Title Page
ABSTRACT
ABSTRACT (KOREAN)
Contents
NOMENCLATURE 21
CHAPTER 1. INTRODUCTION 28
1.1. Research background 28
1.2. Research purpose 34
CHAPTER 2. MODELING OF ENERGY SOURCES 36
2.1. Modeling overview 36
2.2. Non-renewable energy 37
2.2.1. Fuel cell 37
2.2.2. Gas turbine 44
2.2.3. Bottoming cycle 48
2.3. Energy storage systems 52
2.3.1. Adiabatic compressed air energy storage 52
2.3.2. Lead acid battery 60
2.4. Renewable energy 62
2.4.1. Photovoltaics 62
2.4.2. Wind turbine 64
2.5. Conclusion 66
CHAPTER 3. HYBRID SYSTEM BASED ON FUEL CELL 67
3.1. Introduction 67
3.1.1. Background and literature survey 67
3.1.2. Research purpose 68
3.2. System structure and analysis frameworks 69
3.2.1. System layout 69
3.2.2. Research outline 72
3.3. Results and discussion 78
3.3.1. Design performance 78
3.3.2. Off-design performance 81
3.4. Conclusion 85
CHAPTER 4. HYBRID SYSTEM BASED ON ENERGY STORAGE SYSTEM AND GAS TURBINE 87
4.1. Introduction 87
4.1.1. Background and literature survey 87
4.1.2. Research purpose 88
4.2. System structure and analysis frameworks 89
4.2.1. System layout 89
4.2.2. Research outline 92
4.3. Results and discussion 97
4.3.1. Selection of A-CAES capacity 97
4.3.2. Effects of thermal integrations 98
4.3.3. Effects of air injection 102
4.3.4. Part load operation 105
4.4. Conclusion 116
CHAPTER 5. ENERGY MANAGEMENT OF DISTRIBUTED GENERATIONS THROUGH AI-BASED OPTIMIZATION FRAMEWORK 117
5.1. Introduction 117
5.1.1. Background and literature survey 117
5.1.2. Research purpose 118
5.2. System structure and analysis frameworks 119
5.2.1. Layout of distributed generations 119
5.2.2. Research outline 125
5.3. Regression models 129
5.3.1. Construction of regression models 129
5.3.2. Results of regression models 131
5.4. Optimization method 135
5.4.1. Genetic algorithm 135
5.4.2. Operation strategies 136
5.4.3. Objective functions 140
5.5. Results and discussion 145
5.5.1. Optimization results in DG 1 with conventional energy systems (DG_c1) 147
5.5.2. Optimization results in DG 2 with conventional energy systems (DG_c2) 157
5.5.3. Optimization results in DG with next generation energy systems (DG_n) 166
5.5.4. Comparison in values of objective functions among DGs 176
5.6. Conclusion 178
CHAPTER 6. CONCLUSION AND FUTURE WORK 180
6.1. Conclusion 180
6.2. Future work 181
APPENDIX A. HYDROGEN FED DISTRIBUTED GENERATION 183
A.1. Introduction 183
A.2. Hydrogen fed hybrid system based on fuel cell 183
A.3. Hydrogen fed hybrid system based on energy storage system and gas turbine 188
A.4. Energy management in hydrogen fed distributed generations 192
REFERENCE 199
Table 2.1. Composition of reformed fuel. 39
Table 2.2. Specifications for the PAFC modeling. 41
Table 2.3. Performance results of the PAFC stack modeling. 42
Table 2.4. Specifications for the GT modeling. 45
Table 2.5. Design parameters and performance for BC. 51
Table 2.6. Validation results of the C-CAES modeling. 54
Table 2.7. Design parameters and performance of the A-CAES modeling. 56
Table 2.8. Design parameters of a lead acid battery. 60
Table 2.9. Specifications of a PV module. 63
Table 2.10. Specifications of a WT. 65
Table 3.1. Design parameters and performance of a conventional PAFC system. 73
Table 3.2. Data transmitted via spreadsheets in PAFC hybrid system. 76
Table 3.3. Design parameters and performance of PAFC hybrid system. 80
Table 5.1. Performance of energy sources in DGs (ISO conditions). 124
Table 5.2. Cases across various operating conditions in DGs. 128
Table 5.3. Datasets for construction of regression models. 130
Table 5.4. Improvement in the computational efficiency through regression models. 132
Table 5.5. Design variables used for the optimization problem. 136
Table 5.6. Cost of each energy sources for DGs. 142
Table 5.7. Example of GA results in DG_c1 (Maximum load: 30MW, CAES tank pressure: minimum, RE load sharing rate: 0%). 146
Table A.1. Performance results of hydrogen fed PAFC stack modeling. 183
Table A.2. Design parameters and performance of the hydrogen fed conventional PAFC system. 185
Table A.3. Design parameters and performance of the hydrogen fed PAFC hybrid system. 187
Table A.4. Performance of the natural gas fed and hydrogen fed GT modeling. 188
Fig. 1.1. Sustainable Development Goals. 28
Fig. 1.2. World Energy Trilemma Index. 29
Fig. 1.3. Types of power generation. 30
Fig. 1.4. Power generation by 2050 in the 1.5℃ Scenario. 31
Fig. 1.5. Positioning of the energy storage system technologies. 33
Fig. 1.6. Research purpose and significance. 34
Fig. 2.1. Modeling overview for simulation of DGs. 36
Fig. 2.2. Configuration of PAFC. 38
Fig. 2.3. Validation result of the PAFC modeling. 41
Fig. 2.4. Part load curve of PAFC. 43
Fig. 2.5. Temperature and recovered heat diagram of the waste heat recovery system in the PAFC stack. 44
Fig. 2.6. Configuration of GT. 44
Fig. 2.7. Normalized performance map of the GT. 46
