ECONOMIC OPTIMIZATION FOR HEAT & ENERGY PRODUCTION USING RENEWABLE ENERGY FROM LOCAL RESOURCES
Master’s Thesis
submitted in fulfilment of the requirements for the
academic degree ‘Master of Science’
at the University of Graz for the
‘Erasmus Mundus Master's Programme in Industrial Ecology’
by
ARIADNI GEMENETZI
at the Institute Graz University of Technology
Supervisor: Ao. Univ.-Prof. Dipl.–Ing. Dr techn. Michael Narodoslawsky
Graz, 2013
A.G. Gemenetzi i
Topic
Economic optimization for heat and energy production using renewable energy from local resources
Supervisor
Ao. Univ.-Prof. Dipl.–Ing. Dr techn. Michael Narodoslawsky Graz University of Technology, Institute of Process and Particle Engineering
Author Ariadni Gemenetzi, Diploma Engineer Graduate student of the Erasmus Mundus Program in Industrial Ecology
Appointed Degree Double Master of Science degree
Student Nr: 1214904
With the kind support of Energieregion Weiz-Gleisdorf and Energie Steiermark.
Master Thesis Supervisor Ao. Univ.-Prof. Dipl.–Ing. Dr techn. Michael Narodoslawsky
Graz, July 2013
A.G. Gemenetzi iii
Declaration
I hereby declare that this submission is my own work and that, to the best of my knowledge and belief, it contains no material previously published or written by another person nor material which to a substantial extent has been accepted for the award of any other degree or diploma of the university or other institute of higher learning, except where due acknowledgment has been made in the text.
Graz, July 2013
Ariadni Gemenetzi
A.G. Gemenetzi iv
Abstract
The current master thesis is conducted within the framework of ‘iEnergy Weiz-Gleisdorf 2.0’, which is part of the long term energy goal of Styria; to become CO2 neutral until 2050.
Although anaerobic digestion is commonly used for waste management, its use for biogas production has increased over the last decades, as a local response to the pursuit of energy autonomy and economic stimulus. Thus the objective of this work is to assess the feasibility of creating an industrial complex that would deliver: 1) biogas production using locally acquired manure, meadow grass, farmland grass and municipal waste, and 2) electricity and heat production using biogas fueled technologies and biomass gasifiers. Three locations, within the area of ‘Weiz-Gleisdorf’ have been investigated as potentially situating the investigated technologies. Moreover, ELIN was also considered for biogas and/or high temperature heat provision in order to cover its heat demand, which is currently covered by a natural gas-fired boiler.
Thus an energy system has been designed, which is limited by certain system boundaries. The optimization tool used for assessing the optimal solution of the designed energy system is called Process Network Synthesis and is based on the P-graph methodology. The PNS tool was also used to carry out 22 different scenarios, responding to potential resource, energy, financial and geographical limitations.
The main results exhibit that biogas production appears to be economically viable, having an average gross profit of ~224,000 €·y-1. Anaerobic digesters appear to all three locations, but more prominently at Location 1 (Thannhausen North) and Location 2 (Thannhausen South). Biomass gasification favors wood chips’ use over biomass from short rotation and miscanthus chips and appears also at Location 1 and Location 2 and very rarely at Location 3 (Mortantsch). No resource shortage appears to be imminent, except for municipal waste.
Moreover, it appears to be economically optimal to combust biogas for energy and heat production, rather than to inject it in the distribution grid. Thus the main revenue derives from electricity and heat generation, and more rarely from the sales of upgraded biogas. Consequently, the respective tariffs will have a great influence on the future of similar projects. Furthermore, two district heat lines -1 (2,500 MWhth·y ) from Location 1 to Thannhausen and Location 2 to Krottendorf have resulted as economically optimal, whilst considerable amounts of heat are dissipated into the environment. Consequently, heat utilization is anticipated to become a great debate among actors.
Overall it can be concluded that biogas production from local resources is feasible, but doesn’t appear to be cost effective. Thus, the interplay of various stakeholders and their ambitions will be decisive regarding the future implementation of the current and similar projects.
A.G. Gemenetzi v
Zusammenfassung
Die aktuelle Masterarbeit wird im Rahmen des Projekts ‘Weiz-Gleisdorf iEnergy 2.0', als Teil der langfristigen Energiestrategie der Steiermark durchgeführt; CO2-neutral bis zum Jahr 2050 zu werden.
Obwohl anaerobe Vergärung häufig für die Abfallwirtschaft verwendet wird, hat der Einsatz für die Biogasproduktion, wegen der lokalen Reaktion auf die Verfolgung der Energieautonomie und Ökonomie Verbessererung, in den letzten Jahrzehnten zugenommen. Ziel dieser Arbeit ist die Durchführung: 1) der Biogasproduktion von lokal erworbenen, Gülle, Wiesen-, Ackergras und Kommunalsabfälle und 2) der Strom-und Wärmeerzeugung mit Biogas betriebenen Technologien und Biomassevergaser, zu untersuchen. Drei Standorte wurden im Bereich der ‘Weiz-Gleisdorf als potenzielle Situierung der untersuchten Technologien ernannt. Darüber hinaus wurde auch ELIN für Biogas- und/oder Hochtemperaturwärmebereitstellung berücksichtigt, um seinen Wärmebedarf zu decken, der derzeit von einem Erdgas-Heizkessel garantiert wird.
Somit ist ein Energie-System entwickelt, das durch bestimmte Systemgrenzen begrenzt ist. Das verwendete Optiemierungswerkzeug heisst Prozess-Netzwerk-Synthese, und ist auf der P-Graph Methodik basiert. Die PNS wurde auch verwendet, um 22 verschiedene Szenarien durchzuführen, die Ressourcen, energetisch, finanziel und geographisch begrentzt sind.
Es scheint, dass Biogasproduktion, mit einer durchschnittlichen Bruttogewinn von ~ 224,000 €·y-1 realisirbar ist. Biogasanlagen erscheinen auf allen drei Standorten, aber mehr prominent auf Standort 1 (Thannhausen Nord) und Standort 2 (Thannhausen Süd). Biomassvergasung favorisiert die Verwendung von Holzhackschnitzel über Kurzumtrieb- und Miscanthushackschnitzel und erscheint auch bei Standort 1 und Standort 2, aber sehr selten auf Standort 3 (Mortantsch). Es gibt keine Ressourcenknappheit, mit Ausnahme von Kommunalabfälle.
Darüber hinaus erscheint es wirtschaftlich optimal Biogas für Energie-und Wärmeerzeugung zu verbrennen, anstatt sie einzuspeisen. Somit, ergeben sich die wichtigsten Einnahmen aus Strom-und Wärmeerzeugung und selterner aus dem Verkauf des gereinigten Biogas. Folglich, werden die entsprechenden Tarife einen großen Einfluss auf die Zukunft von ähnlichen Projekten haben. -1 Außerdem, wurden zwei Fernwärme-Leitungen (2.500 MWhth y ), von Standort 1 zu Thannhausen und Standort 2 zu Krottendorf, optimal beurteilt, während große Wärmemenge auf die Umgebung abgegeben wird. Folglich, wird erwartet, dass Wärmeabgabe, eine große Debatte zwischen den Aktoren sein wird.
Insgesamt kann gefolgert werden, dass die Biogasproduktion aus lokalen Ressourcen realisirbar, aber nicht kosteneffektiv, ist. Also, wird das Zusammenspiel der verschiedenen Akteure und ihre Ambitionen entscheidend sein, in Bezug auf die künftige Umsetzung dieser und ähnlicher Projekten.
A.G. Gemenetzi vi
Acknowledgements
I would like to express my appreciation to the whole ‘Naro-team’ for the warm welcome and the pleasant working atmosphere. First of all, I thank Dr Narodoslawsky for accepting my thesis placement request and for providing his precious insight throughout this work. I am also grateful to Michael Eder, who introduced me to the topic, as well as to Nora Niemetz, who provided valuable feedback, regarding the Process Network Synthesis structure. Additionally, I wish to acknowledge the contribution of Dr Iris Absenger-Helmli (Energieregion) and Christian Orthofer (Energie Steiermark) toward the development of the investigated project and consequently of this work. Moreover, I offer my special thanks to Stephan Maier for his ceaseless support throughout this thesis.
I thank the Erasmus Mundus Program 2009-2013 for granting me a scholarship and thus giving me the opportunity to participate in the MIND Program. Last but not least I thank my mom, dad, my sister Rika and her boyfriend Kostas for their support throughout this 2 years long journey.
A.G. Gemenetzi vii
Table of Contents
1. INTRODUCTION ...... 1
2. BACKGROUND ...... 2
2.1 SUSTAINABLE DEVELOPMENT ...... 2 2.1.1 People ...... 4 2.1.2 Planet ...... 5 2.1.3. Profit ...... 5
2.2 ENERGY DEMAND AND AVAILABILITY ...... 6 2.2.1 World energy production and consumption ...... 6 2.2.2 Austrian energy production and consumption ...... 7
2.3 REVITALIZATION OF THE REGION ...... 9 2.3.1 The Austrian example ...... 10
2.4 PROCESS NETWORK SYNTHESIS (PNS) ...... 11 2.4.1 P-graph ...... 11 2.4.2 The Science behind PNS - Mathematical description of P-graphs ...... 11 2.4.3 MSG – SSG- ABB ...... 15 2.4.4 PNS as a Tool for region energy optimization ...... 15
3. PROJECT DESCRIPTION ...... 17
3.1 STAKEHOLDERS ...... 17
3.2 WEIZ - GLEISDORF ENERGIEREGION DEMOGRAPHICS AND SURFACE AREA ...... 19
3.3 GEOGRAPHICAL INFORMATION ...... 20
3.4 AVAILABLE RESOURCES ...... 22
3.5 TECHNOLOGY DESCRIPTION ...... 23 3.5.1 Anaerobic digesters ...... 24 3.5.2 MW treatment ...... 31 3.5.3 Gas-fired boilers ...... 32 3.5.4 Gas turbines & micro gas turbines ...... 33 3.5.5 CHP ...... 36 3.5.6 Biomass gasifier ...... 38 3.5.7 Biogas pipelines and upgrading & feed-in ...... 39 3.5.8 District heating ...... 39 3.5.9 Dryers ...... 40 3.5.10 Chopper harvester ...... 40 3.5.11 Transformers ...... 41
4. PNS INPUT STRUCTURE ...... 42
4.1 PURCHASE AND SALE PRICES ...... 45
A.G. Gemenetzi viii
4.2 TRANSPORTATION ...... 46
4.3 TECHNOLOGIES ...... 46 4.3.1 Anaerobic digesters ...... 48 4.3.2 CHPs ...... 52 4.3.3 Gas-fired boilers ...... 53 4.3.4 Micro gas turbines & small turbines ...... 55 4.3.5 Wood Gasifier ...... 56 4.3.6 Biogas Upgrading and feed-in ...... 57 4.3.7 District heating ...... 58 4.3.8 Dryers ...... 58 4.3.9 MW Treatment ...... 59 4.3.10 Chopper ...... 59
5. OPTIMAL STRUCTURE GENERATION – RESULTS & DISCUSSION ...... 60
6. SCENARIO SYNTHESIS – RESULTS & DISCUSSION ...... 64
6.1 DESCRIPTION ...... 64
ST 6.2 1 SCENARIO CATEGORY - DISTRICT HEATING LIMITATION ...... 64 6.2.1 Scenario 1: No DH except for St Ruprecht ...... 65 6.2.2 Scenario 2: 50% limitation of DH ...... 66 6.2.3 Scenario 3: 25% limitation DH ...... 67 6.2.4 Scenario 4: DH unlimited ...... 68
ND 6.3 2 SCENARIO CATEGORY - MORTANTSCH EXCLUSION ...... 69 6.3.1 Scenario 5: Mortantsch exclusion ...... 69
RD 6.4 3 SCENARIO CATEGORY - SENSITIVITY ANALYSIS OF GAS PRICES ...... 71 6.4.1 Scenario 6: Natural gas price increase ...... 71 6.4.2 Scenario 7: Biogas feed-in...... 72
TH 6.5 4 SCENARIO CATEGORY: FEED-IN TARIFF LIMITATION ...... 73 6.5.1 Scenario 8: Retail prices for electricity & biogas ...... 73 6.5.2 Scenario 9: Retail prices for electricity ...... 75 6.5.3 Scenario 10: 100-50% Decrease of the electricity tariffs' provision ...... 76 6.5.4 Scenario 11: 60% Decrease of electricity tariffs provision ...... 77
TH 6.6 5 SCENARIO CATEGORY: ELECTRICITY PRODUCTION LIMITATION ...... 78 6.6.1 Scenario 12: Only one energy producing technology per location...... 78
TH 6.7 6 SCENARIO CATEGORY: RESOURCE LIMITATION ...... 79 6.7.1 Scenario 13: No wood chips available ...... 79 6.1.1 Scenario 14: No MW availability ...... 81 6.7.1 Scenario 15: No grass availability ...... 82 6.7.2 Scenario 16: 33.3% grass availability ...... 83 6.7.3 Scenario 17: 50% grass availability ...... 84
A.G. Gemenetzi ix
6.7.4 Scenario 18: 10% manure availability ...... 86 6.7.5 Scenario 19: 50% manure availability ...... 87 6.7.6 Scenario 20: 33.3% grass - 50% manure Availability ...... 88
TH 6.8 7 SCENARIO CATEGORY: DIGESTATE PRICE VARIATION ...... 89 6.8.1 Scenario 21: Digestate price: 0€ ...... 89 6.8.2 Scenario 22: Digestate price: 8€ ...... 90
6.9 COMPARISON OF RESULTS ...... 92
7. CONCLUSIONS-SUGGESTIONS ...... 102
APPENDIX A ...... 111
A.1 CALCULATION OF TOTAL MASS OF A SUBSTANCE FOR DIFFERENT WATER CONTENTS ...... 111
A.2 RESOURCE AVAILABILITY PER LOCATION ...... 111
A.3 RESOURCES’ DENSITIES ...... 112
A.4 COST ANALYSIS OF ANAEROBIC DIGESTERS ...... 113
A.5 COST ESTIMATION OF GAS-FIRED BOILERS ...... 114
A.6 COST ESTIMATION OF MICRO/SMALL GAS TURBINES ...... 115
A.7 COST ESTIMATION OF CHPS ...... 115
A.8 SCENARIOS’ ENERGY OUTPUT ...... 116 Scenario 1: No DH except for St Ruprecht...... 116 Scenario 2: 50% limitation of DH ...... 117 Scenario 3: 25% limitation DH ...... 118 Scenario 4: DH unlimited ...... 119 Scenario 5: Mortantsch exclusion ...... 120 Scenario 6: Natural gas price increase...... 121 Scenario 7: Biogas feed-in ...... 122 Scenario 8: Retail prices for electricity & biogas ...... 123 Scenario 9: Retail prices for electricity ...... 124 Scenario 10: 100-50% Decrease of the electricity tariffs' provision ...... 125 Scenario 11: 60% decrease of electricity tariffs provision...... 125 Scenario 12: Only one energy producing technology per location ...... 126 Scenario 13: No wood availability ...... 127 Scenario 14: No MW availability ...... 128 Scenario 15: No grass availability ...... 129 Scenario 16: 33.3% grass availability...... 130 Scenario 17: 50% grass availability...... 131 Scenario 18: 10% manure availability ...... 132 Scenario 19: 50% manure availability ...... 133 Scenario 20: 33.3% Grass - 50% Manure Availability ...... 134 Scenario 21: Digestate price: 0€ ...... 134
A.G. Gemenetzi x
Scenario 22: Digestate price: 8€ ...... 135
A.9 SCREENSHOTS OF THE PNS INPUT STRUCTURE ...... 137
A.G. Gemenetzi xi
Table of Tables
TABLE 1. POPULATION AND SURFACE AREA OF THE I-ENERGY 2 PROJECT MUNICIPALITIES...... 20
TABLE 2. DISTANCES BETWEEN THE MUNICIPALITIES, THE POTENTIAL SELECTED LOCATIONS AND ELIN...... 22
TABLE 3. MOISTURE CONTENT AND TONNES PER HECTARE OF THE RESOURCES...... 23
TABLE 4. TOTAL QUANTITIES OF THE AVAILABLE RESOURCES OF ‘ENERGIEREGION WEIZ-GLEISDORF’ & BIRKFELD ...... 23
TABLE 5. TYPES OF DIGESTERS USED IN THE PROJECT...... 28
TABLE 6. DIGESTER DIMENSIONS AND SOLID’S CONTENT...... 30
TABLE 7. PURCHASE PRICES OF THE INPUT MATERIALS...... 45
TABLE 8. SALE PRICES OF THE PRODUCTS...... 45
TABLE 9. TRANSPORTATION COSTS OF THE SELECTED RESOURCES...... 46
TABLE 10. BRIEF OUTLOOK OF THE TECHNOLOGIES USED IN PNS...... 47
TABLE 11. BRIEF OUTLOOK OF THE PIPELINES USED IN THE STRUCTURE...... 47
TABLE 12. DETAILED OUTLOOK OF THE DIGESTERS SET IN THE PNS PROGRAM...... 48
TABLE 12. DETAILED OUTLOOK OF THE DIGESTERS SET IN THE PNS PROGRAM...... 49
TABLE 12. DETAILED OUTLOOK OF THE DIGESTERS SET IN THE PNS PROGRAM...... 50
TABLE 12. DETAILED OUTLOOK OF THE DIGESTERS SET IN THE PNS PROGRAM...... 51
TABLE 13. DETAILED OUTLOOK OF THE CHPS SET IN THE PNS PROGRAM...... 52
TABLE 13. DETAILED OUTLOOK OF THE CHPS SET IN THE PNS PROGRAM...... 53
TABLE 14. DETAILED OUTLOOK OF GAS-FIRED BOILERS SET IN THE PNS PROGRAM...... 53
TABLE 14. DETAILED OUTLOOK OF GAS BOILERS SET IN THE PNS PROGRAM...... 54
TABLE 15. DETAILED OUTLOOK OF GAS TURBINES SET IN THE PNS PROGRAM...... 55
TABLE 15. DETAILED OUTLOOK OF GAS TURBINES SET IN THE PNS PROGRAM...... 56
TABLE 16. DETAILED OUTLOOK OF THE WOOD GASIFIER SET IN THE PNS PROGRAM...... 56
TABLE 17. DETAILED OUTLOOK OF THE BIOGAS CLEANING TECHNOLOGIES SET IN THE PNS PROGRAM...... 57
TABLE 18. DETAILED OUTLOOK OF DISTRICT HEATING PIPELINES SET IN THE PNS PROGRAM...... 58
A.G. Gemenetzi xii
TABLE 19. DETAILED OUTLOOK OF THE DRYERS SET IN THE PNS PROGRAM...... 58
TABLE 20. DETAILED OUTLOOK OF THE MW TREATMENT UNIT SET IN THE PNS PROGRAM...... 59
TABLE 21. DETAILED OUTLOOK OF THE CHOPPER SET IN THE PNS PROGRAM...... 59
TABLE 22. ENERGY RESULT OF THE MAIN TECHNOLOGIES FOR THANNHAUSEN NORTH...... 60
TABLE 23. ENERGY RESULT OF THE MAIN TECHNOLOGIES FOR THANNHAUSEN SOUTH...... 62
TABLE 24. ENERGY RESULT OF THE MAIN TECHNOLOGIES FOR MORTANTSCH...... 62
TABLE 25. AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 62
TABLE 26. TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 63
TABLE 27. SCENARIO CATEGORIES...... 64
TABLE 28. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 1...... 65
TABLE 29. SCENARIO 1: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 65
TABLE 30. SCENARIO 1: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 65
TABLE 31. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 2...... 66
TABLE 32. SCENARIO 2: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 66
TABLE 33. SCENARIO 2: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 66
TABLE 34. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 3...... 67
TABLE 35. SCENARIO 3: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 67
TABLE 36. SCENARIO 3: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 68
TABLE 37. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 4...... 68
TABLE 38. SCENARIO 4: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 69
TABLE 39. SCENARIO 4: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 69
TABLE 40. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 5...... 70
TABLE 41. SCENARIO 5: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 70
A.G. Gemenetzi xiii
TABLE 42. SCENARIO 5: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 70
TABLE 43. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 6...... 71
TABLE 44. SCENARIO 6: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 71
TABLE 45. SCENARIO 6: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 72
TABLE 46. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 7...... 72
TABLE 47. SCENARIO 7: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 73
TABLE 48. SCENARIO 7: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 73
TABLE 49. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 8...... 74
TABLE 50. SCENARIO 8: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 74
TABLE 51. SCENARIO 8: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 74
TABLE 52. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 9...... 75
TABLE 53. SCENARIO 9: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 75
TABLE 54. SCENARIO 9: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 75
TABLE 55. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 10...... 76
TABLE 56. SCENARIO 10: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 76
TABLE 57. SCENARIO 10: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 76
TABLE 58. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 11...... 77
TABLE 59. SCENARIO 11: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 77
TABLE 60. SCENARIO 11: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 78
TABLE 61. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 12...... 78
TABLE 62. SCENARIO 12: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 78
A.G. Gemenetzi xiv
TABLE 63. SCENARIO 12: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 79
TABLE 64. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 13...... 80
TABLE 65. SCENARIO 13: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 80
TABLE 66. SCENARIO 13: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 80
TABLE 67. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 14...... 81
TABLE 68. SCENARIO 14: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 81
TABLE 69. SCENARIO 14: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 81
TABLE 70. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 15...... 82
TABLE 71. SCENARIO 15: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 83
TABLE 72. SCENARIO 15: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 83
TABLE 73. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 16...... 83
TABLE 74. SCENARIO 16: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 84
TABLE 75. SCENARIO 16: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 84
TABLE 76. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 17...... 85
TABLE 77. SCENARIO 17: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 85
TABLE 78. SCENARIO 17: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 85
TABLE 79. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 18...... 86
TABLE 80. SCENARIO 18: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 86
TABLE 81. SCENARIO 18: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 86
TABLE 82. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 19...... 87
TABLE 83. SCENARIO 19: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 87
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TABLE 84. SCENARIO 19: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 87
TABLE 85. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 20...... 88
TABLE 86. SCENARIO 20: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 88
TABLE 87. SCENARIO 20: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 89
TABLE 88. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 21...... 89
TABLE 89. SCENARIO 21: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 90
TABLE 90. SCENARIO 21: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 90
TABLE 91. TECHNOLOGIES APPEARING IN THE OPTIMAL STRUCTURE FOR SCENARIO 22...... 91
TABLE 92. SCENARIO 22: AMOUNT AND PERCENTAGE OF CONSUMED RESOURCES...... 91
TABLE 93. SCENARIO 22: TOTAL INVESTMENT, OPERATING & TRANSPORTATION COST, REVENUE AND GROSS PROFIT...... 91
TABLE 94. DOMINANT TECHNOLOGIES...... 101
TABLE 95. SEMI-DOMINANT TECHNOLOGIES...... 101
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Table of Figures
FIGURE 1. EUROPEAN UNION’S EMISSION REDUCTION PLAN BY 2050 ...... 3
FIGURE 2. PILLARS OF SUSTAINABLE DEVELOPMENT...... 4
FIGURE 3. PRIMARY ENERGY RESOURCES FOR WORLD ENERGY CONSUMPTION IN 2011 ...... 6
FIGURE 4. WORLD DEMAND TREND BY FUEL UNTIL 2030 ...... 7
FIGURE 5. AUSTRIA’S ENERGY BALANCE ...... 8
FIGURE 6. P-GRAPH ...... 11
FIGURE 7. BLOCK DIAGRAM...... 11
FIGURE 8. POWER-INTEREST STAKEHOLDER MAP...... 19
FIGURE 9. GEOPGRAPHICAL POSITION OF THE THREE INVESTIGATED LOCATIONS; THANNHAUSEN NORTH, THANNHAUSEN SOUTH AND MORTANTSCH...... 21
FIGURE 10. INSTALLED AND AVERAGE CAPACITY OF ANAEROBIC DIGESTERS FOR SOME EUROPEAN COUNTRIES IN 2010 ...... 24
FIGURE 11. FOUR MAIN ANAEROBIC DIGESTION STAGES...... 25
FIGURE 12. SKETCH OF A COMPLETE MIXED DIGESTER ...... 27
FIGURE 13. COMPLETE MIXED DIGESTER WITH AN EXTERNALLY MOUNTED MIXER ...... 27
FIGURE 14. MUNICIPAL SOLID WASTE TREATMENT AND DIGESTION ...... 31
FIGURE 15. AN INDUSTRIAL BOILER PRODUCING HIGH TEMPERATURE HEAT...... 32
FIGURE 16. MODULES OF A GAS TURBINE...... 33
FIGURE 17. GAS TURBINE WITH A HEAT RECOVERY UNIT ...... 34
FIGURE 18. CAPSTONE 30 KWEL MICRO GAS TURBINE...... 36
FIGURE 19. CHP UNIT CONSISTING OF A BOILER AND A STEAM TURBINE ...... 37
FIGURE 20. CLEANSTGAS GASIFIER FOR ELECTRICITY AND LOW TEMPERATURE HEAT PRODUCTION. .. 38
FIGURE 21. GENERAL INPUT-STRUCTURE ILLUSTRATION...... 43
FIGURE 22. INPUT-STRUCTURE ILLUSTRATION FOR EACH OF THE THREE LOCATIONS...... 44
FIGURE 23. OPTIMAL STRUCTURE...... 61
FIGURE 24. COST COMPARISON BETWEEN THE 22 SCENARIOS AND THE INITIAL OPTIMAL STRUCTURE...... 93
A.G. Gemenetzi xvii
FIGURE 25. COMPARISON OF THE OVERALL COST, THE REVENUE AND THE GROSS PROFIT OF THE 22 SCENARIOS AND THE INITIAL OPTIMAL STRUCTURE...... 94
FIGURE 26. PERCENTAGE OF THE CONSUMED MANURE FOR BIOGAS PRODUCTION...... 95
FIGURE 27. PERCENTAGE OF FOREST, MEADOW AND FARMLAND AREA USED IN THE 22 SCENARIOS AND THE INITIAL STRUCTURE...... 98
FIGURE 28. ENERGY PRODUCTION IN THE FORM OF HEAT, ELECTRICITY AND BIOGAS, FOR ALL 22 SCENARIOS AND THE INITIAL OPTIMAL STRUCTURE...... 98
FIGURE 29. LOW TEMPERATURE AND HIGH TEMPERATURE WASTE HEAT AND THEIR SUMMATION FOR EACH SCENARIO AND THE INITIAL STRUCTURE...... 99
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Abbreviations and Symbols
AD: Anaerobic Digester BGas: Biogas BG: Biomass Gasifier CHP: Combined Heat and Power DH: District Heating GB: Gas-fired Boiler ha: hectare HT: High Temperature kWhel: kilowatt·hours electric kWhth: kilowatt·hours thermal LT: Low Temperature L1: Location 1, i.e. Thannhausen North L2: Location 2, i.e. Thannhausen South L3: Location 3, i.e. Mortantsch MW·h: Megawatt-hours MGT: Micro Gas Turbine m.c.: Moisture content MW: Municipal Waste MSG: Maximal Structure Generation Pct: Percentage SSG: Solution Structure Generation ABB: Accelerated Branch and Bound (algorithm) N: North P-graph: Process-graph PNS: Process Network Synthesis S: South Sc.: Scenario Srm (Schüttraummeter): Special volume unit for wood, which is less than m3 (1 srm ~ 0.6-0.7 m3). SGT: Small Gas Turbine t: metric tonnes WG: Wood gasifier
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1. Introduction Among the challenges the world is currently facing, the great energy challenge is most prominent. Undoubtedly this century is going to be critical concerning how humankind copes with this challenge, which will affect the prosperity and livelihood of future generations. The challenge is twofold, concerning both energy production and energy consumption. The need of alternative, new sources of energy is provoked by the ceaseless population rise and the ever growing energy consumption in our everyday lifestyles.
