Strategies for Provision of Secondary Reserve Capacity to Balance Short-Term Fluctuations of Variable Renewable Energy
David-Constantin Radu
Master of Science Thesis KTH School of Industrial Engineering and Management Energy Technology EGI_2017-0073-MSC EKV1199 Division of Heat & Power SE-100 44, STOCKHOLM
Master of Science Thesis EGI_2017-0073-MSC
EKV1199
Strategies for Provision of Secondary Reserve Capacity to Balance Short-Term Fluctuations of Variable Renewable Energy
David-Constantin Radu
Approved Examiner Supervisor 2017-09-18 Miroslav Petrov -KTH/ITM/EGI Miroslav Petrov Commissioner Contact person Energynautics GmbH Eckehard Tröster Peter-Philipp Schierhorn
Abstract
Recent trends and projections show a massive shift in power generation towards variable renewable energy (VRE), especially wind and solar PV. Renewable energy technologies are expected, in the upcoming decades, to become the primary source of electricity production and this will attract inherent design and operational challenges of power systems. One such challenge is posed by the ability of power systems to cope with short-term fluctuations, or intermittency, of variable renewable energy technologies. Strategies linked to this issue are related to the use of one specific subset of power system ancillary services, namely the active power control mechanism, responsible for the activation of primary, secondary and tertiary or reserves.
The scope of this thesis is to develop and present a methodology that comes as solution to sizing and allocation of secondary reserves. The first part of the thesis (i.e. dimensioning of system-wide secondary reserve requirement) is based on high-resolution load and VRE generation time series. Input data is processed to represent more accurately the challenges secondary control has to cope with; and statistically analyzed according to predefined security-of-supply levels. Supplementary requirements of secondary reserve (SR) due to VRE extensions are also estimated.
The second part of the project (i.e. reserve allocation throughout a proposed generic grid model) treats the joint optimization of the active power economic dispatch during normal operation (previously available as part of a grid optimization software) and the secondary reserve capacity allocation problem. It is mathematically formulated as a linear programming model and bound by generating units’ technical constraints, as well by the physical limitations of the transmission network. The output of the SR sizing methodology, validated for a case study on the Czech Republic’s power system, serves as an input to the optimization tool subsequently developed.
Sammanfattning
Att upprätthålla en hög nivå av elförsörjningskvalitet till lägsta kostnad har alltid varit av största vikten för energisystemoperatörer. Medan förnybar energi blir mer tillgänglig, och med framtida utvecklingstrender och förväntade prognoser i åtanken, har den här situationen blivit ännu mer uppmärksammad och fått större betydelse. Förnybar energiteknik förväntas bli primärkällan för elproduktion under de kommande decennierna och kommer att utmana den nuvarande design och operationstrategier av dagens energisystem. Orsaken är att en ständig växande elproduktion baserat på solceller och vindkraft skapar en svårhanterad variabilitet i elsystemet som i sin tur översättas till produktionsprofiler med både stor frekvensvariation och begränsad styrbarhet. I detta sammanhang blir vikten av åtgärder för aktivt produktionskontroll och därmed utjämning av medfödda systemfluktuationer uppenbar. Strategier som löser denna frågan är beroende på aktiva systemkontrollmekanismer som kan delas upp i: primärt, sekundärt och tersiärt kontroll (eller således reserver). Syftet med detta examensarbete är att utveckla och testa en metod för optimal dimensionering och fördelning av sekundära reserver i ett nationellt kraftnät. Den första delen (dimensionering av sekundära reserver) tacklar komponenten av systemvarationer på kort sikt, oavsett den variabla kraftkällan (dvs. sol eller vind). De nämnda variationerna händer på en tidskala som är tillräcklig liten så att nödvändiga balanskorrigeringar kan ske via operativa reserver. Modellen är utvecklad runt högupplösningsbelastning och bildningstidsserier. Först, tidserienera applicerar en low-pass filter som tar bort stokastiska, högfrekventa spets för att kunna visa kraven för sekundära reserver under dess aktiveringstider. Användning av skillnaden mellan positiva och negativa fluktuationer leder till en optimering uppåt och neråt av sekundära reserver. Tidserierna klassificeras på grund av relevanta kriterier för varje fluktuationskälla. Analysen av den subsekventa fördelningen koncentrerar sig på de uppsättningar som delar samma statistiska egenskaper. En godtycklig önskad säkerhetsnivå (e.g. 95 %) beaktas och används för alla definerade kategorier, vilka summeras efteråt för att tydliggöra den totala kraven på kraftreserven. Slutligen utvecklas ett heuristisk tillvägagångsätt för att kunna uppskatta det ytterligare behovet av sekundära reserver in samband med den befintliga ökningen av variabel produktion i det studerade systemet. Metoden för dimensionering av sekundära reserver, som har validerats för Tjeckiens kraftsystem, fungerar som inmatning för optimeringsverktyget som har utvecklats därefter. Andra delen av projektet (alltså fördelning av reserver genom en föreslagen generisk nätmodell) behandlar punktoptimeringen för ekonomsik tilldelning av genererad elektricitet och problemet med fördelningen av sekundära reserver. Detta är matematiskt formulerad i form av en linear programmodell och är bunden av tekniska begränsnigar inom elgenereringsenheterna, samt fysiska begränsningar inom transmissionsnätverket. Modellen som avänds för utvecklingen av optimeringsverktyget är ett generisk eltnätverk med allmänna funktioner, likt ett typiskt central- amerikansk eller väst-afrikansk elnätverk (vattenkraftdominerad, hög men outvecklad solkraft och/eller vindkraft potential), med tanke på att elnätets generiska karaktär saknar den föreslagna reservfördelningsmetodens validering.
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Table of Contents
SAMMANFATTNING ...... I TABLE OF CONTENTS ...... II LIST OF FIGURES ...... IV LIST OF TABLES ...... V ABBREVIATIONS ...... VI GLOSSARY ...... VII PHYSICAL QUANTITIES ...... IX 1 INTRODUCTION ...... 1 1.1 CONTEXT ...... 1 1.2 MOTIVATION AND THESIS OBJECTIVES ...... 3 1.3 METHODOLOGY AND TOOLS ...... 4 1.4 CASE STUDIES ...... 5 1.5 THESIS OUTLINE ...... 7 2 VARIABLE GENERATION AND OPERATING RESERVES ...... 8 2.1 RELIABILITY OF POWER SYSTEMS ...... 8 2.2 CHARACTERISTICS OF VARIABLE GENERATION ...... 9 2.3 ANCILLARY SERVICES AND OPERATING RESERVES ...... 11 2.4 IMPACT OF VARIABLE GENERATION ON OPERATIONAL RELIABILITY OF POWER SYSTEMS ...... 15 3 PROVISION OF OPERATING RESERVES ...... 17 3.1 ELECTRICITY MARKET DESIGN ...... 17 3.2 RESERVE PROCUREMENT VS. BALANCING MARKETS ...... 22 3.3 THE IMBALANCE SETTLEMENT ...... 23 3.4 PREQUALIFICATION FOR ACTIVE POWER RESERVE CAPACITY PROVISION ...... 25 3.5 TOWARDS HOMOGENEOUS ACTIVE POWER CONTROL PRODUCTS ...... 26 4 DIMENSIONING SECONDARY RESERVE CAPACITY ...... 29 4.1 SYSTEM OPERATORS’ EXPERIENCE ...... 29 4.2 DATA ACQUISITION AND PRE-PROCESSING ...... 30 4.3 TIME SERIES FILTERING ...... 33 4.4 ASSESSMENT OF GENERATION VARIABILITY...... 35 4.5 CLASSIFICATION OF TIME SERIES ...... 37 4.6 STATISTICAL SIZING OF SECONDARY RESERVE ...... 42 4.7 ADDITIONAL SECONDARY RESERVE REQUIREMENTS WITH VRE CAPACITY EXPANSION ...... 45 4.8 SCENARIO DEVELOPMENT ...... 48 4.9 RESULTS ...... 49 5 JOINT OPTIMIZATION OF THE ACTIVE POWER DISPATCH AND THE SECONDARY RESERVE CAPACITY ALLOCATION ...... 59 5.1 REVIEW OF PREVIOUS WORK ...... 59 5.2 CHARACTERISTICS AND GENERATION ASSETS OF THE STUDIED SYSTEM ...... 60 5.3 OPTIMIZATION TOOL ENAPLAN ...... 63 5.4 SIZING SECONDARY RESERVE REQUIREMENTS ...... 65 5.5 MATHEMATICAL FORMULATION OF THE CO-OPTIMIZATION PROBLEM ...... 66 5.6 RESULTS ...... 68 6 CONCLUSIONS & OUTLOOK ...... 77 6.1 FINAL REMARKS ...... 77 6.2 MODEL LIMITATIONS AND FUTURE WORK ...... 78
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REFERENCES ...... 81 APPENDIX – LIST OF FIGURES ...... 88 APPENDIX – LIST OF TABLES ...... 90 APPENDIX A TIME SERIES FILTERING ...... 91 APPENDIX B TIME SERIES CLASSIFICATION ...... 93 APPENDIX C ADDITIONAL SECONDARY RESERVE REQUIREMENTS WITH VARIABLE GENERATION CAPACITY EXPANSION – STUDIED SYSTEMS ...... 97 APPENDIX D SECONDARY RESERVE REQUIREMENTS – COMPARATIVE RESULTS ...... 99 APPENDIX E SECONDARY RESERVE REQUIREMENTS – SCENARIO BREAKDOWN ...... 106 APPENDIX F JOINT OPTIMIZATION GRID INPUT DATA ...... 136
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List of Figures
Figure 1-1. Merit order effect of VRE development ...... 2 Figure 1-2. Projected global electricity generation by source (1990-2035) ...... 3 Figure 1-3. Czech Republic’s VRE and secondary reserves development (2007-2016) ...... 5 Figure 1-4. Generic model used in the security-constrained joint optimization of economic dispatch and secondary reserve allocation ...... 6 Figure 2-1. Components of power system reliability ...... 9 Figure 2-2. Monthly capacity factors for wind and PV (Germany, 2005) ...... 10 Figure 2-3. Geo-spreading (smoothing) effect on wind (left) and PV (right) output ...... 11 Figure 2-4. Hierarchical control structure of UCTE...... 12 Figure 2-5. Generic frequency control scheme and corresponding actions ...... 13 Figure 2-6. Example of active power control deployment in case of a wide-area power system ... 15 Figure 2-7. Impacts of wind power on large interconnected power systems ...... 16 Figure 3-1. General electricity market models – vertically integrated utility ...... 17 Figure 3-2. General electricity market models – vertical unbundling ...... 18 Figure 3-3. Example of the electricity market timeframe ...... 18 Figure 3-4. Determination of the day-ahead market clearing price ...... 20 Figure 3-5. Development of the intraday market ...... 21 Figure 3-6. Example of prequalification protocol for up- and downward secondary reserves ...... 25 Figure 3-7. Features of active power control standard products...... 27 Figure 4-1. System load and VRE output for an example week in 2014 ...... 31 Figure 4-2. Superposition of June PV output curves (raw data) ...... 32 Figure 4-3. Superposition of June PV output curves (processed data) ...... 32 Figure 4-4. Example of load and VRE generation fluctuations on a one-hour sample...... 33 Figure 4-5. Low-pass filter methods. Example on wind feed-in time series ...... 35 Figure 4-6. Up- (left) and down-fluctuation (right) evaluation ...... 36 Figure 4-7. Time series classification criteria ...... 37 Figure 4-8. Load time series time-of-day classification ...... 38 Figure 4-9. Load time series time-of-day classification ...... 39 Figure 4-10. PV time series time-of-day classification ...... 39 Figure 4-11. Generic pitch-regulated wind turbine power curve ...... 41 Figure 4-12. Wind time series power level classification basis ...... 41 Figure 4-13. The normal distribution “three-sigma” rule ...... 43 Figure 4-14. Determination of the 95th percentile ...... 43 Figure 4-15. Additional requirements with VRE expansion for upward secondary reserve capacity ...... 46 Figure 4-16. Additional requirements with VRE expansion for downward secondary reserve capacity ...... 47 Figure 4-17. Total upward SR requirement for scenario Alpha ...... 50 Figure 4-18. Upward SR requirements for scenario Alpha due to short-term load variations ...... 51 Figure 4-19. Upward SR requirements for scenario Alpha due to short-term PV generation variations ...... 52 Figure 4-20. Upward SR requirements for scenario Alpha due to short-term wind generation variations ...... 52
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Figure 4-21. Overview of the ABC scenarios during one example week in January ...... 53 Figure 4-22. Overview of the ABC scenarios during one example week in July ...... 54 Figure 4-23. SR requirements of individual variation drivers for scenario Alpha ...... 55 Figure 4-24. SR requirements of individual variations drivers for scenario Foxtrot ...... 56 Figure 4-25. Overview of total SR requirements for the DEF scenarios during one example week in January ...... 56 Figure 4-26. Overview of total SR requirements for the DEF scenarios during one example week in July ...... 57 Figure 4-27. Duration curves for upward SR reserve requirement due to PV generation variations ...... 57 Figure 4-28. Duration curves for upward SR reserve requirement due to wind generation variations ...... 58 Figure 5-1. Examples of various system inputs (load, PV and wind feed-in, inflows) ...... 63 Figure 5-2. Generic reservoir reference curves ...... 63 Figure 5-3. Secondary reserve requirements throughout the year...... 65 Figure 5-4. Yearly active power dispatch breakdown by technology (daily averages) ...... 69 Figure 5-5. Breakdown of positive secondary reserve allocation by plant during the entire year (daily averages) ...... 71 Figure 5-6. Breakdown of negative SR allocation by plant during the year (daily averages) ...... 72 Figure 5-7. Daily active power dispatch breakdown by technology during a dry week (November) ...... 73 Figure 5-8. Hourly averages of reserve allocation by plant during the dry season (a week in November) ...... 73 Figure 5-9. Active power dispatch and reserve allocation by plant for a given timestamp during the dry season (15-minute resolution) ...... 74 Figure 5-10. Daily active power dispatch breakdown by technology during the wet season (a week in June) ...... 75 Figure 5-11. Hourly averages of reserve allocation by plant during the wet season (a week in June) ...... 76 Figure 5-12. Active power and reserve allocation by plant for a given timestamp during the wet season (15-minute resolution) ...... 76
List of Tables
Table 2-1. Active power control characteristics in Europe ...... 14 Table 4-1. Parameters of proposed scenarios ...... 49 Table 5-1. List of VRE power plants and their operational characteristics ...... 61 Table 5-2. List of conventional power plants and their operational characteristics ...... 61 Table 5-3. List of hydro power plants and their operational characteristics ...... 62 Table 5-4. List of hydro power plants and their hydrological dependencies ...... 62 Table 5-5. Generation breakdown by fuels ...... 69 Table 5-6. Secondary reserve capacity allocation by plant (hours per annum) ...... 70
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Abbreviations
(m)Hz (mili)Hertz AC Alternative current ACE Area Control Error AGC Automatic Generation Control CAPEX Capital expenditure ČEPS Czech Transmission System Operator DC Direct current DST Daylight Saving Time ENTSO-E European Network of Transmission System Operators ERCOT Electric Reliability Council of Texas EU European Union GW Gigawatt Hm3 Cubic hectometer hr p.a. Hours per annum LOCF Last observation carried forward MW Megawatt NREL National Renewable Energy Laboratory OPF Optimal Power Flow p.u. Per unit PPA Power Purchase Agreement PV Photovoltaic SC Secondary Control SR Secondary Reserve TSO Transmission System Operator TWh Terawatt-hour UNFCCC United Nations Framework Convention on Climate Change VRE Variable Renewable Energy
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Glossary
(n-k) Criteria Security measure that the System Operator uses in modelling the transmission network to avoid potential power interruptions and/or system failure (ability of the system to maintain electricity supply with k out of n power system assets Active power dispatch Actual dispatch of active power during normal operation of the system; based on the cost optimal solution of reaching the system demand Ancillary services Auxiliary services within power systems that aid the transfer of electricity between parties in a safe and reliable fashion Contingency Loss or failure of a power system asset (generator, transmission line, etc.) Control power Active power used in balancing purposes Flexibility of operation The extent to which a power system can variate the electricity production or consumption Liberalized (electricity) Competitive market strategy based on the unbundling of the supply market chain and possibility of consumer to choose its supplier Merit order rule Power plants are scheduled on the electricity market taking into account their marginal production cost Net load Load minus VRE generation output, also known as residual load Operating reserve Generating capacity available within a short time range for system balancing under normal operation or to cover for various system contingencies Optimal power flow Optimization of the power generation allocation in order to meet necessary demand while satisfying the physical constraints of the corresponding transmission lines Penetration level Metric for the share of VRE within a given power system (VRE cumulative capacity relative to the yearly peak load) Reserve allocation Active power (share of installed capacity of a generating unit) capacity reserved (not necessarily activated) for secondary control strategies Secondary control Mechanism (usually automated) used for the secondary reserve activation process Secondary reserve Active power capacity provided by network assets (generators capacity and/or loads) for the second stage of active power control strategies Security constrained Bound to certain contingency constraints, like the (n-k) criteria Synchronous zone Electrical interconnection operating at synchronized frequency
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Transmission System Legal body responsible for operating, ensuring the maintenance and Operator developing the transmission system and corresponding interconnections Value of water The decrease in the total operation cost given an extra unit of available hydro energy Variable renewable Power generation which output depends on variable resource; energy generation within this thesis, generic term for wind and PV generation, not including tidal and run-of-river hydro generators Vertically integrated Structure where one company has monopole on the entire utility electricity supply chain (generation, transmission, distribution, supply) Yearly peak load Maximum demand during one natural year
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Physical quantities
Quantity Physical Meaning Unit
퐴퐶퐸푡 Area Control Error for time instance t MW
퐷푡 System demand at time t MW
퐺푖,푡 Generation of unit i at time t MW 퐻(휔) Transfer function of the Butterworth filter - 퐾 Frequency characteristic of the control area MW/Hz
퐿푗 Capacity of line j MW
퐿푚푎푥(푡) Maximum hourly load for time stamp t in control area of interest MW
푁퐵푖,푡 Negative secondary reserve allocated to unit i at time t MW
푁퐺 Number of generating units # of units
푁퐻 Number of hydro power plants # of units
푁퐿 Number of system transmission lines # of units
푁푘 Number of hydro cascaded units within a given scheme # of units
푃퐵푖,푡 Positive secondary reserve allocated to unit i at time t MW
푃퐶푅푖 Primary reserve contribution of control area i MW
푃퐹푗,푡 Power flow through line j at time t MW
푃퐼퐶,푃푉 PV installed capacity MW 0 푃퐼퐶,푃푉 Base PV installed capacity MW Δ 푃퐼퐶,푃푉 Future marginal PV installed capacity MW
푃퐼퐶,푉퐺 Variable generation installed capacity MW
푃퐼퐶,푤푖푛푑 Wind installed capacity MW 푚푎푥 푃푖 Maximum power of of generating unit i MW 푚푖푛 푃푖 Minimum power of of generating unit i MW
푃푚푎푥,푙표푎푑 Yearly peak load MW
푃푚푒푎푠,푡 Sum of measured active power flows for time instance t MW
푃푝푟표푔,푡 Sum of scheduled active power flows for time instance t MW 푃퐸푁 VRE Penetration level %
푅푅푖 Ramp rate of generating unit i MW/min. 3 푆푖,푡 Spilling of unit i hm
푆퐶푡 Total secondary reserve allocation for time t MW
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Quantity Physical Meaning Unit 푆퐶퐶(푡) Secondary reserve for time t adjusted for VRE expansion MW − 푆푅푡 System-wide negative secondary reserve requirement at time t MW + 푆푅푡 System-wide positive secondary reserve requirement at time t MW 푇 Number of timestamps during a full natural year - 푎, 푏 Empirically determined parameters (a = 10 MW, b = 150 MW) MW
푎푗 j-th term of the moving average -
푐푖,푡 Marginal cost of generation for unit i $/MW 푅− 푐푖,푡 Cost of negative secondary reserve provision for unit i $/MW 푅+ 푐푖,푡 Cost of positive secondary reserve provision for unit i $/MW 푠푝푖푙푙 Cost of spilling for unit i $/hm3 푐푖,푡
푒푖 Amount of electricity generated in control area i MWh
푒푡 Amount of electricity generated system-wide MWh
푓푚푒푎푠 Instantaneous measured frequency Hz
푓0 Set-point frequency Hz 푗, 푘 Base and future penetration levels %
푛퐵 Order of the Butterworth filter -
푛푅퐴 Moving average window size - 푝 푞푃푉,푗(푡) p-th PV sample percentile for time t, corresponding to PV time % series classification key j 푝 푞푙표푎푑,푖(푡) p-th load sample percentile for time t, corresponding to load time % series classification key i 푝 푞푤푖푛푑,푘(푡) p-th wind sample percentile for time t, corresponding to wind time % series classification key i
푟푗, 푟푘 Relative reserve requirement for 푗, 푘 penetration levels %
푠푖 Moving average of subset starting from the i-th term -
푥푡 Holt sample value at time t -
푦푡 Holt sample estimation at time t -
푦푡−1 Holt sample estimation at time t-1 - 훼 Smoothing parameter - 휔 Frequency Hz
휔0 Cut-off frequency Hz
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1 INTRODUCTION
This introduction chapter states and clarifies major aspects of the thesis, on different levels: circumstantial, functional and organizational. The current state of power systems is firstly discussed and serves as context in which this thesis is set. Motivation for exploring the topic and expected outcomes of the work are thereafter discussed. An insight in the proposed methodology precedes a brief introduction of the power systems that will serve as basis in the thesis development. Lastly, the outline of the thesis is presented.
1.1 Context Power grids are complex systems that include generators (power plants), consumers and the connection between the two, i.e. transmission and distribution lines. The hallmark of any power system is that electricity cannot be stored, unless additional conversion steps are applied and, with no storage capabilities, the system has to find itself in permanent equilibrium between generation and consumption. Contingencies of all natures (power plant outages, line faults, etc.) are frequent within power systems, but not the only ones that require attention. More frequent system discrepancies happen in a short time frame and are inherent to variable renewable energy (VRE) generation and its characteristic fluctuating output. With recent and expected developments in this area (De Vos, 2013; IRENA, 2016), balancing load and generation becomes increasingly challenging and thus of significant interest. Electricity generators run on a wide variety of fuels and, therefore have different financial markers, e.g. capital cost (CAPEX) and variable or operating cost (OPEX). The amount of total generated electricity has to meet the demand at any point in time and it is comprised of different volume shares originating from different power plants. The decision on what generator gets to deliver the amount of electricity it bid for is taken based on the merit order rule, which implies that units are scheduled in ascending order of their marginal operating cost (Deane, et al., 2015). VRE technologies (referring here to PV and wind) still have higher capital costs than conventional generation (Black & Veatch, 2012), regardless of the various factors that lead to a decrease in corresponding technology costs in recent years (Samadi, 2016; IEA, 2017). Nevertheless, with support schemes provided to foster renewable development (e.g. priority feed-in, feed-in tariff, PPAs) and taking into account the free-of-charge resource, VRE units are cheaper to operate and subsequently shifted towards the bottom of the merit order (Figure 1-1), hence their power is traded and subsequently used whenever is available. In this regulatory context, integration of VRE in power grids has a two-fold effect on the electricity generation mix. First, an operational effect occurs. Additional PV and wind generating units entering the market are located towards the top of the merit order, according to their marginal operating costs. Every producer with higher marginal operating costs than the most recent VRE addition is shifted right, towards the bottom of the merit order. In other words, given any momentary demand, additional VRE capacity will decrease the market clearing price (MCP)1, with the
1 Detailed information about the MCP provided in Section 3.1 – Electricity Market Design -1- most expensive technologies being left out from the generation schedule. In the example shown in Figure 1-1, increasing variable generation yields in the complete removal of hard coal based generation from the electricity mix and the electricity price decreased, as indicated by the red arrow. The second effect of VRE integration is the environmental gain of producing electricity with no
associated emission, in comparison with the polluting effects of fossil-based power plants.
Demand Renewables
Nuclear
Lignite Op. Cost [$/MWh] Cost Op. Capacity [MW] Hard Coal
Gas
Marginal cost reduction Oil Op. Cost [$/MWh] Cost Op. Capacity [MW]
Figure 1-1. Merit order effect of VRE development. Source: (CLEW, 2017) Environmental policies – the UNFCCC 21st Conference of the Parties (COP21) being the latest and maybe the most important, with the highest desired outcome, given its global coverage – are gaining ground in shaping the way power systems are designed and operated. Decarbonization of electricity generation is one crucial aspect in meeting sustainable targets in terms of emission control, but not only. In these circumstances, renewable energy generation technologies (including here all sources, not only PV and wind) have steadily developed in the last decades, reaching the point where they were expected to become the second-largest source of electricity generation on a global scale by 2015 (Figure 1-2). Projections of the International Energy Agency (IEA, 2013) indicate that the fast growth of renewables will continue and, by 2035 they will reach coal levels as the primary source of electricity generation. As PV and wind generation have the lead in the recent renewable energy growth and with the trend not expected to change (Ackermann, 2012; IEA, 2013; UNEP, 2017), particular features of corresponding electricity generation2 will enhance the complexity of power systems design. This will bring inherent challenges in operating power systems at expected reliability levels, therefore certain related issues, i.e. security of continuous supply, have to be explored and comprehended.
