
8.04 8.04
2.00 2.00
2.00 2.00
6.15 6.15
89.7%
92.7%
3,360 3,360
1,060 1,060
3,360 3,360
2,850 2,850
12.30 12.30
50.00 50.00
50.00 50.00
-
-
-
-
s
s
s
s
m³
m³
m³
m³
kg/s
kg/s
kg/s
kg/s
MW
MW
MW
MW
MWh
MWh
MWh
MWh
Simulation
Simulation
min
max
min
max
SA
SA
charge
charge
charge
STS
charge
STS
P
P
charge
P
P
charge
discharge
discharge
discharge
discharge
t
t
discharge
discharge
Q
Q
ṁ
ṁ
t
t
Q
Q
ṁ
ṁ
System efficency System
System efficency System
Storage efficency Storage Storage efficency Storage
Steam Storage for Flexible Biomass CHP Plants Page 186
48%
48%
1.76 1.76
3.65 3.65
0.82 0.82
7.50 7.50
5.38 5.38
54
1.71 1.71
3.57 3.57
2.41 2.41
7.50 7.50
4.00 4.00
42
75.2%
79.3%
1,810 1,810
2,150 2,150
1,810 1,810
2,850 2,850
10.95 10.95
18.45 18.45
10.94 10.94
12.30 12.30
100.00 100.00
100.00 100.00
50%
50%
1.81 1.81
3.65 3.65
0.82 0.82
9.14 9.14
4.00 4.00
5.38 5.38
53
1.79 1.79
3.57 3.57
2.41 2.41
9.14 9.14
4.00 4.00
4.00 4.00
41
82.7%
85.1%
3,360 3,360
2,150 2,150
3,360 3,360
2,850 2,850
18.45 18.45
12.30 12.30
100.00 100.00
100.00 100.00
51%
52%
1.85 1.85
3.65 3.65
0.82 0.82
8.05 8.05
2.00 2.00
5.38 5.38
52
1.86 1.86
3.57 3.57
2.41 2.41
8.07 8.07
2.00 2.00
4.00 4.00
40
89.5%
90.7%
6,700 6,700
2,150 2,150
6,700 6,700
2,850 2,850
18.45 18.45
12.30 12.30
100.00 100.00
100.00 100.00
48%
47%
1.76 1.76
3.70 3.70
2.41 2.41
7.50 7.50
4.00 4.00
51
1.73 1.73
3.71 3.71
4.67 4.67
7.50 7.50
2.00 2.00
39
78.8%
86.7%
1,810 1,810
2,950 2,950
1,810 1,810
5,920 5,920
10.97 10.97
18.45 18.45
10.99 10.99
12.30 12.30
100.00 100.00
100.00 100.00
49%
49%
1.82 1.82
3.70 3.70
2.41 2.41
9.15 9.15
4.00 4.00
4.00 4.00
50
1.81 1.81
3.71 3.71
4.67 4.67
9.17 9.17
4.00 4.00
2.00 2.00
38
84.5%
89.4%
3,360 3,360
2,950 2,950
3,360 3,360
5,920 5,920
18.45 18.45
12.30 12.30
100.00 100.00
100.00 100.00
50%
51%
1.86 1.86
3.70 3.70
2.41 2.41
8.06 8.06
2.00 2.00
4.00 4.00
49
1.88 1.88
3.71 3.71
4.67 4.67
8.09 8.09
2.00 2.00
2.00 2.00
37
90.1%
92.5%
6,700 6,700
2,950 2,950
6,700 6,700
5,920 5,920
18.45 18.45
12.30 12.30
100.00 100.00
100.00 100.00
47%
53%
1.78 1.78
3.81 3.81
4.67 4.67
7.50 7.50
2.00 2.00
48
1.71 1.71
3.20 3.20
0.82 0.82
9.10 9.10
4.00 4.00
5.38 5.38
6.15 6.15
35
86.6%
85.2%
1,810 1,810
6,090 6,090
3,360 3,360
1,890 1,890
11.03 11.03
18.45 18.45
100.00 100.00
100.00 100.00
48%
57%
1.85 1.85
3.81 3.81
4.67 4.67
9.20 9.20
4.00 4.00
2.00 2.00
47
1.81 1.81
3.20 3.20
0.82 0.82
8.05 8.05