Following this train of thought, 18 Styrian municipalities forming the ‘Energieregion Weiz- Glesdorf’, together with the municipality of Birkfeld and a company called ELIN, are taking on this energy challenge and thus set the objective of this thesis. The current work is conducted under the framework of ‘iEnergy Weiz-Gleisdorf 2.0’and investigates the possibility of setting a large industrial complex in the region of Weiz-Gleisdorf. This industrial complex will deliver renewable energy in the form of biogas, electricity and heat, with the use of local, unutilized resources. Certain types of technologies, with most important biogas plants and wood gasifiers, have been selected for investigation and three potential locations have been appointed for situating these technologies. Moreover, part of this investigation is to assess the possibility of ELIN covering its high temperature heat demand through heat provision from one or more of the three locations or by producing it by using biogas instead of natural gas fueled technologies.
The assessment is carried out with the help of a tool called Process Network Synthesis, PNS, which conducts an economic optimization, i.e. provides the optimal solution in terms of delivering the highest gross profit.
Thus, Chapter 2 contains a brief retrospect of the process through which the world came to the notion of sustainable development and the presentation of its basic characteristics. Furthermore, an analysis of the energy production and consumption in Europe and in Austria is conducted, which is in high relation with the next sub-chapter, which concerns the revitalization of regions. Chapter 2 ends with the presentation of the method used, i.e. PNS.
Chapter 3 includes a thorough project description and relative information, whilst Chapter 4 presents the data inserted in the PNS program. The result of the simulation is called the optimal structure and is presented in Chapter 5. Chapter 6 goes a step further to the conduction of scenarios adjusted to the initial structure formulation and discusses the comparison of the different scenarios. Last but not least, chapter 7 contains the final results of this work and suggestions for future work.
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2. Background
2.1 Sustainable development Circumstances, as the oil crisis in the 1970s, ozone layer depletion, greenhouse effect, et cetera, made people aware that the availability of fossil fuels is not everlasting and the effect of their use will have tremendous effects on the environment. Thus, in the late 1960s the Organization for Economic Co-operation and Development (OECD) was the first to establish central environmental ministries and agencies and adopted frameworks for air and water pollution, which underpinned the current system of environmental policies. This was the first centrally organized action, as before respective actions were sporadic and restrained in local level [1]. In 1987, for the first time the idea of sustainable development was shared internationally through the ´Our common Future´ report of the World Commission on Environment and Development (WCED) and gave the most famous definition of sustainable development: «A development that meets the needs of the present without compromising the ability of future generations to meet their own needs» [2]. The notion of this definition creates a vision all countries can share, despite their development status.
In 1992 the United Nations Conference on Environment and Development (UNCED) or else Rio Earth Summit, assembled the world leaders of the time and endorsed for the first time the idea of sustainable development [3]. Since then, the policy domain has broadened and became more present, e.g. EU’s emission trading scheme (ETS).
Following this trend, the European Union is working intensively towards this direction, setting future, long term targets. One of its commitments is the 20-20-20 goal by 2020, i.e.
20% reduction of CO2 emissions compared to the 1990 levels, 20% energy efficiency improvement and 20% of energy consumption deriving from renewables [4]. A more long term plan is called Energy Roadmap 2050, which aims to achieve a competitive low carbon economy by 2050. The Road-map plan, aims to decrease emissions by 80%-95%, compared to 1990 levels, until the middle of the century, as can be seen by Figure 1, on page 3. However, based on the current EU-policies it is predicted that emissions will be managed to be cut only by about 40% until 2050. It is urgent to set post 2020 agenda strategies, as uncertainty regarding what direction will follow afterwards creates chaos for citizens, investors and governments [5].
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Figure 1. European Union’s emission reduction plan by 2050 [6]. As can be seen in Figure 1, all sectors should contribute to the emission reduction.
Another plan for tackling the energy challenge is the European Strategic Energy Technology (SET) Plan. This plan was initiated in 2006-2007 and it comprises a policy frame for Europe, aiming to achieve increased development and deployment of cost effective, low carbon technologies [6]. The SET-plan includes 8 initiatives and 3 groups; namely, these are [8]:
Initiatives Groups
1) EU electricity grid initiative 1) SET plan Steering Group (SET-Group) 2) Fuel cells and hydrogen (FCH) joint 2) EU energy research alliance (EERA) technology initiative 3) SET plan information system (SETIS)
3) Sustainable nuclear initiative 4) Energy efficiency-the smart cities initiative
5) EU CO2 capture, transport and storage initiative 6) EU industrial bioenergy initiative SET plan 7) EU wind initiative 8) Solar Europe initiative
Following the same policy trend, Austria and more specific Styria, has set goals in alignment with the European Union goals, regarding five sectors; i) energy efficiency/energy saving, ii) renewable energies, iii) district heating and cogeneration, ix) energy, space planning and mobility and v) energy research, education and consultancy [9].
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Figure 2 illustrates the three pillars of sustainable development, commonly known as the three Ps, which are also known as social (people), environmental (planet) and economic pillars (profit). According to Figure 2, all three pillars are equally important for sustainable development.
Figure 2. Pillars of sustainable development [8].
The three pillars of sustainable development are very briefly discussed in the following sub- chapters.
2.1.1 People Sustainable development is often misunderstood as a concept that concerns only the environment and more specific climate change. That is far from true, as the notion of sustainable development goes beyond that, to social and economic aspects. Another characterization of sustainable development derives from Meadomcroft et al. [1]: «Sustainable development may be conceptualized as an overall process of balanced societal change, but, if so, it is a process with multiple dimensions, implications at many scales and subject to uncertainties in many levels». In other words, interactions between governance, the emerging environment and development in general are highly complex as entrenched socio- technological regimes, production and consumption patterns and economic and political elites, that have evolved in mutual interdependencies. Consequently strong governance is needed to coordinate activities for the collective good and steer development to the desired direction [1].
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Socially, some of the notions of sustainable development are as follows [2], [11]:
Peace & security Participation Equity & justice Education Poverty Resource Security Human health Sustainable communities 2.1.2 Planet Planet or else the environmental pillar relates directly to the biosphere. But beside involving the management of the atmosphere, oceans, fresh water, ecosystems, et cetera, it goes beyond environmental management and concerns the resource exploitation patterns as well. Thus, some of the topics of the environmental pillar are namely [2], [11]:
Ecosystem Services Environmental stressors Green Engineering and Chemistry Resource integrity Air quality Food, energy, water consumption Water quality Information regarding energy production and consumption will be provided in subchapter 2.2.
2.1.3. Profit Prosperity is directly linked to economy and our consumption patterns are molded according to the mindset that increased material possession brings happiness. Opposed to that, the idea of decoupling is generated, according to which, decoupling occurs when the growth rate of an environmental pressure is lower than that of its economic driving force over a given period of time [9]. In other words decoupling is the break of the link between environmental bad with economic goods, supported by the motto ‘do more with less’.
Some topics related to the economic pillar are namely [11]:
Job availability-security Natural resource accounting Supply & demand that alter Costs-Prices economic growth, environmental health and social prosperity
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2.2 Energy demand and availability
2.2.1 World energy production and consumption World primary energy consumption is projected to be rising annually by 1.6% per year and is predicted to continue at this pace until 2030, as the population rises [10]. In 2011, the world energy consumption was approximately 16.3 PW of which the contributing energy resources can be seen in Figure 3 [10].
2% Oil 5% 6% 33% Natural Gas
30% Coal
24% Nuclear
Hydro
Renewables
Figure 3. Primary energy resources for world energy consumption in 2011 [10].
The worldwide demand of resources has been predicted until 2030 to follow the trend illustrated in Figure 4, on page 7. It is apparent that renewables (without hydro) grow the fastest till 2030, by 7.6% per year, reaching a ~6.5% share of the total energy demand. Hydro grows by 2% per year, reaching ~9.5% by 2030. The availability of resources whatsoever seems obscure; the time of occurrence of world Peak oil (the point at which world’s oil supplies start declining irreversibly) is strongly debated and is predicted to take place within this decade [14], [15]. World Peak gas (the point at which world’s gas supplies start declining irreversibly) is expected to happen two or three decades later than Peak Oil [15], [16]. Coal on the other hand is predicted to be abundant throughout the 21st century, [12].
Consequently, if the Peak Oil & Peak Gas predictions are correct, new sources of energy will be soon required to cover the world’s energy demand and most likely renewable energy is going to take the lead.
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Figure 4. World demand trend by fuel until 2030 [10].
2.2.2 Austrian energy production and consumption Austria belongs to the OECD countries and is quite advanced regarding the use of renewable energies. Figure 5 on page 8 shows the energy balance for Austria for the year 2010 [13]. It can be seen that the total energy supply is 1,806 PJ, of which 27% accounts for renewables; biomass, wastes, hydropower, solar radiation and wind energy. Austria produces only ~28% of its supplied energy, of which the majority (~73%) accounts for renewable energy production. Renewable energy is mostly consumed for electricity production and district heating. Austria imports both fossil fuel and renewable energy resources. Moreover, it is interesting to note that the gross inland energy consumption was reduced by 3.9% from 2010 to 2011, but most probably due to the warmer weather and the increased oil taxation [14].
The current work focuses on the federal state of Styria, which comprises of 12 districts, divided into 542 municipalities. Approximately all (98%) produced energy in Styria derives from renewable resources. The greatest end use of renewables is for district heating, followed by electricity production. 67% of the gross inland consumed energy is imported, of which only 3.3% is renewable [14].
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Figure 5. Austria’s energy balance [13]. A.G.Gemenetzi 8
2.3 Revitalization of the region As discussed in sub-chapter 2.2.1, the world energy system provision will definitely continue to keep a firm grip to fossil fuels for the next at least 50 years. Nevertheless, the struggle for using cleaner forms of energy will continue. In fact, it has been argued that the resource base of the 21st century might undergo fundamental changes on the way to a renewable energy based energy system [12].
If we choose the path of renewable energies, then it must be considered that many renewable resources are time dependent regarding their availability. Thus there is need for smarter grids and sufficient, cost effective and safe energy storage systems. Storing energy in the form of electricity isn’t efficient nor cost- effective. Among the solutions for stabilizing the energy system, biomass and hydrogen seem appealing, as both produce electricity with an efficiency of 40-60% and give heat as a by-product. By no means is suggested that biomass will solve the world’s energy issue, but it expected to constitute an important element in providing a significant and readily available source of stored energy [12].
As biomass requires a higher transport effort compared to fossils, due to its low energy density, it is optimal to utilize biomass close to its resource. In general, whether we talk about biomass, wind power, solar, et cetera, generous land surface is required. In other words, it is likely that local regions will play a key role as energy suppliers and form a decentralized energy system based on local resources. In this case, one can assume that the major energy companies would not have the same control over dispersed energy sources, which practically would be distributed among multiple actors. This scenario would mean the revitalization of regions, in terms of their economy and political life, which would be interlinked and mutually supportive [12].
Many questions arise though in terms of governance; how complex will the involvement of actors be, how a region is coordinated to establish sustainable local resource exploitation and in result energy dependence and if a shared vision of energy autarky is enough for a long term success. From real life examples, it is observed that small energy regions and local initiatives precede larger scale regional management structures, e.g. the case of Mureck, which led to the development of the first industrial scale biodiesel plant in the world. In the case of Mureck, a small group of farmers, led by a charismatic individual, concentrated on finding an alternative to the expensive and unreliable diesel they were using for their tractors. Undoubtedly there are many motives for local resource utilization, with the most apparent, the boost of local economy and the retaining of labor within the region [12].
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Some points have been identified as to what a region needs; strong identity, which will boost trust among actors, strategic leadership, transparent strategic planning process and economic clout provision.
Narodoslawsky [12] argues that regions will succeed in an economy if they can achieve:
Surplus production of grid distributed energy forms such as electricity and gas; Commodity products’ provision, e.g. basic chemicals as well as biomass in a transportable way, e.g. pellet form; Preserve land fertility without importing fossil fuel based fertilizers, e.g. with the use of manure; Optimal heat utilization, with zero waste heat. Regions who manage to achieve that will have a competitive advantage over other regions.
2.3.1 The Austrian example As mentioned in sub-chapter 2.2.2, Austria is quite advanced in the production of renewable energies. More in specific the term ‘Energieregion’ is used for regional development, which usually focuses on the energy sector. Some examples of regional development in Austria are:
1. The region around Güssing in the state of Burgenland; 2. The region Mureck in Styria; 3. Vulkanland in Styria; 4. Mühlviertel in Upper Austria; 5. Weiz-Gleisdorf in Styria.
Examples 1 and 2, are both small towns, populated only by a few thousands inhabitants, which nevertheless have become frontrunners in the use of cutting edge technologies for renewable energy production. Their gradual progress is based on the continuous involvement of various stakeholders and the diversity in the use of different technologies [12].
Examples 3 and 4, are larger regions, which have integrated regional energy development in their regional development plans for a long term use of renewable energies.
Last but not least, Weiz-Gleisdorf is a region, consisting of 18 municipalities. In 1996, 17 of the 18 municipalities formed a corporation, whose goal was to cope together with future challenges, make common plans and excel as a region. Along the way, the 18th municipality joined too. The epicenter of their vision is ‘energy’ and thus many respective projects have been developed; one of them is the focal point of this thesis [15].
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2.4 Process Network Synthesis (PNS)
2.4.1 P-graph Process graph or P-graph is a unique bipartite graph (bigraph) representing the structure of a process system [16]. Materials (M) are represented by solid circles and operating units (O) by horizontal bars. The flows of the materials are represented by arrows inserting or exiting from an operating unit. Figure 6 shows an example of a P-graph of a separation process synthesis and Figure 7 shows a block diagram corresponding to the P-graph:
A, B, C A,Ø, Ø
A, B, C
Ø, B, A,FigureØ, Ø 6 . PØ,-graph B, [16]. Figure 7. Block diagram [16].
The meaning of bigraph is that the vertices (M-materials & O-operating units) of the graph are in disjunctive sets and there are no flows between vertices belonging to the same set. It is worth mentioning that the assignment of operating units and materials are strictly defined by the relations given and thus a P-graph can be represented by the set of materials and operating units, (M, O ) [17].
Various flowcharts can be used for the representation of a chemical/physical/biological process network or network in general. Nevertheless, a p-graph is unique in the sense that it is semantically rich enough to represent only one structure and avoid confusion. Thus, P-graphs are being used in many fields, e.g. chemical and process engineering, natural sciences, et cetera [16].
2.4.2 The Science behind PNS - Mathematical description of P-graphs
2.4.2.1 Vertices & Arcs definition First, two finite sets should be defined; a finite set of materials, M, which consequently includes the subsets raw materials, R and products, P and a finite set of operating units, O. The two finite sets are disjunctive. Mathematically speaking, these relations can be described as:
P ⊆ M, R ⊆ M, and M ∩ O=Ø (1)
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Each operating unit produces output materials (products or intermediate products) from input materials and therefore two disjunctive sets can be appointed to each operating unit. An arbitrary operating unit is defined as (α, β), where α is the set of input materials and β is the set of output materials connected to the unit. Considering that the output products of an operating unit might not be end products, but intermediate materials and thus input to another operating unit, it can be proven that:
O ⊆ ℘(M)×℘(M) (2), where ℘(M) is the power set of M, i.e. the set of subsets of M and ℘(M)×℘(M) is the set of ℘(M) and ℘(M) pairs.
Assuming that there is a finite set of m, which is a subset of M (m ⊆ M) and a finite set of o, which is a subset of o (o ⊆ O) and assuming there is such a material that is input for one or more operating units and there is such a material which is output of one or more operating units, then:
o ⊆ ℘(m)×℘(m) (3)
Since the P-graph is defined as a bigraph, the set of vertices, V is comprised of the elements of the union of sets of m and o, i.e.
V = m ∪ o (4)
Apparently vertices that are elements of m, are materials and vertices that are elements of o, are operating units.
The set of A edges (flows) of the P-graph are the elements of the union of A1 and A2, i.e.
A = A1 ∪ A2 (5), where
A1 = {(x, y) | y = (α,β) ∈ o and x ∈ α} (6) and
A2 = {(y, x) | y = (α,β) ∈ o and x ∈ β} (7)
Relations (5) to (6) make it obvious that the edges can be A1 and/or A2. A1 is an input flow from an input material vertex (solid circle) to an operating unit vertex (horizontal line) and can be determined by the (x, y) pair; y belongs to the operating unit vertex and x is an element of the input set α. Likewise, A2 is an output flow from an operating unit vertex (horizontal
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line) to an output material vertex (solid sircle) and can be determined by the pair (y, x); y belongs to the operating unit vertex and x in an element of the output set β [17], [18].
2.4.2.2 Process Network Synthesis Problem The relations for constraining and defining the cost of operating units and materials are expressed in relations (8) and (9), which show the constraint on and the function of the cost of operating units oj, respectively.
- + gj(yj, φ(ω (oj)), φ(ω (oj)), zj) ≤ 0, j = 1, 2, …, n (8)
- + fj(yj, φ(ω (oj)), φ(ω (oj)), zj), j = 1, 2, …, n (9) gj: The constraint on the cost of the operation unit oj and its specifications. fj: The cost function for operating unit oj.
φ: the function held for any subset of A. zj: A variable set to operating unit oj, for identification.
- ω (oj): The incoming arcs to vertex oj.
+ ω (oj): The outgoing arcs from vertex oj. yj: A vector determining a sub-graph of (M,O). yj={0,1}
1 ≤ j ≤ n. Apparently yj = 1, if oj is contained in the subgraph of (M, O) and yj = 0 otherwise.
For a fixed yj the functions g and f are usually non-linear, differentiable functions.
The relations for constraining and defining the cost of materials are expressed in relations
(10) and (11), which show the constraint on and the function of the cost of the materials mi, respectively.
’ - + gi (φ(ω (mi)), φ(ω (mi)) ≤ 0, i = 1, 2, …, l (10)
’ - + fi (φ(ω (mi)), φ(ω (mi)) i = 1, 2, …, l (11) gi: The material balances and specifications of the products. fi: Cost of raw materials.
φ: the function held for any subset of A.
- ω (mi): The incoming arcs to vertex mi.
+ ω (mi): The outgoing arcs from vertex mi.
1 ≤ i ≤ l.
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In practice g’ and f’ are usually linear.
Now, assuming a non-empty finite set M = {m1, m2, …, mn} and sets (P, R, O), where P, R, O are non-empty finite sets. Also, if we assume that
P ∩ R=Ø, P ⊆ M, R ⊆ M, O ⊆ ℘(M)×℘(M) and M ∪ (α,β), then the problem is to (α,β) ∈O find:
f j (y j ,( (o j )),( (o j )), z j ) f 'i (( (mi )),( (mi ))) j(1,2,...,n) i(1,2,...,l) Subject to : g (y ,( (o )),( (o )), z ) 0, j 1,2,...,n j j j j j (12) g'i (( (o j )),( (o j ))) 0,i 1,2,...,l y j 0,1, z j 0
[19]
2.4.2.3 Combinatorial Solution of PNS
Not any vector yj defines a feasible process structure, but the feasible process structures share some common combinatorial properties. Some of them are expressed through equations (12). Moreover, five axioms have been defined [18]:
(S1) Each final product is represented in the graph.
(S2) An M-type vertex does not have an input if and only if it represents a raw material.
(S3) Each O-type vertex, representing an operating unit, is defined in the network synthesis problem.
(S4) Each O-type (operating unit) vertex has at least one path leading to an M-type vertex representing a (intermediate or final) product.
(S5) If an M-type vertex belongs to the graph, then there must be at least one route leading to an O-type vertex or a route from an O-type vertex to the given M-type vertex.
For the best combinatorial algorithms the relation between the number of combinatorial possible networks and the size of the problem, can be bound by a polynomial function. However, the complexity of most combinatorial algorithms is higher than polynomial, e.g. exponential or factorial. Therefore, the Maximal Structure Generation (MSG), of which the complexity is polynomial, had been developed [20].
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2.4.3 MSG – SSG- ABB
2.4.3.1 Maximal Structure Generation The maximal structure contains all the combinatorial possible structures and thus, the optimal structure as well. This structure is produced through the elimination of those materials and operating units that are not in alignment with the 5 axioms presented in subchapter 2.4.2.3. The elimination starts from the deepest level of the input structure, i.e. from the raw materials and goes from level to level, examining the vertices of materials and operating units, comparing whether they align with the 5 axioms or not. Apparently, the elimination of a vertex leads to the elimination of further vertices that are connected to it. After the elimination is over, the vertices are linked again from level to level, starting this time from the highest, the final product level [17], [18].
2.4.3.2 Solution Structure Generation The Solution Structure Generation (SSG), results in the production of all the combinatorial solution structures. The SSG is a mathematical tool based on the axiom system and the application of the Decision Mappings (DM), which has been developed by Friedler et al. [17], [20]. This algorithm generates only solutions structures and it generates each only once.
2.4.3.3 Accelerated Branch and Bound algorithm The optimal structure is obtained through an Accelerated Branch and Bound (ABB) algorithm. ABBs are time intensive tree-based exploration methods for solving to optimality combinatorial optimization problems [21]. The ABB algorithm used in PNS is based on the so called decision mapping technique and the axioms of combinatorially feasible structures. It reduces the number and the size of the bounding problem, substantially reducing the computational effort and time needed to obtain the optimal solution [22].
2.4.4 PNS as a Tool for region energy optimization PNS is been used for the assessment of potential energy network solutions for regions. In the same line of thought, PNS is used for the Weiz-Gleisdorf ‘Energy Region’ investigated here.
Practically, the PNS user has the freedom to create the structure from scratch. PNS is used to ascertain the optimal economic solution from a pool of data inserted in the program by the user. There are three categories of materials; raw materials, intermediate products and final products, all of which must be manually defined in the structure. Then, these materials are used in operating units. An operating unit can be a technology or even a virtual operating unit,
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indicating a relationship between two materials. Screenshots of the structure developed here can be found in Appendix A.9.
The program, as mentioned in detail in sub-chapters 2.4.1- 2.4.3, uses the P-graph method and works through energy and material flows. Temporal dependencies, e.g. availability, demand, are part of the optimization process. The optimization input quantities concern:
Mass and energy balances; Investment and operating costs of the considered technologies; Costs for resources and utilities Selling prices for products and services as well as constraints regarding resource supply and product/service demand.
Currently, an online platform, called RegiOpt is available, which uses a default input structure with default values, for technology optimization using the PNS method. The RegiOpt platform is more user friendly than the one used in this project, but has a limited system boundary and is still in an exploratory stage.
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3. Project description
The work conducted here is carried out within the framework of 'i-Energy Weiz-Gleisdorf 2.0' [23]. 'i-Energy Weiz-Gleisdorf 2.0' is comprised of many projects and is a thematic continuity of 'i-Energy Weiz-Gleisdorf 1.0'. 'i-Energy Weiz-Gleisdorf 1.0' has generated a vision for the region until 2020 and 'i-Energy Weiz-Gleisdorf 2.0' is on the verge of implementing this vision.
The projects that have been or are currently being carried out, regard many fields, e.g. energy networks, mobility, communication and information, construction of buildings, smart cities and urban region systems, all of which aim to the region’s goal of becoming CO2 neutral until 2050 [24].
The concept of this work, as mentioned in the introduction, is the delivery of a large industrial complex in the region. This complex will produce renewable energy in the form of biogas, electricity and heat, from local resources. Three locations, within the area of ‘Energieregion Weiz-Gleisdorf’ have been identified as potential locations for situating the investigated technologies.
The technologies’ selection and distribution at the three locations is done with the use of Process Network Synthesis, which has been described in sub-chapter 2.4. PNS carries out an economic optimization, taking into account the data and the system boundaries inserted to the program by the user.
The present chapter includes a brief stakeholder analysis, demographic and geographic data of the ‘Energieregion’, resource availability data and a description of the selected technologies.
3.1 Stakeholders The main stakeholders, their interest and their power in regard to the project, have been identified as follows:
Energieregion & Birkfeld (19 Municipalities): For ‘Energieregion’ this project comprises a small step towards their long term goal; becoming CO2 neutral until 2050 [15], [24]. Overall, for the 19 municipalities, a potential benefit is the boost of the local economy, e.g. profits from the sales of local resources and new vacancies that will keep labor in the region. Furthermore, renewable energy generation is funded by the government and EU, which implies that there is a financial incentive and support for involvement in a respective project.
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ELIN: Beside the 19 municipalities, interest in the project shares also a company called ELIN. ELIN Motoren is a motor manufacturing company, which claims to have sustainable development as one of its core strategies [25]. Thus, this project investigates also the possibility to cover ELIN’s heat demand by using renewable energy from local resources. Their current energy need is 3,412.017 MWh·y-1 of electricity and 2,155.880 MWh·y-1 of high temperature (> 100 °C) heat. At the moment their heat demand is covered by a natural gas boiler with a 290 kWth capacity. Energie Steiermark AG: Energie Steiermark AG participates in the project too, as a consultant and potentially main distributor of the energy that might be produced in the region. Energie Steiermark AG is the 4th biggest energy company in Austria, operating also abroad and is partially privatized since 1998. Its main services and products are electricity generation and provision, natural gas and district heating [26]. Institute of Process and Particle Engineering in TU Graz: The IPPE works as a consultant in this project. Supply companies: If the project is implemented, companies providing equipment, e.g. Capstone, CleanstGas, et cetera, will benefit. These companies are potential suppliers, but could also be potential future partners. Media: In a vague sense the local media can be considered as a stakeholder, as media is the fourth estate, which influences the masses. E.g. if the media were against the project, it is likely that people would react and postpone or even cancel its realization. Government: Central government gives authorization for the project development and provides funding for initiatives that involve renewable energies. Public: Here, the term public refers only to local citizens, who are not directly involved in the project. These people may have an interest on what is taking place close to their home or in the area in general, in terms of affecting their quality of life.