2 Detailed information on VRE generation particularities offered in Section 2.2 – Characteristics of Variable Generation -2-
14 12 10 8
TWh] 6 4 2
Electricity generationElectricity [*1000 0 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 Coal Renewables Gas Nuclear Oil
Figure 1-2. Projected global electricity generation by source (1990-2035). Adapted from: (IEA, 2013)
1.2 Motivation and Thesis Objectives In the current context of VRE rapid growth, increasingly complex power systems are exposed to more frequent challenges and maintaining their performance indicators at desired values requires additional awareness and knowledge of the subsystems that contribute to those issues. VRE units or groups are a good example of such a subsystem. The nature of the corresponding power generation has a strong impact on the power system operation, affecting the system on multiple levels (e.g. its power system stability, due to generation variability; adequacy, due to generation uncertainty; market development, by having an effect on the merit order and therefore on electricity prices). Short-term variability of intermittent renewable generation (PV and wind) within a time frame of seconds to minutes is what this thesis addresses. It is of interest how integration of VRE impacts the reliable functioning of electric networks on a short time perspective, an issue generally dealt with through provision of ancillary services. The motivation of this work is, therefore, to provide a solution approach on this topic by emphasizing the role of secondary active power control3 in the adequate operation of power systems. When integrating VRE in power systems (especially high shares of VRE in relatively small power systems), the following questions ought to be addressed: How much spinning reserves for secondary active power control have to be available system-wide at each point in time and how do VRE fluctuations influence this system parameter? How much additional secondary reserves are required with increasing shares of VRE? What is the value of secondary reserve in the system and how it could be embedded in the optimal economic dispatch problem? How do possible grid congestions influence the adequacy of allocated reserves?
3 Detailed information on active power control strategies provided in Section 2.3 – Ancillary Services and Operating Reserves -3-
The purpose of this thesis is to find an answer to these questions and, to this end, a secondary reserve sizing and allocation methodology is suggested. The main objective is minimizing total system costs at predefined risk shares (i.e. a security of supply level) by taking into account physical limitations of the electric network assets (e.g. generators, transmission lines). Upon completion, this work will provide a comprehensive assessment of secondary reserve requirements, deployment and value within power systems coping with VRE integration.
1.3 Methodology and Tools This thesis is comprised of two distinct, but correlated parts. Throughout the first half of the thesis, a generic methodology for dimensioning secondary reserve capacity in power grids with increasing shares of VRE generation is built. During the latter part, output of the sizing methodology is used, as input data for a generic network model, in enhancing the functionality of an existent, more extensive grid optimization model. For this purpose, a linear algorithm is developed to embed the cost optimization of secondary reserve allocation throughout the grid’s assets into an already available tool that yields the active power economic dispatch during normal operation of the system. For the remaining of the thesis, the initial step of the optimization problem (i.e. the economic dispatch of the grid’s generators) may be simply referred to as the active power dispatch. When it comes to power system services, terminology may differ from one system to another. Therefore, in order to avoid misleads and inconsistencies throughout the report, all definitions and nomenclature will be based on the “Continental Europe Operation Handbook”, as proposed by ENTSO-E (ENTSO-E, 2009). This report makes a distinction between three stages of active power control: primary, secondary and tertiary, each with its particular role in power system operation4. Throughout the thesis, the term “reserve” (SR in case of secondary reserve) denotes the active power capacity used in the generation-consumption balancing process, while “control” (SC, secondary control) is used to express the mechanism responsible for activating active power reserves. The secondary reserve dimensioning methodology proposed in this thesis is intended to have a generic character, applicable to any system as long as basic input (i.e. VRE output and system demand) is available. Starting from demand and VRE generation data, fluctuations are evaluated and secondary reserve is dynamically sized considering temporal and operational characteristics of short-term variations. Data is evaluated by means of statistical procedures and the marginal requirement of secondary reserve due to additional VRE capacity is also accounted for. The output of this model represents forecasted SR requirements given specific system parameters and covers a time horizon of choice (from 1 hour to 1 year). For exemplification and validation purposes, the sizing methodology proposed in this thesis develops around generation and load data from Czech Republic and covers a full year. A generic grid model is thereafter set and serves as basis for the optimization tool for secondary reserve allocation, part of a broader electricity dispatch model. Two major input parameters are given: the generation assets of the proposed network, including their relevant technical and economical characteristics and the SR requirement as determined via the aforementioned
4 The role of each active power control stage is detailed in Section 2.3 – Ancillary Services and Operating Reserves -4- dimensioning methodology. Linear programming is used to simultaneously solve the economic dispatch, as well as the SR allocation problems, for 15-minute intervals throughout a full year. Both sizing and allocation methodologies of secondary reserve are developed in Python. The former is based on an initial model previously developed within Energynautics GmbH (Wagner, et al., 2016). The corresponding work presented in this paper is an enhancement of the initial model functionalities. The secondary reserve allocation tool is developed within and subsequently embedded in Energynautics’ in-house grid simulation software, ENAplan, as an add-on to the already existent economic dispatch optimization tool.
1.4 Case Studies In order to introduce the features of the proposed secondary reserve sizing methodology, availability of certain information is required. In this regard, for reasons that will be later uncovered (Section 4.2), exemplification and validation of the dimensioning procedure are based on Czech Republic’s power system data.
Figure 1-3. Czech Republic’s VRE and secondary reserves development (2007-2016) Czech Republic’s power system, with a yearly peak demand in 2015 of approximately 11 GW and a gross consumption in the same year of around 70 TWh (ERU, 2016), is part of the European interconnected network of ENTSO-E and synchronously connected to the continental Europe grid (formerly known as UCTE grid). During the same year, one third of the country’s gross electricity generation (84 TWh) was covered by nuclear power plants, while over 60% was based on fossil fuels (mainly coal). Rest of the energy mix was ensured by hydro (3.5%), PV (2.7%) and wind (0.7%) power plants (ERU, 2016).
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Figure 1-3 illustrates evolution of variable generation and secondary reserve requirements of Czech Republic over the last 10 years. The upper left plot shows the evolution of total VRE installed capacity (in pale green) (ERU, 2016) and the magnitudes of secondary reserve capacity (both positive and negative) on monthly resolution (ČEPS, 2017b). The dimensioning of SR capacity is done symmetrically, i.e. upward and downward regulation volumes have the same magnitude (ENTSO-e, 2017a). While wind installed capacities developed steadily over the studied time horizon (see upper right plot), a steep increase in PV installations is observed between 2009 and 2010. The latter, caused by a temporary introduction of feed-in tariffs was dimmed by regulatory changes made by the Czech government after 2010 (UNDP, 2013). The plot displaying the geographical distribution of PV and wind capacities in Czech Republic shows wind generation concentrated in the western part of the country, while PV installations are more distributed across the entire territory. The second part of the thesis deals with the joint optimization problem of the active power dispatch and the secondary reserve allocation taking into account transmission constraints. In this regard, for exemplification purposes, a 20-bus generic grid was set (Figure 1-4). It contains two transmission voltage levels, 138 kV (green) and 230 kV (red) and its nodes correspond to five geographical regions with distinct characteristics: on the 230 kV level, the Central area (high demand) and the South area (high VRE production and a cascaded hydro generation complex) and on connected to the 138 kV lines, the West, North and East regions (relatively low demand, distributed thermal generation and PV generation in the latter).
PV Biomass Diesel Hydro PV
Geothermal
Hydro Diesel
Wind
PV Hydro
Wind
Figure 1-4. Generic model used in the security-constrained joint optimization of economic dispatch and secondary reserve allocation
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1.5 Thesis Outline Chapter 2 serves as theoretical background for the secondary reserve sizing methodology developed in Chapter 4. It starts by mentioning key aspects related to power system reliability. Characteristics of power generation from intermittent sources are also presented, together with its effect on the operating reserves of power systems. Also, a short description of operating reserves, with emphasis on secondary reserve is presented. Chapter 3 provides basic theoretical information related to the secondary reserve allocation tool designed in Chapter 5. General aspects of current electricity markets design are introduced before the focus is shifted towards reserve procurement schemes and secondary reserves. Characteristics of common and specific products traded in capacity markets are briefly described, as well as the requirements that producers have to meet in order to provide reserve capacity. Also, the activation of such ancillary services and the subsequent market settlement is discussed. Throughout Chapter 4, the developed reserve dimensioning methodology will be presented in detail. First, a review of the SR dimensioning approaches as adopted by various electric utilities will be conducted. Subsequently, data acquisition, time series filtering methods and classification criteria, as well as the statistical assessment of processed datasets are discussed. A heuristic method of taking into account VRE capacity expansion and its effect on secondary reserve sizing is proposed. All input parameters of the recommended methodology are subsequently centralized in a matrix form which will serve as basis for the scenario development. Finally, results are displayed and discussed. Chapter 5 expands around the active power dispatch - reserve allocation joint optimization tool. It starts with a literature review of similar works on the topic, before discussing general characteristics and exogenous inputs to the proposed generic electric network model. A few words on the grid simulation software used (ENAplan) and its relevant characteristics precede the actual development of the optimization tool. Secondary reserve requirements are determined for a full year and the results are briefly discussed. Before presenting and analyzing the joint optimization process results, its mathematical formulation (objective function and constraints) is given. Finally, Chapter 6 presents the conclusions of the thesis and discusses possible further improvement, as well as the context in which this work can add value to existing and potential case studies.
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2 VARIABLE GENERATION AND OPERATING RESERVES
Along this section, theoretical background for the secondary reserve dimensioning methodology is presented. Issues related to the reliability of power systems are discussed at first, placing the purpose of this thesis in the system security area of power system reliability. Features of variable power generation are presented and inherent characteristics of VRE are introduced. Subsequently, power system ancillary services are succinctly listed and explained before the focus is set on the actual topic of the thesis: secondary reserves. Lastly, the way power systems are influenced by addition and expansion of VRE capacity is briefly discussed.
2.1 Reliability of Power Systems Keeping a high level of supply continuity and quality at least cost is a paramount issue for power systems and recent development brought subsequent changes in the energy business which enhance the importance of a safe and reliable electric grid (Eurelectric, 2004; Paulus, et al., 2011). Continuous ability of electricity provision, as an indicator of economic welfare, is dependent on every step of the supply chain: from access to primary fuels, to actual generation, transmission and distribution capabilities and finally, to consumers’ access to electricity, via trading and retail (Paulus, et al., 2011). Therefore, the security of electricity supply appears as a complex concept, encompassing several independent factors that have to function harmoniously for the existence of a final service of highest quality. A detailed study on this topic conducted by the association of European electricity industry (Eurelectric, 2004) concludes that provision of this commodity has to be done “with a specified level of continuity and quality in a sustainable manner”, emphasizing that a 100% reliability (electricity demand satisfaction in all circumstances) is not economically optimal. Nevertheless, an adequate level of security needs to be provided. Power system reliability expands in two separate time frames. System security expresses the ability of a power system to successfully deal with disturbances, such as losses of generating units or load, transmission line failures or any kind of demand or generation peaks, all of which occurring suddenly and having to be dealt with in a relatively short period of time. Thus, system security, as component of power system reliability, is defined as a short-term operational issue (Holttinen, et al., 2012a; Luickx, 2009). In liberalized electricity markets, it is the Transmission System Operator (TSO)5 that has the responsibility to ensure the system security, using operational standards and ancillary services to maintain system’s parameters within desired limits. Operating reserves, with the particular subclass of secondary reserves, are one type of ancillary services and fundamental in dealing with short-term disturbances. System adequacy refers to the ability of the power system to cope with demand requirements and transmission constraints within the electrical grid at all time instances. Its long-term characteristic
5 Within the European Union, the TSO is legally obliged to “ensure long-term ability of the system to meet reasonable demands for the transmission of electricity, operating, maintaining and developing […] secure, reliable and efficient transmission systems” (EC, 2009). -8- is derived from association with the steady state conditions of the power system. In this regard, nominal ratings of the power system equipment, scheduled outages of generating units or expected contingencies of transmission lines are considered (Holttinen, et al., 2012a; Luickx, 2009) when assessing system’s adequacy.
Power System Reliability
Adequacy Security
Resource System Market
Generation Network
Figure 2-1. Components of power system reliability. Adapted from: (Eurelectric, 2004) Distinct elements are considered when evaluating the adequacy of power systems (Figure 2-1). First, resource adequacy (i.e. access to primary fuels adequacy) implies producers’ right to unconstrained choice of primary energy sources. System adequacy denotes the ability of power systems to convert the resource into electricity and transmit it to consumers in a sustainable manner, which requires simultaneous adequacy of generation (presence of enough generating capacity to cope with load demand at all time) and network (availability of transmission, distribution and cross-border interconnections). Lastly, market adequacy, meaning the ability of the electricity market to foster the development and link between all parties involved (Eurelectric, 2004). For a safe and reliable power system operation, both previously mentioned characteristics have to be maintained. Long-term adequacy is complemented by operational security, with strong operational capabilities increasing the flexibility of an already adequate system (Luickx, 2009). Given the nature of secondary control defined within the operational time scale of power systems, this thesis deals mainly with the system security characteristic. Nevertheless, considering the fact that SR dimensioning will be conducted as a full year estimate and the system’s steady state parameters are of interest in developing the model, this shifts the perspective towards a system adequacy issue, thus emphasizing the interdependency between the two components of power system reliability.
2.2 Characteristics of Variable Generation Electricity production with wind and PV technology is based on using variable, intermittent resource and this particularity comes with an output profile characterized by generation spikes, persistent fluctuations, as well as limited controllability and predictability (De Vos, 2013). Their corresponding stochastic resource (i.e. solar irradiance or wind) is the very reason why the total output of such technology, relative to its hypothetical maximum possible, i.e. the maximum obtainable capacity factor, is lower than that of concurrent conventional technologies. Capacity factors are highly dependent on several aspects, e.g. technology used, location, but typically wind generation has higher (20-50%) average capacity factors than PV, which rounds about 10 to 20% (IEA, 2017), as exemplified on the German system for the year of 2005 in Figure 2-2.
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The variability characteristic of VRE can be observed from two perspectives: a long-term component and a short-term one. The former is prone to temporal (from annual and seasonal down to daily) patterns and estimated through weather forecasts covering different time horizons (from multiple days to hour-ahead), with implicit accuracy improving as the time horizon gets narrower (Wang, et al., 2011). The short-term component is represented by the stochastic output variations that occur a time scale small enough (intra-hour) that any further balance correction is within any market resolution, therefore dealt by operating reserves (De Vos, 2013). The latter component and the way it is counteracted through activation of operating reserves depends on adequate sizing of the reserves, i.e. the topic this thesis addresses. On the other hand, long-term variability and resource uncertainty are beyond the scope of this thesis.
40 35 30 25 20 15 10 Capacity Factor [%] Capacity Factor 5 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Wind PV Average
Figure 2-2. Monthly capacity factors for wind and PV (Germany, 2005). Adapted from: (IEA, 2008)
There are distinct patterns for wind and solar irradiation variability, which will be briefly enumerated. In case of wind, a distinction between seasonal (annual, seasonal) and short-term variations (diurnal, hourly) is accepted (Holttinen, et al., 2012a). In this regard, for the Northern Hemisphere, wind energy is usually more available in winter time, as also observed in the capacity factor evolution for Germany, in Figure 2-2. While seasonal variability may display a definite pattern, depending on the observed location, sub-seasonal timescales often show stochastic fluctuations in wind output (IEA, 2017). On the other hand, variation in solar energy output displays highly predictable variation on two distinct time scales. First, there is a seasonal variation, with more solar irradiation during summer than in winter. Also, the diurnal variation shows a strong pattern, with no solar output between sunset and sunrise. One important issue to mention in the context of VRE integration is related to the limitation of short-term output variability with spatial distribution of such production sites. Figure 2-3 shows what is called the smoothing effect, that is how geographical spreading of wind and PV plants results in less variable output, thus in fewer grid integration problems of VRE for the TSOs (Ackermann, et al., 2012; Bird, et al., 2013). There is potential to reduce hourly output variations in this way from ±30% to ±5% of the installed capacity, when increasing the perspective from a 200x200 km2 area to a nation-wide system (Holttinen, et al., 2012a).
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in - 0.8 0.8 0.6 0.6 0.4 0.4
0.2 0.2 Normalized Normalized feed 00:00 06:00 12:00 18:00 00:00 00:00 06:00 12:00 18:00 00:00 Wind 25 km2 Wind supply area PV 25 km2 PV supply area
Figure 2-3. Geo-spreading (smoothing) effect on wind (left) and PV (right) output. Adapted from: (IEA, 2017) Given single wind power plants, natural resource short-term variations have a considerable effect on the generation curve (Ackermann, et al., 2012). In case of residential or undistributed utility scaled PV generation, common weather events, such as clouds, fog or snow can vary generation in short (minute-to-minute) time instances. Seen from a system perspective (several wind farms or PV parks) and taking into account the smoothing effect of large enough systems, changes in the corresponding output occur relatively slow, with large variations happening in hourly time range and because of extreme conditions, i.e. persisting storms or cloud coverage spreading across larger areas (Ackermann, et al., 2012; Bird, et al., 2013). Still, improvements in output variability are practically limited, even with geo-spreading considered. When talking about wind generation, smoothing effect could have, in theory, unlimited potential, but it is always limited to the geographical boundaries of the system under observation. On the other side, smoothing effect due to geo-spreading of PV plants would be limited the moment the output curve reaches the bell- shaped curve corresponding to solar irradiation under no cloud conditions (IEA, 2017).
2.3 Ancillary Services and Operating Reserves Depending on the size and existing interconnections of the considered power system, its secure operation may be addressed on different resolutions of a hierarchical structure: control area, control block, coordination center or synchronous area (ENTSO-E, 2009), as observed in Figure 2-4. The responsible party for this issue, namely the TSO, delivers a series of ancillary services, i.e. services essential for the functioning of the system, in order to maintain system reliability. The offer of ancillary services would differ from one system to another but, as an example, it includes the following: active power (primary, secondary and tertiary) control, voltage support, compensation for active power losses, black start and islanding capability, system coordination and operational measurement (Beck, et al., 2010). This thesis focuses on secondary reserves, therefore a brief description of only the active power control mechanisms from the enumerated list will follow. Power systems usually feature a series of active power control methods and they can be differentiated based on several criteria: operating vs. contingency, spinning vs. stand-by, positive vs. negative, time of activation, mode of activation (manual vs. automatic) (Ela, et al., 2011; Hirth, et al., 2013). Due to the lack of electricity storability, permanent balance between generation and load has to be kept in order to maintain frequency around nominal levels (50 or 60 Hz, depending on the considered system) and in this purpose active power control actions are performed in successive steps (Figure 2-5), each with distinct characteristics. Therefore, considering a system disturbance severe enough to require activation of all three stages of active power control (primary, secondary and tertiary), the frequency containment and restoration develops as follows.
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Control Control Control Control Control Control Area Area Area Area Area Area
Control Control Control Control Block Block Block Block
Coordination Center Coordination Center
Synchronous Area
Figure 2-4. Hierarchical control structure of UCTE. Source: (ENTSO-E, 2009)6
In case of a frequency deviation, system inertia occurs as the power system’s first countermeasure. Power system inertia is a physical property expressed as the ability of synchronous machines to store kinetic energy of their rotating masses and provide it to or absorb it from the system following an imbalance (Söder, 2011). It basically determines the frequency response (rate of change of frequency – RoCoF) subsequent to such events; the lower the system inertia, the steeper the frequency gradient of the system. (ENTSO-e, 2016a). At this point, within seconds after the disturbance, primary control activates corresponding reserves. This control stage is responsible for primary reserve (Frequency Containment Reserve (FCR) in the ENTSO-E area) deployment. It is an automated control mechanism; for a generator providing primary control, its droop (power output – frequency function) is subjected to actions from certain frequency sensitive equipment (e.g. governor control) that adjusts generation with frequency deviations (Söder, 2011). Primary reserve is the first step in safeguarding the operational stability of the system, by stabilizing the system frequency at an offset (not rated) stationary value in the matter of seconds. Every system has a particular frequency deviation from nominal value set-point at which primary control is triggered, and for the European region this threshold is set at a ±20 mHz “dead band”. The ENTSO-E region has the ability to withstand a 3000 MW sudden loss of generating capacity and the primary control is designed to fully contain it. In this situation, the absolute frequency deviation must be kept within ±200 mHz. In addition, for wide-area interconnected synchronous areas, such as the European ENTSO-E region, primary control may be the subject of a “joint action”. In other words, in order to protect the system’s interconnected operation, primary reserves are distributed across the network’s control areas (ENTSO-E, 2009), and the contribution of each control area in primary reserve control is determined based on their generation share relative to the total amount of electricity generated system-wide (Ela, et al., 2011) (see Equation 2-1). Dimensioning of primary reserve is usually subject to deterministic methods based on the magnitude of a “reference incident” 7 (ENTSO-E, 2009).
푒푖 푃퐶푅푖 = ∗ 푃퐶푅푡 [푀푊] (2-1) 푒푡
6 A control area is a part of an interconnected system (usually defined by the geographical boundaries of countries) operated by a single TSO that may have its own subordinate control of secondary control. A control block comprises one or more control areas working together in the secondary control function. A coordination center is responsible for safeguarding the exchange programs between control blocks (ENTSO-E, 2009). 7 Size of the largest generation unit or generation capacity connected to a single bus bar (ENTSO-E, 2009). -12-
Frequency deviation
Automatically activated Limits deviation from set point at set point deviation
Primary control
Replaces primary control Automatic activation (based on ACE)
Secondary control Restore to set point set to Restore
Replaces secondary control Manual activation TSO Tertiary control
Figure 2-5. Generic frequency control scheme and corresponding actions. Adapted from: (Consentec, 2014) The next control action taken in case of frequency disturbance is the secondary reserve control, whose corresponding reserve capacity is the main focus of this thesis. Subsequent to primary control activation of primary reserves, system frequency is stabilized, but still offset from the nominal value. Furthermore, deviations occur continuously due to load and VRE fluctuations, thus if primary reserve capacity not replaced, it may be difficult for the system to cope with new imbalance situations. Under these conditions, the secondary control occurs, dealing with the following issues: it gradually replaces primary reserve capacity in the matter of minutes, for the latter to be available for future grid imbalances; it maintains the generation-consumption balance previously supported by inertia and primary control (Söder, 2011) and it commences the process of restoring the frequency level back to the nominal set-point (hence, the corresponding active power reserves are named automatic Frequency Restoration Reserve – aFRR - in the ENTSO-E zone), while not impairing the action of the primary control. Any imbalance between generation and demand translates into a frequency deviation in the whole synchronous area which determines the primary control response. During the joint provision of the first control stage, control areas or blocks may encounter subsequent imbalances and, in this case, power interchanges between each individual entity (area or block) can deviate from scheduled values. In order to determine the cause (control area imbalance or provision of primary control) of power interchanges deviations between control areas, the real-time Area Control Error (ACE) is of particular interest. As particular imbalance measure for each individual control area, it is characterized by magnitude and direction (positive or negative). It is defined as the imbalance between actual instantaneous and scheduled active power flows minus the primary control contribution of the respective control area (Equation 2-2). For example, if the set-point system frequency is equal to the measured one, the potential ACE would be associated to a control area imbalance. If, additionally, the power flows are balanced, the ACE would be zero. Each control area is equipped with a secondary controller, whose purpose is to deploy secondary reserves while minimizing the real-time ACE for every time instance. In opposition to the “joint action” of
-13- primary control, SR is usually deployed solely8 in the control areas where the imbalance occurs (or where the ACE is not zero). This is done by pre-setting the system-wide secondary control mechanisms (secondary controllers) in such way that, ideally, only the controller of the affected zone will respond and provide secondary reserves in case of imbalance. The sum of all ACEs within a synchronized system yields zero (ENTSO-E, 2009).