2.00 2.00
5.38 5.38
6.15 6.15
34
89.2%
91.6%
3,360 3,360
6,090 6,090
6,700 6,700
1,890 1,890
18.45 18.45
100.00 100.00
100.00 100.00
50%
51%
1.89 1.89
3.81 3.81
4.67 4.67
8.09 8.09
2.00 2.00
2.00 2.00
46
1.71 1.71
3.32 3.32
2.41 2.41
9.11 9.11
4.00 4.00
4.00 4.00
6.15 6.15
32
92.2%
86.1%
6,700 6,700
6,090 6,090
3,360 3,360
2,650 2,650
18.45 18.45
100.00 100.00
100.00 100.00
49%
55%
1.71 1.71
3.52 3.52
0.82 0.82
7.50 7.50
5.38 5.38
45
1.82 1.82
3.32 3.32
2.41 2.41
8.06 8.06
2.00 2.00
4.00 4.00
6.15 6.15
31
75.8%
91.6%
1,810 1,810
2,080 2,080
6,700 6,700
2,650 2,650
10.93 10.93
12.30 12.30
100.00 100.00
100.00 100.00
51%
49%
1.79 1.79
3.52 3.52
0.82 0.82
9.12 9.12
4.00 4.00
5.38 5.38
44
1.72 1.72
3.50 3.50
4.67 4.67
9.13 9.13
4.00 4.00
2.00 2.00
6.15 6.15
29
83.5%
89.7%
3,360 3,360
2,080 2,080
3,360 3,360
5,590 5,590
12.30 12.30
100.00 100.00
100.00 100.00
52%
53%
1.84 1.84
3.52 3.52
0.82 0.82
8.06 8.06
2.00 2.00
5.38 5.38
43
1.84 1.84
3.50 3.50
4.67 4.67
8.08 8.08
2.00 2.00
2.00 2.00
6.15 6.15
28
90.1%
93.0%
6,700 6,700
2,080 2,080
6,700 6,700
5,590 5,590
12.30 12.30
100.00 100.00
100.00 100.00
-
-
-
-
s
s
s
s
m³
m³
m³
m³
kg/s
kg/s
kg/s
kg/s
MW
MW
MW
MW
MWh
MWh
MWh
MWh
Simulation
Simulation
min
max
min
max
SA
SA
charge
charge
charge
STS
charge
STS
P
P
charge
P
P
charge
discharge
discharge
discharge
discharge
t
t
discharge
discharge
Q
Q
ṁ
ṁ
t
t
Q
Q
ṁ
ṁ
System efficency System
System efficency System
Storage efficency Storage Storage efficency Storage
Steam Storage for Flexible Biomass CHP Plants Page 187
49%
49%
2.64 2.64
5.41 5.41
0.82 0.82
7.50 7.50
5.38 5.38
81
2.57 2.57
5.30 5.30
2.41 2.41
7.50 7.50
4.00 4.00
69
75.6%
79.5%
2,700 2,700
3,190 3,190
2,700 2,700
4,230 4,230
11.00 11.00
18.45 18.45
10.98 10.98
12.30 12.30
150.00 150.00
150.00 150.00
50%
51%
2.73 2.73
5.41 5.41
0.82 0.82
9.18 9.18
4.00 4.00
5.38 5.38
80
2.70 2.70
5.30 5.30
2.41 2.41
9.17 9.17
4.00 4.00
4.00 4.00
68
83.0%
85.4%
5,030 5,030
3,190 3,190
5,030 5,030
4,230 4,230
18.45 18.45
12.30 12.30
150.00 150.00
150.00 150.00
51%
53%
2.78 2.78
5.41 5.41
0.82 0.82
8.08 8.08
2.00 2.00
5.38 5.38
79
2.79 2.79
5.30 5.30
2.41 2.41
8.09 8.09
2.00 2.00
4.00 4.00
67
89.7%
90.9%
3,190 3,190
4,230 4,230
18.45 18.45
12.30 12.30
10,040 10,040
10,040 10,040
150.00 150.00
150.00 150.00
48%
47%
2.65 2.65
5.49 5.49
2.41 2.41
7.50 7.50
4.00 4.00
78
2.59 2.59
5.47 5.47
4.67 4.67
7.50 7.50
2.00 2.00
66
79.2%
86.9%