A power-interest stakeholders’ map is illustrated in Figure 8, on page 19. The term power is interpreted as having the power to influence the project, e.g. the Energieregion has the power to cancel the project. The term interest is interpreted as having an interest on the project, not necessarily having to gain something.
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High
Power
Low
Low Interest High
Figure 8. Power-Interest stakeholder map.
3.2 Weiz - Gleisdorf Energieregion demographics and surface area As mentioned in subchapter 2.3.1, the 'Energieregion Weiz – Gleisdorf' consists of 18 municipalities. Nevertheless, the current case study includes also the municipality of Birkfeld, due to its relative proximity to the region and its availability of resources. The 19 municipalities, sorted in descending total surface area, except roads, are presented in Table 1, on page 20 [27].
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Table 1. Population and surface area of the I-Energy 2 project municipalities [27]. Nr of Municipality Surface area, ha inhabitants 1) Thannhausen 2,356 4,359 2) Gutenberg an der Raabklamm 1,256 2,450 3) Puch bei Weiz 2,100 2,303 4) Naas 1,389 2,052 5) Mitterdorf an der Raab 2,074 1,630 6) Mortantsch 2,011 1,545 7) Unterfladnitz 1,500 1,235 8) Hofstätten an der Raab 2,045 1,166 9) Etzersdorf-Rollsdorf 1,133 1,036 10) Albersdorf-Prebuch 1,987 1008 11) Birkfeld 1,619 982 12) Nitscha 1,448 963 13) St. Ruprecht an der Raab 2,081 958 14) Krottendorf 2,376 909 15) Ludersdorf-Wilfersdorf 2,044 835 16) Labuch 788 507 17) Weiz 8,928 359 18) Gleisdorf 5,766 254 19) Ungerdorf 852 245 Total 43,758 26,817
The surface area includes forests, meadows, arable land, private and public properties, i.e. buildings, their yards, et cetera, and non-utilized spaces. Moreover, the Total area includes roads as well. It is apparent that Weiz and Gleisdorf are the most densely populated areas.
3.3 Geographical information Three locations were selected for potentially locating the investigated technologies; Thannhausen North, Thannhausen South and Mortantsch. To ELIN were located only technologies that use raw biogas as fuel (and the natural gas-fired boiler) in order for ELIN to cover its heat demand. A map of the region is presented in Figure 9, on page 21.
Thannhausen North is represented by the upper right red mark, followed below by Thannhausen South. Mortantsch is represented by the red mark on the left and ELIN is represented by the blue mark.
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Figure 9. Geopgraphical position of the three investigated locations; Thannhausen North, Thannhausen South and Mortantsch.
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The 19 municipalities are spread around and between the marked locations, although it is apparent that Mortantsch is relatively isolated, compared to Thannhausen North and Thannhausen South. More in specific, the distances between each municipality and each of the three locations is shown in Table 2.
Table 2. Distances between the municipalities, the potential selected locations and ELIN. Municipality L1, km L2, km L3, km ELIN, km
1) Albersdorf-Prebuch 17.5 15.9 19 11.8 2) Birkfeld 22.8 23.2 29 25.1 3) Etzersdorf-Rollsdorf 8.2 8.6 14.3 7.3 4) Gleisdorf 17 15.3 18.5 11.3 5) Gutenberg an der Raabklamm 11 10.1 5 11.6 6) Hofstätten an der Raab 22.9 21.2 24.4 19.4 7) Krottendorf 2.5 0.7 6 3.4 8) Labuch 22.5 20.8 23.9 16.8 9) Ludersdorf-Wilfersdorf 20 18.3 21.4 14.3 10) Mitterdorf an der Raab 9.3 7.3 9.1 7 11) Mortantsch 8 7.1 1.9 8.5 12) Naas 7.6 6.8 8.6 8.8 13) Nitscha 19 19.4 25.5 18.3 14) Puch bei Weiz 10 10.4 16.2 12.3 15) St. Ruprecht an der Raab 10.4 8.7 11.8 4.7 16) Thannhausen 0.65 1.8 6.3 6.4 17) Ungerdorf 21.1 19.4 22.6 15.4 18) Unterfladnitz 8.5 4.6 9 1.9 19) Weiz 3.3 2.5 3.7 3.8 20) ELIN 5.9 4.3 7.4 0
L1: Thannhausen North, L2: Thannhausen South, L3: Mortantsch (see Abbreviations).
3.4 Available resources The local resources considered for utilization are:
Wood chips obtained from forest; Meadow grass;
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Arable land which can produce grass, short rotation coppice and miscanthus; Manure from dairy cattle and Communal waste & bio-waste (green wastes & food/kitchen).
In this work grass that derives from meadow will be fully written as meadow grass, opposed to farmland grass, which will be simply mentioned as grass. Communal waste and bio-waste will be referred from now on as Municipal Waste or MW.
Table 3, shows the moisture content and tonnes per hectare for every resource respectively.
Table 3. Moisture content and tonnes per hectare of the resources. Moisture content, % Tonnes per hectare, t/ha 15.0 7.80 Wood chips 50.0 13.26 Meadow grass 5.66 68.0 Grass 3.86 15.0 18.82 Short rotation biomass 60.0 40.00 Miscanthus 14.0 16.15 Manure 93.9 - MW 71.3 - The equation used for calculating the tonnes per hectare for different water contents is shown in Appendix A.1.
Table 4 shows the total quantities of available resources. The quantities presented were all used for the optimization as readily available resources. The area used for grazing has not been taken into account for the optimization and thus the “real” available area will be less than the one presented in Table 4. Detailed tables of resource availability for each municipality are available in Appendix A.2.
Table 4. Total quantities of the available resources of ‘Energieregion Weiz-Gleisdorf’ & Birkfeld [27]. Forest, ha Meadow, Arable land, ha Manure, t MW, t ha Total 16,203.00 3,978.00 281.00 107,974.00 1,627.62
3.5 Technology description In this sub-chapter, a general description, followed by a specific description of each investigated technology is given. Thus, 11 technologies are discussed; namely: 1) Anaerobic digesters, 2) MW treatment, 3) gas-fired boilers, 4) gas turbines & micro/small turbines, 5) CHPs, 6) biomass gasifier, 7) biogas upgrading & biogas pipelines, 8) district heating, 9) dryers, 10) chopper harvester and 11) transformers. A.G.Gemenetzi 23
3.5.1 Anaerobic digesters
3.5.1.1 Development Anaerobic digestion was first reported in the 17th century by Robert Boyle and Stephen Hale and the first biogas plant was built in Bombay, India, in 1859. Since then it has been widely used as part of biodegradable waste treatment and for the last decades it is also considered as a means of renewable energy production [28]. The capacity of anaerobic digesters for some European countries in 2010, is presented in Figure 10 [29].
(kTon/y) Capacity installed Average installed Capacity (kTon/y) Capacity installed Average Total
Figure 10. Installed and average capacity of anaerobic digesters for some European countries in 2010 [29].
3.5.1.2 Anaerobic digestion processes and end products Anaerobic digestion is a collective process in which microorganisms break down biodegradable substances in the absence of oxygen. The feedstock used can be manure, wastes and energy crops and generally almost all organic compounds.
The main anaerobic digestion processes that take place are summarized in four stages and are illustrated in Figure 11, on page 25 [30].
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Carbohydrates Sugars H2CO3 - HCO3 -2 H2 HCO3 CH4 CH3COOH Fats Fatty Acids CxH2x+1OH CO CO 2 H2 2 CO2 NH + 4
Proteins Amino acids
Hydrolysis Acetogenesis Acidogenesis Methanogenesis
Figure 11. Four main anaerobic digestion stages.
Hydrolysis of the large organic polymer chains into sugars, amino acids and fatty acids; Acidogenesis, which results in further break down of the remaining components, by acidogenic bacteria. Volatile fatty acids are created, along with ammonia, carbon dioxide, hydrogen sulfide and other products; Acetogenesis, in which the simple molecules produced by acidogenesis are further processed by acetogens to largely produce acetic acid, as well as carbon dioxide and hydrogen; Methanogenesis, in which methanogens use the intermediate substances of the previous steps and convert them into methane, carbon dioxide and water. This process is sensitive to pH and occurs between 6.5 to 8, optimally at 7.
The above four processes don’t occur consecutively, but simultaneously. The exact processes’ interaction is not clear and thus the maintenance of the operating conditions is critical [30].
Substances, which cannot be used by the bacteria, are digestion remnants, called digestate. Digestate can come in three forms; fibrous, liquor or a sludge combination of them. Its composition depends on the composition of the initial feedstock and on the phase of the digestion. The end digestate is usually comprised of elements unable to biodegrade in anaerobic conditions, e.g. lignin, et cetera, mineralized remains of dead bacteria, nitrogen in the form of ammonia and phosphates. Usually a maturation process under aerobic conditions is applied after the digestion and thus compounds non degradable anaerobically are now degraded, ammonia is oxidized in to nitrates and the overall volume is reduced. Thus, the rich in nitrogen, in the form of nitrates, digestate can be used as a fertilizer. Levels of potential toxic elements should also be assessed before the end use of the digestate. Water is also an end product as it was already present as a moisture content of the original feedstock and is also produced through the processes that take place. [31]. A.G.Gemenetzi 25
3.5.1.3 Types of anaerobic digesters Anaerobic digester types can be distinguished based on their [32]:
Operation mode;
Operation mode can be continuous, batch or semi-continuous. Continuous operation means that feedstock is provided continuously and end products are being removed also continuously/periodically. Opposed to that, in batch processes, feedstock is provided once and is provided again when the process has ended and the end products have been removed, i.e. a new cycle begins. Batch digesters are the simplest form of digesters, they are cheaper and the use of many batch reactors in a plant can ensure continuous gas production.
Temperature and pH;
Depending on the methanogens present in the digester, the optimal temperature can be 30 ° C to 38 ° C, which is referred as mesophilic digestion and 49 ° C to 57 ° C, which is referred as thermophilic digestion. It is proved that thermophilic digestion is more efficient, but the question arises as to whether the gas produced offsets the heat that is needed to be provided. The optimal pH value is 7.
Solids and water content ;
High solids, dry digesters are considered those with a solids’ content between 25-40% and require no additional water provision for their process. The digesters that can be used are vertical plug flow reactors and batch tunnel horizontal digesters.
Wet digesters can be processed in plug flow digesters and in continuously mixed digesters. As high solids, wet digesters are referred those, which have a total suspended solids’ concentration greater than ~20%. They form a thick slurry, which needs a lot of energy to be transported and might also cause abrasion. Low solids, wet digesters have a total suspended solids’ concentration below ~15% and are less energy consuming, as they can be transported by using standard pumps. Also, the liquid phase enables better circulation of the materials, i.e. better contact between the bacteria and their food.
Complexity.
Complexity refers to the installation design, which can be single-stage digestion or multi- stage digestion. The latter needs more than one vessel for the digestion and it is usually the methanogenesis stage that takes place in a different vessel, as it is the process that needs to have stable temperature and pH. Nevertheless, it is not possible to completely isolate the
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reaction phases and thus some biogas is produced in the first tank as well, in which the hydrolysis, acidogenesis and acetogenesis takes place.
Practically a digester can be constructed from concrete, stainless steel or both and it can be covered by concrete or single membrane cover or double membrane cover or have an external biogas storage tank [33]. Figure 12 shows a sketch of a typical complete mixed digester and Figure 13 shows a real, one stage, complete mixed digester:
Figure 12. Sketch of a complete mixed digester [34].
Figure 13. Complete mixed digester with an externally mounted mixer [34].
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The final design also depends on the feedstock used, the purpose of the biogas plant and the available budget.
3.5.1.4 Anaerobic digesters selected Based on the feedstock composition and the different biogas production rates, fifteen digesters were selected to be investigated using the PNS tool. Table 5 shows the fifteen types of digesters investigated.
Table 5. Types of digesters used in the project. Digester A Digester B Digester C Digester D Digester E
80 kWel
160 kWel
250 kWel
Digester A: Feedstock comprised of 100% cattle manure; Digester B: Feedstock comprised of 50% cattle manure and 50% grass, either from meadow, farmland or both; Digester C: Feedstock comprised of 50% cattle manure, 25% grass/meadow grass and 25% MW; Digester D: Feedstock comprised of 75% cattle manure and 25% grass/meadow grass; Digester E: Feedstock comprised of 75% cattle manure, 15% grass/meadow grass and 10% MW.
The three categories, comprising the columns of Table 5 refer to the amount of biogas produced, which is be able to generate 80 kWel, 160 kWel and 250 kWel respectively, when burnt in a CHP unit.
The design of the digesters is complete mixed digesters, continuously stirred. The digestion that takes place is thermophilic, i.e. 49 °C – 57 °C, and the biogas is stored on top of the digester in an inflatable big gas bubble.
Since the main equipment that needs maintenance/replacement are the agitators (lifespan of 8-10 years) and the roof (lifespan 10-20 years), the lifespan of the digesters is estimated to be 15 years [35].
Moreover, beside the digesters, secondary digesters are being used for the 160 kWel and 250 kWel digester categories. The secondary digesters have the same dimensions as the fermenters
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and their use is for further digestion of the digestate and also can serve as storage tanks. When secondary digesters are used, they produce 15-20% of the total biogas production [36]. Both, the main and secondary digesters are assumed to have the same retention time. Digesters A have a retention time of 25 days, while the rest of the digester categories have a retention time of 30 days. Table 6 on page 30 shows the solid’s content and the dimensions of the fermenters. The thorough cost estimation of the digesters can be found in Appendix A.4.
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Table 6. Digester dimensions and solid’s content [37]. Digester A Digester A Digester A Digester B Digester B Digester B Digester C Digester C Digester C
80 kWel 160 kWel 250 kWel 80 kWel 160 kWel 250 kWel 80 kWel 160 kWel 250 kWel
Solids’ 6.1 6.1 6.1 19.1 19.1 19.1 18.2 18.2 18.2 content, % Diameter, m 17 25 29 11 13 15 11 13 16 Height, m 7.5 7.5 7.5 7 7.5 7 7 7.5 7 Volume, m3 1,589 3,436 4,624 618 929 1,149 618 929 1,307 Gas storage 250 250 250 200 250 250 200 250 250 volume, m3 Digester D Digester D Digester D Digester E Digester E DigesterE
80 kWel 160 kWel 250 kWel 80 kWel 160 kWel 250 kWel
Solids’ 12.6 12.6 12.6 12.3 12.3 12.3 content, % Diameter, m 11 15 18 12 16 20 Height, m 7 7.5 7 7 7.5 7 Volume, m3 618 1,237 1,654 735 1,407 2,042 Gas storage 200 250 250 250 250 250 volume, m3
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3.5.2 MW treatment Municipal waste can be used as feedstock for the digesters. The type of digester used and the composition of the waste, define the necessary treatment processes. Figure 14 shows a sketch of MSW treatment that ends up in digesters for heat and gas production [38].
Figure 14. Municipal solid waste treatment and digestion [38].
As can be seen from Figure 14, MW first needs to be collected and then go through separation from other materials that are harmful or not suitable for fermentation. Also, shredding takes place, since the greater the surface of the material, the easier it mingles and interacts with the bacteria in the digester. Mixing might also be necessary before entering the digester.
3.1.1.1 MW Treatment selected
The term municipal waste can differ, but for this project it refers to biodegradable waste, consisting of food and kitchen waste and green wastes from the municipality gardens and parks. Although most likely they don’t contain harmful substances, e.g. toxic substances, heavy metals, et cetera, and they are suitable for fermentation, their composition should be assessed before used. The treatment that is considered for this project is filtering and shredding.
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3.5.3 Gas-fired boilers
3.5.3.1 Gas-fired boiler description A gas fired boiler uses gas as fuel for the production of heat, which is transferred to water or steam. There are many types of gas fired boilers, differing in the design and the quality of produced heat. Figure 15 represents an industrial boiler producing high temperature heat.
Figure 15. An industrial boiler producing high temperature heat [39].
Hot water and steam boilers are quite similar in design. As presented in Figure 15, a three pass shell boiler is a horizontal, cylindrical tube, pressure vessel which is insulated all around. On the one end side of the cylinder tube, there is a burner integrated in a ‘flame tube’, which comprises the ‘1st pass’, as illustrated in Figure 15. In the pressure vessel there is also a reversing chamber, which reverses the flue gases, leading them back in the 2nd smoke tube pass. On the front of the boiler, there is an external reversing chamber, which again reverses the flue gases and leads them to the end of the boiler, in the 3rd smoke tube pass. Hot water boilers are usually completely filled with water during operation, while steam boilers on the other hand are only ¾ filled with water; the upper quarter is the steam space. The exhaust gases are emitted through a flue situated usually at the side of the boiler [39].
In the case of hot water, pumps are the means of transportation, while in the case of steam transport is based on pressure difference. When steam is utilized for heating, it can be directly
A.G.Gemenetzi 32
used by steam using equipment or via heat exchangers that supply hot water to the equipment [39].
3.5.3.2 Gas-fired boiler selected
Four different heat outputs of gas-fired boilers were investigated; 300 kWth, 500 kWth, 1
MWth and 1.5 MWth. For each capacity, the respective amount of heat can be obtained either as low temperature heat or high temperature heat. Of course, the type and cost of the boilers and the operating conditions, i.e. temperature and pressure, change respectively. The life expectancy of the boilers is 15 years. The fuel used is raw biogas obtained from the designed anaerobic digesters, except for the already existing boiler located in ELIN, which uses natural gas. The boilers are made of steel and the empirical equations used for their cost estimation can be found in Appendix A.5.
3.5.4 Gas turbines & micro gas turbines
3.5.4.1 Gas turbine description A gas turbine is a type of internal combustion engine, which operates with a rotary rather than a reciprocating motion. There are three main modules of a gas turbine; a compressor, a combustor and a gas turbine. A respective scheme can be seen in Figure 16.
Figure 16. Modules of a gas turbine [40].
A gas turbine can be distinguished in two sections; the gasifier section and the power section. The compressor and the combustion chamber comprise the gasifier or else gas generator and the turbine and the shaft comprise the power section.
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A gas turbine uses hot gases directly to drive a turbine instead of steam, as in steam turbines. The compressor has a rotor with a series of blades around its outer edge. As the rotor rotates, air between the blades is carried around and thrown out by centrifugal force into the burner, resulting in a pressure rise [40]. The feedstock used can be natural gas, oil, pulverized coal, et cetera. The fuel is injected in the burner, where it combusts. After leaving the gasifier section, the high pressure, high temperature gas expands rapidly and enters the power turbine. Here, it strikes another series of stationary curved blades, which routes it against to a series of rotor curved blades, on the outer edge of the power turbine rotor, causing it to rotate at high speed [40]. The shaft of the turbine is coupled to an electric generator for electricity production or to a machinery to drive it directly. The compressor is driven by the turbine’s power.
The capacity of gas turbines ranges from a few MWel to more than 350 MWel [41]. Thus gas turbines are usually used in large industrial applications. Gas turbines have a big variety of applications and are also used in Combined Heat and Power systems. Figure 17 illustrates an example of a gas turbine with a heat recovery unit.
Figure 17. Gas turbine with a heat recovery unit [42].
The example of Figure 17 comprises a CHP unit and shows heat as a by-product of electricity production. The expanded exhaust gases still have a high temperature, which can be used in a heat exchanger for heat utilization, usually through steam or hot water production, which can be used for industrial or domestic purposes, e.g. district heating/cooling, et cetera. Gas turbines or reciprocating engines are ideal for large industrial or commercial applications, since they deal with ample amounts of heat and electricity.
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3.5.4.2 Micro gas turbines There are various literature findings about the characterization of a turbine as micro gas turbine. In this work a micro-gas turbine is a turbine with a production of less than 100 kWel.
Turbines with a production above 100 kWel and less than 1 MWel are characterized as small turbines [43].
MGTs and small turbines are different from typical turbines and they cannot merely be considered as their smaller versions. MGTs have a small mass in-flow rate, which results in small machine size; the smaller the latter the bigger the rotational speed. In general MGTs differ from GTs in [41]:
The type of turbo-machines used, i.e. MGTs usually use high revving, single stage radial turbo-machines instead of multi-stage axial turbo-machines; MGTs operate in low pressure ratios, higher temperatures and high rotational speeds, which are independent of the grid frequency. High temperatures have resulted in exploration of new materials, i.e. ceramic materials that can cope with the high inlet turbine temperatures (~ 1,400 °C). Furthermore, power electronics are used to convert the high frequency electricity to the grid electricity; Usually a regenerator is used instead of a recuperator. MGTs can operate with lower gas quality, have a lower maintaining and operating costs,
but higher specific investment costs (currency·kWel) and lower electrical efficiencies.
Figure 18 on page 36 presents the modules of a Capstone, 30 kWel (C30) micro gas turbine.
MGTs can operate in two operation modes; cogeneration and non-cogeneration, with the latter being the most popular. MGTs are anticipated to play a significant role in a future decentralized distribution of energy systems, based on local power plants. Some of the advantages of a decentralized system are the reduction of energy losses due to electrical transport and the utilization of thermal energy [41].
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Figure 18. Capstone 30 kWel micro gas turbine [43].
3.5.4.3 Micro gas and small gas turbines selected Two different capacities of micro gas turbines and four different capacities of small turbines were selected; 30 kWel and 65 kWel and 200 kWel, 600 kWel, 800 kWel and 1 MWel, respectively. The specifications of the turbines, i.e. outlet temperatures, efficiencies, et cetera, are acquired from Capstone. The lifespan of the Capstone micro gas turbines is 80,000 operating hours.
Each capacity can give either high temperature heat or low temperature heat. The fuel used is raw biogas obtained from the designed anaerobic digesters. The equations used for cost estimation can be found in Appendix A.6.
3.5.5 CHP
3.5.5.1 CHP description Combined Heat and Power or else co-generation, is the simultaneous production of heat and power from a single fuel source [42]. CHP units combine different technologies and have increased efficiency compared to the sole technologies that comprise them, since they utilize the heat that otherwise would be wasted. This unit integration makes CHPs flexible for modification, according to the end energy needs. Four common CHP units are [42]:
Gas turbine or engine with heat recovery unit; Steam boiler with steam turbine; Combined cycle power plants adapted for CHP;
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Solid oxide fuel cells and molten carbon fuel cells, which have very hot exhaust gases that can be used for heating.
Figure 19 illustrates a CHP unit consisting of a steam boiler and a steam turbine.
Figure 19. CHP unit consisting of a boiler and a steam turbine [42].
In the unit represented in Figure 19, the main product is heat, generated in the form of steam and electricity is a by-product, unlike in Figure 17, where the opposite occurs. The steam drives a steam turbine, whose shaft is coupled to an electric generator for electricity production. Steam turbine based CHP units are used in large industrial applications, where the boiler usually uses solid fuels, e.g. biomass, coal, and waste [42].
3.5.5.2 CHP selected
In the current project, CHP units of seven different capacities were selected; 80 kWel, 160 kWel, 250 kWel, 300 kWel, 500 kWel, 1 MWel and 3 MWel. The lifespan of the CHP depends on its modules and can vary, depending on the operating conditions and maintenance. Empirically, the longest lifespan is 15 years [44].
The feedstock fuel is raw biogas produced from the digesters and/or syngas produced from the biomass gasifier. The efficiencies and costs were estimated based on empirical data obtained from CHP units in Germany in 2011 [44]. The empirical equations used for cost estimation are available in Appendix A.7.
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3.5.6 Biomass gasifier
3.5.6.1 Biomass gasifier description Biomass gasification is a process taking place under high temperature (650 °C – 1100°C) and high pressure, producing syngas, i.e. CO and H2 and often CO2 and CH4. The feedstock used can vary, e.g. agricultural residues, forestry residues, energy crops cultivated solely for this use, urban wood wastes from construction and demolition, et cetera and its composition determines the design of the gasifier [45]. The produced syngas can be directly combusted in a power engine or if it is clean enough to be used in power generation turbines, fuel cells, for chemical reactions, e.g. Fischer-Tropsch.
3.5.6.2 Biomass gasifier selected The specifications of the selected biomass gasifier were provided by the company
CleanstGas. A fixed bed gasifier with a downstream gas motor and a capacity of 250 kWel is used, with a lifespan of 10 years. Thus the produced gas is directly combusted for electricity and low temperature heat production. The gasifier is suitable for biomass in general, but for this project it is considered to have as feedstock wood chips and/or short rotation biomass and/or miscanthus chips. All feedstock substances have a water content of 14 % - 15 %. Figure 20 illustrates the operation of the CleanstGas gasifier.
Figure 20. CleanstGas gasifier for electricity and low temperature heat production [46].
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3.5.7 Biogas pipelines and upgrading & feed-in In the current project raw biogas is used directly at all technologies described above, i.e. CHPs, gas-fired boilers and gas turbines. Biogas upgrading occurs only if biogas is to be fed in to the grid.
3.5.7.1 Raw biogas pipelines There are three raw biogas pipelines, with a flow rate of 150 m3·y-1:
Thannhauser North Thannhausen South Thannhausen South ELIN Mortantsch ELIN
3.5.7.2 Biogas upgrading Biogas is cleaned before being transferred to other locations and before it is fed in to the grid.
The technique used for hydrogen sulfide, H2S, carbon dioxide, CO2 and water vapor, H2O, removal is Pressure Swing Absorption, PSA. This method uses a column filled with a molecular sieve, in our case and most commonly, activated carbon impregnated with potassium iodide, KI, for differential absorption of the gases, e.g. CO2, H2S, H2O, et cetera, letting methane, CH4, pass through. The process takes place in a pressure range of 6.7 to 7.9 bars and a temperature range of 50 °C to 70 °C. The lifespan of a PSA installation is at least 20 years [48].
3.5.7.3 Biogas feed-in Before the upgraded biogas is fed in to the grid, a number of legal standards defining the quality of biogas need to be fulfilled, e.g. quality and quantity measurements, compression, odor addition, et cetera. Odor addition to the otherwise odorless upgraded biogas is necessary in order to easily detect gas leaks. Thus, biogas is blended with gas odorants, usually tert- butylthiol [49].