퐴퐶퐸푡 = 푃푚푒푎푠,푡 − 푃푝푟표푔,푡 + 퐾(푓푚푒푎푠 − 푓0) [푀푊] (2-2)
In most of Europe, secondary control is triggered automatically by AGC (Automatic Generation Control) systems, which adjust active power output of generating units in the time frame of tens of seconds up to 15 minutes after the disturbance (ENTSO-E, 2009; E-Bridge & IAEW, 2015). Depending on the system characteristics, secondary control can be provided by generators, load and pumped storage hydro units (ENTSO-e, 2017a). The amount of secondary control reserves is usually determined by the TSO based on system particularities (e.g. load and VRE variation magnitudes, forecast errors, predefined security levels). Finally, tertiary reserves are activated in case of prolonged SR activation. Activation is usually manual (also called manual Frequency Restoration Reserve, or mFRR, in the ENTSO-E region) and done either to replace SR for later availability or to assist the latter in case of severe disturbances (such as power plant outages or long-lasting load changes) and release primary reserve for subsequent usage (ENTSO-E, 2009). In certain cases, tertiary reserves may be activated preventively in order to cover relatively large foreseen deviations (Consentec, 2014). Table 2-1. Active power control characteristics in Europe. Adapted from: (Hirth, 2014) Primary Reserves Secondary Reserves Tertiary Reserves Full Activation Time 30 sec. 5 min. 15 min. Activation Mode Direct (no schedule), Direct (no schedule), Direct or scheduled, based on frequency centralized (TSO) centralized (TSO) measurements dispatch dispatch Activation Area Synchronized system Balancing area Balancing area Decision Variable Frequency meas. ACE measurements SR availability Suppliers Synchronized Synchronized Synchronized generators generators, stand-by generators, fast- hydro units starting stand-by units Reserved Capacity 3000 in ENTSO-E Decided by TSO Decided by TSO The plot in Figure 2-6 (Beck, et al., 2010) gives an overview image of how the active power control procedures develop in time in case of a severe disturbance related to a generating unit collapse. This particular example describes the active power control strategy following an outage of a French 1200 MW power plant. First reaction (apart from system inertia, not exemplified here) comes from primary control, which is activated fast, automatically and across the entire synchronous zone (here, the ENTSO-E continental area) and stops the frequency decay. Seconds after primary control
8 Activation of SR may occur in neighboring control areas. TSO control reserve cooperation is detailed in (Consentec, 2014). -14- deployment, secondary reserves are provided but, this time, the response is geographically limited to the control area affected by the imbalance. As the frequency is slowly restored to its set point, manually activated tertiary reserves (provided here by four power plants in France) take over to release secondary reserve. After 15 minutes, both primary and secondary reserves earlier deployed are fully available again for future imbalances.
Fault occurrence Primary control Secondary control Tertiary control
50.05 Hz 50 Hz
Frequency 49.95 Hz
1200 5 min 15 min MW
Figure 2-6. Example of active power control deployment in case of a wide-area power system. Adapted from: (Beck, et al., 2010)
2.4 Impact of Variable Generation on Operational Reliability of Power Systems Uncertainty and variability of VRE generation induce inherent challenges in the secure operation of a power system and the extent to which those challenges appear are strongly linked to the size and operational flexibility of the system, as well as to the penetration level of VRE (Holttinen, et al., 2012a). The latter system characteristic is a metric for the share of VRE within a given power system and there are several criteria on which it is calculated (Holttinen, 2012), but for the rest of this thesis the penetration level is to be mentioned as VRE cumulative capacity relative to the yearly peak load (Equation 2-3), i.e. maximum load during one natural year: 푃 푃퐸푁 = 퐼퐶,푉퐺 ∗ 100 [%] (2-3) 푃푚푎푥,푙표푎푑 Figure 2-7 depicts a set of system related issues on which wind integration is likely to have an impact and it would be safe to assume that same impact would be valid for PV integration. Accordingly, secondary reserves, dimensioned based on VRE short-term (inter-hour) variability, are studied from a system-wide perspective. Other sub-systems of the electric grid impacted by VRE (Holttinen, et al., 2012a; Bird, et al., 2013) are outside the scope of this thesis and would therefore not be discussed in detail.
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When analyzing the impact of VRE on operating reserves, such a study should be conducted from the control area perspective. Considering the deterministic approaches widely used for their sizing (see Section 2.3), primary reserves are beyond the scope of this thesis9. Moreover, reserve capacity associated with tertiary control (with an deployment time at the order of tens on minutes) can still be adjusted on the balancing market10 (15-minute- up to 1-hour-ahead bids) to the extent of forecasting update availability (Holttinen, et al., 2012a). Still, even with updated wind and PV forecasts, some of the short-term (intra-hour) fluctuations cannot be predicted. Therefore, it is up for secondary reserve to deal with remaining imbalances or, in other words, the need for flexibility in short time scales (seconds to minute horizons) becomes of interest.
Secondary Primary Reduced control System-wide control emissions Generation Transmission adequacy Relevant area size Grid efficiency stability Regional Congestion Grid management Voltage adequacy management Distribution Power efficiency Local quality
seconds minutes hours day(s) years Relevant time scale
Figure 2-7. Impacts of wind power on large interconnected power systems . Source: (Holttinen, et al., 2009)
Finally, improvements in forecasting models can contribute to a better understanding of weather patterns. As consequence, more accurate generation estimates (and smaller active power reserve requirements) would be available, thus facilitating the secure system operation. Nonetheless, increasing penetration levels of VRE in power systems will certainly have an incremental impact on reserve dimensioning11 (Holttinen, et al., 2012a).
9 In addition, up to a certain VRE penetration level threshold (characteristic to every power system), VRE capacity expansion effect on primary reserve requirements is limited by the geo-spreading effect (Holttinen, et al., 2012a). 10 More about bidding adjustment possibilities on the electricity markets in Chapter 3 – Electricity Markets 11 Estimation of additional reserve requirements due to increasing shares of VRE is discussed in Section 4.7 - Additional Secondary Reserve Requirements with Variable Generation Capacity Expansion -16-
3 PROVISION OF OPERATING RESERVES
This chapter shares the purpose with the previous one, to outline the background for the secondary reserve allocation tool developed in Chapter 5. Basic features of electricity markets are introduced, with emphasis on the European design, before focusing on reserve procurement schemes. Thereafter, reserve activation and the inherent imbalance settlement are briefly discussed. Lastly, producers’ prequalification requirements for secondary reserve capacity provision and details of traded products are enumerated.
3.1 Electricity Market Design Electricity has, from an economic perspective, the hallmark characteristics of commodities. In that regard, it meets the same purpose regardless of its source, it is bound by predetermined minimum standards and it is usually traded on specific markets. Nevertheless, due to constraints imposed by physical laws of electromagnetism, electricity has certain particularities (i.e. lack of feasible large- scale storage solutions, high transmission costs, as well as losses associated with both mentioned issues; perpetual requirement for frequency balance) that differentiate it from other commodities, especially in the manner corresponding markets are designed. Not so long ago, the electricity sector in European countries was organized around vertically integrated utility companies. They basically had monopoly over the entire supply chain, including generation, transmission, distribution and supply (Figure 3-1), and this aspect came with the necessity of an independent market regulator12. In the late 1990s, EU launched a set of legislative packages whose purpose was to unbundle the electricity business (Figure 3-2), fostering market competition, free trade and a transition towards a unified European electric power market. The result of this still ongoing process was the creation of a continuously expanding liberalized market environment for electricity producers, traders and consumers. In the same time, economic reasons lead to transmission and distribution (in most states) of electricity remaining organized as natural monopolies (KU Leuven Energy Institute, 2015).
Vertically Integrated Company
Generation Transmission Distribution Supply Customer(s)
System Operator Regulator Trader(s) Private generator(s)
Figure 3-1. General electricity market models – vertically integrated utility
12 Privately owned generation was (to some extent) possible before the unbundling. In this case, the market structure involved a single buyer, where the utility buys from the generators. In such case, the conflict builds when the utility has no interest in contracting cheaper private generation that reduces the profit for the utility. To avoid such conflicts, systems are currently unbundled. -17-
Generator Distributor Supplier Customer
Generator Distributor Supplier Customer TSO Generator Distributor Supplier Customer
Generator Distributor Supplier Customer
Market Operator Regulator Trader(s)
Figure 3-2. General electricity market models – vertical unbundling The paramount requirement of electricity utilization is the continuous equilibrium between generation and consumption. Failure to maintain it can lead to issues affecting system’s operation at different levels, from to load shedding and, ultimately, system partial or total blackout. In these circumstances, design of electricity markets has evolved accordingly. Different electricity markets, classified by the lead-time between contract and delivery (Hirth, 2014), exist and they are arranged in a sequential order (Figure 3-3).
Market timeframe (multiple actors) Balancing (TSO only)
> Day - 1 Day - 1 Day Real-time
Forward/ Day-ahead futures Intraday
Portfolio adjustment
Reserve capacity markets TSO balancing
Figure 3-3. Example of the electricity market timeframe. Adapted from: (TenneT, 2017)
Before expanding the main features of each market, the corresponding participants and their inherent roles should be addressed. Power systems certainly possess distinct particularities between each other and they may account for several factors, i.e. resource availability, technical constraints related to generation and/or transmission capacities or regulation and legislation.
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Nevertheless, organization of most electricity markets (or power exchanges13) as the flow of currency develops around the presence of several main participants, as follows: Electricity generators; Electricity suppliers – market participants buying electricity from producers and selling it to consumers (the electricity producer and supplier can be the same entity); Transmission system operator (TSO) – legal body ensuring long-distance electricity transport and system operational stability; Distribution system operator (DSO) – legal body delivering electricity to end consumers; Consumers – final users of electricity; Traders – participants acting as intermediaries between suppliers and consumers; Market regulators – legal entity overseeing the functioning of the electricity market. In addition to these main actors there are several others of great importance in certain time horizons throughout the trading process. They will be mentioned and their role will be explained as the chapter develops. The actual trading process is based on exchange participants submitting bids for selling and buying electricity. Taking the wholesale day-ahead market as example, supply offers are auctioned on the basis of the marginal cost of generation and subsequently aggregated based on some predefined method (e.g. the merit order). Electricity demand is bid on two fronts: based on the TSO’s consumption forecast and on a small number of large consumers’ own demand. When all participants submitted their bids, the equilibrium between aggregated curves of supply and demand sets the system price, i.e. market clearing price (Figure 3-4). It should be noted that, given the constraints the electricity market is subjected to, the market clearing price formation is found to develop in three dimensions: time (since limitations in storage makes it impossible to overcome supply or demand extremes), space (electricity transport is subjected to transmission constraints, leading to different prices in different market areas, while one market area shares the same price) and lead-time between contract and actual delivery (pricing of electricity contracted in short notice – minutes – can differ from the one settled upon days or months in advance) (Hirth, 2014). Therefore, in the current European context of market coupling14, it is usual for system prices to be different for each bidding area15, market time unit and market horizon on which electricity is traded (forward/futures, day-ahead, intraday or balancing market).
13 Market platform where electricity is traded; access is granted based on predefined admission requirements for all active market players: producers, suppliers, traders, consumers. Trading on the power exchange is governed by market and operation rules set by the market operator. These regulations usually take into account a broad range of topics, from exchange organization, to market (e.g. price limits) and behavioral aspects of the participants. 14 Term that refers to the integration of multiple electricity markets from different control areas through an implicit cross-border allocation mechanism. 15 The largest geographical area within which market participants are able to trade without cross-border capacity allocation. Within each bidding area, system prices are uniform for a given temporal unit. National power systems may include one (e.g. Romania, France, Spain), several bidding areas (e.g. Sweden, Norway, Italy), or multiple national systems may form one bidding area (e.g. Germany and Austria). -19-
Price ($/MWh) Supply
Market Clearing Price
Demand Volume Traded Volume (MWh)
Figure 3-4. Determination of the day-ahead market clearing price. Adapted from: (NordPool, 2017a) The first electricity market from a temporal perspective is the forward/futures market. This market has practically no temporal limit before the day-ahead market; forwards and futures can be settled upon years before the delivery. Both types are financial derivatives, contracts signed between parties to sell/buy a certain amount of a given commodity at a specified price (strike price) on a given date. While futures are standardized, high-liquidity, low-risk contracts traded through an exchange platform, forwards are mainly the opposite. Usually bi-lateral, not standardized contracts, no exchange is implicated (traded over-the-counter), therefore implying lower liquidity and higher risk. The main reason behind the existence of this market is risk hedging, i.e. electricity producers/consumers protecting themselves against price volatility (Koppenhaver, 2010; KU Leuven Energy Institute, 2015). Further down the electricity market timeframe (Figure 3-3) there is the day-ahead market, which occurs the day before electricity delivery. Electricity bids can be traded bi-laterally (over-the- counter) or on the power exchange. The market closes at a specified hour (e.g. 12 PM for NordPool) the day before the actual delivery. As the main trading platform (in terms of volume traded) for the European power exchanges (EpexSpot, 2017; NordPool, 2017b), its target at market closure is to have balanced each corresponding bidding zone at its temporal resolution (e.g. hourly, in case of NordPool). In Europe, the day-ahead markets of multiple market areas (i.e. Great Britain, France, Benelux, Iberia, Germany and Austria, the Nordic countries, the Baltics, Poland in one group and Romania, Slovakia, Hungary and Czech Republic in another) are currently coupled (KU Leuven Energy Institute, 2015). This means that market players still bid for energy on the exchange platform, but the exchange uses all available cross-border transmission capacity in the process. By doing so, the price difference between multiple market areas is minimized. The intraday market is the following one in the electricity markets sequence, happening in the very day of power delivery. A recent innovation in the market structure, driven by the recent development of VRE technologies and their implicit generation variability and uncertainty, the intraday exchange is not as common outside the ENTSO-E network (e.g. in case of the North or Central American electricity markets the day-ahead market is the last stage before real-time balancing). Given its continuous clearing characteristic with closure merely hours before actual delivery (NordPool, 2017c), this stage allows electricity producers to adjust their generation patterns given improved VRE forecasts (Figure 3-5, left), updated expected or unplanned power
-20- plant outages. As in the day-ahead market, intraday prices are set on a supply and demand basis. The right side of Figure 3-5 shows another feature of the intraday market. At this point, products of lower resolution (e.g. 15-minute) are traded, enabling a better, but still not complete approximation of actual load ramps and VRE fluctuations.
Day-ahead forecast Intraday forecast Actual realization
in
- Feed
Forecast / Measurement / Forecast Time Time Day-ahead forecast error Hourly products Intraday forecast error 15-minute products
Figure 3-5. Development of the intraday market. Source: (TenneT, 2017) Even with the higher product resolution following the intraday trading, differences between planned and actual electricity balance are expected to occur. In the exemplified market structure (Figure 3-3), the real-time generation-demand balance is reached in two steps: a reserve capacity allocation (that can span on months before the actual delivery – see Section 3.2) and a reserve activation step (occurring in real-time, with the TSO dispatching active power reserve capacity in order to contain imbalances). The latter step is done either through (manual active power control is activated via an auction mechanism), or without additional market structures (automatic control schemes are activated without additional auction mechanism). In the case of manually-activated tertiary reserves, the real-time exchange that may serve as trading platform is the balancing market, a structure supervised by the TSO, a market opens after the day-ahead market closure and whose clearing takes place in real-time. Since it is a fairly recent addition to the electricity market structure (Hirth, 2014), this real-time stage of the electricity market, although present in certain control area or blocks (e.g. Germany, the Netherlands, Romania), is not so common in other European balancing zones. In such case, lack of the balancing market for bidding of active power reserve activation is replaced, for example, by reserve activation based on pre-contracted offers, as resulted from the capacity market clearing. At all levels, electricity markets are currently undergoing a major transformation process incentivized by the continuous development that characterizes the power systems in the last decades. The current design has its origin back when vertically integrated companies were dominating the market by means of large-scale, centralized, fossil-fueled power plants that had to supply with power a limited area. Nevertheless, recent developments of the electricity sector have reshaped the structure of corresponding markets (EC, 2015a) and this process is expected to continue within the decarbonization process. The electricity markets are “more European” (BMWi, 2016) and in support to this assertion, several recently occurring aspects are given: the already existent coupling of the wholesale day-ahead market, TSO cooperation or implementation of the common European emission trading scheme. Nevertheless, the process is still far away from the desired outcome. The ultimate goal of the EU regulatory bodies is the development of a common European electricity market at all levels (i.e. on long- and short-term), a feature that will allow
-21- producers from one geographical region (e.g. Sweden) to bid in the market while the receiving end is located in a geographically opposite area (e.g. Spain). Thus, the market competition will be enhanced from a regional to a European level. The currently implemented features are helpful, but not sufficient on their own. In this regard, the European Commission’s initiative for electricity markets development treats several other market aspects that can improve its actual state. Some of these propositions include: developing cross-border markets also for short-term markets (i.e. intraday, balancing), ensuring infrastructure development that would support the fully-integrated European market, enhancing the market flexibility for a better VRE integration or fostering regional (inter-state) and inter-TSO utmost coordination (EC, 2015a).
3.2 Reserve Procurement vs. Balancing Markets Following the intraday market gate closure (i.e. when participants complete their final production schedule), system imbalance due to last-minute power plant outages or short-term fluctuations of load and VRE may still occur. At that point in time, the TSO takes full control of the system and proceeds in maintaining its frequency balance by use of active power control. As mentioned in the previous section, these strategies are divided, according to their activation mode, in two distinct classes: automatic and manual active power control schemes. At least for the European network, this classification basis is important in understanding the way active power reserve procurement is structured across all balancing stages. The automatic active power control strategies include primary and secondary control schemes. Controllers responsible for primary or secondary reserve deployment are set to respond automatically to certain network signals therefore, in terms of energy delivery, there is no additional market mechanism involved. Yet, decision upon which generating assets (generators or loads) are to deliver these reserves is taken beforehand, based on pre-defined criteria. Commonly referred to as reserve capacity markets, their principle relies on participants auctioning for availability of power reserves, instead of bidding energy volumes. A contract for provision of reserve capacity encompasses a series of features which are usually particular to each individual control area or block and depend on the national legislation. In Europe, for example, the procurement scheme of such settlement may include mandatory (with or without remuneration) or voluntary (organized as bilateral or wholesale market) provision. The product resolution in size can vary from under one to 10 MW, while time-wise the contract can cover hours up to a full year of providing the same auctioned product. The lead-time of the contract (i.e. the time ahead from the real time when an agreement takes place) varies from a day to month ahead, depending on the control block. Hence, the capacity provision and the balancing market are distinct and often not simultaneous stages. Product symmetry (equal capacities for up- and downward regulation), price formation scheme (e.g. marginal pricing, pay-as-bid or regulated price), transfer of obligation possibility, as well as the cost recovery scheme (i.e. from whom are the balancing costs recovered) are all additional particularities of the capacity markets in Europe16 (ENTSO-e, 2017a). Considering a power system where active power control is remunerated, the payment for such services can be done differently. The price formation scheme, together with the system demand
16 A comprehensive survey on features of active power control strategies within many of the European countries is provided in (ENTSO-e, 2017a). -22- for active power reserves, yields the capacity price (i.e. the price for keeping the service available). In addition to that, it is possible that the activation of reserves might involve some means of remuneration via an energy price, or the payment for the actual utilization of the service. Thus, according to the power system internal structure, remuneration for automatic active power reserve provision may include one (the capacity price) or two (both prices) components. The last stage of active power control (tertiary) is a manual strategy. Capacity provision for tertiary control keeps the same features as the automatic active power control strategies (ENTSO-e, 2017a). In this regard, grid asset (generators or loads) owners and the system operator settle on active power availability contracts days to months ahead of the actual requirement date and selection of capacity bids is done through certain price formation schemes (e.g. marginal pricing) based on the auctioned capacity price. The subsequent dispatch of tertiary reserve (done at the TSO’s request) can take place in different ways, with no common structure currently existent at European level. For example, given its large enough activation time (see Section 2.3), the TSO is able to run the tertiary reserve deployment process via an additional exchange structure, namely the balancing market17. This electricity market has several distinct features: first, the TSO is the single purchaser, which makes it a single-buyer market; second, the price-inelastic demand is determined by the small, but highly volatile system imbalance volumes that have to be leveled out at any point in time (Bourdette, 2016). In addition to the capacity price, providers of tertiary reserve capacity bid an additional activation price. At this point, a certain activation rule (e.g. merit order, pro-rata) determines the generating assets that will be initiated for this ancillary service. In this situation, service provider remuneration is comprised of two prices (capacity and activation price). As shown in (Hirth, et al., 2015), this structure is currently in operation in the German control areas. Another possibility of tertiary reserve activation removes the necessity of the additional market structure, in which case the merit order of the reserve capacities is also used for the activation. Thus, even though the payment for tertiary reserve provision can include both a capacity and an energy price, decision on which network assets allocate this service is based solely on the former. A comprehensive survey of tertiary control active power reserve capacity and energy allocation is available in (ENTSO-e, 2017a), emphasizing the structural differences between multiple control areas within the same interconnected network.
3.3 The Imbalance Settlement When the generation – demand disequilibrium is propagated in real time, it leads to a power imbalance. According to what type of system event the TSO is responding against, the system imbalance may be positive (when consumption exceeds generation) or negative (the other way around). While the price of the former is always positive (i.e. system operators are paid for balancing actions), in case of the latter prices can be both positive or negative (i.e. suppliers getting paid for ramping down units). Either way, the imbalance can involve the activation of any number of active power control stages (i.e. from a minor deviation from scheduled volumes requiring solely primary control to larger imbalances involving the full sequence of active power control stages). The TSO’s role after the gate closure covers a series of responsibilities: determining the amount of required reserve capacity power; facilitating the reserve power acquisition; activating it, if needed;
17 Applicable or not, depending on the structure of the power sector and the existence of a balancing market (see Section 3.2) -23- financially clearing the system, by allocating various costs. Nevertheless, in some cases it is not the TSO, but the Balance Responsible Parties (BRPs) that have the responsibility of composing and delivering binding generation and load schedules to the TSO. Also, it may be the case that the BRPs are financially responsible for deviations from the submitted schedules (ENTSO-e, 2017a). If one producer associated with a BRP fails to deliver the designated power, costs corresponding to the resulted positive imbalance will be paid by the BRP to the TSO. A BRP may represent electricity producers, suppliers or industrial consumers and its portfolio may include own generation, consumption or traded deals with other BRPs (Hirth, 2014; KU Leuven Energy Institute, 2015). Following the power delivery, in the context of existent system imbalance, the BRPs are engaged in the Imbalance Settlement, a financial mechanism used to charge or pay the BRPs for the imbalance they are responsible for in each Imbalance Settlement Period (i.e. which is of 15 minutes in most of the ENTSO-E network). That means, at every 15 minutes, the TSO determines the net imbalance in its control area which is assigned an Imbalance Price and forwarded to the BRP. This process aims at recovering the costs of power reserve provision and activation (initially supported by the TSO), while also incentivizing the market actors to reduce the imbalance volumes and transferring the financial risk to the BRPs. A typical imbalance settlement between the TSO and a BRP includes the following elements: the energy volume and direction, the settlement period and the imbalance price (EUETS, 2017). Calculation of the imbalance volume for each BRP takes into account three power volumes: the allocated volume18 (usually based on metered values), the final or notified position (reflecting the scheduled net volume of all bi-lateral or market transactions) and the imbalance adjustment19 (volume reflecting the activation of balancing energy bids from the balancing providers associated with the BRP of interest) (EUETS, 2017). Imbalance pricing is conducted separately in each imbalance direction and it is related to what the TSO does in order to restore system balance. In other words, the price of imbalance is determined based on the prices of active power control for the same imbalance settlement period. Currently, there are several pricing methodologies in use within the European power markets. ENTSO-E recommends, in its “Network Code on Electricity Balancing” that the imbalance price for shortage (positive imbalance) shall not be less than the weighted average price for activated positive secondary and tertiary reserves, while the price for surplus (negative imbalance) should not be greater than the weighted average of activated negative reserve capacity (ENTSO-e, 2014). Belgium’s TSO, Elia, imposes an imbalance price based on the highest price paid for upward activations given a certain settlement period (marginal incremental price), for shortage situations, while surplus is priced based on the lowest price received for downward activations for the given quarter-hour of the settlement process (KU Leuven Energy Institute, 2015). One important aspect concerning the Imbalance Settlement is that the TSO has to maintain financial neutrality, meaning that it is not allowed for the operator to gain profit from any balancing energy settlement process (EUETS, 2017).
18 An energy volume physically injected or withdrawn from the system and attributed to a Balance Responsible Party, for the calculation of the Imbalance of that Balance Responsible Party (ENTSO-e, 2017b). 19 An energy volume representing the Balancing Energy from a Balancing Service Provider and applied by the Connecting TSO for an Imbalance Settlement Period to the concerned Balance Responsible Parties, for the calculation of the Imbalance of these Balance Responsible Parties (ENTSO-e, 2017b). -24-
3.4 Prequalification for Active Power Reserve Capacity Provision In order to participate on reserve capacity markets, producers have to provide evidence that their products are able to meet specific technical requirements set by the TSO. This process, called “pre- qualification”, is system-specific (i.e. each system operator is entitled to set its own technical thresholds for active power control products) and develops separately for each control stage (primary, secondary and tertiary) over a period of several months (VDN, 2007). In the following section, such requirements will be detailed for secondary reserve provision, as they are currently demanded in the German power system. In this regard, Annex D2 in the German Transmission Code (VDN, 2007) elaborates the minimum technical and organizational requirements that may enable electricity producers to provide secondary active power reserve capacity. The pre-qualification process is possible at any time, carried out by the control area TSO and one bidder may qualify for provision of several active power control stages. Upon successful completion of the pre-qualification process, the bidder will own a framework agreement with the TSO regarding provision of a specific control power. Bidding control power is dependent on both the pre-qualification process successful completion and the conclusion of the framework agreement. One standard requirement for pre-qualification is a test deployment of active power reserve capacity on the generator of interest. Such a protocol, with its corresponding technical requirements (i.e. ramp rates, pause and reserve provision times), is exemplified in Figure 3-6 for both up- and downward secondary control provision. (Swissgrid, 2012) provides a similar deployment test that includes both control directions in the same run.