2,700 2,700
4,380 4,380
2,700 2,700
8,730 8,730
11.02 11.02
18.45 18.45
11.03 11.03
12.30 12.30
150.00 150.00
150.00 150.00
50%
50%
2.74 2.74
5.49 5.49
2.41 2.41
9.19 9.19
4.00 4.00
4.00 4.00
77
2.72 2.72
5.47 5.47
4.67 4.67
9.21 9.21
4.00 4.00
2.00 2.00
65
84.8%
89.6%
5,030 5,030
4,380 4,380
5,030 5,030
8,730 8,730
18.45 18.45
12.30 12.30
150.00 150.00
150.00 150.00
51%
52%
2.79 2.79
5.49 5.49
2.41 2.41
8.09 8.09
2.00 2.00
4.00 4.00
76
2.83 2.83
5.47 5.47
4.67 4.67
8.12 8.12
2.00 2.00
2.00 2.00
64
90.3%
92.7%
4,380 4,380
8,730 8,730
18.45 18.45
12.30 12.30
10,040 10,040
10,040 10,040
150.00 150.00
150.00 150.00
48%
54%
2.67 2.67
5.61 5.61
4.67 4.67
7.50 7.50
2.00 2.00
75
2.56 2.56
4.78 4.78
0.82 0.82
9.11 9.11
4.00 4.00
5.38 5.38
6.15 6.15
62
86.9%
85.3%
2,700 2,700
8,960 8,960
5,030 5,030
2,820 2,820
11.09 11.09
18.45 18.45
150.00 150.00
150.00 150.00
49%
57%
2.78 2.78
5.61 5.61
4.67 4.67
9.25 9.25
4.00 4.00
2.00 2.00
74
2.72 2.72
4.78 4.78
0.82 0.82
8.07 8.07
2.00 2.00
5.38 5.38
6.15 6.15
61
89.5%
91.7%
5,030 5,030
8,960 8,960
2,820 2,820
18.45 18.45
10,040 10,040
150.00 150.00
150.00 150.00
51%
52%
2.84 2.84
5.61 5.61
4.67 4.67
8.12 8.12
2.00 2.00
2.00 2.00
73
2.57 2.57
4.95 4.95
2.41 2.41
9.12 9.12
4.00 4.00
4.00 4.00
6.15 6.15
59
92.4%
86.2%
8,960 8,960
5,030 5,030
3,950 3,950
18.45 18.45
10,040 10,040
150.00 150.00
150.00 150.00
49%
55%
2.56 2.56
5.23 5.23
0.82 0.82
7.50 7.50
5.38 5.38
72
2.73 2.73
4.95 4.95
2.41 2.41
8.08 8.08
2.00 2.00
4.00 4.00
6.15 6.15
58
76.1%
91.7%
2,700 2,700
3,090 3,090
3,950 3,950
10.96 10.96
12.30 12.30
10,040 10,040
150.00 150.00
150.00 150.00
51%
50%
2.68 2.68
5.23 5.23
0.82 0.82
9.15 9.15
4.00 4.00
5.38 5.38
71
2.58 2.58
5.18 5.18
4.67 4.67
9.14 9.14
4.00 4.00
2.00 2.00
6.15 6.15
56
83.7%
89.8%
5,030 5,030
3,090 3,090
5,030 5,030
8,280 8,280
12.30 12.30
150.00 150.00
150.00 150.00
53%
53%
2.77 2.77
5.23 5.23
0.82 0.82
8.08 8.08
2.00 2.00
5.38 5.38
70
2.76 2.76
5.18 5.18
4.67 4.67
8.09 8.09
2.00 2.00
2.00 2.00
6.15 6.15
55
90.3%
93.1%
3,090 3,090
8,280 8,280
12.30 12.30
10,040 10,040
10,040 10,040
150.00 150.00
150.00 150.00
-
-
-
-
s
s
s
s
m³
m³
m³
m³
kg/s
kg/s
kg/s
kg/s
MW
MW
MW
MW
MWh
MWh
MWh
MWh
Simulation
Simulation
min
max
min
max
SA
SA
charge
charge
charge
STS
charge
STS
P
P
charge
P
P
charge
discharge
discharge
discharge
discharge
t
t
discharge
discharge
Q
Q
ṁ
ṁ
t
t
Q
Q
ṁ
ṁ
System efficency System
System efficency System