3.5.8 District heating Since all energy forms can be converted into heat, all grids can provide heat [12]. Thus, the question that arises is which is the most effective and efficient way for heat provision. For this project, district heating is distributed via heat transmission grids. Heat transmission grids are only suitable for densely populated areas, since there must be a minimum amount of heat per km to make it economically viable. The Austrian norm obliges a minimum demand of 900 MWh·(km·y)-1 [12].
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In this project, fifteen different potential heat transfers are investigated, with an energy flow rate of 2,100 kWh·(m·y)-1 and a lifespan of at least 30 years:
Low temperature heat transfers: Thannhausen North Thannhausen South Krottendorf Thannhausen South Mortantsch Weiz Thannhausen North Mortantsch St. Ruprecht Thannhausen Mortantsch Mortantsch Thannhausen North Weiz ELIN Weiz Thannhausen South Weiz ELIN St. Ruprecht Thannhausen South St. Ruprecht ELIN Unterfladnitz
High temperature heat transfers: Thannhauser North Thannhausen South Thannhausen South ELIN Mortantsch ELIN
The cost estimation is presented in Table 18.
3.5.9 Dryers A plethora of equipment can be used for the drying of solids. Nevertheless, in this project a fluid bed dryer is used. A fluid bed dryer is drying materials by using fluidized hot and/or dehumidified air, which creates turbulence to the material, while flowing through it [50].
The specifications of the dryer, i.e. energy use, costs, et cetera, are those of a ‘DRYON’ fluid bed dryer. In the project, a dryer is considered optionally, only for the location of Mortantsch and only for the drying of short rotation biomass and wood chips. Wood chips enter with a moisture content of 50%, short rotation biomass enters with a moisture content of 60% and finally a moisture content of 15% is achieved as output. There are two dryer capacities; a 1.76 t and a 12.5 t input material.
3.5.10 Chopper harvester A chopper harvester can be a self-propelled unit or it can be attached to a tractor. It consists of a cutter-head or a flywheel with attached knives to it that chops and blows the harvest into a wagon that is connected to the harvester or to another wagon that drives alongside.
The chopper considered for this project is a ‘Krone Big X 650’, which is a self-propelled unit with a capacity of ~ 2,000 tonnes per year [51]. It is considered only for miscanthus chopping.
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3.5.11 Transformers Transformers are static electrical devices, used for transferring energy between circuits. They are significant to the national power grid and are responsible for the transmission of large amounts of high voltage power over long distances. Depending on their intended function and the amount of required power, transformers’ types and size can vary from a size of a fingernail to weighing hundreds of tonnes [55].
A 250 kVA transformer is used for all technologies producing electricity in the current project [56].
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4. PNS input structure
This chapter contains in detail, the data inserted in the PNS program and the system limitations, as for the reader to obtain a full picture of the optimization and an understanding of the results, which are presented in chapter 5. More in specific, the purchase and sale prices of the raw materials and products, the transportation costs and the inserted data for each technology are presented.
As mentioned, the Process Network Synthesis program gives the flexibility to the user to set a structure from scratch. Thus, all resources were introduced for each of the nineteen municipalities and all technologies were allocated to the three locations and ELIN. The technologies allocated to all three locations are the same, except for L3, which can additionally have dryers. ELIN has an already existing gas boiler and isn’t considered for biogas production and biomass gasification, but it is considered for biogas-fired boilers, micro/small turbines and CHPs, in the same range as they are set in the other three locations. Thus potential biogas pipelines from each location to ELIN were considered.
Figure 21, on page 43 shows a general illustration of the structure and Figure 22, on page 44, shows an illustration for one location (as the structure is identical for all three locations), depicturing also the availability of the resources. It should be noted that dryers (for wood chips and short rotation biomass) are only placed at L3 and the DH destinations are not all the same for all locations (see sub-chapter 3.5.9 & 3.5.8, respectively).
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Figure 21. General input-structure illustration. A.G.Gemenetzi 43
Figure 22. Input-structure illustration for each of the three locations.
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4.1 Purchase and sale prices The purchase and sale prices of the raw materials and products, respectively, were acquired from current Austrian retail prices. Tables 7 and 8 summarize the prices used for the raw materials and products, respectively.
Table 7. Purchase prices of the input materials. Input Price Natural gas 42.5 €·MWh-1 [52] Diesel 1,635 €·t-1 [52] Electricity 150 €·MWh-1 [52] Meadow grass (68% MC) 38 €·t-1 [53] Grass (68% MC) 38 €·t-1 [53] Miscanthus (14% MC) 68.18 €·t-1 [54] Short rotation biomass (60% MC) 31.35 €·t-1 [52] Short rotation biomass (15% MC) 66.63 €·t-1 Wood chips (50% MC) 35 €·t-1 [55] Wood chips (15% MC) 59.5 €·t-1 Municipal waste (71.3% MC) 7.4 €·t-1 [53] Manure (93.9% MC) 4 €·t-1 [53]
Table 8. Sale prices of the products. Output Price -1 Domestic heat 35 €·MWth [52] -1 Industrial heat 45 €·MWth [52] Fed-in the grid biogas 70 €·MW-1 [52] -1 Feed-in Ökostrom tariff (biogas), till 250 kWel 195 €·MWel [56] -1 Feed-in Ökostrom tariff (biogas), 250 till 500 kWel 169.3 €·MWel [56] -1 Feed-in Ökostrom tariff (biogas), 500 till 750 kWel 133.4 €·MWel [56] -1 Feed-in Ökostrom tariff (biogas), from 750 kWel 129.3 €·MWel [56] -1 Feed-in Ökostrom tariff (biomass), till 2 kWel 120.3 €·MWel [56] Digestate 4 €·(m³)-1 [57] MW tariff 70 €·t-1 [52] As mentioned in sub-chapter 3.5.1, the digestate can be utilized as a fertilizer. The price presented in Table 8 is not stable, but is highly connected to the demand and the current market prices [57]. Also, it is remarkable that the state gives such a great incentive to utilize municipal waste, with the provision of a high tariff, i.e. 70 €·t-1.
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4.2 Transportation The transportation of the resources was also taken into account, since these costs are significant for the final resource selection and the revenue of the project. Table 9 contains the transportation costs for the investigated resources.
Table 9. Transportation costs of the selected resources [58]. Resources Transportation Type Prices Fixed (40 t/Ride) 6.67 €/Ride Meadow grass/grass Varying (40 t/Ride) 1.63 €·(t DM·km) -1 Miscanthus chips/ Fixed (40 t/Ride) 8.33 €/Ride Short rotation biomass/ Varying (40 t/Ride) 0.4 €·(srm·km)-1 Wood chips/MW Fixed (27 t/Ride) 20 €/Ride Manure Varying (27 t/Ride) 5 €·(t DM·km) -1 The varying transportation costs for miscanthus chips, short rotation biomass, wood chips and municipal waste are calculated based on their density and their respective moisture content. The densities of each material can be found in Appendix A.3.
The program calculates the fixed transportation costs based on the costs presented in Table 9, and the amount of resources transported, whilst for the varying costs, the cost per tonne for each resource and for each potential journey was calculated and manually inserted into the program.
4.3 Technologies The data inserted in the program are thoroughly presented here for each of the investigated technology.
There is only one required flow and six flow limitation set in the initial input structure: 1) -1 high temperature heat required for ELIN, which accounts to 2,155.88 MWhth·y and 2) the upgraded biogas feed-in the grid limit (max flow), which is 200 m3·h-1 for the three locations -1 altogether and the DH, which is 1,700 MWhth·y (max flow) for St Ruprecht and 2,500 -1 MWhth·y (max flow) for the rest of the locations to which DH lines were appointed. Moreover, the availability of raw materials from each municipality can also be considered as a limitation.
The pay-out time and the operating hours were appointed as default values for all the operating units set in the PNS program. Pay-out time was set to 10 years and operating hours per year were set as 8,000 hours or else ~334 days.
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Table 10 summarizes the main technologies and table 11 summarizes the pipelines set in the PNS program.
Table 10. Brief outlook of the technologies used in PNS. Technology Capacity range Location Gas-fired boiler: Natural gas 290 kW ELIN or biogas (existent) th
Biogas-fired boiler 300|500|1000|1500 kWth 1. ELIN
CHP 80|160|250|300|500|1000|3000 kWel 2. Thannhausen North 3. Thannhausen South (Micro)-biogas turbine 30|65|200|600|800|1000 kW el 4. Mortantsch 1. Thannhausen North
Wood gasifier 250 kWel, 430 kWth 2. Thannhausen South 3. Mortantsch Anaerobic digester: 100% Manure 1. Thannhausen North
50% Manure - 50% Grass 80|160|250 kWel 2. Thannhausen South 50% Manure - 25%Grass - 3. Mortantsch 25% MW
Table 11. Brief outlook of the pipelines used in the structure. Transfers Specifications Location 1. Thannhausen North South (HT, LT) 2. Thannhausen SouthELIN (HT) 3. Thannhausen SouthWeiz (HT) 4. Thannhausen SouthSt Ruprecht (LT) District heating 65 DN 5. Mortantsch ELIN (HT) 6. Mortantsch Weiz (LT) 7. Mortantsch St Ruprecht (LT) 8. ELIN Weiz (LT) 9. ELIN St Ruprecht (LT) 1. Thannhausen North South No flow rate limit Raw biogas pipeline 2. Thannhausen SouthELIN 90 DN, 150 ·h-1 3. MortantschELIN 1. Thannhausen North Feed-in limit: 200 m³·h-1 of Biogas Cleaning 2. Thannhausen South cleaned biogas 3. Mortantsch
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4.3.1 Anaerobic digesters Table 12. Detailed outlook of the digesters set in the PNS program. Technology Location Efficiency Input Output •Investment cost: 518,927 € •Operating cost: 11,791 €·y-1 CHP 80 kW : •Biogas: 1,897 MW·y-1 L1, L2, L3 el •Electricity: 38.4 MW·y-1 η =0.78 •Digestate: 21,306 t·y-1 total •Heat, LT: 803.3 MW·y-1 •Manure: 21,815 t·y-1 •Investment cost: 1,126,189 € •Operating cost: 20,523 €·y-1 CHP 160 kW : •Biogas: 3,900 MW·y-1 Digester A L1, L2, L3 el •Electricity: 76.8 MW·y-1 η =0.80 •Digestate: 43,792 t·y-1 total •Heat, LT: 1,651 MW·y-1 •Manure: 44,839 t·y-1 •Investment cost: 1,505,634 € •Operating cost: 34,302 €·y-1 CHP 250 kW : •Biogas: 5,432 MW·y-1 L1, L2, L3 el •Electricity: 120 MW·y-1 η =0.81 •Digestate: 60,995 t·y-1 total •Heat, LT: 2,300 MW·y-1 •Manure: 62,453 t·y-1 •Investment cost: 395,586 € •Operating cost: 9,984 €·y-1 -1 -1 CHP 80 kWel: •Electricity: 38.4 MW·y •Biogas: 1,897 MW·y Digester B L1, L2, L3 -1 -1 ηtotal=0.78 •Heat, LT: 120.2 MW·y •Digestate: 2,859 t·y •Manure: 1,632 t·y-1 •Grass: 1,632 t·y-1
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Table 12. Detailed outlook of the digesters set in the PNS program. Technology Location Efficiency Input Output •Investment cost: 682,465 € •Operating cost: 15,583 €·y-1 -1 -1 CHP 160 kWel: •Electricity: 76.8 MW·y •Biogas: 3,900 MW·y L1, L2, L3 -1 -1 ηtotal=0.80 •Heat, LT: 253 MW·y •Digestate: 6,027 t·y •Manure: 3,441 t·y-1 •Grass: 3,441 t·y-1 Digester B •Investment cost: 850,922 € •Operating cost: 19,514 €·y-1 -1 -1 CHP 250 kWel: •Electricity: 120 MW·y •Biogas: 5,432 MW·y L1, L2, L3 -1 -1 ηtotal=0.81 •Heat, LT: 353 MW·y •Digestate: 8,395 t·y •Manure: 4,793 t·y-1 •Grass: 4,793 t·y-1 •Investment cost: 395,586 € •Operating cost: 10,220 €·y-1 •Electricity: 38.4 MW·y-1 •Biogas: 1,897 MW·y-1 CHP 80 kW : Digester C L1, L2, L3 el •Heat, LT: 152 MW·y-1 •Digestate: 3,636 t·y-1 η =0.78 total •Manure: 2,065 t·y-1 •Grass: 1,0323 t·y-1 •MW: 1,033 t·y-1
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Table 12. Detailed outlook of the digesters set in the PNS program. Technology Location Efficiency Input Output •Investment cost: 857,409 € •Operating cost: 199,514 €·y-1 •Electricity: 120 MW·y-1 CHP 250 kW : •Biogas: 5,432 MW·y-1 Digester C L1, L2, L3 el •Heat, LT: 435 MW·y-1 η =0.81 •Digestate: 10,408 t·y-1 total •Manure: 5,912 t·y-1 •Grass: 2,956 t·y-1 •MW: 2,956 t·y-1 •Investment cost: 395,586 € •Operating cost: 9,984 €·y-1 •Biogas: 1,897 MW·y-1 CHP 80 kW : •Electricity: 38.4 MW·y-1 L1, L2, L3 el •Digestate: 5,341 t·y-1 η =0.78 •Heat, LT: 214 MW·y-1 total •Manure: 4,354 t·y-1 •Grass: 1,451 t·y-1 •Investment cost: 791,220 € •Operating cost: 17,645 €·y-1 •Biogas: 3,900 MW·y-1 CHP 160 kW : •Electricity: 76.8 MW·y-1 Digester D L1, L2, L3 el •Digestate: 10,978 t·y-1 η =0.80 •Heat, LT: 440 MW·y-1 total •Manure: 8,950 t·y-1 •Grass: 2,983 t·y-1 •Investment cost: 1,016,043 € •Operating cost: 21,916 €·y-1 -1 -1 CHP 250 kWel: •Electricity: 120 MW·y •Biogas: 5,432 MW·y L1, L2, L3 -1 -1 ηtotal=0.81 •Heat, LT: 612 MW·y •Digestate: 15,291 t·y •Manure: 12,465 t·y-1 •Grass: 4,155 t·y-1
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Table 12. Detailed outlook of the digesters set in the PNS program. Technology Location Efficiency Input Output •Investment cost: 417,889 € •Operating cost: 10,300 €·y-1 •Electricity: 38.4 MW·y-1 CHP 80 kW : •Biogas: 1,897 MW·y-1 L1, L2, L3 el •Heat, LT: 246 MW·y-1 η =0.78 •Digestate: 6,215 t·y-1 total •Manure: 5,012 t·y-1 •Grass: 668 t·y-1 •MW: 1,002 t·y-1 •Investment cost: 792,927 € •Operating cost: 17,800 €·y-1 •Electricity: 76.8 MW·y-1 CHP 160 kW : •Biogas: 3,900 MW·y-1 Digester E L1, L2, L3 el •Heat, LT: 506 MW·y-1 η =0.80 •Digestate: 12,774 t·y-1 total •Manure: 10,302 t·y-1 •Grass: 1,374 t·y-1 •MW: 2,060 t·y-1 •Investment cost: 1,055,558 € •Operating cost: 22,433 €·y-1 •Electricity: 120 MW·y-1 CHP 250 kW : •Biogas: 5,432 MW·y-1 L1, L2, L3 el •Heat, LT: 705 MW·y-1 η =0.81 •Digestate: 17,792 t·y-1 total •Manure: 14,349 t·y-1 •Grass: 1,913 t·y-1 •MW: 2,870 t·y-1
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4.3.2 CHPs Table 13. Detailed outlook of the CHPs set in the PNS program. Capacity Location Efficiency Input Output •Investment cost: 119,534.3 € η =0.78 tot •Operating cost: 13,437 €·y-1 •Heat, HT: 329 MW·y-1 η =0.35 80 kW ELIN, L1, L2, L3 el •Electricity: 38.4 MW·y-1 •Heat, LT: 457 MW·y-1 el η =0.18 th, HT •Biogas: 1,829 MW·y-1 •Electricity: 640 MW·y-1 η =0.25 th, NT •Transformer cost: 4,000 €·y-1 •Investment cost: 164,881 € •Operating η =0.8 tot cost: 19,295 €·y-1 •Heat, HT: 623 MW·y-1 η =0.37 160 kW ELIN, L1, L2, L3 el •Electricity: 76.8 MW·y-1 •Heat, LT: 865 MW·y-1 el η =0.18 th, HT •Biogas: 3,459 MWh/y •Electricity: 1,280 MW·y-1 η =0.25 th, NT •Transformer cost: 4,000 €·y-1 •Investment cost: 202,816.4 € η =0.81 tot •Operating cost: 24,356 €·y-1 •Heat, HT: 947 MW·y-1 η =0.38 250 kW ELIN, L1, L2, L3 el •Electricity: 120 MW·y-1 •Heat, LT: 1,316 MW·y-1 el η =0.18 th, HT •Biogas: 5,263.2 MW·y-1 •Electricity: 2,000 MW·y-1 η =0.25 th, NT •Transformer cost: 4,000 €·y-1 •Investment cost: 220,720.7 € η =0.81 tot •Operating cost: 26,788.5 €·y-1 •Heat, HT: 1,137 MW·y-1 η =0.38 300 kW ELIN, L1, L2, L3 el •Electricity: 144 MW·y-1 •Heat, LT: 1,579 MW·y-1 el η =0.18 th, HT •Biogas: 6,316 MW·y-1 •Electricity: 2,400 MW·y-1 η =0.25 th, NT •Transformer cost: 4,000 €·y-1 •Investment cost: 279,757 € -1 ηtot=0.82 •Operating cost: 34,975 €·y -1 -1 •Heat, HT: 1,895 MW·y ηel=0.39 •Electricity: 240 MW·y -1 500 kWel ELIN, L1, L2, L3 -1 •Heat, LT: 2,737 MW·y ηth, HT=0.18 •Biogas: 10,526 MW·y -1 -1 •Electricity: 4,000 MW·y ηth, NT=0.25 •Transformer cost: 4,000 €·y
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Table 13. Detailed outlook of the CHPs set in the PNS program.
Capacity Location Efficiency Input Output •Investment cost: 38,588.6 € η =0.85 tot •Operating cost: 50,221.6 €·y-1 •Heat, HT: 3,428.6 MW·y-1 η =0.42 1 MW ELIN, L1, L2, L3 el •Electricity: 480 MW·y-1 •Heat, LT: 4,761.9 MW·y-1 el η =0.18 th, HT •Biogas: 19,048 MW·y-1 •Electricity: 8,000 MW·y-1 η =0.25 th, NT •Transformer cost: 4,000 €·y-1 •Investment cost: 642,455.6 € η =0.85 tot •Operating cost: 89,114.4 €·y-1 •Heat, HT: 10,285.7 MW·y-1 η =0.42 3 MW ELIN, L1, L2, L3 el •Electricity: 1,440 MW·y-1 •Heat, LT: 14,285.7 MW·y-1 el η =0.18 th, HT •Biogas: 57,143.2 MW·y-1 •Electricity: 24,000 MW·y-1 η =0.25 th, NT •Transformer cost: 4,000 €·y-1
4.3.3 Gas-fired boilers Table 14. Detailed outlook of gas-fired boilers set in the PNS program. Capacity Location Efficiency Input Output •Operating cost: 2,230 €·y-1
ELIN 80% •Electricity: 2,304 MW·y-1 • Heat: 2,155.88 MW·y-1 290 kW , (NG/BG) th •Natural gas/Biogas: 2,695 MW·y-1 •Investment cost: 29,588 € •Operating cost: 2,959 €·y-1 300 kW , HT ELIN, L1, L2, L3 93% • Heat: 2,400 MW·y-1 th •Electricity: 14 MW·y-1 •Biogas: 2,5801 MW·y-1 •Investment cost: 16,139 € •Operating cost: 1,614 €·y-1 300 kW , NT ELIN, L1, L2, L3 93% •Heat: 2,400 MW·y-1 th •Electricity: 14 MW·y-1 •Biogas: 2,581 MW·y-1
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Table 14. Detailed outlook of gas boilers set in the PNS program.
Capacity Location Efficiency Input Output •Investment cost: 40,347 € •Operating cost: 4,035 €·y-1 500 kW , HT ELIN, L1, L2, L3 η =0.93 •Heat: 4,000 MW·y-1 th th •Electricity: 23.3 MW·y-1 •Biogas: 4,301 MW·y-1 •Investment cost: 22,415 € •Operating cost: 2,242 €·y-1 500 kW , NT ELIN, L1, L2, L3 η =0.93 •Heat: 4,000 MW·y-1 th th •Electricity: 23.3 MW·y-1 •Biogas: 4,301 MW·y-1 •Investment cost: 64,257 € •Operating cost: 6,426 €·y-1 1 MW , HT ELIN, L1, L2, L3 η =0.93 •Heat: 8,000 MW·y-1 th th •Electricity: 46.7 MW·y-1 •Biogas: 8,602 MW·y-1 •Investment cost: 44,830 € •Operating cost: 4,483 €·y-1 1 MW , NT ELIN, L1, L2, L3 η =0.93 •Heat: 8,000 MW·y-1 th th •Electricity: 46.7 MW·y-1 •Biogas: 8,602 MW·y-1 •Investment cost: 96,385 € •Operating cost: 9,639 €·y-1 1.5 MW , HT ELIN, L1, L2, L3 η =0.93 •Heat: 12,000 MW·y-1 th th •Electricity: 120 MW·y-1 •Biogas: 12,903 MW·y-1 •Investment cost: 67,246 € •Operating cost: 6,725 €·y-1 1.5 MW , NT ELIN, L1, L2, L3 η =0.93 •Heat: 12,000 MW·y-1 th th •Electricity: 120 MW·y-1 •Biogas: 12,903 MW·y-1
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4.3.4 Micro gas turbines & small turbines Table 15. Detailed outlook of gas turbines set in the PNS program. Capacity Location Efficiency Input Output •Investment cost: 52,500 € -1 -1 ηtot=0.87 •Operating cost: 8,053 €·y •Heat: 560 MW·y -1 -1 30 kWel ELIN, L1, L2, L3 ηel=0.26 •Electricity: 2.1 MW·y •Electricity: 240 MW·y -1 ηth=0.61 •Biogas: 920 MW·y •Transformer cost: 4,000 €·y-1 •Investment cost: 113,750 € -1 ηtot=0.83 •Operating cost: 12,057 €·y -1 -1 •Heat: 960 MW·y 65 kWel ELIN, L1, L2, L3 ηel=0.29 •Electricity: 2.9 MW·y -1 -1 •Electricity: 520 MW·y ηth=0.54 •Biogas: 1,792 MW·y •Transformer cost: 4,000 €·y-1 •Investment cost: 249,823 € -1 ηtot=0.82 •Operating cost: 21,679 €·y -1 -1 •Heat: 2,360 MW·y 200 kWel ELIN, L1, L2, L3 ηel=0.31 •Electricity: 9.3 MW·y -1 -1 •Electricity: 1,600 MW·y ηth=0.49 •Biogas: 4,848 MW·y •Transformer cost: 4,000 €·y-1 •Investment cost: 539,036 € - ηtot=0.80 •Operating cost: 38,467 €·y -1 1 -1 •Heat: 6,880 MW·y 600 kWel ELIN, L1, L2, L3 ηel=0.33 •Electricity: 28 MW·y -1 -1 •Electricity: 4,800 MW·y ηth=0.47 •Biogas: 14,544 MW·y •Transformer cost: 4,000 €·y-1 •Investment cost: 659,287.8 € -1 ηtot=0.81 •Operating cost: 44,700 €·y -1 -1 •Heat: 9,200 MW·y 800 kWel ELIN, L1, L2, L3 ηel=0.33 •Electricity: 37.3 MW·y -1 -1 •Electricity: 6,400 MW·y ηth=0.46 •Biogas: 19,392 MW·y •Transformer cost: 4,000 €·y-1
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Table 15. Detailed outlook of gas turbines set in the PNS program.
Capacity Location Efficiency Input Output •Investment cost: 770,747 € -1 ηtot=0.81 •Operating cost: 50,222 €·y -1 -1 •Heat: 11,520 MW·y 1 MWel ELIN, L1, L2, L3 ηel=0.33 •Electricity: 46.7 MW·y -1 -1 •Electricity: 8,000 MW·y ηth=0.46 •Biogas: 24,240 MW·y •Transformer cost: 4,000 €·y-1
4.3.5 Wood Gasifier Table 16. Detailed outlook of the wood gasifier set in the PNS program. Capacity Location Efficiency Input Output •Investment cost: 1,350,000 € •Operating cost: 23,000 €·y-1 •Heat, LT: 3,440 MW·y-1 250 kW L1, L2, L3 η =74% •Electricity: 120 MW·y-1 el total •Electricity: 2,000 MW·y-1 •Fuel LHV: 7,351 MW·y-1 •Transformer cost: 4,000 €·y-1
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4.3.6 Biogas Upgrading and feed-in Table 17. Detailed outlook of the biogas cleaning technologies set in the PNS program. Technology Location Losses Input Output •Investment cost: 510,684 € Methane content in raw •Operating cost: 16,200 €·y-1 PSA L1, L2, L3 •Upgraded Biogas: 15,904 MW·y-1 biogas: 55% •Electricity: 40 MW·y-1 •Raw biogas: 17,455 MW·y-1 Raw biogas pipeline •Investment cost: Material cost (incl. Muffs): 1. S1 S2 6.1 €·m-1 2. S2ELIN - Transport (incl. Planning): 3. S3ELIN 16 €·m-1 Compression: 20,000 € •Electricity: 24 MW·y-1 • Investment cost: 160,000 € •Upgraded biogas: 15,904 MW·y-1 •Operating cost: 5,030 €·y-1 Biogas feed-in station L1, L2, L3 - •Electricity: 1 MW·y-1 •Upgraded biogas: 15,904 MW·y-1
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4.3.7 District heating Table 18. Detailed outlook of district heating pipelines set in the PNS program. Technology Location Losses Input Output S1 S2 (HT, LT) S1 Weiz, Thannhausen (LT) S2ELIN (HT) S2 Weiz, StRuprecht, •Investment cost: 300 €·km-1 District heating Krottendorf (LT) 20 % •Electricity use: 0.5% •1,680 MW·(km·y)-1 pipelines S3 ELIN (HT) •2,100 MW·(km·y)-1 S3 Weiz, StRuprecht, Mortantsch (LT) ELIN Weiz, StRuprecht, Unterfladnitz (LT)
4.3.8 Dryers Table 19. Detailed outlook of the dryers set in the PNS program. Capacity Location Input Output •Investment cost: 168,827 € •Operating cost: 5,065 €·y-1 1.76 t·h-1 S3 •Electricity: 118.06 MW·y-1 •Drier output: 7,454 t·y-1 •Heat, LT: 5,512 MW·y-1 •Drier input: 14,080 t·y-1 • Investment cost: 532,800 € •Operating cost: 15,984 €·y-1 12.5 t·h-1 S3 •Electricity: 9748.46 MW·y-1 •Drier output: 52,941.18 t·y-1 •Heat, LT: 56,488 MW·y-1 •Drier input: 100,000 t·y-1
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4.3.9 MW Treatment Table 20. Detailed outlook of the MW treatment unit set in the PNS program. Technology Location Input Output
•Investment cost: 175,000 € Filtering & shredding L1, L2, L3 • MW treated: 1,620 t·y-1 •MW: 1,628 t·y-1
4.3.10 Chopper Table 21. Detailed outlook of the chopper set in the PNS program. Capacity Location Input Output •Investment cost: 219,500 € 245.7 t·h-1 Mobile •Diesel: 1,093.4 t ·y-1 • Miscanthus chips: 245.7 t·h-1 •Miscanthus: 245.7 t·h-1
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5. Optimal Structure Generation – Results & discussion
The optimal structure is generated through the accelerated branch and bound algorithm, (see sub-chapter 2.4.3) and the result obtained, includes the gross profit, the materials and the operating units that were used in the optimal solution. Thus, the amount of resources and energy used and produced are available in the solution. Figure 23, on page 61, illustrates the optimal structure generated based on the input presented in Chapter 4, whilst Tables 22, 23 and 24 present the energy output of the main technologies of the optimal structure at each location. The framework, the required flows and the limits of the input structure were presented in Chapter 4.