Figure 3-6. Example of prequalification protocol for up- and downward secondary reserves. Adaptation from: (regelleistung.net, 2017b)
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The German Transmission Code divides the pre-qualification requirements for secondary reserve capacity provision (VDN, 2007) into four distinct sections, as follows: Technical capabilities of the technical units, including here requirements related to: connection point of the generator in the grid; its technology used and fuel, ramp rates, minimum, rated and maximum power, dead times, availability factors, etc.; Technical requirements for the secondary control pool 20; Communication (IT) requirements containing: issues related to implementation and use of IT connections for the safe and reliable provision of active power control, availability of data upon request for service provision assessment; Administrative issues, including here: contact between the parties, notification issues, third party involvement aspects and other obligations of the parties.
3.5 Towards Homogeneous Active Power Control Products The electricity markets in the ENTSO-E region are subjected to continuous changes since the market liberalization process commenced and the coordinator of this common action between the member states is the European Union (EU), through its legislative packages for electricity markets21. The ENTSO-E Network Code on Electricity Balancing (ENTSO-e, 2014) comes in support of the EU legislation by providing technical requirements the European TSOs have to comply with in the aforementioned legislative context. One such requirement (currently at the proposition level) is related to the standardization of products traded on capacity markets. Specific rules are currently elaborated and to be implemented system wide in the future in order to ease the assessment of balancing energy providers and compare their products on specific criteria for a more clear and competitive market participation. This approach is expected to have a positive impact in the cost optimization of balancing energy provision (ENTSO-e, 2016b). Currently, TSOs use and bid high numbers of products which are not directly comparable, even though some of them have similar features. In the actual regional or national capacity and/or balancing market division, liquidity may not be a big issue. Yet, with the EU main goal of fostering the creation of common Europe-wide electricity markets, liquidity in such a large system would be a liability in the absence of standard products. The need for harmonization led to the recent development of standard products for balancing energy and balancing capacity. A bid for such a product is required to contain at least the following technical characteristics (temporal features exemplified in Figure 3-7) (EC, 2017): Preparation period (PP) – duration between the TSO request for balancing energy and the actual start of energy delivery; Ramping period (RP); Full activation time (FAT) – time horizon from the TSO request until the full activation of the product; Minimum and maximum quantity (mQ, MQ);
20 Providers of SR are obliged to pool their capacities (i.e. individual technical units are operated by different operators at different locations within a control area) in order to increase the operational flexibility (VDN, 2007). 21 Currently in use is the “European Union’s Third Energy Package”, adopted in 2009 and targeting, among others, electricity market development towards a more international, open platform for electricity trading. -26-
Deactivation period (DP) – time period for down ramping to a pre-defined set point; Minimum and maximum duration of delivery period (mDP, MDP) – the minimum/maximum time period during which the product provider can be requested to physically deliver active power reserve capacity; Validity period – defined by a beginning and an end time, reflecting the time horizon when the auctioned product can be activated; Mode of activation – the way the product is activated, automatically or manually by the TSO. In addition to these characteristics, other variable features shall be made available by the service provider prior to or at the bid submission time (EC, 2017): Price of the bid; Divisibility – possibility for the TSO to use only a share of the auctioned product, both in terms of time or magnitude; Connection point; Minimum duration between the end of deactivation period and the following activation.
Figure 3-7. Features of active power control standard products. Adapted from: (ENTSO-e, 2016b)
Implementation of standard products for balancing energy and balancing capacity improves market liquidity, enhancing the market’s transparency and competitiveness. Nevertheless, different TSOs have distinct balancing needs, according to their network peculiarities, and this comes with inherent requirements for non-standardized bids, an issue addressed by specific products. Since standard products have been developed as a cost optimization solution for the balancing requirements of systems, the European Commission regulation on electricity balancing (EC, 2017) proposes that bids for specific products shall include a series of mandatory features, as follows: Clear definition of the specific product and the corresponding time horizon;
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A demonstration that standard products are not adequate to guarantee operational security of the grid or a demonstration that certain balancing sources cannot be standardized; A set of measures intended to minimize the use of specific products (mainly for economic reasons); Proof that specific products under discussion do not generate system inefficiencies within the balancing market; If applicable, the rules and corresponding information required to convert the bids from specific products into balancing energy bids from standard products. As part of the “Europe 2020” strategy, the design features mentioned above are expected to be implemented in the course of this decade by all regional TSOs. Of course, these characteristics are merely a fraction of the European Commission guideline on electricity balancing (EC, 2017), a regulation that is intended to serve as backbone of future European market design developments.
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4 DIMENSIONING SECONDARY RESERVE CAPACITY
This chapter will provide a detailed explanation of the proposed secondary reserve sizing methodology. It starts with a short description of the procedures currently in the operational handbooks of several TSOs. Input data availability and pre-processing is briefly discussed before the actual SR sizing procedure is introduced. Time series filtering, assessment of VRE fluctuation magnitude, time series classification and statistical analysis of processed data are explained. Subsequently, a heuristic method of estimating additional SR requirements due to VRE expansions is proposed. The “Scenario Development” part of this chapter introduces the six scenarios that are detailed later in the results section.
4.1 System Operators’ Experience Sizing of operating reserves has been a hot topic in the specialized literature and, with current and foreseen VRE evolution in the market (IRENA, 2016), its importance is expected to increase (Holttinen, et al., 2012b). Throughout literature sources, two distinct classes of methods are proposed in this regard: deterministic and probabilistic methods (Ehsani, et al., 2009; Holttinen, et al., 2012b; Ortega-Vazquez, et al., 2009; Vasilj, et al., 2016). Deterministic methods are the more traditional solutions for reserve dimensioning. Implemented in order for the system to cope with severe disturbances (e.g. (n-k) criteria), a deterministic method fails to take into account events of minor severity but with higher occurrence rates, e.g. VRE short- term variations (Holttinen, et al., 2012b). By integrating wind and PV into the system, the grid has to be able to compensate also for the short-term, unpredicted variation in the corresponding generation (and demand) and, at some instances (and given high enough VRE penetration levels) the cumulative magnitude of these variations may exceed the size of the system’s characteristic disturbance size, thus the (n-k) criteria may underestimate actual SR requirements. Probabilistic methods rely on statistical properties of the system events. Also called statistical methods, their complexity can vary on several assumptions: correlation between events, type of continuous distribution function that encompasses certain events (e.g. load and PV short-term variability may be assumed to follow a normal, or Gaussian, distribution, while a Weibull or Gamma distribution may be more accurate in case of wind data (Holttinen, et al., 2012b)), aggregation of different fluctuation drivers (e.g. geometric aggregation, convolution) or the potential dynamic character of reserve allocation (i.e. different levels of reserve allocation at different times, according to VRE variability). The ENTSO-E Operation Handbook (ENTSO-E, 2009) expresses different operational needs of different control areas, due to different generation and demand characteristics. In order to cope with these particularities, four distinct deterministic or/and probabilistic methods are proposed. First, the “Empiric Noise Management Sizing Approach” relies on a square root equation (Equation 4-1) with empirically determined coefficients and maximum hourly load as variable in order to determine the SR requirements. Next, the “Probabilistic Risk Management Sizing Approach” is based on the requirement to maintain the ACE to zero during a certain number of hours (set as a confidence level) per year. Third, the deterministic “Largest Generation Unit or Power Infeed” method dimensions the control reserve capacity requirement based on the size of the largest system contingency.
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Lastly, the “Extra-ordinary Sizing of Reserves” method develops around certain unexpected criteria that shall influence the size of reserves. Getting into detailed specifications of TSO methodologies within the former UCTE, now ENTSO-E, a report conducted on eight different systems shows that, at least at the moment of publication, sizing of control power in the corresponding control areas was based on deterministic methods (Rebours, et al., 2005).
2 (4-1) 푆퐶(푡) = √푎퐿푚푎푥(푡) + 푏 − 푏 Same trend applies in the Central American interconnected system22, where the still used secondary reserves dimensioning method develops around a percentage of hourly demand that has to be kept as reserve in each of its control areas (EOR, 2002). On the other hand, separation from deterministic criteria can be observed in case of one TSO from the USA. Sizing of the same product in the ERCOT region23 is done using a probabilistic method, with secondary reserve capacity having to cover the maximum between the 95th percentile of historical deployed regulation and the same percentile of net load variability (ERCOT, 2017). The German TSOs use an improved probabilistic approach in this regard. Probability density functions of all fluctuation drivers are estimated and an overall statistical distribution is computed by use of recursive convolution, assuming that the individual random variables are not correlated. Subsequently, the magnitude of active power for secondary control strategies is determined based on a pre-defined security of supply level (Hirth, et al., 2013). Probabilistic approaches, under various forms, started to gain ground in the operating reserve sizing issue and that is observable in the vast number of methods on this topic presented and reviewed in literature (Ela, et al., 2011; Holttinen, et al., 2012b; Dowell, et al., 2016; Zhou, et al., 2016).
4.2 Data Acquisition and pre-Processing The secondary reserve sizing methodology that will be detailed in the following sections expands around several procedures that define the generic characteristic of the model. Providing it with basic system parameters (yearly peak demand, PV and wind installed capacities) and actual demand and VRE data is sufficient for an estimation of SR requirement. The probabilistic and dynamic character of the proposed method implies two important features of the input data required in order to enhance the statistical relevance of the model: high sample resolution and availability of a relatively large sample size (Lenth, 2001). Given these constraints, the one-minute resolution Czech data covering five consecutive years (2012-2016) of load and VRE generation was chosen for exemplification purposes. Data was available free of charge on the internet platform of the Czech Transmission System Operator (ČEPS, a.s., 2017a). Load time series used reflect the real-data one-minute average of the instantaneous active power consumed in the Czech power system including power plants’ auxiliary consumption and network losses. PV and wind time series indicate one-minute averages of the aggregated instantaneous production values of each technology. Estimates of production figures for plants lacking output measurement equipment are also considered (ČEPS, a.s., 2017a). All data is expressed in MW.
22 Central American Electrical Interconnection System; power grid interconnection of six Central American nations: Panama, Costa Rica, Honduras, Nicaragua, El Salvador, Guatemala. 23 Independent System Operator of Texas, USA. -30-
Figure 4-1. System load and VRE output for an example week in 2014
Figure 4-1 shows the demand curve, as well as the PV and wind production curves of the Czech system during one example week in 2014. Next to a usual daily demand pattern, with summer afternoon peaks and weekend decrease, a PV feed-in with an over 1000 MW peak and a minor wind contribution are observable. Time series pre-processing included a number of data clean-up methods used to perform simple checks on the datasets, e.g. monotonicity of the timestamps, occurrence of duplicates or existence of data gaps within the time series, some of which will be detailed in the following. First, for simplification reasons, but without affecting the outcome of the model in any way, all input data (load, PV and wind) was adjusted taking into account the Day-light Saving Time (DST). Every time series included the inherent effects of the DST, i.e. lack of one hour in March and the duplicate of another in October, and by adapting data to this issue time series ended up expressing the winter time all year round. Another encountered issue was particular to the load time series. Assuming measurement device down-times or other data communication errors, around 0.6% of the total number of load samples were found to be missing (reflected as time stamp jumps accounting for several consecutive minutes or even hours). Time series symmetry, i.e. identical sample resolution and time stamps for each of the datasets, was inalienable for later data analysis procedures. In this regard, the last observation carried forward (LOCF) method proposed and discussed in (Zhu, 2014) is embraced for data resampling (i.e. last known sample is used for the entire consecutive subset of unavailable entries). The last major pre-processing issue was characteristic to the PV time series and involved two separate anomalies (Figure 4-2). First, a frequent problem was the existence of non-zero PV output values outside the sunshine hours. The second abnormality (assumed to be the byproduct of distributed PV generation data aggregation) can be seen in the zoomed area, where unusually high output is present in the very vicinity of sunset or sunrise, respectively, followed or preceded by large stepwise development of corresponding generation. -31-
Considering the wind datasets, the stochastic nature of the corresponding output made it impossible to qualitatively evaluate the available data which was furtherly assessed without any prior processing.
Figure 4-2. Superposition of June PV output curves (raw data)
The processed version of PV data is shown in Figure 4-3. The “false” sunrise/sunset issue was taken care of by comparing available every day sunrise/sunset hours with actual sunrise/sunset times, as they were separately computed for Prague (Beaudet, et al., 2017). In this way, any non- negative PV output extending outside the daytime limits was equaled to zero. The other PV data acquisition abnormality, the one concerning unusual PV output at both daytime boundaries, was corrected by replacing the offset samples with entries within the data spread at the corresponding time stamp. Both improvements to this dataset can be seen in the zoomed area of Figure 4-3.
Figure 4-3. Superposition of June PV output curves (processed data)
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Input: time series containing un-processed measurement data Key assumptions: Daylight Saving Time effects removed from time series (winter time all year round); LOCF method used to replace load sample gaps; Sunrise/sunset real times determined for the city of Prague.
Output: one-minute resolution measurement time series for each of the fluctuation drivers (load, PV, wind), covering five consecutive years (2012-2016).
4.3 Time Series Filtering Measured data, regardless of its nature (load or VRE) is defined by the presence of persistent and stochastic fluctuations originated in the irregular demand, in case of load time series, and in the variability of weather, i.e. solar irradiance and wind, in case of VRE. Since the secondary control activation time is considered to be somewhere on the order of tens of seconds (E-Bridge & IAEW, 2015), it will be the case of another regulating mechanism (primary control) to cope with sample spikes as seen in Figure 4-4. Therefore, for a better display of challenges that power plants face in order to provide secondary reserve, a low-pass filter was applied to the time series. Three filter methods have been used in the purpose of data smoothing, i.e. removing random variations from the observations: The rolling average; The Butterworth low-pass filter; Holt single exponential method.
Figure 4-4. Example of load and VRE generation fluctuations on a one-hour sample
4.3.1 Rolling average The rolling average consists of computing mean values of sequential subsets of the original time series. Also called moving average, the term comes from the way this method computes the average, by dropping the oldest sample and including the first next sample for every new calculation. This method relies on the assumption that there is a high probability that consecutive observations are -33- close in magnitude, hence averaging all samples in the proximity of a studied point results in a decent estimate of the sample trend, while simultaneously discarding data outliers (Hyndman 2008). The data subset size, also known as window, is the number of observations used for calculating the statistic and may vary for calculation purposes. The larger the window, the flatter and smoother the real data estimation will be. Throughout the model, a subclass of the rolling average was used, namely the centered moving average, which associates the computational output with the midpoint of the subset (Hyndman, 2008). Given a time series, a corresponding n-window moving average can be defined as seen in Equation 4-2: 푖+푛−1 1 푠푖 = ∑ 푎푗 (4-2) 푛푅퐴 푗=푖
4.3.2 Butterworth filter This method implies a signal processing filter that is designed to have a nearly flat pass-band (range of original frequencies that can pass through the filter) (Lacanette, 1995). Its mathematical expression, given as transfer function is: 1 퐻(휔) = 휔 2푛퐵 (4-3) 1 + ( ) 휔0
This filter type is characterized by two parameters: the filter order (nB in Equation 4-3) and the cutoff frequency (ω0). The order of the filter is given as a positive integer number and it dictates the shape of the response curve, with lower filter orders providing smoother estimations of the raw data (Lacanette, 1995). As a model particularity, given the Python built-in functions used to apply the Butterworth filter on the time series, the cutoff frequency is input as normalized frequency (with a value of one being corresponding to the Nyquist frequency, or half the sampling rate).
4.3.3 Holt single exponential method This last applied method can be seen as a weighted form of the moving average. It depends on a positive smoothing parameter (α) that weights the contribution of both already evaluated samples, as well as of original, raw data. As α decreases, it delivers a flatter, smoother estimate of the time series (Hyndman, et al., 2017). Its mathematical expression is given in Equation 4-4:
푦푡 = 훼푥푡 + (1 − 훼)푦푡−1 (4-4)
Translated into words, this equation shows that, at time t, the estimated value is a weighted average of the actual value (xt) and the estimation of the last known observation (yt-1), with the weight represented by a proper fraction. The outcome of applying the aforementioned low-pass filtering methods on the wind time series can be observed in Figure 4-5. It is obvious that, for the considered filter parameters, all three methods provide similar results, even though the techniques involved in each of these filters take
-34- different directions. In fact, the residuals subplot shows almost identical behavior of the rolling average and the Butterworth filter at the considered input parameters, while the Holt single exponential smoother displays a minor offset for the given smoothing parameter.
Figure 4-5. Low-pass filter methods. Example on wind feed-in time series
It is important to mention that each of the proposed signal filtering methods described above have their particular strengths and weaknesses, but this topic goes beyond the scope of the thesis. Nevertheless, certain features of available data samples or even specific modelling requirements can define the choice of the low-pass filter method used.
Input: time series of measurement data Key assumptions: - Output: time series containing filtered measurement data (high-frequency, stochastic spikes removed)
4.4 Assessment of Generation Variability Wind and PV feed-in are functions of the resource availability and power systems including VRE tend to be subjected to more frequent deviations of the power balance, thus with a more recurrent reserve activation (Wagner, et al., 2016). The proposed secondary reserve dimensioning methodology is intended to cover for the short-term system variations only and to this end, its development is based solely on measurement data (i.e. on the minute-resolution variation magnitudes of load and VRE generation, as determined from measurement time series). At this point, a time interval relevant for variation assessment and corresponding secondary reserve demand is decided upon. Given the fact that the methodology treats the short-term variability of the system due to VRE integration, the time interval has to be small enough that the forecast errors would be negligible within the considered window. Also, considering the maximum full activation time of 15 minutes in the ENTSO-E continental system (see Section 2.3), a 10-minute window with a five-minute step is an acceptable trade-off between high resolution of the results and computational speed.
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The following step in this direction is implemented to distinguish between the directions in which the fluctuations occur. In this regard, a distinction between upward (incremental) and downward (decremental) secondary reserve capacity is made. Upward control occurs if the system is undersupplied (i.e. demand is higher than generation, situation that results in a negative frequency gradient), while downward control is required when the system produces more energy than requested (therefore the frequency of the system will increase above the nominal set-point). The idea behind this feature is that different technologies could provide SR in different directions, at different costs, thus a non-symmetrical reserve sizing may be more economically efficient for the market participants. During each 10-minute window, unique up- and down-fluctuation values are determined, based on filtered data, as the summation of time series increments and decrements, respectively (Figure 4-6).
Figure 4-6. Up- (left) and down-fluctuation (right) evaluation . Example using the rolling average method
This procedure is identically applied for all three fluctuation drivers (load, PV and wind), during the whole historical time span studied (five consecutive years). The result of this process consists in separate time series of absolute system fluctuations for load, PV and wind, respectively, all on a five-minute resolution. The last addition to this section concerns data normalization, a step that is required in order to have a basis for reserve sizing estimations of future scenarios. In this regard, load data was normalized to the yearly peak load. In case of VRE, the normalized values of their corresponding fluctuations were determined based on wind and solar end-of-year installed capacity data (EurObserv'ER, 2017).
Input: filtered data time series Key assumptions: Up- and down-fluctuations determined on a 10-minute moving window with a five- minute step basis; Up- and down-fluctuations estimated as the summation of increments and decrements, respectively, within any given window; Load data normalized to yearly peak load, VRE data to end-of-year installed capacity.
Output: time series of normalized values of the system fluctuations
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4.5 Classification of Time Series The amount of reserves allocated for a particular time stamp has to maintain a certain level of electricity supply security. Secondary reserves have to cover for the magnitude of system imbalances due to load and VRE implied by the system characteristic security quota. In order for the dimensioning methodology to abide to this aspect, the actual sizing process is based on certain statistical procedures. Statistical properties of the given time series may show different behaviors at different moments in time, i.e. load fluctuations are more frequent during evenings, PV variations are more common outside summer and winter or wind feed-in magnitudes are highest in winter. Considering this, it seems convenient to differentiate between subsets of the time series that share similar statistical properties and assess them separately.
Load PV Wind
Seasons Seasons Seasons quarterly (spring, monthly (Jan., Feb., quarterly (spring, summer, etc.), bi- or etc.), quarterly, bi- or summer, etc.), bi- or tri-annually (wet, dry, tri-annually (wet, dry, tri-annually (wet, dry, etc.) etc.) etc.)
Day of week Time of day Power Level weekday / weekend morning (down- *fraction of wind IC ramps), day, evening (up-ramps), night Time of day (no output) morning (up-ramps), day (peak), evening (down-ramps), night (off-peak)
Figure 4-7. Time series classification criteria
The time series categorization, as proposed in this thesis, is shown in Figure 4-7. In case of load data, the seasonal classification comes first, and its output depends on the climate of the observed system. A day-of-week classification follows, differentiating between weekdays and weekend days. Finally, a time-of-day classification is applied in order to divide the 24-hour day into distinct subclasses with similar fluctuation statistical properties. PV time series undergoes a similar to load seasonal classification, but in this particular case a monthly categorization proves to be helpful. Also, a time-of-day classification is used to distinguish between time intervals within the 24-hour day that share similar statistical properties. Finally, wind data is subjected to the same seasonal classification as used for load data, followed by a power level classification of the corresponding feed-in.
4.5.1 Load Time Series Given a climate specific for the system under observation, seasonal classification of load data can divide samples into quarterly (for temperate climate), tri-annual or bi-annual (tropical climates) seasons. Throughout this report, as the study is conducted with data from the Czech system, the
-37- seasonal classification output corresponds to the seasons characteristic to temperate climates, i.e. winter, spring, summer and autumn. Load magnitudes and daily curves show different patterns in different seasons (see Appendix B), therefore will display different statistical properties. Weekdays and weekends also generate different load patterns in terms of magnitude and trajectory. The main reason for this behavioral change is the lower commercial activity during weekends. The last step of load data classification takes into account the existence of distinct demand curve time intervals during one day. Applying this classification starts from computing the aggregated curve of load profiles that correspond to each of the day-of-week class, i.e. “autumn-weekday”, “autumn-weekend”, “spring-weekday”, etc. After that, pre-set time stamps are attributed to morning and evening boundaries; duration of mornings and evenings is limited between pre- defined temporal boundaries (e.g. from a demand perspective, mornings do not occur earlier than 5:00 AM). Given these constraints, analysis of the load curve gradient sets the definitive separation timestamps. On the aggregate demand profile, four different time intervals, each with distinct statistical properties from the fluctuation occurrence point of view, are located (Figure 4-9): night hours (I), corresponding to the off-peak region of the demand curve; morning time (II), characterized by steep upward load ramps; day time (III), or the peak region and evening (IV), with steep downward ramps and, thus high downward fluctuations. A significant difference can be observed between the two subplots in Figure 4-9, showing demand time-of-day classification for winter weekdays and weekend days, respectively. Due to industrial activity during weekdays, the morning ramps show a steep evolution in a relatively short time span (between 04:00 and 07:15), while this is not the case during weekends, where the morning ramp has a shallower development across a longer time span (05:45 to 10:30). Daytime activity is lower in magnitude during weekends, as it is during nights. Evening hours show a peculiarity of the aggregated load curve for weekend days. While the weekday evening time ends at 23:45, the initial model constraint, during weekend this interval is shortened by two hours due to an increase in aggregated consumption at that point in time (21:45). Still, supporting plots in Appendix B show that this is only a particularity of the system, with the weekend demand curves for other seasons not showing similar behavior.
Figure 4-8. Load time series time-of-day classification. Example for winter time, weekday
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Figure 4-9. Load time series time-of-day classification. Example for winter time, weekend
4.5.2 PV Time Series PV feed-in first undergoes a seasonal classification that is extended, in this case, to distinguish between entries correlated to each month of the year. The justification for this approach is two- fold: first, the superposition of daily PV output shows relevant enough variation on a monthly basis (Appendix B) and second, it is related with the next classification level. The reason for applying the time-of-day classification for PV feed-in is to locate time intervals during the 24-hour day in which time series data share similar statistical properties from the PV feed-in variation occurrence perspective. It is obvious that a night-/daytime classification of PV output is relevant since there is a strict correlation between solar irradiation and PV output. Nevertheless, this is still not sufficient, as daytime hours could still be divided into relevant subclasses. During sunrise, the PV feed-in develops in a steep upward manner, thus the system downward reserve requirements are higher (generators of other technologies ready to step down production in case of steep solar activity). That is in opposition with the evening expectations when, due to high PV negative output gradients, the upward SR requirements decrease.