Storage efficency Storage Storage efficency Storage
Steam Storage for Flexible Biomass CHP Plants Page 188
Appendix III – Calculation of the AI
The additional income is calculated with the time series of the results. The monthly average price is subtracted from the current market price. Each value above is the potential additional price.
The additional price is multiplied with the electricity generated during baseload and flexible operation (even during baseload, energy is generated at high price period). The difference of both total values is the additional income of the flexible operation.
The full calculation of AI or any other details can be requested by the author.
Steam Storage for Flexible Biomass CHP Plants Page 189
Appendix IV – ANSYS/CFX Modell Settings
ANSYS Modell Settings ANSYS Workbench Version ANSYS Workbench 2020 R2 CFX Version CFX (including CFX Post) 2020 R2
Geometry (Quarter pipe) Pipe diameter 0.013 m Pipe length 10 m Concrete module diameter 0.064 m
Variables Input massflow Constant Input pressure Constant or time dependet pressure profile Input temperature Constant or time dependent pressure profile
Initial conrete temperature fixed value or temperature profile for each point in the mesh
Mesh Mesh Methodes Sweep Method and Edge Sizuing Mesh Metric Jacobian Ration (Corner Nodes) Bias Factor (Concrete) 50 Bias Factor (Pipe) 12 Sweept Divider 140 Element Size Default Element 62720 Elements Element Metrics (Quality) 51100 Elemtents = 1,19
Model Setup Simulation Type Transient Simulation Min Coeff. Loop / Max Coeff. Loop 1 / 30 Residual Target 0.001 Transient Sccheme Second order Backward Euler Turbulence Numerics High Resulution
Fluid Domain Material Steam (CFXLibary) Settings Continues Fluid, No Buoyancy, Stationary Domain, No Mesh Deformation Fluid Model Thermal Energy (CFX) Turbulence Modell k-Epsilon , Eddy Diffusitivity 0.90,
Solid Domain Material Own defined (from Laing) Specific Heat Capacity ((0.7 [K]) + (T - (273.13 [K])) * 10^-4* 8.75) * 1 [kJ kg^-1 K^-2] Density 2300 kg m^-3 Thermal Conductivity ((1.467 [K]) - (T - (273.13 [K])) * 10^-4*6.6667) * 1 [W m^-1 K^-2]
Settings Continious Solid, Stationary, No Mesh Deformation Solid Model Thermal Energy (CFX), no thermal radiation
Fluid Solid Interface Interface Model Conservative Heat Flux Model / Thin Material / Steel 2.5 mm
Steam Storage for Flexible Biomass CHP Plants Page 190