According to the solution, it is profitable for all three locations to have a biogas plant, with most popular being digester B, but only L1 and L2 were deemed optimal for wood gasification. District heating is only optimal for Thannhausen and Krottendorf, with the rest of the produced heat being dissipated into the environment. ELIN has no additional technologies, but the already existing natural gas fired boiler, which is currently used to cover its heat demand.
Thannhausen North
Table 22. Energy result of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 1,200 MWh·y-1 WG LT Heat: 2,064 MWh·y-1 Electricity: 1,122 MWh·y-1 CHP 160 kW LT Heat: 758 MWh·y-1 el HT Heat: 1,493 MWh·y-1 HT Heat: 546 kWh·y-1 Electricity: 2,000 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester A kWel Biogas 1,196 MWh·y - -1 Digester B 250 kWel Biogas: 4,123 MWh·y - -1 Digester C 160 kWel Biogas: 2,976 MWh·y - DH to Thannhausen Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
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Figure 23. Optimal structure. A.G.Gemenetzi 61
Thannhausen South
Table 23. Energy result of the main technologies for Thannhausen South. Technology Energy output Waste heat Electricity: 1,251 MWh·y-1 WG LT Heat: 2,151 MWh·y-1 Electricity: 2,000 MWh·y-1 HT Heat: 947 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester B 250 kWel Biogas: 5,263 MWh·y - DH to Krottendorf 2,500 MWh·y-1 625 MWh·y-1 (losses)
Mortantsch
Table 24. Energy result of the main technologies for Mortantsch. Technology Energy output Waste heat Electricity: 2,000 MWh·y-1 HT Heat: 947 MWh·y-1 CHP 250 kW LT Heat: 1,316 MWh·y-1 el LT Heat: 507 MWh·y-1 HT Heat: 947 MWh·y-1 -1 Digester A kWel Biogas 1,303 MWh·y - -1 Digester B 250 kWel Biogas: 3,961 MWh·y - According to the solution structure it is economically optimal for ELIN to produce its heat demand via a natural gas-fired boiler, rather than obtain it from L1, L2 or L3. Thus, the high temperature heat produced from the CHP units in all three locations is not utilized.
Table 25 presents the amount and percentage of the consumed resources in relation to the total resources presented in Table 4. Table 26, on page 63, presents the total investment cost, operating cost, transportation cost, resources cost, gross revenue and net revenue.
Table 25. Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 275 2,145 1.7 Meadow grass 2,290 12,960 57.6 Farmland 113 436 40.2 Total 2,678 15,540 13.1 Manure - 43,742 40.5 MW - 1,628 100 The only fully consumed resource is municipal waste and the second most used resource is meadow grass. MW was expected to be fully consumed, since although its available amount is small, the revenue gained from its utilization is substantial. Meadow grass used for biogas
A.G.Gemenetzi 62
on the other hand is in competition with grazing and thus is practically less available than the considered amount. The consumption of the rest of the resources though is ‘safe’ in terms of availability. Especially for wood chips, the used amount is negligible compared to the available amount.
Table 26. Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €640,202 €347,904 €85,610 €937,905 €2,286,496 €274,875
The most important result of Table 26 is the gross profit, which can be considered low, considering that labor and other unforeseen costs were not taken into account in the optimization. Operating costs include maintenance costs and electricity.
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6. Scenario synthesis – Results & discussion
6.1 Description Scenario synthesis is the next step after the optimal structure generation. As mentioned in sub-chapter 2.4, PNS is a flexible program, i.e. gives to the user the possibility to change the structure and thus the system boundaries. With scenario synthesis, new optimal structures are generated based on various limitations of the initial input structure. In other words, the initial input structure will be changed accordingly for each scenario and then the simulation will be run again, using the same algorithm, ABB (Accelerated Branch and Bound algorithm), to produce the new optimal structure.
Twenty two scenarios are formulated, in which various limitations are investigated, as to define the respective optimal structure. After the scenario synthesis is completed, an overall comparison and discussion is made.
For all scenarios, the pay-out time and the operating hours were appointed as default values; i.e. 10 years and 8,000 hours (~334 days), respectively. The twenty two scenarios are divided in seven main categories, as presented in Table 27.
Table 27. Scenario categories. 1. DH demand limitation: 4 scenarios 2. Exclusion of Location 3: 1 scenario 3. Sensitivity analysis of gas prices: 2 scenarios 4. Feed-in tariffs limitation: 4 scenarios 5. Electricity production limitation: 1 scenario 6. Resources limitation: 8 scenarios 7. Digestate price variation: 2 scenarios
The next sub-chapters present the main results of each scenario, in terms of the appearance of the technologies per location, resource availability and economics. The energy output per location can be found in Appendix A.8. Finally, an overall comparison is conducted.
Moreover, if a location is not mentioned it means it doesn’t appear in the solution, whilst if ELIN isn’t mentioned, then it maintains its current status (natural gas boiler) regarding its heat demand covering.
6.2 1st Scenario category - District heating limitation The initial input structure considers a limited district heating scheme, as shown in Table 18. Scenarios were formed, as to investigate the new optimal generated structures, assuming lower or increased district heat demand.
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6.2.1 Scenario 1: No DH except for St Ruprecht The municipality of St Ruprecht is the only known municipality which can still utilize some -1 heat, i.e. a heat demand amounting to 1,700 MWth·y . Thus, all other DH units were excluded from the structure and only the district heat pipeline to St Ruprecht, limited as in the initial -1 input structure to 1,700 MWth·y , was kept. Table 28 summarizes the technologies that appear in the new optimal structure.
Table 28. Technologies appearing in the optimal structure for scenario 1. Thannhausen N Thannhausen S Mortantsch ELIN
CHP 250 kWel CHP 250 kWel CHP 250 kWel Biogas pipeline from L2
Digester A 80 kWel Digester B 160 kWel Digester A 80 kWel CHP 250 kWel
Digester B 250 kWel Digester B 250 kWel Digester B 250 kWel SGT 200 kWel
Digester C 160 kWel MW Treatment
Tables 29 and 30 show the amount of consumed resources and the economics of the scenario, respectively.
Table 29. Scenario 1: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 0 0 0 Meadow grass 2,868 16,232 72.1 Farmland 175 676 62.3 Total 3,043 16,908 14.9 Manure - 47,628 44.1 MW - 1,628 100
Table 30. Scenario 1: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €612,887 €341,465 €107,360 €844,705 €2,112,431 €206,015
Summary of results of scenario 1
The only available DH line, to St Ruprecht, is not economically optimal. The optimal structure includes biogas production at all three locations, with L2 producing the biggest amount and the most popular digester being digester B. The heat needed for the digestion is
provided via a CHP unit per location, with a capacity of 250 kWel. Thus, no biomass
gasification is considered optimal. ELIN is producing its required heat using a 250 kWel CHP
A.G.Gemenetzi 65
unit and a 200 kWel small gas turbine (HT). The biogas ELIN burns is supplied from L2 (Thannhausen South).
Due to the DH limitation in combination with the use of CHPs, there is low and high temperature waste heat at all three locations and low temperature waste heat at ELIN. The resource use is increased compared to the initial optimal structure (see Table 25), but no resource shortage appears.
The gross profit is ~25% lower than the gross profit of the initial optimal structure (see Table 26).
6.2.2 Scenario 2: 50% limitation of DH This scenario limits the heat demand set on the initial structure to 50%. The purpose of the scenario is to see whether it is still optimal to utilize heat as DH (lower heat demand). Table 31 summarizes the technologies that appear in the new optimal structure. Table 31. Technologies appearing in the optimal structure for scenario 2. Thannhausen N Thannhausen S Mortantsch
CHP 160 kWel CHP 160 kWel CHP 250 kWel
CHP 250 kWel CHP 250 kWel Digester A 80 kWel
Digester B 250 kWel Digester B 250 kWel Digester B 250 kWel
Digester D 250 kWel Digester C 160 kWel DH to Thannhausen MW Treatment DH to Krottendorf
Table 32 shows the amount of resources consumed and Table 33 shows the economics of scenario 2.
Table 32. Scenario 2: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 0 0 0 Meadow grass 2,923 16,541 73.5 Farmland 175 676 62.3 Total 2,678 15,540 15.1 Manure - 38,844 36.0 MW - 1,628 100
Table 33. Scenario 2: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €543,797 €350,351 €96,102 €844,705 €2,150,011 €223,929
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Summary of results of scenario 2
Beside the DH demand limitation, DH lines appear from L1 and L2 to Thannhausen and Krottendorf, respectively. Biogas production is optimal at all three locations, with L2 producing the biggest amount and the most popular digester being digester B. The heat needed for the digestion is provided via two CHP units (160 kWel and 250 kWel) at each of the two locations, L1 and L2, and one CHP unit (250 kWel) at L3. There is no change in ELIN’s technology use, as it is covering its heat demand by the use of its natural gas-fired boiler.
Due the use of CHPs, there is high temperature waste heat at all three locations. Moreover, there is low temperature waste heat at L3. Regarding the resource use it is higher than in scenario 1, except for manure consumption, which is lower due to lesser use of digester A.
As expected the gross profit is less than the gross profit of the initial structure, but higher than the one in scenario 1, in which there is no DH.
6.2.3 Scenario 3: 25% limitation DH Again, this scenario aims to see if DH appears to be economically optimal, when heat demand is reduced to 25% of the demand of the initial structure. Table 34 summarizes the technologies of the generated optimal structure, whilst Table 35 and Table 36, on page 68, show the resource consumption and economics, respectively.
Table 34. Technologies appearing in the optimal structure for scenario 3. Thannhausen N Thannhausen S Mortantsch ELIN
CHP 250 kWel CHP 250 kWel CHP 250 kWel Biogas pipeline from L2
Digester B 250 kWel Digester A 80 kWel Digester A 80 kWel CHP 250 kWel
DH to Thannhausen Digester B 160 kWel Digester B 250 kWel SGT 200 kWel
Digester B 250 kWel
Digester C 160 kWel MW Treatment
Table 35. Scenario 3: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 0 0 0 Meadow grass 2,889 16,352 72.6 Farmland 175 676 62.3 Total 3,064 17,027 15.0 Manure - 46,710 43.3 MW - 1,628 100
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Table 36. Scenario 3: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €630,207 €342,466 €110,098 €845,567 €2,150,011 €206,094
Summary of results of scenario 3 Unlike scenario 2, DH appears to be economically optimal only from L1 to Thannhausen. Biogas production occurs at all three locations, with L2 producing the biggest amount and the most popular digester being digester B. The heat needed for the digestion is provided via a
CHP unit per location, with a capacity of 250 kWel. ELIN is producing its required heat using
a 250 kWel CHP unit and a 200 kWel small gas turbine (HT). The biogas ELIN uses is supplied from L2 (Thannhausen South).
Due to the DH limitation in combination with the use of CHPs, there is low and high temperature waste heat at all three locations and low temperature waste heat at ELIN. The resource use is approximately the same as in scenario 1. The gross profit is ~25% lower than the one of the initial optimal structure (see Table 26).
6.2.4 Scenario 4: DH unlimited The last scenario of the DH category, regards how the heat distribution would be if heat demand was unlimited for any of the destinations set in the initial structure. A summary of the new optimal structure is presented in Table 37.
Table 37. Technologies appearing in the optimal structure for scenario 4. Thannhausen N Thannhausen S Mortantsch
WG WG CHP 250 kWel
CHP 160 kWel CHP 250 kWel Digester A 80 kWel
CHP 250 kWel SGT 200 kWel Digester B 250 kWel
Digester B 250 kWel Digester B 160 kWel
Digester D 250 kWel Digester B 250 kWel
DH to Thannhausen Digester C 160 kWel MW Treatment DH to Krottendorf
Table 38, on page 69, shows the amount of resources consumed and Table 39, on page 69, summarizes the costs and the profit of the scenario.
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Table 38. Scenario 4: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 448.8 3,501 2.8 Meadow grass 3,162 17,894 79.5 Farmland 209 807 74.4 Total 3,819 22,201 18.7 Manure - 40,570 37.6 MW - 1,628 100
Table 39. Scenario 4: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €855,002 €445,581 €111,562 €1,207,386 €2,961,221 €341,690
Summary of results of scenario 4 DH lines appear from L1 and L2 to Thannhausen and Krottendorf, respectively. Biogas production is optimal at all three locations, with L2 producing the biggest amount and the most popular digester being digester B. The heat needed for the digestion is provided via a
wood gasifier and two CHP units (160 kWel and 250 kWel) at L1 and a wood gasifier, a CHP
unit (250 kWel) and a small gas turbine (200 kWel, LT) at L2 and one CHP unit (250 kWel) at L3. There is no change in ELIN’s technology use, as it is covering its heat demand by the use of its natural gas-fired boiler.
Due the use of CHPs, there is high temperature waste heat at all three locations. Moreover, there is low temperature waste heat at L3. Regarding the use of resources, it is higher than all optimal structures generated so far, but no shortage appears. Nevertheless, extensive use of grass increases the competition with grazing.
The gross profit is higher than the gross profit of the initial structure due to the higher electricity and DH production, i.e. higher revenue (see Table 26).
6.3 2nd Scenario category - Mortantsch exclusion
6.3.1 Scenario 5: Mortantsch exclusion This scenario excludes L3 from the initial structure and thus only L1 and L2 are being considered. This might also be a realistic case, since as seen in sub-chapter 3.3, Mortantsch is relatively isolated from the municipalities and thus the stakeholders might want to consider only Thannhausen North and South. Table 40, on page 70, summarizes the optimal structure.
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Table 40. Technologies appearing in the optimal structure for scenario 5. Thannhausen N Thannhausen S WG WG
CHP 160 kWel & CHP 250 kWel CHP 250 kWel
Digester A 80 kWel Digester B 250 kWel
Digester B 250 kWel DH to Krottendorf
Digester C 160 kWel MW Treatment DH to Thannhausen
Table 41 presents the amount and percentage of the consumed resources and Table 42 presents the total investment cost, operating cost, transportation cost, resources cost, gross revenue and net revenue.
Table 41. Scenario 5: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 275 2,145 1.7 Meadow grass 1,689 9,557 42.5 Farmland 89 344 31.7 Total 2,678 15,540 10.0 Manure - 25,273 23.4 MW - 1,628 100
Table 42. Scenario 5: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €518,256 €731,305 €56,108 €266,147 €1,813,512 €241,695
Summary of results of scenario 5 DH lines appear from L1 and L2 to Thannhausen and Krottendorf, respectively. Biogas production is optimal at both locations, with L1 producing the biggest amount and the most popular digester being digester B. The heat needed for the digestion is provided via a wood
gasifier and two CHP units (160 kWel and 250 kWel) at L1 and a wood gasifier and a CHP unit
(250 kWel). There is no change in ELIN’s technology use, as it is covering its heat demand by the use of its natural gas-fired boiler.
Due the use of CHPs, there is high temperature waste heat at both locations. Regarding the use of resources, it is lower than all optimal structures generated so far (the previous 4 reviewed scenarios and the initial optimal structure) due to the exclusion of L3 (see Table 25).The gross profit is ~13% lower than the gross profit of the initial structure (see Table 26). A.G.Gemenetzi 70
6.4 3rd Scenario category - Sensitivity analysis of gas prices
6.4.1 Scenario 6: Natural gas price increase In the initial optimal solution, ELIN is still using the currently existing natural gas-fired boiler. A sensitivity analysis was conducted in order to pinpoint the natural gas price above which, it is optimal for ELIN to use a biogas fueled technology. It was determined that for a natural gas price of more than 43 €·(MW·h)-1 the natural gas boiler is replaced by other technologies, e.g. for a natural gas price of 44 €·(MW·h)-1 a CHP unit and a small gas turbine
200 kWel (HT) are used.
It is worth mentioning that the current price of natural gas is ranging from 40€·(MW·h)-1- 45€·(MW·h)-1, so this scenario is very realistic. The price used in the initial input structure was 42.5€·(MW·h)-1. Table 43 presents a summary of the technologies that appear in the new optimal structure.
Table 43. Technologies appearing in the optimal structure for scenario 6. Thannhausen N Thannhausen S Mortantsch ELIN
WG WG CHP 250 kWel Biogas pipeline from L2
CHP 160 kWel CHP 250 kWel Digester A 80 kWel CHP 250 kWel
CHP 250 kWel Digester B 160 kWel Digester B 250 kWel SGT 200 kWel
Digester A 80 kWel Digester B 250 kWel
Digester B 250 kWel Digester C 160 kWel DH to Thannhausen MW Treatment DH to Krottendorf Table 44 shows the amount of resources consumed and Table 45, on page 72, summarizes the costs and the profit of the scenario.
Table 44. Scenario 6: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 341 2,658 2.1 Meadow grass 2,882 16,312 72.5 Farmland 175 676 62.3 Total 3,398 19,646 16.6 Manure - 46,670 43.2 MW - 1,628 100
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Table 45. Scenario 6: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €857,366 €408,211 €109,698 €1,002,068 €2,649,352 €272,010
Summary of results of scenario 6 DH lines appear from L1 and L2 to Thannhausen and Krottendorf, respectively. Biogas production is optimal at all three locations, with L2 producing the biggest amount and the most popular digester being digester B. The heat needed for the digestion is provided via a
wood gasifier and two CHP units (160 kWel and 250 kWel) at L1, a wood gasifier and a CHP
unit (250 kWel) at L2 and one CHP unit (250 kWel) at L3. ELIN is producing its required heat
using a 250 kWel CHP unit and a 200 kWel small gas turbine (HT). The biogas ELIN burns is supplied from L2 (Thannhausen South).
Due to the use of CHPs, there is high temperature waste heat at all three locations. Low temperature waste heat appears at L3 and at ELIN. Regarding the use of resources, it is higher than the initial optimal structures, but no shortage appears (see Table 25). Nevertheless, extensive use of grads increases the competition with grazing.
The gross profit is slightly less than the gross profit of the initial optimal structure (see Table 26).
6.4.2 Scenario 7: Biogas feed-in A sensitivity analysis was conducted to determine the biogas tariff for which it is profitable to feed biogas in the distribution grid. The price was determined to be any price above 76€·(MW·h)-1. Table 46 summarizes the technologies that appear in the optimal structure.
Table 46. Technologies appearing in the optimal structure for scenario 7. Thannhausen N Thannhausen S Mortantsch WG WG WG
CHP 250 kWel CHP 250 kWel Digester B 160 kWel
Digester A 80 kWel Digester B 250 kWel Digester B 250 kWel
Digester B 250 kWel DH to Krottendorf Digester C 160 kWel
DH to Thannhausen Digester D 250 kWel MW Treatment Table 47, on page 73, shows the amount of resources consumed and Table 48, on page 73, summarizes the costs and the profit of the scenario.
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Table 47. Scenario 7: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 450 3,510 2.8 Meadow grass 3,716 21,033 93.4 Farmland 247 953 87.9 Total 4,413 25,496 21.6 Manure - 48,988 45.4 MW - 1,628 100
Table 48. Scenario 7: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €878,992 €451,328 €169,942 €1,366,397 €3,156,786 €290,130
Summary of results of scenario 7 DH lines appear from L1 and L2 to Thannhausen and Krottendorf, respectively. Biogas production is optimal at all three locations, with L3 producing the biggest amount and the most popular digester being digester B. The heat needed for the digestion is provided via a
wood gasifier and a CHP unit (250 kWel) per L1 and L2 and a wood gasifier at L3. It is one of the few scenarios where wood gasifiers appear at all three locations. There is no change in ELIN’s technology use, as it is covering its heat demand by the use of its natural gas-fired boiler.
Due the use of CHPs, there is high temperature waste heat at L1 and L2. Moreover, there is low temperature waste heat at L3. Regarding the use of resources, it is higher than all optimal structures generated so far, but no shortage appears (see Table 25). Nevertheless, extensive use of grass increases the competition with grazing.
The gross profit is higher than the gross profit of the initial structure due to additional revenue deriving from the upgraded biogas sale (see Table 26).
6.5 4th Scenario category: Feed-in tariff limitation
6.5.1 Scenario 8: Retail prices for electricity & biogas The Austrian government has set tariffs for electricity generated from biogas fueled technologies and for upgraded biogas that is fed in the grid [56], [52]. This scenario substitutes the tariffs with regular retail market prices for electricity and natural gas, respectively. Table 49 on page 74 presents the technologies which appear in the optimal structure.
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Table 49. Technologies appearing in the optimal structure for scenario 8. Thannhausen N Thannhausen S Mortantsch ELIN
WG WG WG CHP 1 MWel
DH to Thannhausen Digester B 160 kWel Biogas pipeline from L2
Digester B 250 kWel DH to Unterfladnitz
Digester C 160 kWel
MW Treatment DH to Krottendorf Tables 50 and 51 show the amounts of resources consumed and summarize the costs and the profit of scenario 8, respectively.
Table 50. Scenario 8: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 673 5,251 4.2 Meadow grass 1,648 9,328 41.3 Farmland 88 340 31.9 Total 2,409 14,919 11.8 Manure - 11,288 10.5 MW - 1,628 100
Table 51. Scenario 8: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €850,010 €296,648 €53,412 €736,817 €2,188,522 €251,634
Summary of results of scenario 8 DH lines appear from L1, L2 and ELIN to Thannhausen, Krottendorf and Unterfladnitz, respectively. Biogas production is optimal only at L2 and the most popular digester is digester B. The heat needed for the digestion is provided via a wood gasifier. This is one of the few scenarios, in which a wood gasifier appears at all three locations. ELIN is covering its high
temperature heat demand via a CHP unit (1 MWhel), which is fueled with biogas from L2.
Due the use of wood gasifiers and no anaerobic digesters to provide heat to, there is low temperature waste heat at L1 and L3. Regarding the use of resources, it is lower (except for wood chips) than all optimal structures reviewed so far, due to the fewer biogas plants.
Nevertheless, the gross profit is only a bit lower (by ~8%) than the gross profit of the initial structure. That is because the retail prices, which are set for electricity produced from biogas and for natural gas are high.
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6.5.2 Scenario 9: Retail prices for electricity In this scenario, biogas tariff is provided, but the electricity prices are the same as the regular retail prices. A summary of the new optimal structure is presented in Table 52.
Table 52. Technologies appearing in the optimal structure for scenario 9. Thannhausen N Thannhausen S Mortantsch WG WG WG
DH to Thannhausen DH to Krottendorf Digester B 160 kWel
Digester B 250 kWel
Digester C 160 kWel
Digester D 250 kWel MW Treatment Biogas upgrade & feed-in Table 53 shows the amount of consumed resources and Table 54 summarizes the costs and the profit of the scenario.
Table 53. Scenario 9: Amount and percentage of consumed resources. Pct of the Surface area, ha Resource amount, t total, % Forest 673 5,251 4.2 Meadow grass 2,399 13,580 60.3 Farmland 111 429 39.5 Total 4,413 25,496 15.6 Manure - 23,938 22.2 MW - 1,628 100
Table 54. Scenario 9: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €790,458 €331,218 €102,955 €1,066,795 €2,558,548 €267,126
Summary of results of scenario 9 DH lines appear from L1 and L2 to Thannhausen and Krottendorf, respectively. Biogas production is optimal only at L3 and the most popular digester is digester B. This is one of the few scenarios, in which biogas injection in to the distribution grid occurs. The heat needed for the digestion is provided via a wood gasifier. Wood gasifier appears at all three locations. There is no change in ELIN’s technology use, as it is covering its heat demand by the use of its natural gas-fired boiler.
A.G.Gemenetzi 75
Due the use of wood gasifiers, there is low temperature waste heat at all three locations. Regarding the use of resources, only meadow grass presents higher use than the initial optimal structure, due to the fact that no digester A is used (i.e. 100% manure).
The gross profit is slightly lower (by ~3%) than the gross profit of the initial structure. That is because the market prices, which are set for electricity produced from biogas and for natural gas are high and also due to the revenue gained from the sale of the upgraded biogas.
6.5.3 Scenario 10: 100-50% Decrease of the electricity tariffs' provision A sensitivity analysis was conducted as to determine, for which electricity prices, technologies producing electricity don’t result as optimal solution. It was determined that for 100% to 50% reduction of the current tariffs, electricity production doesn’t occur in the optimal solution. For this reduction range the structure remains the same and is briefly presented in Table 55. Table 55. Technologies appearing in the optimal structure for scenario 10. Thannhausen N
GB 300 kWel Digester B 160 kWel Digester B 250 kWel Digester C 160 kWel Digester D 250 kWel MW Treatment Table 56 shows the amount of resources consumed and Table 57 summarizes the costs and the profit of the scenario.