Figure 4-10. PV time series time-of-day classification . Example for April data
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Given these assumptions, for each month, the aggregated curve of PV feed-in is computed and the hours that delimit each part of the day are determined (Figure 4-10). In order to get the temporal boundaries between the desired time intervals, the curvature of the aggregated curve is observed (i.e. when the aggregated curve changes curvature from convex to concave or the other way around). Night time (I), between 19:20 and 04:40, accounts for no solar activity whatsoever, therefore both upward and downward reserve requirement due to PV fluctuations are zero across this time horizon. In the example pictured above, data is provided for the month of April thus two assumptions have to be emphasized: hours are adjusted for DST (the displayed figures represent the winter time) and the aggregation of all April PV feed-in curves suppose that the first non-zero entry in all historical data corresponding to the mentioned month represents the first different than zero entry of the aggregated curve. The implication of this last assumption plays a notable role in further calculations. In case of days towards the beginning of April, the night subclass ends where it does for the last day of the month, where the day is the longest. Therefore, no fluctuation activity can be found in the first morning minutes of first days of April, even though that would count as night time. This translates, of course, in some minor alteration of the results while saving computational time by avoiding a time-of-day classification made on a daily basis. Nevertheless, this issue relates with the second justification for choosing a seasonal classification on a monthly basis, with the effect described above being more severe in a quarterly-, tri-annual- or bi-annual- based season categorization. Back to Figure 4-10, daytime hours are divided into three distinct intervals. The morning time (II), in the exemplified case between 04:40 and 07:20, accounts for hours with steep increase in PV feed-in, correlated with a higher requirement for downward SR due to PV short-term fluctuations. The hour boundaries are found between the first non-negative entry of the monthly aggregated curve and the point where the curve changes curvature from convex to concave. The actual daytime (III), from 07:20 to 15:30, accounts for the entire concave part of the curve. Lastly, the evening time (IV), lasting from the high-end boundary of daytime until the aggregated PV curve sunset time (which is also the latest sunset time of all daily PV curves for the considered month), represents an interval with steep decrease in PV output, thus with high demand in upward SR provision. Sunrise and sunset occurs at different times for different latitudes, therefore another advantage of the time of day classification for PV feed-in is that the sunrise/sunset hours can be tuned according to the geographical coordinates of the system under study. An overview of the monthly aggregated curves of PV feed-in in the Czech power system is provided in Appendix B.
4.5.3 Wind Time Series Wind time series classification starts by taking into consideration the seasonal variability of wind generation (Wan, 2012; Holttinen, et al., 2012a). In this regard, a seasonal classification similar to the quarterly-based one that categorizes the load time series is used. From that point on, a more detailed temporal classification is discarded, as it proves to add no value on this particular dataset (no distinct pattern is seen in Czech diurnal or even monthly wind output variability). It should be mentioned though that, in case of the presence of a more refined resolution of the variability pattern, characteristic to certain locations (Holttinen, et al., 2012a), this should be accounted for in terms of a superior classification level (e.g. day-/night time). The non-linear dependence of wind power output of a wind turbine with the wind velocity (Ackermann, et al., 2012), as shown in Figure 4-11, would, on the other hand, justify a classification
-40- of wind generation data in separate bins expressing relative wind generation with respect to the total wind installed capacity. This categorization, referred to as power level classification, can be seen in Figure 4-12 where wind superposed output is divided into bins each representing a 10% additional normalized generation. For illustration purposes, the depiction shows the superposition of daily wind generation curves covering for the entire study period, i.e. 2012-2016, even though this step is applied subsequent to each seasonal subclass.
Figure 4-11. Generic pitch-regulated wind turbine power curve . Source: (Sohoni, et al., 2016)24
Figure 4-12. Wind time series power level classification basis
24 Power curve computed using actual data for a group of wind turbines at a wind farm.
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Input: time series of system fluctuations by nature (load, PV, wind); Key assumptions: Seasonal classification divides load and wind data into quarterly seasons, while PV time series into months; Load time-of-day classification assumes temporal constraints on morning (04:00 – 08:00) and evening (17:00 – 23:45) times; PV time-of-day classification relies on the monthly aggregated curve of diurnal PV feed- in, therefore length of day changes within one month are not considered; Wind data is split into 10 power level bins;
Output: dictionary objects with classified relative fluctuation time series.
The output structure of the classification methods, regardless of the fluctuation origin (load, PV or wind) is a dictionary object25, each classified subset of data having a unique key that is related to its corresponding values.
4.6 Statistical Sizing of Secondary Reserve Based on background information provided in Section 4.1, the methodology proposed in this thesis follows a probabilistic and quasi-dynamic approach. SR sizing is based on statistical evaluation of data subsets with different distribution properties of the fluctuations induced in the system in relatively short time frames (from minutes to one hour). With time series of load, PV and wind classified into subsets characterized by similar statistical properties of short-term variation occurrence, the next step in the model build-up is made towards estimating the SR requirements. Pre-defined levels of security of supply are applied as quantiles of each individual time series, at every time step. The output is thereafter aggregated, thus expressing the total amount of secondary reserve required. Figure 4-13 depicts the “three-sigma” rule of normal distributions (Nikulin, 2011), according to which 95% of samples lie within a bandwidth of twice the standard deviation (2휎) from the mean of the samples, while a bandwidth of three times the standard deviation (3휎) includes 99.7% of samples. In security of supply terms, a 95% confidence level would be translated into 438 hours per year when the system deviates from normal operation. Expressed as the impact this has on the secondary control of interconnected systems, this is equivalent to higher ACEs for the area where the confidence level is exceeded, while in case of an isolated system more drastic measures may be required (i.e. load or generation shedding) in order to maintain system functioning. A 99.7% security level is equivalent to roughly 26 hours of expected generation-load imbalance not to be fully covered by active power control. In case secondary reserve studies (10 minutes to 1 hour), bandwidths of ±2휎 to ±3휎 have been previously used (Holttinen, et al., 2008) and the methodology proposed in this thesis adheres to this range.
25 “Key – value” characteristic structure of Python. In this example, the key is the name of a specific subset of data (e.g. “Load – Winter – Weekday – Morning”) and the values are all samples corresponding to that key. -42-
Figure 4-13. The normal distribution “three-sigma” rule A detailed example on how the 95th percentiles (equivalent to a 95% security level) are determined for each of the time series is given in Figure 4-14. This example looks only at upward SR sizing for an example day, between 12:00 and 13:00. First, load, PV and wind classified subsets that correspond to the respective timestamp are extracted and their histograms are computed. Then, the 0.95 quantile is determined. The 95th percentile, or 0.95 quantile stands for the sample value with 95% of corresponding time series values found below this entry. In the figure below, 95% of load time series values grouped under the unique subclass having the “winter”, “weekday” and “day“ attributes are situated under 1.12% of the yearly peak load. Therefore, the SR requirement due to load short-term variations for the entire subclass defined above, considering a 95% security level, is 1.12% of the early peak load. The same procedure is valid for PV and wind series, with their variations expressed relative to corresponding installed capacities.
Figure 4-14. Determination of the 95th percentile . Example for upward reserves, 17th of February, afternoon At this point, short-term variations due to each of the three drivers and for one particular time stamp are determined. Assuming that the load and VRE productions are not correlated, a geometric aggregation of their characteristic fluctuations is proposed (Holttinen, 2012). Equation 4-5 expresses demand for secondary reserve capacity at a specific moment in time, t, as a function of peak load, VRE installed capacities and associated quantiles:
푝 2 푝 2 푝 2 푆퐶(푡) = √(푞푙표푎푑,푖(푡) ∗ 푃푚푥,푙표푎푑) + (푞푃푉,푗(푡) ∗ 푃퐼퐶,푃푉) + (푞푤푖푛푑,푘(푡) ∗ 푃퐼퐶,푤푖푛푑) [푀푊] (4-5)
This way, a dynamic sizing of SR requirements is done by evaluating historical data of short-term variations of load and VRE. While historical data processing has been discussed up to this point, one issue has to be added regarding the quantile computation. In case of load and PV data, the
-43- desired percentile is determined based solely on temporal attributes (date and time). For wind data, this is not the case. Wind power level classification (see Section 4.5.3) is based on the relative wind generation with respect to the respective year installed capacity, or a “normalized wind generation”, which adds to the temporal attributes needed for SR prediction. In order to generate a SR prediction for a future year, given known or assumed peak load and VRE installed capacities, one cannot rely on long-term (year-ahead) forecasting. The accuracy on such temporal horizons is practically not existent (Holttinen, et al., 2009), therefore the SR sizing would be affected. Instead, the use of a range of historical wind generation time series is proposed. Assuming that wind generation expansion will come in line with its geographical distribution, it is expected for the overall capacity factor of the wind generation fleet to decrease, since areas with poorer resource will also be exploited. On the other hand, technological improvements in this domain are likely to compensate for this geo-spreading side-effect (Ackermann, 2012). Therefore, it is assumed within the model that wind capacity factors will stabilize in time, thus the generation patterns for a future year will be similar to the historical ones, regardless of the expansion scale. In this particular case, a range containing all five years with wind generation historical data is adopted, with SR sizing being evaluated for minimum, maximum, average and median values for each time stamp of the time series. Last thing to be considered is the extent to which this methodology applies. As briefly explained in Section 2.3, one of the purposes for secondary control is to replace primary control26 for later use. Therefore, dimensioning SR may be dependent on the size of primary reserve requirements. In these circumstances, in order to evaluate the applicability of this methodology, it is important to take into account several aspects. First, the penetration level of VRE in the studied system and the corresponding magnitude of system short-term fluctuations due to VRE. For example, in case of a system with a low VRE penetration level and limited short-term variations relative to the single largest contingency, the magnitude of the “reference incident” would be of interest when conducting the SR dimensioning. In this case, secondary reserves are closer to what NREL defines as “contingency reserves” (Ela, et al., 2011). If higher penetration levels of VRE are considered, the amplitude of aggregated short-term variations in the systems may be higher than the size of the largest contingency case (i.e. estimated with deterministic methods, such as the (n-k) criteria) therefore the relevance of the latter on the sizing of secondary reserves would decrease (e.g. during daytime, with high load and VRE short-term fluctuations, sizing SR would be a function of the predicted system variations; during night time, with no PV output and relatively low demand fluctuation, the magnitude of the largest single contingency may overcome the confidence level of aggregated short-term deviations). In addition to that, other variables, such as topographic particularities of the studied system (whether it is interconnected or isolated), market changes (TSO cooperation, regulatory actions) or updates in dimensioning methodologies might also have an influence on the secondary reserve sizing (Hirth, et al., 2015). The effect of simultaneous action of all these exogenous factors can be observed in the SR reserve that ENTSO-E recommends in for the system under observation (i.e. Czech Republic –VRE penetration rate of around 20%); a bandwidth of 150-200 MW of secondary reserve is proposed (computed according to the “Empiric Noise Management Sizing Approach” mentioned in Section 4.1) even though the size of the largest generating unit exceeds 1000 MW.
26 Primary reserve capacity is deterministically dimensioned based on the magnitude of a “reference incident” (i.e. size of the largest generation unit or generation capacity connected to a single bus bar ) (ENTSO-E, 2009). -44-
This work does not take into account probabilities concerning conventional generation unplanned unavailability, nor effects of external variables (i.e. the ones previously discussed). The proposed methodology relies solely on VRE and load short-term fluctuation data resulting in SR requirements closer to what NREL interpret as “non-event reserves” (Ela, et al., 2011), i.e. reserve capacity required in order to maintain system balance during highly frequent short-term variations characteristic to normal operation.
Input: classified short-term variation time series; Key assumptions: Load and VRE productions are not correlated; Knowledge of system parameters forecast (peak load, installed capacities); Historical wind generation patterns assumed valid for reserve prediction;
Output: secondary reserve requirements at desired resolution.
4.7 Additional Secondary Reserve Requirements with VRE Capacity Expansion The statistical procedure described in the previous section offers an estimation of secondary reserve requirements without taking into account VRE installed capacity expansions. In this context, linear upscaling should be avoided (Holttinen, et al., 2012a), since the fluctuations magnitude will not increase linearly with wind and solar capacity expansions, with geo-spreading of generating units having a smoothing effect on the inherent fluctuations. Variable generation upscaling depends heavily on resource availability, which is often a particularity of the system under consideration (Holttinen, et al., 2012a). Any approach focused on one system would generate more relevant output exclusively for that particular system, but the general character of the proposed methodology would be lost. Therefore, a more heuristic approach, based on similar metrics as the study shown in (Holttinen, 2012), is applied. Information related to secondary reserve allocation, peak load and VRE installed capacity starting from 2005 is retrieved for a list of control areas/blocks, i.e. Austria (AT)27, Belgium (BE)28, Switzerland (CH)29, Germany (DE)30, Spain (ES)31, Finland (FI)32, France (FR)33, Greece (GR)34, Italy (IT)35, Netherlands (NL)36, Portugal (PT)37, Romania (RO)38 and Texas, USA (TX)39. These systems display distinct
27 (APG, 2017) 28 (elia, 2017) 29 (swissgrid, 2017) 30 (regelleistung.net, 2017a) 31 (RedElectrica, 2017) 32 (FINGRID, 2017) 33 (RTE, 2017) 34 (ADMIE, 2017) 35 (GME, 2017) 36 (TenneT, 2017) 37 (REN, 2017) 38 (ANRE, 2017) 39 (ercot, 2017) -45- characteristics in terms of size (yearly peak load taken as metric), VRE penetration or, to a certain extent, geographical positioning. Evolution of SR requirement (percentage of VRE installed capacity) with VRE penetration (with respect to yearly peak load), as encountered in the aforementioned power systems between 2005 and 2016, is depicted as scatter pairs in Figure 4-15 (for upward regulation) and Figure 4-16 (for downward reserves). For each SR direction (upward, downward), all scatter pairs are bound between two exponential curves, thus resulting in ranges of SR requirements (shown in dark grey) for different levels of VRE penetration. For example, in case of positive regulation (Figure 4-15), given a 20% penetration level, the SR requirements account for 2 to 29% of the VRE installed capacity, while for a 50% penetration level the SR dimensioning would vary between 1 and 10% of the VRE installed capacity. The trend develops as expected; low shares of variable generation in the electricity mix come with relatively high secondary reserve capacity requirements with respect to VRE capacity and, as the VRE penetration increases, relative requirements decrease exponentially. That is equivalent, as shown in Section 4.9, with a marginal increase of SR allocation with generation expansion.
Figure 4-15. Additional requirements with VRE expansion for upward secondary reserve capacity
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Figure 4-16. Additional requirements with VRE expansion for downward secondary reserve capacity There are two important issues to mention regarding the use of this approach. The first one is related to the interpretation of input data. With improvements in energy efficiency measures and relatively stable economic development, the average electricity final consumption in the European zone flattened in the last years (Eurelectric, 2015) and, as seen in a North-American control zone, due to same reasons, the peak demand is forecasted to increase at very small annual rates (NYISO, 2016). Therefore, it is assumed that development of SR requirements for the studied systems are attributed solely to VRE up-scaling, with load characteristics having no effect. Accordingly, SR sizing is adjusted solely for PV and wind data, while active power reserve capacity due to load fluctuations is kept constant as before generation expansion. The second issue is intended to deal with the fact that for each VRE penetration level a corresponding range of relative SR requirement is valid. For calculation purposes, this is adapted into a one-to-one correlation by means of a curve fit (see red colored exponential in Figure 4-15 and Figure 4-16) within the range. Theoretically, there are an unlimited number of curves that fit inside the given boundaries and each of them will generate marginally different outputs. In this case, assessment of SR capacity marginality with VRE upscaling is done based on one example exponential curve fit that intersects each of the VRE penetration levels on close to average range values.
푝 푝 푟푗 − 푟푘 (4-6) 푆퐶퐶(푡) = 푞 (푡) ∗ 푃0 + 푞 (푡) ∗ 푃Δ ∗ | | [푀푊] 푃푉,푖 퐼퐶,푃푉 푃푉,푖 퐼퐶,푃푉 푗 − 푘 Finally, in order to relate VRE installed capacities with required security levels, calculation of SR requirements is conducted based on the function given in Equation 4-6 as an example for PV data.
In this equation, the confidence level is as a function of system’s VRE capacity (푞 = 푞(푃퐼퐶)).
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The amount of secondary reserves adjusted for VRE capacity expansions are comprised of a base term, computed using the chosen quantile (see Section 4.6) and the PV (or wind) installed capacity in the base year, i.e. the last full year covered in the historical input data, and a marginal term. The latter is the product of the same quantile, the marginal PV (or wind) installed capacity, i.e. future minus base capacity and an additional correction factor accounting for the marginal requirements of SR. This correction factor is calculated as the absolute value of a step-wise curve slope bound by VRE penetration of base and future year, respectively.
Input: secondary reserve requirement for base year; Key assumptions: SR procurement for studied systems are attributed solely to VRE; Calculation based on curve that fits within the range;
Output: secondary reserve requirements adjusted for VRE expansion.
4.8 Scenario Development In the interest of result evaluation, the secondary reserve dimensioning methodology described throughout this chapter was run for a full natural year and in different system and model characteristics, including: Yearly peak load, in GW; PV installed capacity (assumed at the end of natural year), in GW; Wind installed capacity (assumed at the end of natural year), in GW; Filtering method: Rolling average (with window size); Butterworth filter (with filter order and cut-off frequency); Holt single exponential smoother (with smoothing factor); Window size relevant for fluctuation assessment, in minutes; Fluctuation aggregation method; Level of security (percentile used). Correlation of all listed model parameters resulted in six different running scenarios, i.e. Alpha, Bravo, Charlie, Delta, Echo and Foxtrot, with their corresponding attributes shown in Table 4-1.
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Table 4-1. Parameters of proposed scenarios
40 41 42 푃푚푎푥,푙표푎푑 푃퐼퐶,푃푉 푃퐼퐶,푤푖푛푑 PEN Filter Aggregation Percentile [MW] [MW] [MW] [%] - - [%]
43 Alpha 11500 2050 280 20.3 R RMS 99.7 44 Bravo 11500 2050 280 20.3 B RMS 95 45 Charlie 11500 2050 280 20.3 H SUM 99.7 46 Delta 12000 3000 500 29.2 R RMS 99.7 47 Echo 12000 4000 1000 41.7 R RMS 99.7 48 Foxtrot 12000 4500 2000 54.2 R RMS 99.7 First three scenarios (Alpha, Bravo and Charlie – the ABC group of scenarios) are based on load and installed capacities of the base year and their main purpose is to emphasize the difference that model parameters, i.e. filtering method, aggregation mode and chosen security levels (percentile of variations) bring to the secondary reserve sizing. Simultaneously, a model validation is performed based on these first scenarios by comparing the computed SR estimates with the ENTSO-E quota of minimum recommended secondary reserve capacity for Czech Republic, according to the “Empiric Noise Management Sizing Approach” mentioned in Section 4.1. Last three runs (Delta, Echo and Foxtrot – the DEF group) look at the influence that additional VRE installations (system characteristics) have on system requirements, in the same time maintaining the model parameters the same for all scenarios.
4.9 Results Throughout this section, a comprehensive discussion of the model results, including all six proposed scenarios, will follow. In order to keep an adequate flow level of the thesis, discussion of results will develop around selected subsets of output data (e.g. extreme weather cases, detailed discussion on one particular scenario or on one certain reserve direction). Additional plots in Appendix D (Comparative Results) and Appendix E (Scenario Breakdown) show detailed results for all six scenarios. In addition, as discussed in Section 4.6, the model is designed to evaluate SR demand for minimum, maximum, average and median values of historical wind generation. In this regard, only the SR requirement corresponding to the median value of wind generation is available.
40 Peak load and VRE installed capacities of the last historical year (2016) for Czech power system were considered as values for base year. Assuming a time perspective of the study limited to 10-20 years and given a forecasted yearly increase of peak demand of 0.2% (NYISO, 2016), the peak demand magnitude evolution in this time frame would mean extreme values for “future” scenarios are assumed to reach 12 GW. VRE installed capacities expansions were considered arbitrarily so they would correspond with different penetration levels. 41 Filter methods: R - rolling average, B – Butterworth filter, H – Holt single exponential smoother; 42 Aggregation methods: SUM – linear summation of fluctuations, RMS – root-mean-square method; 43 Filter window – 10 minutes; 44 Filter order – 1; Cut-off frequency – 0.2; 45 Smoothing parameter – 0.5; 46 Filter window – 10 minutes; 47 Filter window – 10 minutes; 48 Filter window – 10 minutes. -49-
4.9.1 Model Validation This part will treat three main points. First, output analysis of an example scenario will be conducted. Daily and seasonal patterns of secondary reserve requirements are observed on all levels of SR demand (total requirement, load, PV and wind fluctuations). A comparative analysis of the ABC scenarios will follow in order to emphasize the impact that model parameters (e.g. filtering or aggregation method, confidence level) have on the final output. Simultaneously with the latter, a model validation method will be proposed. Each of the plots depicted in Figure 4-17 to Figure 4-20 (also provided in Appendix E, in addition to all other scenarios) are comprised of three figures describing the requirements of upward SR for scenario Alpha: on the top, the evolution of SR requirements over the entire year; on the lower left side, the daily patterns superposition according to the proposed dimensioning methodology; on the lower right side, a heat map with daily and hourly resolution on the X and Y axes, respectively.
Figure 4-17. Total upward SR requirement for scenario Alpha Starting with the SR requirements due to demand fluctuations (Figure 4-18), a strong seasonal pattern, divided in distinct subclasses according to the classification criteria proposed in Section 4.5, is observable. Secondary reserve needs are at their maximum during winter, when demand is at its highest, and reach minimum during summer time, when consumption is lower due to factors such as lower heating demand and availability of natural lighting49. A weekly pattern is also present, with notable higher upward SR demand during spring, summer and autumn weekend mornings; these spikes can be assigned to a more unpredictable behavior of the demand curve during
49 While winter peak demand is characteristic to Central Europe, it may not be the case in other power systems (e.g. ERCOT, Italy, Spain), where the peak consumption occurs during summer time, due to extensive cooling demand. -50- weekends than during weekdays (see Appendix B). Looking at the winter time requirements, it is the exact opposite. Higher SR demand due to load variations is present during weekdays and the same factors mentioned above are supposed to generate this evolution. The daily superposition (lower left) shows a pronounced morning up-ramp characteristic to winter weekdays. Relative steady daily development of SR requirements is notable, even at different magnitudes according to the corresponding weather. The heat map (lower right) supports the seasonal trend depicted in the first figure. An hourly shift is notable with increasing/decreasing length of daytime across the year. Moreover, the highest SR requirement due to short-term load variations is observed during winter mornings (dark areas).
Figure 4-18. Upward SR requirements for scenario Alpha due to short-term load variations Figure 4-19 shows the evolution of upward SR requirement caused by PV output fluctuations. The yearly development (top figure) depicts an interesting trend. Reserve needs increase with higher solar irradiance availability (from January to April) is followed by a decrease over May and June. Typical weather characteristics (i.e. fewer clouds to interfere with PV output which translates into smaller fluctuations) are likely the reason behind this trend. In July, SR requirement increase again and subsequently starts to decrease as reaching the end of the year. The daily pattern (lower left) shows very low upward SR capacity demand during mornings (when, due to high PV output up- ramps, downward SR are needed), while evenings are characterized by relative high activity (caused by the down-ramps of PV generation). The heat map clearly shows the expansion hourly SR requirement with increasing daytime. Also, it can be seen that level of solar irradiance availability is strongly correlated with the demand of SR due to PV output fluctuations (dark areas during the day from mid-spring until end-summer). Demand of upward reserves due to wind short-term variations is detailed in Figure 4-20. A more stochastic development of SR requirements is obvious in each of the three subplots. The seasonal evolution (top figure) shows slightly higher demand for reserve capacity during spring and summer
-51- than in case of autumn or winter time and this is proportional to the wind resource short-term variability (i.e. autumn and winter are characterized by more stable wind resource than spring and summer). The figure showing the daily trend of SR demand shows no particular pattern, while the heat map consolidates the higher demand during spring and summer time. Finally, Figure 4-17 depicts the overall SR requirement computed by aggregation of all variations discussed above.
Figure 4-19. Upward SR requirements for scenario Alpha due to short-term PV generation variations
Figure 4-20. Upward SR requirements for scenario Alpha due to short-term wind generation variations -52-
The SR requirements, as previously presented, are characteristic to one particular scenario having the following characteristics: peak load and VRE installed capacity at current levels in Czech Republic, the rolling average method used as a low-pass filter, the root-mean-square aggregation method proposed in Section 4.6 and a 99.7% security level. Therefore, a sensitivity analysis on model parameters will follow in order to observe the extent to which they affect the secondary reserve capacity demand. Considering this, two other scenarios (with corresponding parameters given in Table 4-1) are defined. In addition to that, a way of validating the methodology is proposed starting from the ENTSO-E minimum recommended quota of SR. The ABC scenarios (Alpha – black, Bravo – red and Charlie – green) as well as the recommended ENTSO-E SR levels50 are shown in two opposite weather cases, i.e. winter (Figure 4-21) and summer time (Figure 4-22). These plots display the total aggregated secondary reserve demand and several differences between the ABC scenarios are notable. First and foremost, a magnitude difference of the SR demand between the first two scenarios (black and red on the figure) is notable and the decrease in SR requirement in case of scenario Bravo is assigned to a lower confidence level (95%) in comparison to the 99.7% used for scenario Alpha. In addition to a different quantile, different filtering methods were used in the two scenarios. As prior test runs have shown, the method used to filter raw data brings only marginal differences if other parameters are maintained. Nevertheless, different filters might be more applicable for different datasets, depending on the characteristics of the time series.
Figure 4-21. Overview of the ABC scenarios during one example week in January It can also be seen that, in case of weekday mornings, the upward requirement for the two cases develop differently. By increasing the security level, inflation of SR demand appears during early hours (more visible during winter time) and the reason behind this are the load-driven positive variations particular to this time horizon, which usually have a large increment characteristic. Due to the nature of daily demand curves, increased SR reservation during morning hours would be
50 The ENTSO-e recommendations were calculated as a symmetric bandwidth based on the 5-year historical load data. -53- expected (steep up-ramps should be covered by increased amount of active power reserve capacity) therefore the development of the Bravo case, taking into account a 95% confidence level, might not truly reflect actual operational conditions.
Figure 4-22. Overview of the ABC scenarios during one example week in July Comparing scenarios Alpha and Charlie reveals another effect that a certain parameter has on the model’s output. Both cases were run for a 99.7% security level and the low-pass filtering methods, even though different, added no relevant quantitative value. Still, the aggregation techniques used make a difference. While scenario Alpha uses the root-mean-square method, the linear summation was used in case of Charlie (green on the plot). It is evident that, regardless of the season, day of the week or hour of the day, SR dimensioning in case of Charlie is very high comparing to Alpha (sometimes almost double the size). A peculiarly high demand of SR can be observed in case of upward control during daytime. In addition to that, very low downward requirement relative to daily peak might be inaccurate. In these circumstances, superposition of the ENTSO-E minimum secondary reserve capacity quota (light grey bandwidth) shows that the first scenario (Alpha) is the closest to what the European Network of TSOs recommends as minimum for a system with similar characteristic. Same figures (Figure 4-21 and Figure 4-22) also show a clear difference between the winter and summer SR requirements. First, it is the obvious magnitude contrast, with summer requirements lower than the need during winter time. This is mainly fueled in this example by the pronounced decrease in electricity demand whose magnitude prevails if compared to the higher PV output corresponding to summer time (also considering wind generation in this example not to play a major role in the process). Also with regard to magnitude, a slightly higher SR demand is observed during weekend days. It might be the higher unpredictability of load profiling across these days that would make operators reserve more active power control capacities during this time span. Still, even though largely affected by load-driven fluctuations, these weekday/weekend differences are not as striking as seen in Figure 4-18 and the reason for this is the aggregation (especially the geometric aggregation used in scenarios Alpha and Bravo) of load, PV and wind short-term variations that has a reducing effect on the total SR demand profile.
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4.9.2 Effects of Variable Generation Expansions Throughout this part, the influence that VRE expansion has on secondary reserve capacity demand is discussed. Starting from the values of PV and wind installed capacities known for Czech Republic at the end of year 2016 (base scenario – Alpha), three cases are developed: Delta (roughly 1 GW of PV and 200 MW of wind additional capacity, thus increasing the VRE penetration share from 20 to 29%), Echo (2 GW of PV and 700 MW of wind additional capacity, taking the VRE penetration rate to 42%) and Foxtrot (2.5 GW of PV and 1.7 GW of wind additional capacity, with VRE penetration reaching 54%). As mentioned in Section 4.7, linear upscaling of SR with variable generation expansions would not be accurate. Figure 4-23 and Figure 4-24 show a comparative view of the non-aggregated secondary reserve demand for the base (Alpha) and Foxtrot scenarios. Since the yearly peak load does not change throughout the scenarios, the SR need due to load short-term fluctuations appear to be identical. Looking at the VRE-driven secondary reserve year-round requirement, things are different. Between the two scenarios there is a roughly 120% and 600% increase in installed capacity of PV and wind, respectively. However, it can be seen that the SR requirement due to VRE short-term variations does not proportionally increase. Instead, a 30-40% additional SR to cover for PV fluctuations, depending on the month of the year, are found. Also, a seven-fold wind capacity expansion does not imply a similar SR capacity margin. First of all, the boundaries of SR due to wind fluctuations are shifted upwards in case of generation enhancement. Second, a 150- 200% increase in upward SR capacity requirement was determined across the year.
Figure 4-23. SR requirements of individual variation drivers for scenario Alpha
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Figure 4-24. SR requirements of individual variations drivers for scenario Foxtrot Next, evolution of SR requirement (both upward and downward) for the DEF scenarios in comparison to the base one (Alpha), during example weeks of what are considered to be extreme weather cases, i.e. winter (Figure 4-25) and summer (Figure 4-26), is presented. Same magnitude difference between the two plots, as the one mentioned in the previous section, is notable here, with summer SR requirements being lower than during winter.
Figure 4-25. Overview of total SR requirements for the DEF scenarios during one example week in January
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Figure 4-26. Overview of total SR requirements for the DEF scenarios during one example week in July Differences between total requirements between the considered scenarios are marginal. Keeping in mind that the model parameters (e.g. filtering and aggregation method, security level) are kept the same for all cases and the yearly peak load is constant, the contrast between scenarios comes from different VRE installed capacities. In addition, duration curves support the significant rise of reserve capacity demand with increasing generation capacity of PV (Figure 4-27) and wind (Figure 4-28).
Figure 4-27. Duration curves for upward SR reserve requirement due to PV generation variations
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Figure 4-28. Duration curves for upward SR reserve requirement due to wind generation variations By considering the yearly peak load magnitude relative to variable generation capacity and placing both in the aggregation method defined in Equation 4-5, it appears that the size of the former dilutes the effect of VRE expansions on secondary reserve capacity dimensioning. Hence, the aggregation method used for these scenarios proves to have a major impact on the final results.
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5 JOINT OPTIMIZATION OF THE ACTIVE POWER DISPATCH AND THE SECONDARY RESERVE CAPACITY ALLOCATION
Throughout this chapter the joint optimization tool of the active power dispatch during normal operation of the power system and the secondary reserve provision will be presented. The section commences with a brief literature review of attempts made on similar topics. Subsequently, the proposed generic grid is established. Its general characteristics, generation assets, exogenous inputs are presented and the reasons behind the current grid design are explained. The grid simulation software used (ENAplan) is thereafter introduced, while its main characteristics, as well as the mathematical formulation of the joint optimization procedure are explained. Output of the secondary reserve dimensioning methodology described in Section 4 and applied on the given system is also displayed before results of the co-optimization problem are presented and discussed.
5.1 Review of Previous Work In essence, the active power dispatch – reserve allocation joint optimization procedure targets to improve the way electricity generating plants in current power systems and markets are managed. One approach towards this purpose (and the approach used within this thesis) is the cost minimization for electricity producers. Such strategies are far from being novelties in the TSOs “modus operandi”, nor as academic topics. In the following section, a brief review of previous work on this topic will be presented. The “Co-Optimization of Energy and Ancillary Service Markets” chapter in (Grant Read, 2010) is a good start in getting the basic technical and economic concepts, as well as understanding the mathematical formulation of the joint optimization problem that is the subject of this section. Presenting all related articles in a chronological order, (Gan, et al., 2002) proposes a joint optimization tool particularly based on modelling the lost opportunity costs corresponding to generating units providing reserves. In this regard, four different financial compensation regimes are assessed: generators receive availability remuneration, lost opportunity cost payments or both of them. The problem is treated as a linear programming model. (Wong, 2005) proposes a simultaneous dispatch model (energy and reserve dispatch) based on a multiple bus generic model, where reserve capacity is determined through minimization of total system costs. Also based on a linear model, this attempt uses also stochastic programming tools in order to determine reserve activation probability in a security constrained grid. A mixed integer model is used in the same purpose, with slight conceptual differences (Ehsani, et al., 2007). Participation to the energy market is constrained by provision of spinning reserve and the financial compensation comes as additional payment of the lost opportunity cost associated with reserve provision. The transmission grid is also security constrained and linear load flow equations are used. (Bazardeh, et al., 2009) treats the same issue by developing a genetic algorithm on a generic IEEE 30-bus system. It concludes that the joint dispatch of the two services is superior to the sequential allocation market. A report that focuses on hydro-based systems treats the same issue following the Norwegian market design (Fodstad, et al., 2009) during an example winter week, -59- on a 24-hour resolution. A different work (Delikaraoglou, et al., 2012) proposes a simultaneous optimization on a more refined (5-minute bidding window) market design by modelling the marginal prices of energy generation and ancillary services, respectively. In (Zugno, et al., 2013), the authors tackle the same issue on power systems with high shares of VRE by using mixed integer programming. The same power system characteristic is taken into account in (Chen, et al., 2013), where a risk-based multi-objective optimization model based on Loss of Load Expectation (LOLE) is proposed. Subsequently, a fuzzy algorithm is applied to transform the problem into a single- objective one. (Street, et al., 2013) propose a mixed-integer version of the joint optimization by taking into account the effect of upward and downward regulation on the transmission network, as well as potential outages in the grid. Research conducted during a thesis work (Chatzis, 2015) concerning islanding operation in Switzerland during critical situations proposes a framework for joint scheduling of energy and reserves based on a linear DC security constrained optimal power flow model. Its results indicate massive cost reductions, mainly attributed to improved hydro reservoir management. In (Motalleb, et al., 2016), a similar security constrained joint optimization model is set and reserve distribution is determined based on stochastic analysis of contingencies rather than on predefined active power control levels. In an additional work, (Li, et al., 2017) proposes to enhance the joint aforementioned joint optimization model by dividing the power system into several geographical areas (zones) and optimizing each of them individually.
5.2 Characteristics and Generation Assets of the Studied System The power system model used in this thesis is a generic model built to include the typical characteristics of a small developing country with a high share of renewable energy. With generation mainly based on hydropower and medium speed diesel generators and a transmission grid at 138 kV and 230 kV, the model has a structure like it can be found in countries in Central and South America as well as, to some degree, in West Africa. The model was not set up to resemble a certain real-life system, but to reflect characteristics and issues found in many different countries: High share of hydro reservoirs, leading to a highly seasonal generation pattern and the need for storage management; Hydro and wind generation far off the load centers, leading to high grid loading on the connecting corridors; Diesel generation close to the load to provide power during the dry season; Grid bottlenecks between the load centers, especially in the 138 kV grids, leading to the need for re-dispatch measures especially during the dry season; Rising share of wind and centralized PV generation, with resources being concentrated within small areas. The hydro inflow patterns used are generic, but based on data from Central America, showing the typical seasonality of the northern tropics between Atlantic and Pacific. VRE resource data is derived from reanalysis of the same area provided by the REatlas program of Aarhus University in Denmark (Andresen, et al., 2015). Developing a grid model resembling power networks in the aforementioned geographical regions implies existence of bi-annual seasons. That is, a wet season (lasting from May until October) and a dry one (from November until April). Accordingly, all resource data has this seasonal characteristic. In addition to that, the model is run for a leap year
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(2012, in this case), accounting for 8784 hours. The reason for the use of such a grid model to test the implementation of the reserve dispatch in ENAplan’s already existent active power economic dispatch optimization tool is that in the past years, as proven by Energynautics’s project experience, several small systems with the abovementioned characteristics have experienced rapid renewable energy development in a relative small-time frame, leading to the grid operators having to revise their operating regimes. Such systems present a good study case as reserve dispatch is impacted by several factors during different times of the year, such as hydro availability, grid congestion and renewable energy fluctuation. The grid assets of interest for the joint optimization problem (generating units) are listed below. Power plants are grouped in variable generation (Table 5-1), thermal (Table 5-2) and hydro units ( Table 5-3). Each table contains technical information of each plant (installed capacity, reserve allocation share, technology, connection node) and corresponding (generation and reserve capacity) costs. In addition to that, hydro power plants’ hydrological dependencies and reservoir size (if applicable) are listed in Table 5-4. Table 5-1. List of VRE power plants and their operational characteristics
51 52 53 54 R+ 55 R- Units Pn.G Pn,plant Type Node c R+ R- c c [#] [MW] [MW] [-] [-] [$/MW] [%] [%] [$/MW] [$/MW] PVCap 2 70 140 PV C02 -10 0 0 3 0 PVEast01 2 70 140 PV E01 -10 0 0 3 0 PVEast02 2 70 140 PV E02 -10 0 0 3 0 WindEast 1 100 100 Wind S04 -5 0 0 5 0 WindWest 1 100 100 Wind S05 -5 0 0 5 0
Table 5-2. List of conventional power plants and their operational characteristics
R+ R- Units Pn.G Pn,plant Type Node c R+ R- c c [#] [MW] [MW] [-] [-] [$/MW] [%] [%] [$/MW] [$/MW] CoGen 2 7.5 15 Biomass E02 36 10 20 40 0 DieselCap 16 17 272 Diesel C02 105 5 10 110 -5 DieselNord 14 18 252 Diesel N03 172 5 10 180 -5 GeoWest 2 58 116 Geoth. W04 25 10 20 30 0 GT 2 35 70 NG N02 389 10 10 395 -10
51 Rated power of one single unit; 52 Rated power of the power plant; 53 Operational cost of each generating unit (modelled here as a fixed cost representing the marginal cost of production encompassing both fixed and variable expenses); 54 Upward and downward (to the right) range of allocated balancing capacity, given as percentage of power plant rated power; 55 Cost of upward and downward (to the right) reserve provision. -61-
Table 5-3. List of hydro power plants and their operational characteristics
56 R+ R- Units Pn.G Pn,plant Type Node c R+ R- c c [#] [MW] [MW] [-] [-] [$/MW] [%] [%] [$/MW] [$/MW] HyCasc1 4 35 140 Res W01 20 35 40 22 0 HyCasc2 4 17 68 Casc W02 20 35 40 22 0 HyCasc3 4 60 240 Res W03 20 35 40 22 0 HyRes1 4 75 300 Res S01 35 35 40 37 0 HyRes2 3 50 150 Casc S02 35 35 40 37 0 HyRes3 2 60 120 Casc S03 35 35 40 37 0 HyRoR 4 12 48 RoR E01 -5 0 0 3 0
Table 5-4. List of hydro power plants and their hydrological dependencies
57 Type Turbines to… Spills to… Reservoir [-] (if applicable) (if applicable) [hm3] HyCasc1 Res HyCasc2 HyCasc3 56 HyCasc2 Casc HyCasc3 HyCasc3 0 HyCasc3 Res - - 269 HyRes1 Res HyRes2 HyRes2 1280 HyRes2 Casc HyRes3 HyRes3 0 HyRes3 Casc - - 0 HyRoR RoR - - 0
In addition to the information given in the tables, Figure 5-1 and Figure 5-2 provide supplementary information with respect to the system’s input data. The first plot depicts the yearly load curve, while the remainders are examples of VRE resource (PV, wind and hydro inflows) for specific nodes/power plants in the modelled grid. Full-size, as well as additional graphs are available in Appendix F. The second image shows reference level curves, as they were considered for the three hydro reservoirs included in the power system. It can be observed that the curve corresponding to the grid’s largest reservoir (HyRes01) has a pattern which is characteristic to seasonal regulated reservoirs, while the other displays expected production patterns mainly shifted towards the wet season (i.e. reservoirs are kept at full capacity during the dry months).
56 There are three distinct types of hydro power plants: with an associated water reservoir (Res), without (Casc) their own water reservoir, but supplied by reservoir(s) upstream and run-of-river (RoR); 57 In case of reservoir hydro power plants, the reservoir size is input as live storage (i.e. the amount of water that can be withdrawn; equal to the difference between the total and the dead capacity). -62-
Figure 5-1. Examples of various system inputs (load, PV and wind feed-in, inflows)
Figure 5-2. Generic reservoir reference curves
5.3 Optimization Tool ENAplan The secondary reserve allocation optimization tool developed in this thesis is implemented in Python and embedded in Energynautics’ broader grid optimization software. ENAplan is the in- house tool used to recreate, simulate and analyze DC grids and one of its features, the one on top of which the reserve allocation tool was added, is the economic dispatch optimization tool. The latter is a linear programming wholesale electricity market model calculating the generation mix,
-63- transmission and hydro storage capacities and market clearing prices at 15-minutes resolution for one year. In the particular case of this thesis, power generation is modelled as seven different technologies (for properties see Table 5-1 to Table 5-4): PV and wind, as VRE, four thermal technologies (a cogeneration biomass plant, two medium-speed Diesel groups, a natural gas turbine and a geothermal plant) and hydro power (three generating groups with reservoir, three additional cascaded groups and a run-of-river plant). Their electricity production cost is given by the marginal generation cost of each generating unit. A distinct case in terms of generation cost is met for the VRE units (PV, wind and run-of-river); negative generation costs were considered in order to emulate a priority feed-in strategy. Thus, curtailing VRE generation would cost more than dispatching their output. The rationale behind setting the cost of secondary reserve capacity is particular to each direction of active power control. The cost of upward SR capacity is set merely above the marginal generation cost of the generating unit. In this way, the opportunity cost of reserve provision is reproduced with respect to the forgone profit of the power plant on the day-ahead market. Allocation of SR capacity is done, as the active power dispatch, based on a merit order. Given the cost setting as described above, the merit order of the reserve capacity allocation is the same as for the day-ahead power dispatch, only slightly offset by the marginal cost implied by reserve provision opportunity cost. In this situation, power groups with lower marginal generation costs (e.g. hydro units) will also be at the top of the capacity allocation merit order, thus having economic priority in providing this service (at least as long as the resource availability can sustain it – the water value plays a role here). Costs associated to downward SR capacity are set on two levels. Fossil fuel (Diesel and natural gas) units are assigned a negative cost of providing downward reserves; this way, the model prioritizes expensive and polluting generators to curtail production when the system requires it. The remainder of the generating units that are eligible for SR provision have a zero cost for downward SR. It is assumed that no profit is forgone when reserve capacity is provided, since the power plant does not reduce its (already remunerated) active power output. Hence, there is no cost associated to this process. Moreover, in order to emulate the value of water, a specific cost is assigned to water spills corresponding to hydro power reservoirs. These cost is artificially set high so the water is spilled only if there is no other option available. All costs are considered to remain constant throughout the year. Under these circumstances, the model’s objective function is to minimize system costs including active power dispatch during normal conditions, reserve capacity allocation and water spilling by taking into account an array of technical constraints. Since the model is linear, it does not contain any integer constraints and one consequence of such modelling is that must-run constraints or start-up costs cannot be explicitly input. In the proposed example, there are two thermal generating technologies having must-run constraints: the cogeneration biomass plant and the geothermal unit. Their must-run constraints are set as minimum generation levels, while the rest of their capacity is freely optimized, as it is the case of hydro units. Considering the storage-based hydro power plants, their reservoir levels throughout the year are given as input, as seen in Figure 5-2. VRE, regardless of technology, is limited by the resource availability. In addition, they do not participate in active power control strategies. Provision of secondary reserve is modelled synchronously with the active power dispatch, starting from a maximum share of each generating unit installed capacity that the producer is willing to bid on the
-64- reserve capacity market. In case of thermal units this share is set assuming that, in a hydro dominated system (and in the context of the previously discussed cost assumptions), such power plants will allocate rather small shares for reserve capacity provision (i.e. between 10 and 25% of their installed capacity). The proportion of maximum capacity for active power control of hydro power units was estimated based on Norway historical data (NordPool, 2017). The reason for choosing Norway’s hydro generation data is based on the fact that its electricity mix relies almost entirely on hydro power plants. Hence, studying volumes and prices traded on the day-ahead and the reserve capacity markets offers a good perspective on the strategies hydro power producers are currently following; in this case, what are the shares of generation capacity divided into active power during normal operation and reserve provision. Costs of providing secondary reserve are limited to capacity reservation costs since the activation step is not considered. Finally, demand is considered perfectly price inelastic, i.e. supply will be met regardless of the electricity price.
5.4 Sizing Secondary Reserve Requirements In addition to system inputs expanded in Section 5.1, another important prerequisite of the joint optimization is the magnitude of secondary reserve requirements throughout the year. Calculation of this parameter is conducted following the steps provided in Section 4. VRE and load data is provided as input and subsequently filtered, classified and statistically analyzed. Fixed VRE capacities are considered and wind induced short-time fluctuations are assessed solely based on the input generation time series.
Figure 5-3. Secondary reserve requirements throughout the year Figure 5-3 shows positive and negative SR requirements of the considered system, given the input weather and load data. In both cases, output values of each 15-minute time step are the aggregation result of the three fluctuation drivers: load, PV and wind short-time fluctuations. The upper side of the graph shows the upward reserve requirements peaking at roughly 40 MW during the dry season. Due to usually lower wind feed-in across the wet season (Figure 5-1), necessity for positive SR capacity decreases in average during this time of the year. On the other side, negative SR peaks
-65- in the beginning of the dry season (October), at values around 35 MW, while average values during the wet season settle around 20 MW. The reason behind the October increase was found to be PV-related; solar VRE spikes during this month after the lower levels corresponding to the wet season (Figure 5-1) and because of the monthly-based classification of PV short-term variations, this increase is extended throughout all month of October due to subsequent statistical procedures. Secondary reserve sizing takes into account only the “non-event” requirements for secondary active power control, ones that are associated solely with perpetual variations in power generation and/or consumption. Back-testing of the joint optimization problem showed that, given the amount of estimated secondary reserve capacity needed to cover for short-term variability, the largest hydro power plant (having also the largest water reservoir - HyRes01) is able to provide the entire reserve capacity by itself during a considerable part of the year. This situation is usually undesired for two reasons. First, unplanned outages may affect the availability of the considered power group and, therefore jeopardize the operational security of the entire system. Second, as ENTSO-E recommends in its operational handbook (ENTSO-e, 2014), network assets responsible for balancing strategies should be situated in locations less affected by transmission bottlenecks. Thus, distributing the reserve capacity across a control area known for its implicit transmission limitations may reduce this risk as well. In order to avoid this potential issue and to better understand the results of the joint optimization problem, the upward reserves were supplemented with the so- called “event” or contingency secondary reserves, which are dimensioned in order for the system to cope with the failure of its largest asset. In this regard, the size of one HyRes01 generator (75 MW) was considered as contingency reserve and linearly added to the already determined upward secondary reserve capacity requirement. It was assumed that disconnection or losses of loads are statistically far less possible, therefore no such adjustment was made for negative SR requirements.
5.5 Mathematical Formulation of the Co-Optimization Problem The joint optimization tool of active power dispatch and secondary reserve allocation is built as a linear problem. That means the objective function of the problem (minimizing system costs), as well as the inherent constraints (technical constraints of generating units, transmission constraints) are linear. In the following section, the linear mathematical model of the proposed joint optimization tool is presented.
푇 푁퐺 푠푝푖푙푙 푅+ 푅− (5-1) 푚푖푛푖푚푖푧푒 ∑ ∑(퐺푖,푡푐푖 + 푆푖,푡푐푖 + 푃퐵푖,푡푐푖 + 푁퐵푖,푡푐푖 ) 푡=1 푖=1 Equation 5-1 represents the mathematical formulation of the objective function. The scope of the joint optimization tool is to minimize total system costs comprising of generation, water spilling, positive and negative secondary reserve capacity costs for each 15-minute time stamp of the year. The objective function is subject to an array of constraints defined below: 푁 ∑ 퐺푖,푡 = 퐷푡, ∀ 푡 = 1̅̅̅,̅푇̅ (5-2) 푖=1
퐺푖,푡−퐺푖,푡−1 ≤ 푅푅푖, ∀ 푡 = 1̅̅̅,̅푇̅; 푖 = 1̅̅,̅̅푁̅̅퐺̅ (5-3)
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푚푎푥 ̅̅̅̅̅ ̅̅̅̅̅̅̅ 퐺푖,푡 + 푃퐵푖,푡 ≤ 푃푖 , ∀ 푡 = 1, 푇; 푖 = 1, 푁퐺 (5-4) 푚푖푛 ̅̅̅̅̅ ̅̅̅̅̅̅̅ 퐺푖,푡 − 푁퐵푖,푡 ≥ 푃푖 , ∀ 푡 = 1, 푇; 푖 = 1, 푁퐺 푁퐺 + ∑ 푃퐵푖,푡 = 푆푅푡 , ∀ 푡 = 1̅̅̅,̅푇̅ 푖=1 (5-5) 푁퐺 − ∑ 푁퐵푖,푡 = 푆푅푡 , ∀ 푡 = 1̅̅̅,̅푇̅ 푖=1
+ ̅̅̅̅̅ ̅̅̅̅̅̅̅ 푃퐵푖,푡 ≤ 푆푅푖,푡, ∀ 푡 = 1, 푇; 푖 = 1, 푁퐺 (5-6) − ̅̅̅̅̅ ̅̅̅̅̅̅̅ 푁퐵푖,푡 ≤ 푆푅푖,푡, ∀ 푡 = 1, 푇; 푖 = 1, 푁퐺
−퐿푗 ≤ 푃퐹푗,푡 ≤ 퐿푗, ∀ 푡 = 1̅̅,̅푁̅̅퐿̅ (5-7) The first set of physical constraints (Equations 5-2 to 5-7) describes the basic system limitations. First (Equation 5-2), at any time step, balance between generation and consumption has to be attained. Equation 5-3 expresses the physical constraints of the generating units; difference between generation at consecutive time steps has to be within the ramp rate of each unit. Thereafter, additional physical limitations of the generators are proposed in Equation 5-4: the first inequality limits the summation of active power dispatch during normal operation and secondary reserve capacity allocation to the maximum output of each generator, while the second one does the same for the negative SR capacity with respect to the minimum output level of the generator. Furthermore, for each time step, system-wide requirement of positive and negative SR has to be met (Equation 5-5). Equation 5-6 limits the magnitude of reserve allocation to predefined shares of installed capacity specific to every generator, while Equation 5-7 comes as network constraint. That is, for any moment in time, the optimal power flow including additional flows resulted from potential reserve activation has to fall within the physical capabilities of the transmission lines.