Table 56. Scenario 10: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 0 0 0 Meadow grass 2,370 13,415 59.6 Farmland 124 479 44.1 Total 2,494 13,894 12.2 Manure - 23,594 21.9 MW - 1,628 100
Table 57. Scenario 10: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €383,786 €149,818 €74,524 €748,632 €1,373,542 €16,780
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Summary of results of scenario 10 Technologies appear only at L1. The technologies that appear at L1 include biogas production with most popular digester being digester B, biogas injection in to the distribution grid and a biogas-fired boiler (300 kWth), which provides heat to the digesters. There is no change in ELIN’s technology use, as it is covering its heat demand by the use of its natural gas-fired boiler.
This is the only scenario, in which zero waste heat occurs (beside the losses of the DH transmission). The total use of resources is low, but still grass consumption is higher than that of the initial optimal structure (see Table 25). The gross profit is the lowest of all conducted scenarios presented in this work.
6.5.4 Scenario 11: 60% Decrease of electricity tariffs provision Following the same train of thought as the previous scenario, the electricity tariffs are decreased by 40%. Starting from this price, it is profitable to produce electricity, but only for electricity prices reduced no more than 10%, technologies appear to the other locations as well. Table 58 shows the technologies that appear in the new optimal structure. Table 58. Technologies appearing in the optimal structure for scenario 11. Thannhausen N
WG Digester B 160 kWel Digester B 250 kWel Digester C 160 kWel Digester D 250 kWel MW Treatment Table 59 presents the amount of resources consumed and Table 60, on page 78, summarizes the costs and the profit of the scenario. Table 59. Scenario 11: Amount and percentage of consumed resources. Pct of the Surface area, ha Resource amount, t total, % Forest 134.6 1,050 0.8 Meadow grass 2,390 13,530 60.1 Farmland 124 479 44.1 Total 2,649 15,059 13.0 Manure - 23,938 22.2 MW - 1,628 100
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Table 60. Scenario 11: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €470,622 €175,467 €77,021 €816,850 €1,570,164 €30,208
Summary of results of scenario 11 The structure generated is the same as in scenario 10 with the only difference the biogas-fired boiler is replaced by a wood gasifier. Thus technologies appear only at L1. The technologies that appear at L1 include biogas production with most popular digester being digester B, upgraded biogas injection in to the distribution grid and a wood gasifier, which provides heat to the digesters.
Due to the wood gasifier, there is low temperature waste heat. The total use of resources is low, but still grass consumption is higher than that of the initial optimal structure (see Table 25). The gross profit is the second lowest of all conducted scenarios presented in this work.
6.6 5th Scenario category: Electricity production limitation
6.6.1 Scenario 12: Only one energy producing technology per location In this scenario, the input structure is set so that each location is limited to use only one technology that produces electricity. Table 61 shows the new generated optimal structure. Table 61. Technologies appearing in the optimal structure for scenario 12. Thannhausen N Thannhausen S Mortantsch ELIN
CHP 250 kWel CHP 250 kWel CHP 250 kWel Biogas pipeline from L2
Digester B 250 kWel Digester A 80 kWel Digester A 80 kWel SGT 200 kWel
DH to Thannhausen Digester B 250 kWel Digester B 250 kWel
Digester C 160 kWel
MW Treatment
Tables 62 presents the resource consumption and Table 63, on page 79, presents the economics of the new optimal solution.
Table 62. Scenario 12: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 0 0 0 Meadow grass 2,494 14,115 62.7 Farmland 113 436 40.2 Total 2,607 14,551 12.7 Manure - 44,234 41.0 MW - 1,628 100
A.G.Gemenetzi 78
Table 63. Scenario 12: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €572,354 €294,426 €92,512 €741,609 €1,901,635 €200,735
Summary of results of scenario 12 DH appears to be economically optimal only from L1 to Thannhausen. Biogas production appears at all three locations, with L2 producing the biggest amount and the most popular digester being digester B. The heat needed for the digestion is provided via a CHP unit per
location, with a capacity of 250 kWel. ELIN is producing its required heat using a 200 kWel small gas turbine (HT). The biogas ELIN uses is supplied from L2.
Due to the DH limitation in combination with the use of CHPs, there is high temperature waste heat at all three locations and low temperature waste heat at L2 and L3. The resource use is approximately the same as in scenario 1.
The gross profit is lower (by ~37%) than the one of the initial optimal structure (see Table 26).
6.7 6th Scenario category: Resource limitation The main resources, i.e. wood chips from forests, meadow grass, grass, miscanthus chips, short rotation biomass, manure and municipal waste (bio-waste), are limited at different percentages as to assess the respective optimal solution. It is essential to conduct resources' related scenarios, since the large number of stakeholders involved implies unforeseen barriers, which could potentially influence the resource availability. In total, 8 resource related scenarios were formed.
6.7.1 Scenario 13: No wood chips available The optimal structure and most of the structures investigated here use only a small amount of wood chips, i.e. ~0.8% - 4.5 %, so it was preferred to make the extreme scenario in which no wood chips are available. Table 64, on page 75, summarizes the technologies that appear in the optimal structure.
A.G.Gemenetzi 79
Table 64. Technologies appearing in the optimal structure for scenario 13. Thannhausen N Thannhausen S Mortantsch
BG CHP 160 kWel CHP 250 kWel
CHP 160 kWel CHP 250 kWel Digester A 80 kWel
CHP 250 kWel SGT 200 kWel Digester B 250 kWel
Digester A 80 kWel Digester B 160 kWel
Digester B 160 kWel Digester B 250 kWel
Digester B 250 kWel Digester C 160 kWel DH to Thannhausen MW Treatment DH to Krottendorf
Table 65 shows the amount of resources consumed and Table 66 summarizes the costs and the profit of the scenario.
Table 65. Scenario 13: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 0 0 0 Meadow grass 3,211 18,172 80.7 Farmland 213 1,585 75.8 Total 3,424 19,757 16.4 Manure - 48,437 44.9 MW - 1,628 100
Table 66. Scenario 13: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit
€689,903 €399,688 €128,216 €1,100,951 €2,571,567 €251,893
Summary of results of scenario 13 DH lines appear from L1 and L2 to Thannhausen and Krottendorf, respectively. Biogas production is optimal at all three locations, with L2 producing the biggest amount and the most popular digester being digester B. The heat needed for the digestion is provided via a
(short rotation) biomass gasifier and two CHP units (160 kWel and 250 kWel) at L1, two CHP
units (160 kWel and 250 kWel) and a small gas turbine (200 kWel, LT) at L2 and one CHP unit
(250 kWel) at L3. There is no change in ELIN’s technology use, as it is covering its heat demand by the use of its natural gas-fired boiler.
Due to the use of CHPs, there is high temperature waste heat at all three locations. Low temperature waste heat appears at L3. Regarding the use of resources, it is higher than the
A.G.Gemenetzi 80
initial optimal structures, but no shortage appears. Nevertheless, extensive use of grass increases the competition with grazing.
The gross profit is a bit less (by ~8%) than the gross profit of the initial optimal structure.
6.1.1 Scenario 14: No MW availability The amount of municipal waste is enough to fuel, as a blend, only one digester. Nevertheless this scenario is not of negligible effect. The generated optimal structure is summarized in Table 67.
Table 67. Technologies appearing in the optimal structure for scenario 14. Thannhausen N Thannhausen S Mortantsch
WG WG CHP 250 kWel
CHP 160 kWel CHP 250 kWel Digester A 80 kWel
CHP 250 kWel Digester B 250 kWel Digester B 250 kWel
Digester A 80 kWel DH to Krottendorf
Digester B 160 kWel
Digester B 250 kWel DH to Thannhausen
Tables 68 presents the resource consumption and Table 69 presents the economics of the new optimal solution.
Table 68. Scenario 14: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 275 2,145 1.7 Meadow grass 2,439 13,806 61.3 Farmland 113 436 40.2 Total 2,827 16,386 13.8 Manure - 43,742 40.5 MW - 0 0
Table 69. Scenario 14: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €619,995 €345,874 €80,306 €958,020 €2,162,408 €158.212
A.G.Gemenetzi 81
Summary of results of scenario 14 DH lines appear from L1 and L2 to Thannhausen and Krottendorf, respectively. Biogas production is optimal at all three locations, with L1 producing the biggest amount and the most popular digester being digester B. The heat needed for the digestion is provided via a wood gasifier and two CHP units (160 kWel and 250 kWel) at L1, a wood gasifier and a CHP unit (250 kWel) at L2 and one CHP unit (250 kWel) at L3. There is no change in ELIN’s technology use, as it is covering its heat demand by the use of its natural gas-fired boiler.
Due to the use of CHPs, there is high temperature waste heat at all three locations. Low temperature waste heat appears at L3. Regarding the use of resources, they are the same as in the initial optimal structure, except for the consumption of meadow grass, which is slightly more.
Despite the small amount of municipal waste, it has a noticeable effect on the revenue. The reason for this is that the revenue obtained in the initial structure is relatively low and thus the exclusion of the tariff provided for municipal waste utilization makes a noticeable difference.
6.7.1 Scenario 15: No grass availability Grass (meadow/farmland) is one of the most important resources and its lack means that only fermenters operating with manure (as MW is set in the structure only in combination with grass) will appear in the structure. Table 70 summarizes the technologies that appear in the optimal structure. Table 70. Technologies appearing in the optimal structure for scenario 15. Thannhausen N Thannhausen S
WG WG
CHP 160 kWel DH to Krottendorf
Digester A 160 kWel DH to Thannhausen
Tables 71 and 72, on page 83, show the amount of resources consumed and summarize the costs and the profit of scenario 15, respectively.
A.G.Gemenetzi 82
Table 71. Scenario 15: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 428 3,340 2.6 Meadow grass 0 0 0 Farmland 0 0 0 Total 428 3,340 2.1 Manure - 26,903 24.9 MW - 0 0
Table 72. Scenario 15: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €384,863 €125,512 €36,233 €420,897 €1,002,592 €35,087
Summary of results of scenario 15 Technologies appear only at L1 and L2. DH lines appear from L1 and L2 to Thannhausen and Krottendorf, respectively. Biogas production is limited and it is optimal only at L1. The heat
needed for the digestion is provided via a wood gasifier and a CHP unit (160 kWel). There is also a wood gasifier at L2, for the provision of the low temperature heat for the DH line. There is no change in ELIN’s technology use, as it is covering its heat demand by the use of its natural gas-fired boiler.
Due to the use of the CHP, there is high temperature waste heat at L1. Regarding the use of resources, it is the lowest of all scenarios. The gross profit is the third lowest of all scenarios.
6.7.2 Scenario 16: 33.3% grass availability The amount of meadow grass/ farmland grass is reduced by 66.67%, which is slightly less than the amount used in average, in the investigated structures. The new optimal structure is summarized in Table 73.
Table 73. Technologies appearing in the optimal structure for scenario 16. Thannhausen N Thannhausen S Mortantsch
WG WG CHP 250 kWel
CHP 250 kWel CHP 250 kWel Digester A 80 kWel
Digester A 80 kWel Digester B 250 kWel Digester C 160 kWel
Digester B 250 kWel DH to Krottendorf MW Treatment DH to Thannhausen
Table 74, on page 84, shows the amount of resources consumed and Table 75, on page 84, summarizes the costs and the profit of the scenario.
A.G.Gemenetzi 83
Table 74. Scenario 16: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 348 2,715 2.2 Meadow grass 1,245 7,044 31.3 Farmland 85 327 30.1 Total 1,677 10,085 8.2 Manure - 43,742 40.5 MW - 1,628 100
Table 75. Scenario 16: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €564,457 €265,601 €90,812 €743,002 €1,884,911 €221,040
Summary of results of scenario 16 DH lines appear from L1 and L2 to Thannhausen and Krottendorf, respectively. Biogas production is optimal at all three locations, with L1 producing the biggest amount and the most popular digester being digester B. The heat needed for the digestion is provided via a
wood gasifier and a CHP unit (250 kWel) at L1, a wood gasifier and a CHP unit (250 kWel) at
L2 and one CHP unit (250 kWel) at L3. There is no change in ELIN’s technology use, as it is covering its heat demand by the use of its natural gas-fired boiler.
Due to the use of CHPs, there is high temperature waste heat at all three locations. Low temperature waste heat appears at L3. Regarding the use of resources, they are slightly less than the set limit. This amount of used grass is a safe valve regarding the competition with grazing. The gross profit obtained is by ~20% less than the gross profit of the initial optimal structure (see Table 26).
6.7.3 Scenario 17: 50% grass availability The amount of meadow grass/grass is reduced to 50%, which is slightly more than the amount used in average, in the investigated structures. Table 76, on page 85, summarizes the technologies that appear in the optimal structure.
A.G.Gemenetzi 84
Table 76. Technologies appearing in the optimal structure for scenario 17. Thannhausen N Thannhausen S Mortantsch
WG WG CHP 250 kWel
CHP 250 kWel CHP 250 kWel Digester A 80 kWel
Digester A 80 kWel Digester B 250 kWel Digester B 250 kWel
Digester C 160 kWel DH to Krottendorf MW Treatment DH to Thannhausen
Table 77 shows the amount of resources consumed and Table 78 summarizes the costs and the profit of the scenario.
Table 77. Scenario 17: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 328 2,559 2.0 Meadow grass 1,575 8,915 39.6 Farmland 105 403 37.2 Total 2,008 11,877 9.8 Manure - 43,742 40.5 MW - 1,628 100
Table 78. Scenario 17: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €590,259 €292,030 €85,562 €807,701 €2,020,553 €244,999
Summary of results of scenario 17 DH lines appear from L1 and L2 to Thannhausen and Krottendorf, respectively. Biogas production is optimal at all three locations, with L3 producing the biggest amount and the most popular digester being digester B. The heat needed for the digestion is provided via a
wood gasifier and a CHP unit (250 kWel) at L1, a wood gasifier and a CHP unit (250 kWel) at
L2 and one CHP unit (250 kWel) at L3. There is no change in ELIN’s technology use, as it is covering its heat demand by the use of its natural gas-fired boiler.
Due to the use of CHPs, there is high temperature waste heat at all three locations. Low temperature waste heat appears at L3. Regarding the use of resources, they are less than the set limit. This amount of used grass can also (as in scenario 16) be considered as a safe valve regarding the competition with grazing. The gross profit obtained is by ~10% less than the gross profit of the initial optimal structure (see Table 26).
A.G.Gemenetzi 85
6.7.4 Scenario 18: 10% manure availability Manure availability is reduced by 90%, which means there is not enough manure for digesters type A, i.e. 100% fueled with manure. It is expected that all the amount of manure set as available (i.e. 10% of the total) will be provided to the digesters. What is interesting is to see the technology distribution among the locations. The technologies that appear in the optimal structure are presented in Table 79. Table 79. Technologies appearing in the optimal structure for scenario 18. Thannhausen N Thannhausen S Mortantsch
WG WG CHP 250 kWel
CHP 250 kWel CHP 250 kWel Digester B 250 kWel
Digester B 250 kWel Digester B 80 kWel
DH to Thannhausen Digester C 160 kWel MW Treatment DH to Krottendorf
Table 80 shows the amount of resources consumed and Table 81 summarizes the costs and the profit of the scenario.
Table 80. Scenario 18: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 311 2,422 1.9 Meadow grass 1,557 8,815 39.2 Farmland 94 363 33.5 Total 1,962 11,600 9.6 Manure - 10,797 10.0 MW - 1,628 100
Table 81. Scenario 18: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €493,484 €238,683 €52,472 €662,469 €1,649,253 €202,146
Summary of results of scenario 18 DH lines appear from L1 and L2 to Thannhausen and Krottendorf, respectively. Biogas production is optimal at all three locations, with L2 producing the biggest amount and the most popular digester being digester B. The heat needed for the digestion is provided via a
wood gasifier and a CHP unit (250 kWel) at L1, a wood gasifier and a CHP unit (250 kWel) at
L2 and one CHP unit (250 kWel) at L3. There is no change in ELIN’s technology use, as it is covering its heat demand by the use of its natural gas-fired boiler.
A.G.Gemenetzi 86
Due to the use of CHPs, there is high temperature waste heat at all three locations. Low temperature waste heat appears at L3. As expected, the use of manure is equal to the set limit. Thus the use of grass is limited as well, since grass fuels the digesters only as a blend with manure (or manure & municipal waste). The gross profit obtained is by ~26% less than the gross profit of the initial optimal structure (see Table 26).
6.7.5 Scenario 19: 50% manure availability On the same train of thought, manure is reduced to 50% as to see if the structure will be the same as the initial optimal structure, in which manure consumption accounts to 40.5%. The results are summarized in Table 82. Table 82. Technologies appearing in the optimal structure for scenario 19. Thannhausen N Thannhausen S Mortantsch
WG WG CHP 250 kWel CHP 250 kWel CHP 160 kWel Digester B 250 kWel Digester B 250 kWel CHP 250 kWel DH to Thannhausen Digester B 250 kWel Digester C 160 kWel MW Treatment DH to Krottendorf Table 83 shows the amount of resources consumed and Table 84 summarizes the costs and the profit of the scenario.
Table 83. Scenario 19: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, %
Forest 275 2,145 1.7 Meadow grass 2,690 15,225 67.6 Farmland 113 436 40.2 Total 3,078 17,806 16.5 Manure - 17,280 16.0 MW - 1,628 100
Table 84. Scenario 19: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit
€612,418 €343,138 €63,676 €918,101 €2,195,124 €257,789
A.G.Gemenetzi 87
Summary of results of scenario 19 DH lines appear from L1 and L2 to Thannhausen and Krottendorf, respectively. Biogas production is optimal at all three locations, with L2 producing the biggest amount and the most popular digester being digester B. The heat needed for the digestion is provided via a wood gasifier and a CHP unit (250 kWel) at L1, a wood gasifier and two CHP units (160 kWel and 250 kWel) at L2 and one CHP unit (250 kWel) at L3. There is no change in ELIN’s technology use, as it is covering its heat demand by the use of its natural gas-fired boiler.
Due to the use of CHPs and wood gasifiers, there is high temperature waste heat at all three locations and low temperature waste heat at L2 and L3. The use of manure is significantly below the set limit, since digester A was not used in any location. Thus the use of grass is high, but no shortage appears.
The gross profit obtained is only by ~6% less than the gross profit of the initial optimal structure (see Table 26).
6.7.6 Scenario 20: 33.3% grass - 50% manure Availability This scenario combines a reduction to the two main resources; grass (meadow/farmland) and manure. Table 85 summarizes the results of the new optimal structure. Table 85. Technologies appearing in the optimal structure for scenario 20. Thannhausen N Thannhausen S WG WG
CHP 250 kWel CHP 250 kWel Digester E 250 kWel Digester B 250 kWel MW Treatment DH to Krottendorf DH to Thannhausen Table 86 shows the amount of resources consumed and Table 87, on page 89, summarizes the costs and the profit of the scenario.
Table 86. Scenario 20: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 312 2,433 1.9 Meadow grass 1,147 6,492 28.8 Farmland 78 300 27.6 Total 1,537 9,225 7.5 Manure - 16,509 15.3 MW - 1,628 100
A.G.Gemenetzi 88
Table 87. Scenario 20: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit
€457,247 €209,432 €59,843 €595,348 €1,517,785 €195,914
Summary of results of scenario 20 The industrial complex is limited to L1 and L2. DH lines appear from L1 and L2 to Thannhausen and Krottendorf, respectively. Biogas production is equally optimal at both locations. It is the only scenario that selects the use of digester E as optimal. The heat needed
for the digestion is provided via a wood gasifier and a CHP unit (250 kWel) for each location. There is no change in ELIN’s technology use, as it is covering its heat demand by the use of its natural gas-fired boiler.
Due to the use of CHPs, there is high temperature waste heat at both locations. The use of manure is significantly below the set limit, since digester A was not used in any location. The use of grass approaches the set resource limit.
The gross profit obtained is by ~28% less than the gross profit of the initial optimal structure (see Table 26).
6.8 7th Scenario category: Digestate price variation The current price given for the digestate is 4€, but this price can vary depending on the region and the respective market [57]. Thus it was deemed reasonable to make two scenarios for a lower and for a higher price, based on relative market prices.
6.8.1 Scenario 21: Digestate price: 0€ The sale price of the digestate is set to 0 € for all three locations producing biogas. Table 88 summarizes the optimal structure. Table 88. Technologies appearing in the optimal structure for scenario 21. Thannhausen N Thannhausen S WG WG
CHP 250 kWel CHP 250 kWel
Digester B 250 kWel Digester B 160 kWel
DH to Thannhausen Digester C 160 kWel MW Treatment DH to Krottendorf
A.G.Gemenetzi 89
Table 89 presents the amount and percentage of the consumed resources and Table 90 presents the total investment cost, operating cost, transportation cost, resources cost, gross revenue and net revenue.
Table 89. Scenario 21: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, %
Forest 284 2,212 1.8 Meadow grass 1,419 8,029 35.7 Farmland 70 270 24.9 Total 1,772 10,511 8.7 Manure - 9,890 9.2 MW - 1,599 98.7
Table 90. Scenario 21: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit
€451,695 €217,060 €34,188 €612,712 €1,467,379 €151,723
Summary of results of scenario 21 The industrial complex is limited to L1 and L2. DH lines appear from L1 and L2 to Thannhausen and Krottendorf, respectively. Biogas production is optimal at both locations, with L2 producing the biggest amount and the most popular digester being digester B. The
heat needed for the digestion is provided via a wood gasifier and a CHP unit (250 kWel) for each location. There is no change in ELIN’s technology use, as it is covering its heat demand by the use of its natural gas-fired boiler.
Due to the use of CHPs, there is high temperature waste heat at both locations. The use of resources has decreased compared to the initial optimal structure (see Table 25). The reason for this decrease is that no revenue can be gained from the digestate; thus fewer digesters appear in the solution, which consequents to lesser use of resources. The effect on the gross profit is obvious as it becomes almost half of the gross profit of the initial optimal structure (see Table 26).
6.8.2 Scenario 22: Digestate price: 8€ Respectively, the sale price of the digestate was set to 8€ for all three locations. Table 91, on page 93, summarizes the technologies that appear in the new optimal structure.
A.G.Gemenetzi 90
Table 91. Technologies appearing in the optimal structure for scenario 22. Thannhausen N Thannhausen S Mortantsch ELIN
WG WG CHP 160 kWel Biogas pipeline from L2
CHP 160 kWel CHP 160 kWel CHP 250 kWel CHP 250 kWel
CHP 250 kWel CHP 250 kWel Digester A 80 kWel SGT 200 kWel
Digester A 80 kWel Digester A 80 kWel Digester B 250 kWel
Digester B 250 kWel Digester B 250 kWel
DH to Thannhausen Digester C 160 kWel
Digester D 250 kWel MW Treatment DH to Krottendorf
Table 92 shows the amount of resources consumed and Table 93 summarizes the costs and the profit of the scenario.
Table 92. Scenario 22: Amount and percentage of consumed resources. Surface area, ha Resource amount, t Pct of the total, % Forest 365 2,845 2.3 Meadow grass 3,241 18,346 81.5 Farmland 213 822 75.8 Total 3,819 22,013 18.7 Manure - 92,331 85.5 MW - 1,628 100
Table 93. Scenario 22: Total investment, operating & transportation cost, revenue and gross profit. Investment cost Operating cost Transportation cost Cost of resources Revenue Gross profit €1,042,705 €520,689 €266,370 €1,278,649 €3,736,071 €627,657
Summary of results of scenario 22
DH lines appear from L1 and L2 to Thannhausen and Krottendorf, respectively. Biogas production is optimal at all three locations, with L2 producing the biggest amount and the most popular digester s being A and B. The heat needed for the digestion is provided via a
wood gasifier and two CHP units (160 kWhel and 250 kWhel) at L1, a wood gasifier and two
CHP units (160 kWhel and 250 kWhel) at L2 and two CHP units (160 kWhel and 250 kWhel) at
L3. ELIN is producing its required heat using a 250 kWel CHP unit and a 200 kWel small gas turbine (HT). The biogas ELIN uses, is supplied from L2 (Thannhausen South).
Due to the use of CHPs, there is high temperature waste heat at all three locations and low temperature waste heat at L3 and ELIN. The total resource use is the second highest of all
A.G.Gemenetzi 91
scenarios (first being scenario 7), but no resource shortage occurs. As expected, the use of manure is the highest compared to all scenarios, since digesters A deliver the highest amount of digestate. The high use of grass increases the competition with grazing.
The gross profit is the highest compared to all scenarios; it is more than twice as great as the gross profit of the initial optimal structure (see Table 26).
6.9 Comparison of results The comparison of the different scenarios is made on the basis of economics, resource consumption and energy production/waste heat. The first two aspects are important for the assessment of the feasibility of the project, whilst the latter reflects the optimization margin. Furthermore, the frequency of appearance of the technologies, at each location, gives the opportunity to assess which technologies are dominant, which are semi-dominant and which appear only rarely.
Comparison based on costs and gross profit Figure 24, on page 93, presents the overall costs considered in the structure, distributed among investment cost, operating cost, cost of resources and transportation cost. The highest cost is due to the purchase of the resources, followed by the investment costs. Operation costs and transportation costs are directly dependent on the investment costs and the resources, respectively.
Figure 25, on page 94, illustrates the sum of the costs presented in Figure 24, the revenue and the gross profit; the latter being equal to the difference of the first two.
It can be observed that the revenue and overall cost of the initial optimal structure is above the average of the 22 scenarios. Average gross profit values occur for scenario 2 and scenario16, i.e. setting half than initially set DH demand and 66.7% reduction of grass availability, respectively. The highest costs and revenue are observed for scenario 22, in which the digestate is sold as a fertilizer to the price of 8€·t-1, which is double than the initially considered price. High costs and revenue are also observed, as anticipated, in scenarios 7 and 4, which regard a higher biogas feed-in tariff and unlimited DH demand, respectively. Opposed to that, the least costs and revenue occur for scenarios 10 and 15, which respond to 100%-50% reduction of the current electricity tariffs and to zero (meadow and farmland) grass availability, respectively. Low electricity tariffs make electricity production not optimal and thus reduce the revenue and also the costs. No grass availability limits the digestion fuel only to manure, which has lower biogas yield, compared to grass and thus is not optimal for high biogas production rates. A.G.Gemenetzi 92
€ 1,600,000
€ 1,400,000
€ 1,200,000
€ 1,000,000
Investment cost, €/y € 800,000 Operating cost, €/y Cost of resources, €/y Transportation cost, €/y € 600,000
€ 400,000
€ 200,000
€ 000
Opt
Sc 1 Sc 2 Sc 3 Sc 4 Sc 5 Sc 6 Sc 7 Sc 8 Sc 9 Sc
Sc 17Sc Sc 10Sc 11Sc 12Sc 13Sc 14Sc 15Sc 16Sc 18Sc 19Sc 20Sc 21Sc 22Sc
Figure 24. Cost comparison between the 22 scenarios and the initial optimal structure.