퐺푖,푡 − 푃퐵푖,푡 ≥ 0, ∀ 푡 = 1̅̅̅,̅푇̅; 푖 = 1̅̅,̅̅푁̅̅퐺̅ (5-8)
푁푘 + + ̅̅̅̅̅ ̅̅̅̅̅̅̅ ̅̅̅̅̅̅ (5-9) 푆푅푖,푡,0 − (∑ 푆푅푖,푡,푘) ≥ 0, ∀ 푡 = 1, 푇; 푖 = 1, 푁퐻; 푘 = 1, 푁푘 푘=0 The last two constraints of the proposed model express certain limitations of generating units in terms of reserve capacity provision. First, it was assumed that for any generating unit providing reserve capacity the magnitude of active power dispatch during normal operation should be higher than or equal to the plant’s allocation of positive secondary reserve capacity, at any point in time. Cases where generating groups would provide positive SR an order of magnitude more than the corresponding active power dispatch during normal operation were considered unusual and through this constraint adjusted. Second, in case of the two hydro cascaded schemes in the generation fleet, it was assumed that the amount of available secondary reserve capacity is dependent on the most upstream plant only (i.e. the “feeding” group, with an associated reservoir). More exactly, for any moment in time, reserve capacity provided by any of the downstream hydro
-67- plants linked to a cascaded scheme cannot be higher than what the most upstream hydro group in that scheme can provide. It can be economically optimal to provide reserve capacity from a hydro power plant without an associated reservoir in a larger share than from one upstream having its own storage. Yet, in case of reserve activation, this situation may be physically impossible when studying the water availability of the downstream plant (i.e. given the plants’ hydrological coupling, there is not enough water available to sustain reserve provision with the downstream plant more than the upstream plant does). Thus, the latter constraint is added to deal with this issue.
5.6 Results Outcomes of the joint optimization process described above will be presented and discussed in the following section. First, the overall year-round active power economic dispatch and reserve allocation will be analyzed, followed by a more detailed examination of weekly results corresponding to each of the two main seasons (dry and wet). The proposed methodology is a solution approach for secondary reserve capacity cost optimal allocation. It is important to mention that, due to the generic characteristic of the proposed electric grid and its related inputs (VRE resource, hydro inflows, reservoirs reference curves), there is no validation process involved. While testing of the model’s functionalities is conducted on the input power network, its application on real grid models would provide a benchmarking solution. Figure 5-4 displays the results of the first outcome of the joint optimization model, namely the yearly active power economic dispatch problem. Correlation of the hydrological inflows, VRE resource patterns (Figure 5-1, supporting plots in Appendix F) and the reservoir reference levels (Figure 5-2) results in a distinct evolution of hydro-thermal system operation throughout the year. High VRE feed-in during the first months of the year (dry season) are sufficient to keep the thermal generation relatively low, even though hydro production is highly constrained by limited inflows and reservoir reference levels. Transition into the wet season (after the 100th day) comes with a shift in natural resources. While hydro inflows increase, VRE resources (especially wind) become scarce in the months to come. This, as well as decreasing levels of the reference reservoir levels, enables hydro power plants to increase the generation, often covering 2/3 of the electricity demand. Later during the year, before the beginning of the dry season (300th day), according to their preset reference levels, the reservoirs start to fill-up and in order to support this, corresponding production is decreased midway through the wet season (the inherent constraints are observable in the flat curves associated with hydro production between the 200th and the 300th day of the year). This aspect, correlated with another period of wind resource scarcity, yields in high thermal contribution to the electricity mix. Another interesting aspect of the yearly active power dispatch results, due to the implications in the grid’s physical capabilities, is the geothermal contribution. Geothermal generation is modelled as a must-run technology and it reaches full and unconstrained output only during times when hydro power production is limited. During the other hours of the year, production varies and the reason behind is related to the transmission grid constraints set by the power flows through the lines. Since hydro generating groups are modelled so they imply high spilling costs, geothermal generation is curtailed whenever there is enough water to use the hydro generators58.
58 Given the resource perpetual characteristic, curtailing geothermal is not associated with spilling energy. In opposition, curtailing hydro power implies energy loss the moment water is spilled. -68-
Figure 5-4. Yearly active power dispatch breakdown by technology (daily averages) The overall electricity generation breakdown by technology is depicted and detailed in Table 5-5. With a total yearly electricity demand of about 7.1 TWh, system’s requirements are roughly half met by hydro power plants. VRE covers additional 20% of the demand, while the geothermal group accounts for another 13%. A small portion of the electricity is generated with the must-run biomass cogeneration plant, while the remainder (11%) is produced via fossil fuels (i.e. Diesel and natural gas, mostly the former, while the latter is used in peaking conditions). Table 5-5. Generation breakdown by fuels
Generation Fuel [TWh] [%] Geothermal 0.94 13.24 Biomass 0.12 1.63 Wind 0.83 11.63 PV 0.7 9.86 Hydro 3.71 52.19 Thermal 0.81 11.45
Demand 7.11 100
In continuation, results of the second outcome of the joint optimization model, i.e. the reserve allocation tool, are discussed. Table 5-6 shows the number of hours per annum each plant provides
-69- upward and downward secondary reserves. Considering the manner in which costs for SR capacity are set (see Section 5.3), the cheapest power plants (i.e. hydro) are providing positive secondary reserve capacity for the most number of hours, at different capacity levels. The largest one (HyRes01, having also the largest reservoir) provides reserve capacity for 8778 out of 8784 hours of the leap year considered. The Diesel plant in the Northern region provides positive reserves for a relatively large number of hours, mainly during the hydro production shortage at the end of the wet season or because of the congested North-South transmission corridor. In contrast, the other plants capable of providing reserve capacity (the cogeneration biomass plant, the Diesel plant in the Centre region, the geothermal or the gas unit) are supplying reserve for a small number of hours and, even when they do, the corresponding magnitude is small (in the order of less than 10 MW). This result may seem counterintuitive; one may expect cheap generating units to dispatch active power during normal operation, while the more expensive ones to be more involved in reserve provision. Yet, in addition to the reserve capacity allocation merit order set by the given costs, there are two other factors that support this outcome. First, operation of hydro power plants is modelled including a value of water. In order to maximize this parameter while maintaining water levels within limits set by the reference levels, active power dispatch may be replaced by reserve capacity provision during certain time periods. Second, reserve provision is strictly dependent on the status of the generating group (see Section 5.5). If the power plant does not dispatch active power during normal operation, it is not able to provide reserve capacity. The fact that active power dispatch associated with hydro plants is the cheapest after the VRE feed-in results in these plants operating most of the time and thus available for reserve provision. The negative cost associated with downward SR capacity (based on their high operating costs – see Section 5.3) from thermal units (Diesel and natural gas) results in these plants providing this service for the largest number of hours during the year. In addition to that, the largest hydro power plant is also active for almost 3000 hours per annum given the inherent value of the saved water throughout the year. Table 5-6. Secondary reserve capacity allocation by plant (hours per annum)
R+ R- R+ R- Plant [hr p.a.] [hr p.a.] Plant [hr p.a.] [hr p.a.] CoGen 39 59 HyCasc1 5523 21 DieselC 73 2455 HyCasc2 3904 78 DieselN 674 5485 HyCasc3 5107 86 GT 0 3215 HyRes1 8778 2977 GeoWest 9 14 HyRes2 7011 465 HyRes3 5517 104 Supporting those numbers, Figure 5-5 and Figure 5-6 depict how reserves are allocated at power plant level throughout the year. In both cases (positive and negative SR) only power plants with relevant (both time-wise and in magnitude) secondary reserve capacity allocation are depicted. The first plot shows how the largest share of positive SR capacity is covered by the largest hydro plant (HyRes01). This is happening due to water availability (i.e. large enough storage to support potential reserve activation) correlated with low costs of positive reserve capacity provision (Section 5.3). Because of the constraints related to active power dispatch – reserve allocation dependencies formulated in the previous section, the two patterns are similar. In fact, reserve allocation by this -70- generating group is limited during the same times electricity production from hydro plants is restrained (see Figure 5-4). This shortage is compensated by the downstream power plant (HyRes02) which, given its own natural inflows, can sustain supplementary potential active power dispatch. Diesel generating groups, even though they are providing this service for a large number of hours, are limited in the magnitude of active power they reserve.
Figure 5-5. Breakdown of positive secondary reserve allocation by plant during the entire year (daily averages) Figure 5-6, recreating the breakdown of negative SR allocation by plant shows how the temporal distribution of negative SR capacity provided by HyRes01 affects the way Diesel groups supply reserve capacity throughout the year, with the Capital group replacing hydro generation in this regard during the water shortage. During almost all this time, the largest hydro plant provides basically no downward SR capacity. What actually happens is that the water inflows shortage, correlated with the filling process of the reservoir set by the reference levels, forces the hydro plant to lower its output (as it does for the entire hydro fleet – see Figure 5-4) and be replaced by the Diesel group located in the Capital. The optimization process target is to minimize system costs, hence hydro (with lower operating cost than the Diesel group) active power dispatch during normal operation will not be curtailed, even for downward control strategies. In this context, the operational Diesel group covering for the hydro shortage is providing required downward SR capacity, in accordance to the corresponding merit order (Section 5.3). In addition to that, the most expensive generating group (i.e. the gas turbines) is steadily providing negative reserve capacity also due to the corresponding opportunity costs. Finally, other hydro power plants with relatively large number of hours of reserve allocation provide rather small amounts of SR to the grid.
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Figure 5-6. Breakdown of negative SR allocation by plant during the year (daily averages) Focusing on more detailed results, performance of the joint optimization tool during one week of the dry and wet season, respectively, is of interest. In this regard, the scheduled active power dispatch and the system-wide secondary reserve allocation breakdown between generating groups on a weekly basis are studied, as well as the active power dispatch – reserve allocation shares of the system’s power plants for a specific moment in time. From Figure 5-7 to Figure 5-9 the previously mentioned aspects during a usual week during the dry season (here, one week in November) are presented. The first plot (Figure 5-7) shows the generating units active power dispatch. Geothermal serves as baseload, must-run unit, as does the biomass cogeneration plant. The run-of-river hydro unit follows the magnitude of its corresponding inflows, with brief interruptions during the afternoon of weekdays due to transmission constraints. High feed-in of PV in the same grid node causes hydro curtailment during daytime peaks and, as long as this PV input maintains high levels, thermal contribution is relatively low. As soon as the solar resource fades out during the evening peak, thermal generation share in the electricity mix becomes significant and it is kept as such until the morning hours.
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Figure 5-7. Daily active power dispatch breakdown by technology during a dry week (November)
Figure 5-8. Hourly averages of reserve allocation by plant during the dry season (a week in November) Figure 5-8 shows the reserve allocation plan between power plants during the same time span. During the considered week, all upward secondary reserve is provided by hydro power plants (see Section 5.3 on pricing SR capacity and explanation of Figure 5-5); most of it from the cascaded scheme containing HyRes01, HyRes02 and HyRes03. Furthermore, the other cascaded scheme completes the system-wide requirement for positive active power control, where HyCasc02 is highly limited in this strategy due to its lack of natural inflows. The lower part of the plot shows how Diesel and natural gas generators, with high corresponding operating costs, are the most utilized for downward secondary reserve provision. In addition to that, the largest hydro group (HyRes01)
-73- is responsible for completing most of the need for negative SR capacity (see Section 5.3 on pricing SR capacity and explanation of Figure 5-6). The last plot in this group (Figure 5-9) depicts the scheduled active power dispatch – secondary reserve allocation breakdown by plant for a particular timestamp, or the way power plants are supposed to act in the next day generation pool. The plot, characteristic for an evening in November (therefore no PV generation), shows Diesel generation overcoming three quarters of its capacity in the Capital group, while the must-runs (geothermal and biomass) are run at their full output. In this situation, given hydrological limitations, the hydro power plants are all run in part load; HyRes01 or HyCasc02 run at 10 – 15% of their installed capacity, a situation that is not unlikely to happen in real systems if not all generating units within a power plant are committed (i.e. running only one unit of HyRes01 out of its 4 available aggregates). According to the reserve capacity merit order, upward SR capacity is provided by the largest hydro plant and the ones downstream, while the downward control is ensured through reserve capacities available on both Diesel generators.
Figure 5-9. Active power dispatch and reserve allocation by plant for a given timestamp during the dry season (15- minute resolution) Figure 5-10 to Figure 5-12 describe the same features for an example week during the wet season (mid-June). The first plot, Figure 5-10, details the active power economic dispatch of the system’s generating assets during normal operation. Shortage of wind resource is translated into low corresponding generation which is compensated by hydro power plants; given the water availability and the constraints set by the reference reservoir levels, the two cascaded schemes produce enough electricity to reduce the need for thermal generation. These hydrological conditions have an effect on the geothermal generation, as the latter is often curtailed in order to prevent water spilling.
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Figure 5-10. Daily active power dispatch breakdown by technology during the wet season (a week in June) Next, Figure 5-11 illustrates the secondary reserve allocation strategy between power plants, during the same time horizon. As hydro inflows during this time of the year allow hydro units to cover a large share of electricity generation, upward SR allocation strategies also build up around these generating groups. More specifically, the largest hydro power plant dominates the reserve capacity allocation, while the share of other hydro groups is reduced in comparison to the dry season example week. In addition to that, the Northern Diesel group also reserves capacity for positive control. This can be explained by the fact that, during this example within the wet season, hydro power plants (excluding the largest one – HyRes01) are run at full output in order to minimize costs related to the day-ahead active power dispatch. In these conditions, reserve capacity is left available only for the power plant associated with the very large water storage (HyRes01) and this is seen in its dominant position in the reserve capacity allocation scheme. Downward SR capacity is allocated mainly from expensive, fossil-fueled units, as well from the main hydro power group in order to profit from potential water savings. Lastly, the scheduled power plant management, as depicted in Figure 5-12 for an afternoon time in mid-June, shows interesting results. With little variable generation feed-in, the demand is mainly met by four hydro units running at or close to full output, accompanied by the must-run technologies. The Diesel group in the Northern region is also contributing to meeting the demand, by deploying two out its 14 generating units. Downward secondary reserve capacity is ensured entirely by the operational Diesel unit, while the positive control is entirely taken care of with reserve capacity provided by one of the hydro power schemes (HyRes01, HyRes02 and HyRes03).
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Figure 5-11. Hourly averages of reserve allocation by plant during the wet season (a week in June)
Figure 5-12. Active power and reserve allocation by plant for a given timestamp during the wet season (15-minute resolution)
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6 CONCLUSIONS & OUTLOOK
This final chapter offers an outcome overview of the two methodologies this thesis has approached. First, a conclusion of the thesis features and the value added they bring is discussed. Subsequently, inherent limitations of both secondary reserve sizing and joint optimization models are mentioned and further work on related aspects is recommended.
6.1 Final Remarks It is expected that evolution of energy systems over the following decades to be highly influenced by globally embraced drivers, such as electricity sector decarbonization, enhanced access to electricity supply or shifting the transportation away from oil-based fuels. In all these situations, variable renewable energy will play a vital role and implicit challenges will arise from this transition process. An example of such challenge is the issue this thesis tackled, namely the impact of VRE on the net load short-term variability, or how the active power reserve requirement magnitude will be affected. Additionally, in the context of perpetual evolution of the mechanisms surrounding electricity markets, it is of interest how to distribute active power reserve capacity across the power grid’s generating assets in a cost-effective manner, thus fostering maximization of producers (through optimizing their operational costs) and society (by maintaining high reliability levels) welfare. Based on a blend of academic insight and available project experience, the proposed solution involved the development of a robust, flexible and computationally fast methodology, features that are highly desired in project-oriented tasks. The secondary reserve dimensioning methodology developed within this thesis is based on the previous work of (Wagner, et al., 2016). Although delivering approximate results for secondary reserve demand required to balance short-term fluctuations due to load and VRE generation, the work carried out in this thesis incorporates a series of additional features that enhances the relevance of its output, as well as its applicability in future projects. The low-pass filtering process applied to input time series is the first example. Load and VRE generation patterns are highly stochastic in short time horizons. Especially in the case of solar PV or wind, their output development is often characterized by high-magnitude, short-duration spikes caused by various meteorological phenomena (e.g. clouds, wind gusts), behaviour that the filtering process smoothens. Considering the activation times of active power control stages, such events are buffered through the response of primary control. Therefore, the resultant (post-filtering) time series expresses more accurately the challenges secondary reserve has to cope with as part of the active power control strategies. Distinction of positive and negative control is another model improvement this thesis addresses. Different power plants may provide reserve capacities in different directions (up- and downward) at different costs. Hence, the current approach of non- symmetrical SR capacity dimensioning enhances the subsequent optimization of system costs. The second addition to the model is part of the time series classification procedure. Categorizing input data into subsets of entries that share similar statistical properties is paramount for a plausible estimation of required SR capacity. This work improves the load data classification through a time- of-week and time-of-day levels (see Section 4.5.1). A similar time-of-day classification (although determined on different criteria) is incorporated to the PV sample categorization. Addition of these
-77- classification levels offers higher temporal resolution to the SR sizing procedure, thus enhancing the statistical relevance of the input data. Initially, aggregation of SR requirement due to individual fluctuation drivers (load, PV and wind) was done linearly (i.e. as an algebraic summation), feature that was most likely to overestimate the need of reserve capacity since the causes of variations are assumed non-correlated. The current model is using the geometric aggregation of SR demand due to individual variation sources. This feature is considered a more precise approach towards estimating overall requirements in power systems with VRE generators geographically spread across the territory. As presented in the validation process (Section 4.9.1), inclusion of the aforementioned additions delivered results that are very close to what real operation of the Czech Republic’s power system implies. Additional requirements of secondary reserve capacity due to VRE installed capacity expansion is the last enhancement brought to the methodology. Rapid development of PV and wind comes with implicit challenges for power system operators and one such challenge is posed by the impact that additional VRE has on the adequate magnitude of reserve capacity required to safeguard system security. The approach suggested in this thesis (see Section 4.7) is based on historical data from a number of control areas; a link between VRE penetration levels and SR requirements relative to VRE capacity. It develops as a method that evaluates marginal secondary reserve requirements as a function of historical data, while diverging from the linear up-scaling procedure that would fail to take into consideration inherent characteristics of VRE capacity expansion (e.g. the smoothing effect). The second part of the work conducted in this thesis aimed at developing a cost optimization algorithm for secondary reserve capacity allocation. The model is developed based on SR capacity requirements (i.e. estimated via the proposed sizing methodology) of an input generic network and subsequently embedded into a more complex grid optimization software (ENAplan). Initially, ENAplan included an active power economic dispatch tool emulating solely the functionality of a day-ahead market, while completely disregarding reserve capacity allocation throughout the power grid. Integrating the latter into a joint optimization of both active power dispatch and reserve allocation problem offers a more complex overview of the states that network assets (e.g. generating units, transmission lines) have to deal with during real operation of power systems. Thus, enhancement via the reserve capacity allocation tool improves the functionality of the optimization software altogether. The joint optimization model is tested within the proposed generic grid and its results are consistent considering the provided inputs and assumptions. Moreover, given its problem formulation, the optimization model is expected to deliver relevant results if a benchmarking procedure (i.e. a validation process based on real power system models) is considered.
6.2 Model Limitations and Future Work The secondary reserve sizing methodology, as presented in Chapter 4, is a generic approach towards one very important aspect of grid integration of renewable energy. This thesis used Czech power system data for exemplification purposes and, as seen in Section 4.9.1, results were actually close to what current circumstances require. Nonetheless, this methodology was developed around a model and comes with implicit limitations. First of all, the model is highly dependent on the availability of high-resolution historical data. In order to evaluate short-term variations of load and variable generation, time series at a relevant
-78- resolution (up to 10-15 minutes) are required. Second, the current model uses load and VRE generation data as input. That means that it is assumed that PV and wind generation is already in place at the moment of the study. Third, smooth evolution of VRE installed capacity during the studies time horizon is preferred. Steep expansions of VRE in short time horizons would affect the statistical relevance of the input data. Several model improvements are proposed: resolution enhancement of PV time-of-day classification that would better correlate SR needs with the variable sunrise/sunset hours during one month (see Section 4.5.2) and using some sort of interpolation means for result data classes that would refine the stepwise development of SR requirement as seen in the yearly diagrams. Also, the method used for statistical aggregation may be subject to changes; using convolution of fluctuation drivers’ probability distribution functions (Holttinen, et al., 2012b; Van den Bergh, et al., 2017) instead of geometrically aggregating individual short-term variation figures may provide significant output differences. The active power dispatch – reserve provision joint optimization tool continues on the generic basis the secondary control sizing methodology is built. Technical and economic information about the assets of the power system (generators, transmission lines, reservoirs), as well as its demand, resource availability and secondary reserve requirements should suffice in order to perform a relevant evaluation of the grid’s generating units’ management. Yet, by its very nature as model, the proposed tool implies specific simplifications. First and maybe the most important simplification is that the optimization problem is built on system’s linearized DC equations. This interpretation leads to the optimal power flow through the grid’s connection lines to take into account only active power flows, thus disregarding reactive power flows and voltage of the transmission network. Another simplification is related to the way must-run constraints are set in the absence of integer variables; such technologies are constrained through explicit variables (as low-bound share of rated power of the generating unit). The remainders (e.g. hydro or VRE plants) are freely dispatched, without any minimum generation level. Shifting to the reserve allocation part of the optimization tool, one simplification implied that variable generation units (PV, wind, hydro run-of-river) are not responsible for reserve capacity allocation. Electricity from such sources is dispatched as long it is available, with the exception of possible curtailment due to grid contingencies. Provision of upward or downward reserve capacity comes with specific lost opportunity costs and technical challenges for VRE producers and further analysis on this topic could support the ability of these units to provide such services. In fact, (Gesino, 2010; Jansen, et al., 2013; De Vos, et al., 2014; Gevorgian, et al., 2016) are only a few papers that advocate the possibility of PV or wind to take part in operating reserve strategies. The joint optimization process evaluates the system, in each of its timestamps, as in its steady state. In this context, at each moment in time, demand and generation are balanced by the active power dispatch tool that acts as a day-ahead wholesale market. As no actual imbalance is seen in advance, reserve activation is therefore not considered. This leads to another potential improvement of the model. Optimal load flows are determined taking into account each plant’s allocated reserve capacity, therefore the source of additional active power dispatch under normal conditions, but not the receiving end of that supplementary energy (i.e. the node where the imbalance shall occur). It is expected from the power flow distribution through the transmission lines to change according to the group of nodes of interest and taking into account the influence of the end node of the reserve allocation strategy is an aspect of the optimization tool that can be enhanced.
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Furthermore, electricity producers’ strategies on various electricity markets are a rather complex issue that the proposed model treats in a simplified manner. The goal of any electricity provider part of a liberalized market is profit-driven and in this regard, when competing on different markets, each having specific features, evaluations based on technical and financial decision factors are the core of subsequent operational strategies. Given a generating unit with limited installed capacity, the producer will allocate and bid certain capacity shares to certain electricity markets (wholesale, intraday, balancing) and this process requires an optimization on its own. A better understanding of how these decisions are made based on exogenous factors (fuel prices, hydro reservoir levels, electricity price forecasts, etc.) and how capacity shares develop along the year and subsequent addition to the methodology would certainly add value to the joint optimization model developed in this thesis. Lastly, the optimization problem can be developed around minimization of other variables, such as various reliability functions of the power system (Loss of Load Probability – LOLP, or the Expected Energy Not Served – EENS).