A.G.Gemenetzi 93
€ 4,000,000
€ 3,500,000
€ 3,000,000
€ 2,500,000
Revenue, €/y € 2,000,000 Total cost, €/y Gross profit, €/y € 1,500,000
€ 1,000,000
€ 500,000
€ 000
Opt
Sc 5 Sc Sc 1 Sc 2 Sc 3 Sc 4 Sc 6 Sc 7 Sc 8 Sc 9 Sc
Sc 11Sc 12Sc 13Sc 14Sc 15Sc 16Sc 17Sc 18Sc 19Sc 20Sc 21Sc 22Sc Sc 10Sc Figure 25. Comparison of the overall cost, the revenue and the gross profit of the 22 scenarios and the initial optimal structure.
A.G.Gemenetzi 94
Comparison based on resource consumption
Figure 26 presents the manure consumption for biogas production and Figure 27 on page 96, presents the consumption of wood chips, meadow grass, farmland and the overall land use for biogas production and biomass gasification, in the 22 scenarios. It is important to mention that grazing is in competition with the surface area that was considered for the optimization, since grazing was not taken into account (i.e. excluded from the total area).
100%
90%
80%
70%
60%
50% Manure 40%
30%
20%
10%
0%
Opt
Sc 1 Sc 2 Sc 3 Sc 4 Sc 5 Sc 6 Sc 7 Sc 8 Sc 9 Sc
Sc 16Sc Sc 11Sc 12Sc 13Sc 14Sc 15Sc 17Sc 18Sc 19Sc 20Sc 21Sc 22Sc Sc 10Sc
Figure 26. Percentage of the consumed manure for biogas production.
The average manure use is ~36%, excluding the scenarios limiting manure availability, i.e. scenarios 18, 19 and 20. As anticipated, the greatest and lowest manure consumption occurs in scenario 22 and 21 respectively, since in the first case digestate is a source of revenue, whilst in the latter it provides no revenue at all. It is interesting to mention that the areas which most often providing manure are Thannhausen, Mortantsch, Krottendorf and Weiz, with the three first comprising approximately 40% of manure availability. The areas of Gutenberg, Naas and Unterfladnitz appear less often in the solution, whilst the rest very rarely.
The percentages of use for each resource illustrated in Figure 27 are percentages of the respective area only, e.g. meadows, and not of the total available surface area. The cyan colored columns though illustrate the percentage of use of the total available area.
A.G.Gemenetzi 95
100%
90%
80%
70%
60%
Forest 50% Meadow gras Farmland 40% Total
30%
20%
10%
0%
Opt
Sc 2 Sc Sc 1 Sc 3 Sc 4 Sc 5 Sc 6 Sc 7 Sc 8 Sc 9 Sc
Sc 17Sc Sc 11Sc 12Sc 13Sc 14Sc 15Sc 16Sc 18Sc 19Sc 20Sc 21Sc 22Sc Sc 10Sc Figure 27. Percentage of forest, meadow and farmland area used in the 22 scenarios and the initial structure.
A.G.Gemenetzi 96
It can be observed that the resource used the least is wood chips, derived from forest. The average use is low, 1.66%, with a range of 0%-4.15%, due to the limited use of wood chips. The areas that appear in the solutions providing wood chips are Thannhausen for L1, Krottendorf for L2 and Mortantsch for L3.
Wood chips, short rotation biomass and miscanthus chips are only used for biomass gasification and direct combustion, thus whenever biomass gasifiers don’t appear in the optimal solution, the use of this resource is 0%, i.e. scenarios 1, 2, 3, 10, 12 and 13.
The second least used resource is resources from farmland. Short rotation biomass doesn’t appear in any of the solutions, whilst miscanthus chips appear only in scenario 13, in which no wood chips are available and thus the biomass gasifier has to use another resource fuel. On the other hand, grass from farmland appears to all solutions, except for scenario 15, in which there is no grass availability. The average farmland use is ~51% and the average meadow grass use is ~64%, without considering the scenarios involving grass limited availability. Meadow grass is the highest used resource, as it yields higher amounts of biogas than manure and municipal waste. The greatest use of grass, both from farmland and meadows, is observed in scenario 7, in which the biogas tariff is increased in order for upgraded biogas feed-in the grid to become an optimal solution. The lowest amount of grass used appears in scenario 21, since digestate is not a revenue source and thus digesters producing a small amount of digestate are preferred.
The municipalities that appear most often in the solutions providing meadow grass are Gutenberg, Krottendorf, Mittersdorf, Mortantsch, Naas, Thannhausen, Unterfladnitz and Weiz, whilst more municipalities are involved for the provision of farmland grass, due to its low availability.
The average of the total use of land is ~13%, with a range of 7.5%-22.0%, not including the land necessary for graze.
The percentage of use of municipal waste was not illustrated, since it is used in all scenarios by nearly 100%, except for the scenario which assumes no municipal waste availability.
Comparison based on energy production & waste heat
Figure 28 on page 98 illustrates the total heat that was utilized as district heating, the total waste heat, the total produced electricity and the total biogas that was upgraded and fed-in the distribution grid. Figure 29 on page 99, presents in more detail, the high & low temperature waste heat and their summation, for all 22 scenarios and the initial optimal structure.
A.G.Gemenetzi 97
18,000
16,000
14,000
12,000
10,000 District heat Waste heat
MWh/y 8,000 Electricity Upgraded biogas
6,000
4,000
2,000
000
Opt
Sc 7 Sc Sc 1 Sc 2 Sc 3 Sc 4 Sc 5 Sc 6 Sc 8 Sc 9 Sc
Sc 11Sc 12Sc 13Sc 14Sc 15Sc 16Sc 17Sc 18Sc 19Sc 20Sc 21Sc 22Sc Sc 10Sc Figure 28. Energy production in the form of heat, electricity and biogas, for all 22 scenario s and the initial optimal structure.
A.G.Gemenetzi 98
6000
5000
4000
LT heat 3000
MWh/y HT heat Total waste heat
2000
1000
0
Opt
Sc 1 Sc Sc 2 Sc 3 Sc 4 Sc 5 Sc 6 Sc 7 Sc 8 Sc 9 Sc
Sc 11Sc 12Sc 13Sc 14Sc 15Sc 16Sc 17Sc 18Sc 19Sc 20Sc 21Sc 22Sc Sc 10Sc
Figure 29. Low temperature and high temperature waste heat and their summation for each scenario and the initial structure.
A.G.Gemenetzi 99
It can be observed from Figure 28, that biogas is upgraded and fed-into the grid only in scenarios 7, 9, 10 and 11. Approximately 186 m3·h-1 and 168 m3·h-1 of biogas are injected to the grid for scenario 10 and for scenarios 7, 9 and 11, respectively. In scenario 7, the minimum biogas tariff was determined, for which biogas upgrading was economically optimal to appear in the solution. Scenarios 9, 10 and 11 on the other hand belong to the 4th scenarios’ group i.e. that of electricity tariff reduction scenarios. This explains why the electricity production is the least for these scenarios and it is chosen instead to inject upgraded biogas in the grid, i.e. biogas feed- in was yielding higher revenue than electricity produced from biogas combustion.
Electricity is produced in all scenarios, except scenario 10, in which it is not economically -1 optimal. The average electricity production for the rest scenarios accounts to 8.4 GWhel·y .
-1 Heat production appears to all scenarios, since 2,155.88 MWhth·y of heat is set as a required amount that needs to be provided to ELIN. Moreover, most scenarios, except 10 and 11, provide DH lines from Location 1 to Thannhausen and Location 2 to Krottendorf. No DH lines have resulted to Weiz or St Ruprecht and only scenario 8 results to a DH line from ELIN to -1 Unterfladnitz. Average heat production is 6.3 GWhth·y .
Waste heat, means that heat is not utilized, but it is instead dissipated into the environment. Almost all scenarios, except one, include waste heat. Exception comprises scenario 10, in which electricity tariffs are reduced from 100%-50%. The optimal structure in this case comprises of a low heat producing boiler, of which all the produced heat is provided for anaerobic digestion and thus no heat is dissipated into the environment (except for losses). The average amount of waste heat is ~3,200 MWh·y-1, which could be used for space heating of ~219 households, based on data of 2010 [59]. The average LT waste heat is ~950 MWh·y-1 and the average HT waste heat accounts to ~2,260 MWh·y-1.
The great amounts of high temperature waste heat can be explained due to the fact, that the only utilization for HT heat set in the program is providing this heat to ELIN, to cover its energy demand. Apparently it is optimal for ELIN to produce this amount of heat, rather than install a pipeline transferring it from one of the three locations.
Scenarios 1, 3, 8 and 9 present the highest amounts of LT waste heat. Scenarios 1 and 3 involve no DH demand and a 75% reduced DH demand, respectively. Thus, there was no means of utilization for the heat produced. Scenarios 8 and 9 regard no biogas and electricity tariffs and no electricity tariffs, respectively. Nevertheless, biomass gasifiers appear in the solution, which produce LT heat, of which most is not utilized. For most scenarios, the great amounts of HT
A.G.Gemenetzi 100
waste heat appear mainly due to the CHP units, which are preferred over biogas-fired boilers as a solution, probably because the produced electricity comprises a revenue source.
Waste heat utilization is expected to become a great debate among the various stakeholders.
Dominant technologies
Based on the frequency of appearance of the technologies in the solutions, some technologies were assessed as dominant over others. The dominant technologies for each location can be viewed in Table 94, whilst Table 95 presents the semi-dominant technologies.
Table 94. Dominant technologies. Thannhausen N Thannhausen S Mortantsch ELIN
Digester B 250 kWel Digester B 250 kWel BG BG Existing NG-fired - CHP 250 kWel CHP 250 kWel boiler DH to Thannhausen DH to Krottendorf
Table 95. Semi-dominant technologies. Thannhausen N Thannhausen S Mortantsch
Digester B 160 kWel Digester A 80 kWel Digester A 80 kWel Digester C 160 kWel Digester B 250 kWel CHP 160 kWel MW Treatment CHP 250 kWel
This categorization is not sufficient for an overall assessment of the optimal establishment of a technology. E.g. Mortantsch appears having no dominant technologies, but overall, the appearance of different types of digesters provides a high frequency of biogas production.
A.G.Gemenetzi 101
7. Conclusions-Suggestions
Overall it can be concluded that the Process Network Synthesis method is a useful tool in developing optimal scenarios, which respond to energy and market availability and demand.
Based on the PNS program results, it can be concluded that biogas production in ‘Energieregion Weiz-Gleisdorf’ appears to be optimal. Biogas plants, i.e. anaerobic digesters, and biomass gasifiers have appeared in the solution of all scenarios and in specific, more prominent for locations 1 and 2, i.e. Thannhausen N and S, respectively. Location 3, i.e. Mortantsch, has also been selected for biogas production, but in lesser scenarios than Locations 1 and 2.
Defining a solution as optimal is something relative and it always depends on the boundaries of the system investigated. In other words, there is no such thing as optimal structure in general, but there is an optimal structure for certain criteria and specifications. Thus, it is always up to the stakeholders to decide for a certain location or certain technology application.
The PNS program doesn’t have the ability to assess whether the inserted mass and energy balances are correct. Its function is limited in using the information inserted, to assess the optimal economic solution. In addition to this characteristic, its not user-friendly interface makes the user susceptible to mistakes and a lot of caution is necessary when setting a structure. Of course the user can assess from the results if something is unrealistic or needs to change.
In the investigated project, the main source of revenue derives from biogas combustion for electricity and heat production, thus high revenues cannot be expected, as for example from the sale of biodiesel. Only in four scenarios biogas grid injection appears to be optimal, whilst electricity production appears to all, except one, scenarios.
Both products, electricity from biogas and biogas, are subsidized by the Austrian government; in other words, the revenue depends on legislative decisions regarding the respective tariffs. Nevertheless, the scenario in which upgraded biogas and electricity have the current regular retail prices for natural gas and electricity respectively results still an above average (from the set of scenarios) revenue. It should also be noted that the future aim of the province of Styria is mainly to upgrade and feed biogas into the grid, rather than use it for electricity production and district heating [9].
All scenarios have resulted positive gross profit, which means that at a first glance the project seems profitable and viable. Nevertheless, it must be considered that neither labor costs nor taxes have been taken into account. According to Baasel, for a good project, i.e. it is suggested to A.G.Gemenetzi 102
invest in it, the Return of Investment (ROI) based on pretax earnings should be at least 30% [58]. Assuming the total capital cost is equal to the investment cost, the current project has an average ROI of 3%. Thus, the question arises as to how profitable the realization of the respective project would end up being and if the various stakeholders would still choose to realize a project in which the biogas production is just enough to set off the costs.
Another important point is the length of payout time. According to Baasel [59], if only the fixed capital is considered, then the payout time should not exceed 3-5 years. Thus, it is suggested to discuss this point with investors and set a 5 years payout time in the PNS.
Heat utilization can be better optimized, as the currently suggested scenarios dissipate great amounts of heat into the environment. Utilization of waste heat is anticipated to become a debate among the different stakeholders and an important point in decision making and legislations. More stakeholders need to be included in the discussions in order to discover unknown potential linkages. One of the main 2025 goals of the province of Styria is to decrease waste heat by increasing district heating.
Biogas production from local resources is an example of materially closed loop systems and has additional benefits, both for the environment and the local economy and community. Thus biogas production continues to spread and to become a popular energy carrier. Furthermore, biogas could play a key role in stabilizing the energy system in a potential transition toward renewable energies. If Styria wants to meet its 2025 energy goals though, profit cannot afford to be the only criterion in decision making. To the contrary, different aspect criteria, e.g. emissions’ reduction, closing material cycles, liveability, et cetera, aiming in long-term results should be considered. In other words, short-term visions should be placed in the light of the long-term visions and ambitions. This applies not only at socio-technological level, but also at policy level.
The local municipalities are important players in the realization of this project. Thus, strong governance and co-operation among the different municipality-actors is needed in order to go a step further. Moreover, potential interests of the involved companies might influence the future development of the project and more stakeholders are anticipated to get involved.
Overall it can be concluded that the social aspects will be decisive regarding the implementation of innovative energy systems based on renewable energies.
A suggestion for further work is to identify and/or suggest theoretical frameworks that can describe the socio-technological transition taking place in the ‘Energieregion Weiz-
A.G.Gemenetzi 103
Gleisdorf’.One suggested framework is transition management, which combines technological transitions with insights from complex theory, social theories and governance approaches [60].
Last, but not least a suggestion for further work is the ecological footprint assessment of the current energy system using the Sustainable Process Index, SPI, tool. SPI provides a good view of the interplay between ecological and economical aspects.
A.G.Gemenetzi 104
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Appendix A
A.1 Calculation of total mass of a substance for different water contents M The expression for moisture content on wet basis is: m.c. H 2O (A.1). where M total
M total. M solid M H 2O and M denominates mass.
Example: if wood with moisture content of 50% has a total mass of 13.26 tonnes, then we can use expression (A.1) to calculate its new total mass for a moisture content of 15%:
M 0.5 H 2O M 6.63t and M 13.26 6.63 6.63t 13.26 H 2O solid
As Msolid remains constant, for 15% m.c.:
M H 2O 0.15 M H 2O 1.17t , i.e. Mtotal=1.17 + 6.63 = 7.8 t M H 2O 6.63
A.2 Resource availability per location Table A.2.1. Forest, meadow and arable land availability for each municipality. Forest Meadow Arable land, ha
ha t (15% W.C.) ha t (68% W.C.) Albersdorf/Prebuch 604 4,711.20 101 571.66 7 Birkfeld 860 6,708.00 57 322.62 13 Etzersdorf/Rollsdorf 781 6,091.8 57 322.62 11 Gleisdorf 164 1279.20 29 164.14 4 Gutenberg/Raabklam 1,967 15,342.60 339 1,918.74 18 m Hofstätten/Raab 442 3,447.60 199 1,126.34 14 Krottendorf 504 3,931.20 124 701.84 10 Labuch 235 1,833.00 137 775.42 7 Ludersdorf- 399 3,112.20 135 764.10 13 Wilfersdorf Mitterdorf 878 6,848.40 247 1,398.02 33 Mortantsch 827 6,450.60 489 2,767.74 24 Naas 1,280 9,984.00 670 3,792.20 19 Nitscha 588 4,586.40 120 679.20 13 Puch bei Weiz 1,788 13,946.40 259 1,465.94 16 St. Ruprecht/Raab 570 4,446.00 111 628.26 18
A.G.Gemenetzi 111
Forest Meadow Arable land, ha Thannhausen 3,307 25,794.60 735 4,160.10 40 Ungerdorf 116 904.80 50 283.00 1 Unterfladnitz 584 4,555.20 94 532.04 20 Weiz 309 2,410.20 25 141.50 0 Total 16,203 126,383.40 3,978 22,515.48 281
Table A.2.2. Manure and MW availability for each municipality. Manure, t Biowaste, t Communal Waste, t Albersdorf/Prebuch 2,522 50.07 23.84 Birkfeld 1,813 28.55 13.60 Etzersdorf/Rollsdorf 3,127 145.30 69.19 Gleisdorf 9,179 31.65 15.07 Gutenberg/Raabklamm 4,267 51.53 24.54 Hofstätten/Raab 4,840 59.88 28.51 Krottendorf 1,889 19.86 9.46 Labuch 4,103 51.51 24.53 Ludersdorf-Wilfersdorf 6,073 52.27 24.89 Mitterdorf 18,469 50.68 24.13 Mortantsch 13,967 35.00 16.67 Naas 2,369 36.49 17.38 Nitscha 4,905 52.92 25.20 Puch bei Weiz 3,117 52.44 24.97 St. Ruprecht/Raab 20,210 59.37 28.27 Thannhausen 1,717 21.47 10.22 Ungerdorf 3,886 37.80 18.00 Unterfladnitz 223 224.99 107.14 Weiz 1,298 40.80 19.43 Total 107,974 1,102.58 525.04
A.3 Resources’ densities The densities that were used to calculate the transportation costs for wood chips, miscanthus chips, short rotation biomass and MW, as presented in Table 9 in sub-chapter 4.2, are:
Table A.3.1. Densities of wood chips, miscnthus chips, short rotation biomass and MW on dry basis. Density, kg (D.S.)·m-3 Wood chips 196 Miscanthus chips 108 Short rotation 119 MW 175
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A.4 Cost analysis of anaerobic digesters Table A 4.1 shows an exemplary cost analysis for digesters B. The same specifications were considered for the rest digester types. Table A.4.2 shows the considered operating costs.
Table A.4.1. Cost estimation for all capacity sizes of digester type B.
Digester B 80 kWel Digester B 160 kWel Digester B 250 kWel Tank construction €55,000 €120,000 €135,000 Coating (35€·m-2) €11,793 €30,733 €35,461 Isolation (35€·m-2) €11,793 €30,733 €35,461 Gas storage (above digester) €30,000 €70,000 €70,000 Final disposal (incl. gas storage) €110,000 €220,000 €340,000 Rest equipment Electronics, control systems, etc 20,000€ €23,000 €25,000 Gas related equipment €20,000 €30,000 €30,000 Substrate related equipment €12,000 €20,000 €25,000 Heating system €20,000 €30,000 €35,000 Agitator/pumps €38,000 €38,000 €50,000 Feedstock loading €30,000 €30,000 €30,000 Gas flare €12,000 €15,000 €15,000 Construction, miscellaneous €25,000 €25,000 €25,000 Total €395,586 €682,465 €850,921
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Tale A.4.2 Operating costs for digesters B.
Digester B 80 kWel Digester B 160 kWel Digester B 250 kWel
Maintenance costs 7,584 €·y-1 12,783 €·y-1 16,514 €·y-1
Insurance costs €2,400 €·y-1 2,800 €·y-1 3,000 €·y-1
Total €9,984 15,583 €·y-1 19,514 €·y-1
A.5 Cost estimation of gas-fired boilers Investment Cost Operating Cost
300 kWel HT €66/kWhth
300 kWel LT €36/kWhth
500 kWel HT €54/kWhth
500 kWel LT €30/kWhth 10 % of Investment Cost 1,000 kWel HT €43/kWhth
1,000 kWel LT €30/kWhth
1,500 kWel HT €43/kWhth
1,500 kWel LT €30/kWhth
As the above prices were form 2004, the final investment cost was corrected with the use of M&S indexes (for 2012 and 2004).
A.G.Gemenetzi 114
A.6 Cost estimation of micro/small gas turbines The investment and operating costs of micro/small turbines were calculated based on empirical equations [59], [44].
Investment Cost Operating Cost
30 kWel €1,750/kWel
65 kWel €1,750/kWel
0.7 200 kWel 17.0538000 Pel Px 0.478 C60kWel 100P 600 kWel el P60kWel 800 kWel P: Capacity, C: Cost 1,000 kWel
A.7 Cost estimation of CHPs The investment and operating costs of the CHPs were also estimated based on empirical equations [44] Investment Cost Operating Cost
80 kWel
160 kWel
250 kWel 15,648 Pel 300 kW el 100P 0.536 el 500 kWel
1,000 kWel
3,000 kWel
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A.8 Scenarios’ energy output
Scenario 1: No DH except for St Ruprecht Thannhausen North Table A.10.1. Scenario 1: Main energy output of the technologies for Thannhausen North. Technology Energy output Waste heat -1 Electricity: 2,000 MWh·y -1 -1 HT Heat: 947 MWh·y CHP 250 kWel LT Heat: 1,316 MWh·y LT Heat: 534 MWh·y-1 HT Heat: 947 MWh·y-1 -1 Digester A kWel Biogas 1,229 MWh·y - -1 Digester B 250 kWel Biogas: 4,035 MWh·y -
Thannhausen South Table A.10.2. Scenario 1: Energy output of the main technologies for Thannhausen South. Technology Energy output Waste heat -1 Electricity: 1,983 MWh·y -1 -1 HT Heat: 939 MWh·y CHP 250 kWel LT Heat: 1,405 MWh·y LT Heat: 460 MWh·y-1 HT Heat: 939 MWh·y-1 -1 Digester B 160 kWel Biogas: 3,900 MWh·y - -1 Digester B 250 kWel Biogas: 5,432 MWh·y - -1 Digester C 160 kWel Biogas: 2,976 MWh·y -
Mortantsch Table A.10.3. Scenario 1: Energy output of the main technologies for Mortantsch. Technology Energy output Waste heat -1 Electricity: 2,000 MWh·y -1 -1 HT Heat: 947 MWh·y CHP 250 kWel LT Heat: 1,316 MWh·y LT Heat: 507 MWh·y-1 HT Heat: 947 MWh·y-1 -1 Digester A kWel Biogas 1,303 MWh·y - -1 Digester B 250 kWel Biogas: 3,961 MWh·y -
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ELIN Table A.10.4. Scenario 1: Energy output of the main technologies for ELIN. Technology Energy output Waste heat Electricity: 1,562 MWh·y-1 -1 -1 CHP 250 kWel LT Heat: 1,028 MWh·y LT Heat: 1,028 MWh·y HT Heat: 740 MWh·y-1 Electricity: 960 MWh·y-1 SGT 200 kWel - HT Heat: 1,416 MWh·y-1 Biogas pipeline from L2 Biogas: 7,019 MWh·y-1 - Scenario 2: 50% limitation of DH Thannhausen North Table A.10.5. Scenario 2: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 1,280 MWh·y-1 -1 CHP 160 kWel LT Heat: 865 MWh·y HT Heat: 623 MWh·y-1 HT Heat: 1,570 Electricity: 2,000 MWh·y-1 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester B 250 kWel Biogas 5,432 MWh·y - -1 Digester D 250 kWel Biogas: 3,290 MWh·y - DH to Thannhausen Heat delivered: 1,166 MWh·y-1 291 MWh·y-1 (losses)
Thannhausen South Table A.10.6. Scenario 2: Energy output of the main technologies for Thannhausen South. Technology Energy output Waste heat Electricity: 1,164 MWh·y-1 -1 CHP 160 kWel LT Heat: 566 MWh·y HT Heat: 786 MWh·y-1 HT Heat: 1,514 MWh·y-1 Electricity: 2,000 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester B 250 kWel Biogas: 5,432 MWh·y - -1 Digester C 160 kWel Biogas: 2,976 MWh·y -
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Mortantsch Table A.10.7. Scenario 2: Energy energy output of the main technologies for Mortantsch. Technology Energy output Waste heat -1 Electricity: 2,000 MWh·y -1 -1 HT Heat: 947 MWh·y CHP 250 kWel LT Heat: 1,316 MWh·y LT Heat: 507 MWh·y-1 HT Heat: 947 MWh·y-1 -1 Digester A kWel Biogas 1,303 MWh·y - -1 Digester B 250 kWel Biogas: 3,961 MWh·y -
Scenario 3: 25% limitation DH Thannhausen North Table A.10.8. Scenario 3: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat -1 Electricity: 2,000 MWh·y -1 -1 HT Heat: 947 MWh·y CHP 250 kWel LT Heat: 1,316 MWh·y LT Heat: 193 MWh·y-1 HT Heat: 947 MWh·y-1 -1 Digester B 250 kWel Biogas 5,263 MWh·y - DH to Thannhausen Heat delivered: 625 MWh·y-1 156 MWh·y-1 (losses)
Thannhausen South Table A.10.9. Scenario 3: Energy output of the main technologies for Thannhausen South. Technology Energy output Waste heat -1 Electricity: 2,000 MWh·y -1 -1 HT Heat: 947 MWh·y CHP 250 kWel LT Heat: 1,316 MWh·y LT Heat: 60 MWh·y-1 HT Heat: 947 MWh·y-1 -1 - Digester A 80 kWel Biogas: 1,139 MWh·y -1 Digester B 160 kWel Biogas: 2,807 MWh·y -1 Digester B 250 kWel Biogas: 5,432 MWh·y - -1 Digester C 160 kWel Biogas: 2,976 MWh·y -
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Mortantsch Table A.10.10. Scenario 3: Energy output of the main technologies for Mortantsch. Technology Energy output Waste heat -1 Electricity: 2,000 MWh·y -1 -1 HT Heat: 947 MWh·y CHP 250 kWel LT Heat: 1,316 MWh·y LT Heat: 507 MWh·y-1 HT Heat: 947 MWh·y-1 -1 Digester A kWel Biogas 1,303 MWh·y - -1 Digester B 250 kWel Biogas: 3,961 MWh·y -
ELIN Table A.10.11. Scenario 3: Energy output of the main technologies for ELIN. Technology Energy output Waste heat Electricity: 1,562 MWh·y-1 -1 -1 CHP 250 kWel LT Heat: 1,028 MWh·y LT Heat: 1,028 MWh·y HT Heat: 740 MWh·y-1 Electricity: 960 MWh·y-1 SGT 200 kWel - HT Heat: 1,416 MWh·y-1 Biogas: 7,019 MWh·y-1 -
Scenario 4: DH unlimited Thannhausen North Table A.10.12. Scenario 4: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 2,000 MWh·y-1 WG LT Heat: 3,440 MWh·y-1 Electricity: 1,280 MWh·y-1 -1 CHP 160 kWel LT Heat: 865 MWh·y HT Heat: 1,570 MWh·y-1 HT Heat: 623 MWh·y-1 Electricity: 2,000 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester B 250 kWel Biogas 5,432 MWh·y - -1 Digester D 250 kWel Biogas: 3,290 MWh·y - DH to Thannhausen Heat delivered: 3,918 MWh·y-1 980 MWh·y-1 (losses)
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Thannhausen South Table A.10.13. Scenario 4: Energy output of the main technologies Thannhausen South. Technology Energy output Waste heat Electricity: 2,000 MWh·y-1 WG LT Heat: 3,440 MWh·y-1 Electricity: 2,000 MWh·y-1 -1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y HT Heat: 947 MWh·y-1 Electricity: 1,600 MWh·y-1 SGT 200 kWel LT Heat: 2,360 MWh·y-1 -1 Digester B 160 kWel Biogas: 2,340 MWh·y - -1 Digester B 250 kWel Biogas: 4,795 MWh·y - -1 Digester C 160 kWel Biogas: 2,976 MWh·y - DH to Krottendorf Heat delivered: 5,131 MWh·y-1 1,283 MWh·y-1 (losses)
Mortantsch Table A.10.14. Scenario 4: Energy output of the main technologies for Mortantsch. Technology Energy output Waste heat -1 Electricity: 2,000 MWh·y -1 -1 HT Heat: 947 MWh·y CHP 250 kWel LT Heat: 1,316 MWh·y LT Heat: 500 MWh·y-1 HT Heat: 947 MWh·y-1 -1 Digester A kWel Biogas 1,324 MWh·y - -1 Digester B 250 kWel Biogas: 3,938 MWh·y -
Scenario 5: Mortantsch exclusion Thannhausen North Table A.10.15. Scenario 5: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 1,200 MWh·y-1 WG LT Heat: 2,064 MWh·y-1 Electricity: 1,122 MWh·y-1 CHP 160 kW LT Heat: 758 MWh·y-1 el HT Heat: 1,493 MWh·y-1 HT Heat: 546 kWh·y-1 Electricity: 2,000 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester A kWel Biogas 1,196 MWh·y - -1 Digester B 250 kWel Biogas: 4,123 MWh·y - -1 Digester C 160 kWel Biogas: 2,976 MWh·y - DH to Thannhausen Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
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Thannhausen South Table A.10.16. Scenario 5: Energy output of the main technologies for Thannhausen South. Technology Energy output Waste heat Electricity: 1,251 MWh·y-1 WG LT Heat: 2,151 MWh·y-1 Electricity: 2,000 MWh·y-1 HT Heat: 947 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester B 250 kWel Biogas: 5,263 MWh·y - DH to Krottendorf 2,500 MWh·y-1 625 MWh·y-1 (losses)
Scenario 6: Natural gas price increase
Thannhausen North Table A.10.17. Scenario 6: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 1,488 MWh·y-1 WG LT Heat: 2,559 MWh·y-1 Electricity: 2,000 MWh·y-1 HT Heat: 947 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester A 80 kWel Biogas 1,139MWh·y - -1 Digester B 250 kWel Biogas: 4,125 MWh·y - DH to Thannhausen Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
Thannhausen South Table A.10.18. Scenario 6: Energy output of the main technologies for Thannhausen South. Technology Energy output Waste heat Electricity: 1,550 MWh·y-1 WG LT Heat: 2,665 MWh·y-1 Electricity: 1,983 MWh·y-1 HT Heat: 939 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,305 MWh·y HT Heat: 939 MWh·y-1 -1 Digester B 160 kWel Biogas: 3,900 MWh·y - -1 Digester B 250 kWel Biogas: 5,432 MWh·y - -1 Digester C 160 kWel Biogas: 2,976 MWh·y - DH to Krottendorf Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
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Mortantsch Table A.10.19. Scenario 6: Energy output of the main technologies for Mortantsch. Technology Energy output Waste heat -1 Electricity: 2,000 MWh·y -1 -1 HT Heat: 947 MWh·y CHP 250 kWel LT Heat: 1,316 MWh·y LT Heat: 507 MWh·y-1 HT Heat: 947 MWh·y-1 -1 Digester A kWel Biogas 1,303 MWh·y - -1 Digester B 250 kWel Biogas: 3,961 MWh·y -
ELIN Table A.10.20. Scenario 6: Energy output of the main technologies for ELIN. Technology Energy output Waste heat Electricity: 1,5620 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,028 MWh·y HT Heat: 740 MWh·y-1 LT Heat: 1,028 MWh·y-1 Electricity: 960 MWh·y-1 SGT 200 kWel HT Heat: 1,416 MWh·y-1 -1 Biogas pipeline from L2 Biogas: 7,019 MWh·y -
Scenario 7: Biogas feed-in Thannhausen North Table A.10.21. Scenario 7: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 1,560 MWh·y-1 WG LT Heat: 2,683 MWh·y-1 Electricity: 2,000 MWh·y-1 HT Heat: 947 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester A 80 kWel Biogas 1,485 MWh·y - -1 Digester B 250 kWel Biogas: 3,778 MWh·y - DH to Thannhausen Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
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Thannhausen South Table A.10.22. Scenario 7: Energy output of the main technologies for Thannhausen South. Technology Energy output Waste heat Electricity: 1,251 MWh·y-1 WG LT Heat: 2,151 MWh·y-1 Electricity: 2,000 MWh·y-1 HT Heat: 947 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester B 250 kWel Biogas: 5,263 MWh·y - DH to Krottendorf Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
Mortantsch Table A.10.23. Scenario 7: Energy output of the main technologies for Mortantsch. Technology Energy output Waste heat -1 Electricity: 1,200 MWh·y -1 WG LT Heat: 607 MWh·y LT Heat: 2,064 MWh·y-1 -1 Digester B 160 kWel Biogas 3,900 MWh·y - -1 Digester B 250 kWel Biogas 5,432 MWh·y - -1 Digester C 160 kWel Biogas: 2,976 MWh·y - -1 Digester D 250 kWel Biogas 5,432 MWh·y Scenario 8: Retail prices for electricity & biogas Thannhausen North Table A.10.24. Scenario 8: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 2,000 MWh·y-1 WG HT Heat: 315 MWh·y-1 LT Heat: 3,440 MWh·y-1 DH to Thannhausen Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
Thannhausen South Table A.10.25. Scenario 8: Energy output of the main technologies for Thannhausen South. Technology Energy output Waste heat Electricity: 2,000 MWh·y-1 WG - LT Heat: 3,440 MWh·y-1 -1 Digester B 160 kWel Biogas: 3,690 MWh·y - -1 Digester B 250 kWel Biogas: 5,432 MWh·y -1 Digester C 160 kWel Biogas: 2,976 MWh·y DH to Krottendorf Heat delivered: 2,087 MWh·y-1 522 MWh·y-1 (losses)
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Mortantsch Table A.10.26. Scenario 8: Energy output of the main technologies for Mortantsch. Technology Energy output Waste heat -1 Electricity: 2,000 MWh·y -1 WG LT Heat: 3,440 MWh·y LT Heat: 3,440 MWh·y-1
ELIN Table A.10.27. Scenario 8: Energy output of the main technologies for ELIN. Technology Energy output Waste heat Electricity: 5,030 MWh·y-1 -1 - CHP 1 MWel LT Heat: 2,994 MWh·y HT Heat: 2,156 MWh·y-1 DH to Unterfladnitz Heat delivered: 2,395 MWh·y-1 522 MWh·y-1 (losses) Biogas pipeline from L2 Biogas: 11,977 MWh·y-1 -
Scenario 9: Retail prices for electricity Thannhausen North Table A.10.28. Scenario 9: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 2,000 MWh·y-1 WG LT Heat: 315 MWh·y-1 LT Heat: 3,440 MWh·y-1 DH to Thannhausen Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
Thannhausen South Table A.10.29. Scenario 9: Main technologies’ energy result for Thannhausen South. Technology Energy output Waste heat Electricity: 2,000 MWh·y-1 WG HT Heat: 315 MWh·y-1 LT Heat: 3,440 MWh·y-1 DH to Krottendorf Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
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Mortantsch Table A.10.30. Scenario 9: Energy output of the main technologies for Mortantsch. Technology Energy output Waste heat -1 Electricity: 2,000 MWh·y -1 WG LT Heat: 1,983 MWh·y LT Heat: 3,440 MWh·y-1 -1 Digester B 160 kWel Biogas 3,900 MWh·y - -1 Digester B 250 kWel Biogas 5,432 MWh·y - -1 Digester C 160 kWel Biogas: 2,976 MWh·y - -1 Digester D 250 kWel Biogas 5,432 MWh·y Biogas feed-in Biogas 16,165 MWh·y-1
Scenario 10: 100-50% Decrease of the electricity tariffs' provision Thannhausen North Table A.10.31. Scenario 10: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat -1 GB 300 kWel LT Heat: 1,440 MWh·y - -1 Digester B 160 kWel Biogas: 3,900 MWh·y - -1 Digester B 250 kWel Biogas 5,432 MWh·y - -1 Digester C 250 kWel Biogas: 2,976 MWh·y - -1 Digester D 250 kWel Biogas: 5,282 MWh·y - Biogas feed-in Biogas: 14,617 MWh·y-1 -
Scenario 11: 60% decrease of electricity tariffs provision Thannhausen North Table A.10.32. Scenario 11: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 1,200 MWh·y-1 WG LT Heat: 1,493 MWh·y-1 LT Heat: 2,064 MWh·y-1 -1 Digester B 160 kWel Biogas: 3,900 MWh·y - -1 Digester B 250 kWel Biogas 5,432 MWh·y - -1 Digester C 250 kWel Biogas: 2,976 MWh·y - -1 Digester D 250 kWel Biogas: 5,282 MWh·y - Biogas feed-in Biogas: 16,165 MWh·y-1 -
A.G.Gemenetzi 125
Scenario 12: Only one energy producing technology per location Thannhausen North Table A.10.33. Scenario 12: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 2,000 MWh·y-1 -1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y HT Heat: 947 MWh·y-1 -1 Digester B 250 kWel Biogas: 5,263 MWh·y - DH to Thannhausen Heat delivered: 779 MWh·y-1 195 MWh·y-1 (losses)
Thannhausen South Table A.10.34. Scenario 12: Energy output of the main technologies for Thannhausen South. Technology Energy output Waste heat -1 Electricity: 1,928 MWh·y -1 -1 LT Heat: 195 MWh·y CHP 250 kWel LT Heat: 1,268 MWh·y HT Heat: 913 MWh·y-1 HT Heat: 913 MWh·y-1 -1 Digester A 80 kWel Biogas: 1,139 MWh·y - -1 Digester B 250 kWel Biogas: 5,432 MWh·y - -1 Digester C 160 kWel Biogas: 2,976 MWh·y -
Mortantsch Table A.10.35. Scenario 12: Energy output of the main technologies for Mortantsch. Technology Energy output Waste heat -1 Electricity: 1,928 MWh·y -1 -1 LT Heat: 507 MWh·y CHP 250 kWel LT Heat: 1,268 MWh·y HT Heat: 947 MWh·y-1 HT Heat: 913 MWh·y-1 -1 Digester A 80 kWel Biogas: 1,303 MWh·y - -1 Digester B 250 kWel Biogas: 3,961 MWh·y -
ELIN Table A.10.36. Scenario 12: Energy output of the main technologies for ELIN. Technology Energy output Waste heat Electricity: 1,462 MWh·y-1 SGT 200 kWel - HT Heat: 2,156 MWh·y-1 -1 Biogas pipeline from L2 Biogas: 4,429 MWh·y -
A.G.Gemenetzi 126
Scenario 13: No wood availability Thannhausen North Table A.10.37. Scenario 13: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 1,200 MWh·y-1 WG LT Heat: 2,064 MWh·y-1 Electricity: 990 MWh·y-1 -1 CHP 160 kWel LT Heat: 669 MWh·y HT Heat: 1,429 MWh·y-1 HT Heat: 482 MWh·y-1 Electricity: 2,000 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester A 80 kWel Biogas 1,139 MWh·y - -1 Digester B 160 kWel Biogas: 2,340 MWh·y - -1 Digester B 250 kWel Biogas: 4,461 MWh·y DH to Thannhausen Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
Thannhausen South Table A.10.38. Scenario 13: Energy output of the main technologies for Thannhausen South. Technology Energy output Waste heat Electricity: 768 MWh·y-1 -1 CHP 160 kWel LT Heat: 519 MWh·y HT Heat: 374 MWh·y-1 -1 Electricity: 2,000 MWh·y -1 -1 HT Heat: 1,321 MWh·y CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 Electricity: 1,418 MWh·y-1 SGT 200 kWel LT Heat: 2,018 MWh·y-1 -1 Digester B 160 kWel Biogas: 3,227 MWh·y - -1 Digester B 250 kWel Biogas: 5,432 MWh·y -1 Digester C 160 kWel Biogas: 2,976 MWh·y - DH to Krottendorf Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
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Mortantsch Table A.10.39. Scenario 13: Energy output of the main technologies for Mortantsch. Technology Energy output Waste heat -1 Electricity: 2,000 MWh·y -1 -1 LT Heat: 507 MWh·y CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 HT Heat: 947 MWh·y-1 -1 Digester A 80 kWel Biogas 1,303 MWh·y - -1 Digester B 250 kWel Biogas: 3,961 MWh·y -
Scenario 14: No MW availability Thannhausen North Table A.10.40. Scenario 14: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 1,200 MWh·y-1 BG LT Heat: 2,064 MWh·y-1 Electricity: 1,080 MWh·y-1 -1 CHP 160 kWel LT Heat: 730 MWh·y HT Heat: 1,473 MWh·y-1 HT Heat: 525 MWh·y-1 Electricity: 2,000 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester A 80 kWel Biogas 1,263 MWh·y - -1 Digester B 160 kWel Biogas: 2,340 MWh·y - -1 Digester B 250 kWel Biogas: 4,578 MWh·y DH to Thannhausen Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
Thannhausen South Table A.10.41. Scenario 14: Energy output of the main technologies for Thannhausen South. Technology Energy output Waste heat Electricity: 1,251 MWh·y-1 BG LT Heat: 2,151 MWh·y-1 Electricity: 2,000 MWh·y-1 HT Heat: 947 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester B 250 kWel Biogas: 5,263 MWh·y DH to Krottendorf Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
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Mortantsch Table A.10.42. Scenario 14: Energy output of the main technologies for Mortantsch. Technology Energy output Waste heat -1 Electricity: 2,000 MWh·y -1 -1 LT Heat: 507 MWh·y CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 HT Heat: 947 MWh·y-1 -1 Digester A 80 kWel Biogas 1,303 MWh·y - -1 Digester B 250 kWel Biogas: 3,961 MWh·y -
Scenario 15: No grass availability Thannhausen North Table A.10.43. Scenario 15: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 2,000 MWh·y-1 BG LT Heat: 3,440 MWh·y-1 Electricity: 866 MWh·y-1 HT Heat: 421 MWh·y-1 -1 CHP 160 kWel LT Heat: 585 MWh·y HT Heat: 421 MWh·y-1 -1 Digester A 160 kWel Biogas 2,340 MWh·y - DH to Thannhausen Heat delivered: 2,428 MWh·y-1 607 MWh·y-1 (losses)
Thannhausen South Table A.10.44. Scenario 15: Energy output of the main technologies for Thannhausen South. Technology Energy output Waste heat Electricity: 1,817 MWh·y-1 BG - LT Heat: 3,125 MWh·y-1 DH to Krottendorf Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
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Scenario 16: 33.3% grass availability Thannhausen North Table A.10.45. Scenario 16: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 1,635 MWh·y-1 WG LT Heat: 2,813 MWh·y-1 Electricity: 1,876 MWh·y-1 HT Heat: 889 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,235 MWh·y HT Heat: 889 MWh·y-1 -1 Digester A 80 kWel Biogas 1,679 MWh·y -1 Digester B 250 kWel Biogas 3,259 MWh·y DH to Thannhausen Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
Thannhausen South Table A.10.46. Scenario 16: Energy output of the main technologies for Thannhausen South. Technology Energy output Waste heat Electricity: 1,466 MWh·y-1 WG LT Heat: 2,522 MWh·y-1 Electricity: 1,239 MWh·y-1 HT Heat: 587 MWh·y-1 -1 CHP 250 kWel LT Heat: 815 MWh·y HT Heat: 587 MWh·y-1 -1 Digester B 250 kWel Biogas: 3,259 MWh·y - DH to Krottendorf Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
Mortantsch Table A.10.47. Scenario 16: Energy output of the main technologies for Mortantsch. Technology Energy output Waste heat -1 Electricity: 1,642 MWh·y -1 -1 HT Heat: 778 MWh·y CHP 250 kWel LT Heat: 1,080 MWh·y NT Heat: 273 MWh·y-1 HT Heat: 778 MWh·y-1 -1 Digester A 80 kWel Biogas: 1,344 MWh·y - -1 Digester C 160 kWel Biogas: 2,976 MWh·y -
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Scenario 17: 50% grass availability Thannhausen North Table A.10.48. Scenario 17: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 1,674 MWh·y-1 WG LT Heat: 2,878 MWh·y-1 Electricity: 1,698 MWh·y-1 HT Heat: 805 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,117 MWh·y HT Heat: 805 MWh·y-1 -1 Digester A 80 kWel Biogas 1,493 MWh·y - -1 Digester C 160 kWel Biogas 2,976 MWh·y - DH to Thannhausen Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
Thannhausen South Table A.10.49. Scenario 17: Energy output of the main technologies for Thannhausen South. Technology Energy output Waste heat Electricity: 1,251 MWh·y-1 WG LT Heat: 2,151 MWh·y-1 Electricity: 2,000 MWh·y-1 HT Heat: 947 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester B 250 kWel Biogas: 5,263 MWh·y - DH to Krottendorf Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
Mortantsch Table A.10.50. Scenario 17: Energy output of the main technologies for Mortantsch. Technology Energy output Waste heat -1 Electricity: 1,832 MWh·y -1 -1 HT Heat: 868 MWh·y CHP 250 kWel LT Heat: 1,206 MWh·y NT Heat: 405 MWh·y-1 HT Heat: 868 MWh·y-1 -1 Digester A 80 kWel Biogas: 1,360 MWh·y - -1 Digester B 250 kWel Biogas: 3,462 MWh·y -
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Scenario 18: 10% manure availability Thannhausen North Table A.10.51. Scenario 18: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 2,354 MWh·y-1 BG LT Heat: 1,368 MWh·y-1 Electricity: 1,584 MWh·y-1 HT Heat: 750 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,042 MWh·y HT Heat: 750 MWh·y-1 -1 Digester B 250 kWel Biogas 4,169 MWh·y - DH to Thannhausen Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
Thannhausen South Table A.10.52. Scenario 18: Energy output of the main technologies for Thannhausen South. Technology Energy output Waste heat Electricity: 1,399 MWh·y-1 WG LT Heat: 2,407 MWh·y-1 Electricity: 1,564 MWh·y-1 HT Heat: 741 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,029 MWh·y HT Heat: 741 MWh·y-1 -1 Digester B 80 kWel Biogas: 1,139 MWh·y - -1 Digester C 160 kWel Biogas: 2,976 MWh·y - DH to Krottendorf Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
Mortantsch Table A.10.53. Scenario 18: Energy output of the main technologies for Mortantsch. Technology Energy output Waste heat -1 Electricity: 1,239 MWh·y -1 -1 HT Heat: 868 MWh·y CHP 250 kWel LT Heat: 815 MWh·y NT Heat: 405 MWh·y-1 HT Heat: 587 MWh·y-1 -1 Digester B 250 kWel Biogas: 3,259 MWh·y -
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Scenario 19: 50% manure availability Thannhausen North Table A.10.54. Scenario 19: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 1,251 MWh·y-1 BG LT Heat: 2,151 MWh·y-1 Electricity: 2,000 MWh·y-1 HT Heat: 947 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester B 250 kWel Biogas 5,263 MWh·y - DH to Thannhausen Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
Thannhausen South Table A.10.55. Scenario 19: Energy output of the main technologies for Thannhausen South. Technology Energy output Waste heat Electricity: 1,200 MWh·y-1 WG LT Heat: 2,064 MWh·y-1 Electricity: 1,147 MWh·y-1 -1 -1 CHP 160 kWel LT Heat: 775 MWh·y HT Heat: 1,506 MWh·y HT Heat: 558 MWh·y-1 LT Heat: 4,415 MWh·y-1 Electricity: 2,000 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester B 250 kWel Biogas: 5,388 MWh·y - -1 Digester C 160 kWel Biogas: 2,976 MWh·y - DH to Krottendorf Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
Mortantsch Table A.10.56. Scenario 19: Energy output of the main technologies for Mortantsch. Technology Energy output Waste heat -1 Electricity: 1,239 MWh·y -1 -1 HT Heat: 868 MWh·y CHP 250 kWel LT Heat: 815 MWh·y NT Heat: 405 MWh·y-1 HT Heat: 587 MWh·y-1 -1 Digester B 250 kWel Biogas: 3,259 MWh·y -
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Scenario 20: 33.3% Grass - 50% Manure Availability Thannhausen North Table A.10.57. Scenario 20: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 1,495 MWh·y-1 BG LT Heat: 2,572 MWh·y-1 Electricity: 1,747 MWh·y-1 HT Heat: 828 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,150 MWh·y HT Heat: 828 MWh·y-1 -1 Digester E 250 kWel Biogas 4,598 MWh·y - DH to Thannhausen Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
Thannhausen South Table A.10.58. Scenario 20: Energy output of the main technologies for Thannhausen South. Technology Energy output Waste heat Electricity: 1,285 MWh·y-1 WG LT Heat: 2,210 MWh·y-1 Electricity: 1,879 MWh·y-1 HT Heat: 890 MWh·y-1 -1 CHP 160 kWel LT Heat: 1,236 MWh·y HT Heat: 890 MWh·y-1 -1 Digester B 250 kWel Biogas: 4,945 MWh·y - DH to Krottendorf Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
Scenario 21: Digestate price: 0€ Thannhausen North Table A.10.59. Scenario 21: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 1,251 MWh·y-1 BG LT Heat: 2,151 MWh·y-1 Electricity: 2,000 MWh·y-1 HT Heat: 947 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester B 250 kWel Biogas: 5,263 MWh·y - DH to Thannhausen Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
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Thannhausen South Table A.10.60. Scenario 21: Energy output of the main technologies for Thannhausen South. Technology Energy output Waste heat Electricity: 1,277 MWh·y-1 BG LT Heat: 2,196 MWh·y-1 Electricity: 2,000 MWh·y-1 HT Heat: 947 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester B 160 kWel Biogas: 2,340 MWh·y - -1 Digester C 160 kWel Biogas: 2,923 MWh·y DH to Krottendorf 2,500 MWh·y-1 625 MWh·y-1 (losses)
Scenario 22: Digestate price: 8€ Thannhausen North Table A.10.61. Scenario 22: Energy output of the main technologies for Thannhausen North. Technology Energy output Waste heat Electricity: 1,424 MWh·y-1 BG LT Heat: 2,449 MWh·y-1 Electricity: 768 MWh·y-1 -1 CHP 160 kWel LT Heat: 519 MWh·y HT Heat: 1,314 MWh·y-1 HT Heat: 374 MWh·y-1 Electricity: 1,997 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,314 MWh·y HT Heat: 946 MWh·y-1 -1 Digester A 80 kWel Biogas 1,897 MWh·y - -1 Digester B 250 kWel Biogas: 5,432 MWh·y - DH to Thannhausen Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
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Thannhausen South Table A.10.62. Scenario 22: Energy output of the main technologies for Thannhausen South. Technology Energy output Waste heat Electricity: 1,827 MWh·y-1 BG LT Heat: 3,143 MWh·y-1 Electricity: 768 MWh·y-1 CHP 160 kW LT Heat: 519 MWh·y-1 el HT Heat: 1,321 MWh·y-1 HT Heat: 374 MWh·y-1 Electricity: 2,000 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,316 MWh·y HT Heat: 947 MWh·y-1 -1 Digester A 80 kWel Biogas: 1,877 MWh·y - -1 Digester B 250 kWel Biogas: 5,432 MWh·y -1 Digester C 160 kWel Biogas: 2,976 MWh·y - -1 Digester D 250 kWel Biogas: 4,144 MWh·y - DH to Krottendorf Heat delivered: 2,500 MWh·y-1 625 MWh·y-1 (losses)
Mortantsch Table A.10.63. Scenario 22: Energy output of the main technologies for Mortantsch. Technology Energy output Waste heat Electricity: 768 MWh·y-1 -1 CHP 160 kWel LT Heat: 519 MWh·y HT Heat: 374 MWh·y-1 HT Heat: 1,319 MWh·y-1 Electricity: 1,997 MWh·y-1 LT Heat: 676 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,314 MWh·y HT Heat: 946 MWh·y-1 -1 Digester A 80 kWel Biogas 1,897 MWh·y - -1 Digester B 250 kWel Biogas: 5,432 MWh·y -
ELIN Table A.10.64. Scenario 22: Energy output of the main technologies for ELIN. Technology Energy output Waste heat Electricity: 1,562 MWh·y-1 -1 CHP 250 kWel LT Heat: 1,028 MWh·y HT Heat: 740 MWh·y-1 LT Heat: 1,028 MWh·y-1 Electricity: 960 MWh·y-1 SGT 200 kWel HT Heat: 1,416 MWh·y-1 -1 Biogas pipeline from L2 Biogas: 7,019 MWh·y -
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A.9 Screenshots of the PNS input structure 107 raw materials, 98 intermediate products, 37 end products and 676 operating units were set in the PNS program. Consequently, only a few examples are provided.
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