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Appendix – List of Figures
Figure A-1. Time series filtering. Detail for the rolling average used on wind data ...... 91 Figure A-2. Time series filtering. Detail for the Butterworth filter used on wind data ...... 91 Figure A-3. Time series filtering. Detail for the Holt single exponential method used on wind data ...... 92 Figure B-1. Superposition of seasonally classified demand curves ...... 93 Figure B-2. Load data time-of-day classification ...... 93 Figure B-3. Superposition of seasonally classified PV output curves (January – April) ...... 94 Figure B-4. Superposition of seasonally classified PV output curves (May - August) ...... 95 Figure B-5. Superposition of seasonally classified PV output curves (September - December) ... 96 Figure D-1. Overview of the ABC scenarios during one random week in April (spring time) ..... 99 Figure D-2. Overview of the ABC scenarios during one random week in July (summer time) ... 99 Figure D-3. Overview of the ABC scenarios during one random week in October (autumn time) ...... 100 Figure D-4. Overview of the ABC scenarios during one random week in January (winter time) ...... 100 Figure D-5. Overview of the DEF scenarios during one random week in April (spring time) .. 101 Figure D-6. Overview of the DEF scenarios during one random week in July (summer time) . 101 Figure D-7. Overview of the DEF scenarios during one random week in October (autumn time) ...... 102 Figure D-8. Overview of the DEF scenarios during one random week in January (winter time) ...... 102 Figure D-9. Duration curve of upward total SR requirement ...... 103 Figure D-10. Duration curve of downward total SR requirement ...... 103 Figure D-11. Duration curve of upward SR requirement due to PV fluctuations ...... 104 Figure D-12. Duration curve of downward SR requirement due to PV fluctuations ...... 104 Figure D-13. Duration curve of upward SR requirement due to wind fluctuations ...... 105 Figure D-14 Duration curve of downward SR requirement due to wind fluctuations ...... 105 Figure E-1.Total upward SR requirement (Alpha) ...... 106 Figure E-2. Total downward SR requirement (Alpha) ...... 106 Figure E-3. Upward secondary SR due to load short-term fluctuations (Alpha) ...... 107 Figure E-4. Downward SR requirement due to load short term fluctuations (Alpha) ...... 107 Figure E-5. Upward SR requirement due to PV output short-term fluctuations (Alpha) ...... 108 Figure E-6. Downward SR requirement due to PV output short-term fluctuations (Alpha) ...... 108 Figure E-7. Upward SR requirement due to wind output short-term fluctuations (Alpha)...... 109 Figure E-8. Downward SR requirement due to wind output short-term fluctuations (Alpha) ... 109 Figure E-9. Upward SR requirement prior to data aggregation (Alpha) ...... 110 Figure E-10. Downward SR requirement prior to data aggregation (Alpha) ...... 110 Figure E-11. Total upward SR requirement (Bravo) ...... 111 Figure E-12. Total downward SR requirement (Bravo) ...... 111 Figure E-13. Upward SR requirement due to load short-term fluctuations (Bravo) ...... 112 Figure E-14. Downward SR requirement due to load short-term fluctuations (Bravo) ...... 112 Figure E-15. Upward SR requirement due to PV output short-term fluctuations (Bravo) ...... 113 Figure E-16. Downward SR requirement due to PV output short-term fluctuations (Bravo) .... 113
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Figure E-17. Upward SR requirement due to wind output short-term fluctuations (Bravo) ...... 114 Figure E-18. Downward SR requirement due to wind output short-term fluctuations (Bravo) . 114 Figure E-19. Upward SR requirement prior to data aggregation (Bravo) ...... 115 Figure E-20. Downward SR requirement prior to data aggregation (Bravo) ...... 115 Figure E-21. Total upward SR requirement (Charlie) ...... 116 Figure E-22. Total downward SR requirement (Charlie) ...... 116 Figure E-23. Upward SR requirement due to load short-term fluctuations (Charlie) ...... 117 Figure E-24. Downward SR requirement due to load short-term fluctuations (Charlie) ...... 117 Figure E-25. Upward SR requirement due to PV output short-term fluctuations (Charlie) ...... 118 Figure E-26. Downward SR requirement due to PV output short-term fluctuations (Charlie) .. 118 Figure E-27. Upward SR requirement due to wind output short-term fluctuations (Charlie) .... 119 Figure E-28. Downward SR requirement due to wind output short-term fluctuations (Charlie) ...... 119 Figure E-29. Upward SR requirement prior to data aggregation (Charlie) ...... 120 Figure E-30. Downward SR requirement prior to data aggregation (Charlie) ...... 120 Figure E-31. Total upward SR requirement (Delta) ...... 121 Figure E-32. Total downward SR requirement (Delta) ...... 121 Figure E-33. Upward secondary SR due to load short-term fluctuations (Delta) ...... 122 Figure E-34. Downward SR requirement due to load short-term fluctuations (Delta) ...... 122 Figure E-35. Upward SR requirement due to PV output short-term fluctuations (Delta) ...... 123 Figure E-36. Downward SR requirement due to PV output short-term fluctuations (Delta) ..... 123 Figure E-37. Upward SR requirement due to wind output short-term fluctuations (Delta) ...... 124 Figure E-38. Downward SR requirement due to wind output short-term fluctuations (Delta) .. 124 Figure E-39. Upward SR requirement prior to data aggregation (Delta) ...... 125 Figure E-40. Downward SR requirement prior to data aggregation (Delta) ...... 125 Figure E-41. Total upward SR requirement (Echo) ...... 126 Figure E-42. Total downward SR requirement (Echo) ...... 126 Figure E-43. Upward SR requirement due to load short-term fluctuations (Echo) ...... 127 Figure E-44. Downward SR requirement due to load short-term fluctuations (Echo) ...... 127 Figure E-45. Upward SR requirement due to PV output short-term fluctuations (Echo) ...... 128 Figure E-46. Downward SR requirement due to PV output short-term fluctuations (Echo) ..... 128 Figure E-47. Upward SR requirement due to wind output short-term fluctuations (Echo) ...... 129 Figure E-48. Downward SR requirement due to wind output short-term fluctuations (Echo) .. 129 Figure E-49. Upward SR requirement prior to data aggregation (Echo) ...... 130 Figure E-50. Downward SR requirement prior to data aggregation (Echo) ...... 130 Figure E-51. Total upward SR requirement (Foxtrot) ...... 131 Figure E-52. Total downward SR requirement (Foxtrot) ...... 131 Figure E-53. Upward SR requirement due to load short-term fluctuations (Foxtrot) ...... 132 Figure E-54. Downward SR requirement due to load short-term fluctuations (Foxtrot) ...... 132 Figure E-55. Upward SR requirement due to PV output short-term fluctuations (Foxtrot) ...... 133 Figure E-56. Downward SR requirement due to PV output short-term fluctuations (Foxtrot) . 133 Figure E-57. Upward SR requirement due to wind output short-term fluctuations (Foxtrot) ... 134 Figure E-58. Downward SR requirement due to wind output short-term fluctuations (Foxtrot) ...... 134 Figure E-59. Upward SR requirement prior to data aggregation (Foxtrot) ...... 135 Figure E-60. Downward SR requirement prior to data aggregation (Foxtrot) ...... 135 Figure F-1. Per-unit system load ...... 136
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Figure F-2. Per-unit PV feed-in for PVCap plant (node C02) ...... 136 Figure F-3. Per-unit PV feed-in for PVEast01 plant (node E01) ...... 137 Figure F-4. Per-unit PV feed-in for PVEast02 plant (node E02) ...... 137 Figure F-5. Per-unit wind feed-in for WindEast plant (node S04) ...... 137 Figure F-6. Per-unit wind feed-in for WindWest plant (node S05) ...... 138 Figure F-7. Natural inflows of HyRes01 plant ...... 138 Figure F-8. Natural inflows of HyRes02 plant ...... 138 Figure F-9. Natural inflows of HyRes03 plant ...... 139 Figure F-10. Natural inflows of HyCasc01 plant ...... 139 Figure F-11. Natural inflows of HyCasc02 plant ...... 139 Figure F-12. Natural inflows of HyCasc03 plant ...... 140 Figure F-13. Natural inflows of HyRoR01 plant ...... 140
Appendix – List of Tables
Table C-1. Correlation between secondary control procurement and variable generation penetration for studied systems between 2005 and 2016 (part 1) ...... 97 Table C-2. Correlation between secondary control procurement and variable generation penetration for studied systems between 2005 and 2016 (part 2) ...... 98
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Appendix A TIME SERIES FILTERING
Figure A-1. Time series filtering. Detail for the rolling average used on wind data
Figure A-2. Time series filtering. Detail for the Butterworth filter used on wind data
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Figure A-3. Time series filtering. Detail for the Holt single exponential method used on wind data
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Appendix B TIME SERIES CLASSIFICATION
Figure B-1. Superposition of seasonally classified demand curves
Figure B-2. Load data time-of-day classification. Hour boundaries based on seasonal aggregated curves
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Figure B-3. Superposition of seasonally classified PV output curves (January – April). Aggregated monthly curve in dark red
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Figure B-4. Superposition of seasonally classified PV output curves (May - August) . Aggregated monthly curve in dark red
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Figure B-5. Superposition of seasonally classified PV output curves (September - December). Aggregated monthly curve in dark red
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Appendix C ADDITIONAL SECONDARY RESERVE REQUIREMENTS WITH VARIABLE GENERATION CAPACITY EXPANSION – STUDIED SYSTEMS
Table C-1. Correlation between secondary control procurement and variable generation penetration for studied systems between 2005 and 2016 (part 1)
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 DE REG+ [MW] N/A N/A N/A 2983.0 2802.0 2402.0 2128.0 2091.0 2108.0 2036.0 2053.0 2008.0 REG- [MW] N/A N/A N/A 2403.0 2163.0 2129.0 2084.0 2131.0 2066.0 1963.0 2025.0 1938.0 VG IC [MW] N/A N/A N/A 29940.0 36200.0 45120.0 54200.0 63860.0 70270.0 76450.0 83910.0 90490.0 RP [% PEAK] N/A N/A N/A 38.7 46.8 58.3 70.1 82.6 90.8 98.8 108.5 117.0 RG_UP[% IC] N/A N/A N/A 10.0 7.7 5.3 3.9 3.3 3.0 2.7 2.4 2.2 RG_DN[% IC] N/A N/A N/A 8.0 6.0 4.7 3.8 3.3 2.9 2.6 2.4 2.1 ES REG+ [MW] 702.0 712.0 524.0 717.0 718.0 727.0 716.0 709.0 691.0 677.0 685.0 682.0 REG- [MW] 506.0 510.0 379.0 526.0 526.0 530.0 526.0 522.0 512.0 502.0 512.0 509.0 VG IC [MW] 11541.0 14301.0 19488.0 22259.0 23546.0 25427.0 27318.0 27642.0 27666.0 27676.0 27689.0 27722.0 RP [% PEAK] 28.6 35.5 48.3 55.2 58.4 63.1 67.7 68.5 68.6 68.6 68.7 68.7 RG_UP[% IC] 6.1 5.0 2.7 3.2 3.0 2.9 2.6 2.6 2.5 2.4 2.5 2.5 RG_DN[% IC] 4.4 3.6 1.9 2.4 2.2 2.1 1.9 1.9 1.9 1.8 1.8 1.8 PT REG+ [MW] N/A N/A N/A 107.0 136.0 195.0 197.0 194.0 183.0 177.0 182.0 190.0 REG- [MW] N/A N/A N/A 52.0 60.0 95.0 90.0 85.0 90.0 80.0 75.0 70.0 VG IC [MW] N/A N/A N/A 2927.0 3634.0 3992.0 4431.0 4756.0 5001.0 5365.0 5534.0 5734.0 RP [% PEAK] N/A N/A N/A 34.0 42.2 46.3 51.4 55.2 58.0 62.3 64.2 66.5 RG_UP[% IC] N/A N/A N/A 3.7 3.7 4.9 4.4 4.1 3.7 3.3 3.3 3.3 RG_DN[% IC] N/A N/A N/A 1.8 1.7 2.4 2.0 1.8 1.8 1.5 1.4 1.2 RO REG+ [MW] N/A N/A N/A N/A 39.0 45.0 57.0 52.0 63.0 64.0 62.0 59.0 REG- [MW] N/A N/A N/A N/A 59.0 57.0 55.0 64.0 52.0 60.0 67.0 64.0 VG IC [MW] N/A N/A N/A N/A 14.3 419.3 464.3 1030.0 2962.0 3752.0 4352.0 4421.0 RP [% PEAK] N/A N/A N/A N/A 0.2 4.9 5.5 12.1 34.9 44.2 51.3 52.1 RG_UP[% IC] N/A N/A N/A N/A 272.7 10.7 12.3 5.0 2.1 1.7 1.4 1.3 RG_DN[% IC] N/A N/A N/A N/A 412.6 13.6 11.8 6.2 1.8 1.6 1.5 1.4 TX REG+ [MW] N/A N/A 764.0 815.0 851.0 857.0 488.0 451.0 420.0 377.0 321.0 301.0 REG- [MW] N/A N/A 828.0 845.0 825.0 834.0 551.0 525.0 470.0 440.0 362.0 326.0 VG IC [MW] N/A N/A 4785.0 8005.0 8916.0 9415.0 9646.0 10489.0 11186.0 12663.0 16052.0 18160.0 RP [% PEAK] N/A N/A 6.9 11.5 12.8 13.5 13.9 15.1 16.1 18.2 23.1 26.1 RG_UP[% IC] N/A N/A 16.0 10.2 9.5 9.1 5.1 4.3 3.8 3.0 2.0 1.7 RG_DN[% IC] N/A N/A 17.3 10.6 9.3 8.9 5.7 5.0 4.2 3.5 2.3 1.8 FI REG+ [MW] N/A N/A N/A 378.0 530.0 480.0 589.0 598.0 745.0 715.0 617.0 823.0 REG- [MW] N/A N/A N/A 377.0 443.0 607.0 482.0 411.0 451.0 473.0 519.0 462.0 VG IC [MW] N/A N/A N/A 143.2 146.2 188.2 197.2 289.2 488.2 627.2 1008.2 1553.0 RP [% PEAK] N/A N/A N/A 1.1 1.1 1.4 1.5 2.1 3.6 4.6 7.4 11.4 RG_UP[% IC] N/A N/A N/A 264.0 362.5 255.0 298.7 206.8 152.6 114.0 61.2 53.0 RG_DN[% IC] N/A N/A N/A 263.3 303.0 322.5 244.4 142.1 92.4 75.4 51.5 29.7 IT REG+ [MW] N/A N/A N/A 1316.0 1423.0 789.0 564.0 704.0 1025.0 1109.0 N/A N/A REG- [MW] N/A N/A N/A 1282.0 1319.0 1689.0 554.0 426.0 576.0 517.0 N/A N/A VG IC [MW] N/A N/A N/A 4181.0 5869.0 9262.0 19487.0 24455.0 26153.0 27099.0 N/A N/A RP [% PEAK] N/A N/A N/A 7.0 9.8 15.5 32.7 41.0 43.8 45.4 N/A N/A RG_UP[% IC] N/A N/A N/A 31.5 24.2 8.5 2.9 2.9 3.9 4.1 N/A N/A RG_DN[% IC] N/A N/A N/A 30.7 22.5 18.2 2.8 1.7 2.2 1.9 N/A N/A
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Table C-2. Correlation between secondary control procurement and variable generation penetration for studied systems between 2005 and 2016 (part 2)
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 GR REG+ [MW] N/A N/A N/A N/A 333.0 333.0 334.0 313.0 366.0 422.0 483.0 480.0 REG- [MW] N/A N/A N/A N/A 162.0 162.0 162.0 109.0 100.0 100.0 100.0 100.0 VG IC [MW] N/A N/A N/A N/A 1135.2 1406.5 2250.3 3285.3 4442.8 4575.0 4754.0 4979.0 RP [% PEAK] N/A N/A N/A N/A 11.6 14.3 22.9 33.5 45.3 46.6 48.4 50.7 RG_UP[% IC] N/A N/A N/A N/A 29.3 23.7 14.8 9.5 8.2 9.2 10.2 9.6 RG_DN[% IC] N/A N/A N/A N/A 14.3 11.5 7.2 3.3 2.3 2.2 2.1 2.0 NL REG+ [MW] N/A N/A 424.0 417.0 504.0 506.0 456.0 353.0 348.0 356.0 337.0 402.0 REG- [MW] N/A N/A 513.0 501.0 584.0 562.0 511.0 504.0 398.0 412.0 412.0 587.0 VG IC [MW] N/A N/A 1801.0 2268.0 2282.0 2336.0 2457.0 2747.0 3443.0 3947.0 4790.0 6219.0 RP [% PEAK] N/A N/A 10.1 12.8 12.8 13.2 13.8 15.5 19.4 22.2 27.0 35.0 RG_UP[% IC] N/A N/A 23.5 18.4 22.1 21.7 18.6 12.9 10.1 9.0 7.0 6.5 RG_DN[% IC] N/A N/A 28.5 22.1 25.6 24.1 20.8 18.3 11.6 10.4 8.6 9.4 AT REG+ [MW] N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 200.0 180.0 REG- [MW] N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 200.0 180.0 VG IC [MW] N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 3338.0 3703.0 RP [% PEAK] N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 29.4 32.7 RG_UP[% IC] N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 6.0 4.9 RG_DN[% IC] N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 6.0 4.9 BE REG+ [MW] N/A N/A N/A N/A N/A N/A N/A N/A 143.0 149.0 144.0 140.0 REG- [MW] N/A N/A N/A N/A N/A N/A N/A N/A 143.0 149.0 145.0 140.0 VG IC [MW] N/A N/A N/A N/A N/A N/A N/A N/A 4480.0 5060.0 5450.0 5825.0 RP [% PEAK] N/A N/A N/A N/A N/A N/A N/A N/A 34.1 38.5 41.5 44.4 RG_UP[% IC] N/A N/A N/A N/A N/A N/A N/A N/A 3.2 2.9 2.6 2.4 RG_DN[% IC] N/A N/A N/A N/A N/A N/A N/A N/A 3.2 2.9 2.7 2.4 FR REG+ [MW] N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 659.0 651.0 REG- [MW] N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 659.0 651.0 VG IC [MW] N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 16860.0 18805.0 RP [% PEAK] N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 18.4 20.5 RG_UP[% IC] N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 3.9 3.5 RG_DN[% IC] N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 3.9 3.5 CH REG+ [MW] N/A N/A N/A N/A N/A N/A 360.0 337.0 325.0 298.0 346.0 N/A REG- [MW] N/A N/A N/A N/A N/A N/A 364.0 364.0 337.0 336.0 313.0 N/A VG IC [MW] N/A N/A N/A N/A N/A N/A 211.0 437.0 756.0 1076.0 1450.0 N/A RP [% PEAK] N/A N/A N/A N/A N/A N/A 2.1 4.3 7.4 10.6 14.3 N/A RG_UP[% IC] N/A N/A N/A N/A N/A N/A 170.6 77.1 43.0 27.7 23.9 N/A RG_DN[% IC] N/A N/A N/A N/A N/A N/A 172.5 83.3 44.6 31.2 21.6 N/A With 푅퐸퐺 + [푀푊] = average upward procured secondary control during corresponding year; 푅퐸퐺 − [푀푊] = average downward procured secondary control during corresponding year; 푉퐺 퐼퐶 [푀푊] = total variable generation installed capacity (PV and wind); 푅푃[%] = variable generation penetration relative to yearly peak load ; 푅퐺_푈푃[%] = upward secondary control procurement relative to variable generation installed capacity; 푅퐺_퐷푁[%] = downward secondary control procurement relative to variable generation installed capacity.
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Appendix D SECONDARY RESERVE REQUIREMENTS – COMPARATIVE RESULTS
Figure D-1. Overview of the ABC scenarios during one random week in April (spring time)
Figure D-2. Overview of the ABC scenarios during one random week in July (summer time)
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Figure D-3. Overview of the ABC scenarios during one random week in October (autumn time)
Figure D-4. Overview of the ABC scenarios during one random week in January (winter time)
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Figure D-5. Overview of the DEF scenarios during one random week in April (spring time)
Figure D-6. Overview of the DEF scenarios during one random week in July (summer time)
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Figure D-7. Overview of the DEF scenarios during one random week in October (autumn time)
Figure D-8. Overview of the DEF scenarios during one random week in January (winter time)
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Figure D-9. Duration curve of upward total SR requirement
Figure D-10. Duration curve of downward total SR requirement
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Figure D-11. Duration curve of upward SR requirement due to PV fluctuations
Figure D-12. Duration curve of downward SR requirement due to PV fluctuations
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Figure D-13. Duration curve of upward SR requirement due to wind fluctuations
Figure D-14 Duration curve of downward SR requirement due to wind fluctuations
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Appendix E SECONDARY RESERVE REQUIREMENTS – SCENARIO BREAKDOWN
Scenario Alpha
Figure E-1.Total upward SR requirement (Alpha)
Figure E-2. Total downward SR requirement (Alpha)
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Figure E-3. Upward secondary SR due to load short-term fluctuations (Alpha)
Figure E-4. Downward SR requirement due to load short term fluctuations (Alpha)
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Figure E-5. Upward SR requirement due to PV output short-term fluctuations (Alpha)
Figure E-6. Downward SR requirement due to PV output short-term fluctuations (Alpha)
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Figure E-7. Upward SR requirement due to wind output short-term fluctuations (Alpha)
Figure E-8. Downward SR requirement due to wind output short-term fluctuations (Alpha)
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Figure E-9. Upward SR requirement prior to data aggregation (Alpha)
Figure E-10. Downward SR requirement prior to data aggregation (Alpha)
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Scenario Bravo
Figure E-11. Total upward SR requirement (Bravo)
Figure E-12. Total downward SR requirement (Bravo)
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Figure E-13. Upward SR requirement due to load short-term fluctuations (Bravo)
Figure E-14. Downward SR requirement due to load short-term fluctuations (Bravo)
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Figure E-15. Upward SR requirement due to PV output short-term fluctuations (Bravo)
Figure E-16. Downward SR requirement due to PV output short-term fluctuations (Bravo)
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Figure E-17. Upward SR requirement due to wind output short-term fluctuations (Bravo)
Figure E-18. Downward SR requirement due to wind output short-term fluctuations (Bravo)
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Figure E-19. Upward SR requirement prior to data aggregation (Bravo)
Figure E-20. Downward SR requirement prior to data aggregation (Bravo)
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Scenario Charlie
Figure E-21. Total upward SR requirement (Charlie)
Figure E-22. Total downward SR requirement (Charlie)
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Figure E-23. Upward SR requirement due to load short-term fluctuations (Charlie)
Figure E-24. Downward SR requirement due to load short-term fluctuations (Charlie)
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Figure E-25. Upward SR requirement due to PV output short-term fluctuations (Charlie)
Figure E-26. Downward SR requirement due to PV output short-term fluctuations (Charlie)
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Figure E-27. Upward SR requirement due to wind output short-term fluctuations (Charlie)
Figure E-28. Downward SR requirement due to wind output short-term fluctuations (Charlie)
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Figure E-29. Upward SR requirement prior to data aggregation (Charlie)
Figure E-30. Downward SR requirement prior to data aggregation (Charlie)
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Scenario Delta
Figure E-31. Total upward SR requirement (Delta)
Figure E-32. Total downward SR requirement (Delta) -121-
Figure E-33. Upward secondary SR due to load short-term fluctuations (Delta)
Figure E-34. Downward SR requirement due to load short-term fluctuations (Delta)
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Figure E-35. Upward SR requirement due to PV output short-term fluctuations (Delta)
Figure E-36. Downward SR requirement due to PV output short-term fluctuations (Delta)
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Figure E-37. Upward SR requirement due to wind output short-term fluctuations (Delta)
Figure E-38. Downward SR requirement due to wind output short-term fluctuations (Delta)
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Figure E-39. Upward SR requirement prior to data aggregation (Delta)
Figure E-40. Downward SR requirement prior to data aggregation (Delta)
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Scenario Echo
Figure E-41. Total upward SR requirement (Echo)
Figure E-42. Total downward SR requirement (Echo)
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Figure E-43. Upward SR requirement due to load short-term fluctuations (Echo)
Figure E-44. Downward SR requirement due to load short-term fluctuations (Echo)
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Figure E-45. Upward SR requirement due to PV output short-term fluctuations (Echo)
Figure E-46. Downward SR requirement due to PV output short-term fluctuations (Echo)
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Figure E-47. Upward SR requirement due to wind output short-term fluctuations (Echo)
Figure E-48. Downward SR requirement due to wind output short-term fluctuations (Echo)
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Figure E-49. Upward SR requirement prior to data aggregation (Echo)
Figure E-50. Downward SR requirement prior to data aggregation (Echo)
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Scenario Foxtrot
Figure E-51. Total upward SR requirement (Foxtrot)
Figure E-52. Total downward SR requirement (Foxtrot)
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Figure E-53. Upward SR requirement due to load short-term fluctuations (Foxtrot)
Figure E-54. Downward SR requirement due to load short-term fluctuations (Foxtrot)
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Figure E-55. Upward SR requirement due to PV output short-term fluctuations (Foxtrot)
Figure E-56. Downward SR requirement due to PV output short-term fluctuations (Foxtrot)
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Figure E-57. Upward SR requirement due to wind output short-term fluctuations (Foxtrot)
Figure E-58. Downward SR requirement due to wind output short-term fluctuations (Foxtrot)
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Figure E-59. Upward SR requirement prior to data aggregation (Foxtrot)
Figure E-60. Downward SR requirement prior to data aggregation (Foxtrot)
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Appendix F JOINT OPTIMIZATION GRID INPUT DATA
Figure F-1. Per-unit system load
Figure F-2. Per-unit PV feed-in for PVCap plant (node C02)
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Figure F-3. Per-unit PV feed-in for PVEast01 plant (node E01)
Figure F-4. Per-unit PV feed-in for PVEast02 plant (node E02)
Figure F-5. Per-unit wind feed-in for WindEast plant (node S04)
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Figure F-6. Per-unit wind feed-in for WindWest plant (node S05)
Figure F-7. Natural inflows of HyRes01 plant
Figure F-8. Natural inflows of HyRes02 plant
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Figure F-9. Natural inflows of HyRes03 plant
Figure F-10. Natural inflows of HyCasc01 plant
Figure F-11. Natural inflows of HyCasc02 plant
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Figure F-12. Natural inflows of HyCasc03 plant
Figure F-13. Natural inflows of HyRoR01 plant
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