Fig. 2.8. Part load curve of the GT. 47
Fig. 2.9. Predicted part load curve of the GT. 47
Fig. 2.10. Configuration of a BC. 48
Fig. 2.11. Configurations of a GTCC and GT CHP. 51
Fig. 2.12. Configurations of CAESs. 53
Fig. 2.13. Expansion power according to the expansion ratios in the A-CAES (Design discharged air mass flow rate: 56.0 kg/s). 55
Fig. 2.14. Operating schedule of an A-CAES. 55
Fig. 2.15. Normalized performance map used in an A-CAES. 57
Fig. 2.16. Characteristics of an A-CAES in the charging process. 58
Fig. 2.17. Characteristics of an A-CAES in the discharging process. 59
Fig. 2.18. Characteristics of the pressurized air tank in the charging and discharging processes. 60
Fig. 2.19. Cycle-to-failure versus DoD for a typical deep-cycle lead acid battery. 62
Fig. 2.20. Validation result of a PV module. 64
Fig. 2.21. Validation result of a WT. 66
Fig. 3.1. Configuration of a conventional PAFC system. 69
Fig. 3.2. Configuration of the PAFC hybrid system. 71
Fig. 3.3. Normalized map of a blower. 75
Fig. 3.4. Block diagram for a coupled calculation method of the PAFC hybrid system (mode_el). 77
Fig. 3.5. Effects of the variation in the SPHT steam outlet temperature on the PAFC hybrid system. 78
Fig. 3.6. Effects of ambient temperature on main parameters PAFC hybrid system. 81
Fig. 3.7. Effects of the ambient temperature on the performance of the PAFC hybrid system. 83
Fig. 3.8. Changes in the main parameters of the PAFC hybrid system at part load operation. 84
Fig. 3.9. Changes in the performance of the PAFC hybrid system at part load operation. 85
Fig. 4.1. Configurations of the A-CAES and GT systems. 90
Fig. 4.2. Configuration of the regenerative A-CAES-GT hybrid system. 91
Fig. 4.3. Regenerative A-CAES-GT hybrid system with air injection. 92
Fig. 4.4. Overall process for the analyses of the regenerative A-CAES-GT hybrid system. 96
Fig. 4.5. Performance improvement of the A-CAES-GT hybrid system according to the design discharged air flow rate. 97
Fig. 4.6. HPE and LPE inlet temperature based on the thermal integrations between the A-CAES and GT. 98
Fig. 4.7. Expansion power of the A-CAES based on the thermal integrations between the A-CAES and GT. 99
Fig. 4.8. Water temperature after heating expanders based on the thermal integrations between the A-CAES and GT. 100
Fig. 4.9. Performance based on the thermal integrations between the A-CAES and GT. 101
Fig. 4.10. Changes in the main parameters based on the injected air flow rate in the regenerative A-CAES-GT hybrid system. 102
Fig. 4.11. Changes in the power of A-CAES and GT based on the injected air flow rate in the regenerative A-CAES-GT hybrid system. 103
Fig. 4.12. Changes in the performance of the regenerative A-CAES-GT hybrid system based on the injected air flow rate. 104
Fig. 4.13. Changes in the mass flow rates, pressures, and GT load at part load operation of the regenerative A-CAES-GT hybrid system (Control 1). 106
Fig. 4.14. Changes in the temperature of the main streams at part load operation of the regenerative A-CAES-GT hybrid system (Control 1). 107
Fig. 4.15. Changes in the properties of the air tank and thermal power at part load operation of the regenerative A-CAES-GT hybrid system (Control 1). 108
Fig. 4.16. Changes in the mass flow rates, pressures, and GT load at part load operation of the regenerative A-CAES-GT hybrid system (Control 2). 109
Fig. 4.17. Changes in the temperature of the main streams at part load operation of the regenerative A-CAES-GT hybrid system (Control 2). 110
Fig. 4.18. Changes in the properties of the air tank and thermal power at part load operation of the regenerative A-CAES-GT hybrid system (Control 2). 111
Fig. 4.19. Changes in the mass flow rates, pressures, and GT load at part load operation of the regenerative A-CAES-GT hybrid system (Control 3). 112
Fig. 4.20. Changes in the temperature of the main streams at part load operation of the regenerative A-CAES-GT hybrid system (Control 3). 113
Fig. 4.21. Changes in the properties of the air tank and thermal power at part load operation of the regenerative A-CAES-GT hybrid system (Control 3). 114
Fig. 4.22. Comparison of the properties of the air tank at part load operation of the regenerative A-CAES-GT hybrid system based on control strategies. 115
Fig. 4.23. Comparison of the thermal power at part load operation of the regenerative A-CAES-GT hybrid system based on control strategies. 116
Fig. 5.1. Configuration of DG_c1. 121
Fig. 5.2. Configuration of DG_c2. 122
Fig. 5.3. Configuration of DG_n. 123
Fig. 5.4. Operational combinations of non-renewable energy sources in DGs. 124
Fig. 5.5. Characteristics of the AI-based optimization framework. 125
Fig. 5.6. Flow chart of the AI-based optimization framework for energy management in DGs. 126
Fig. 5.7. Annual average meteorological data and demand profile. 126
Fig. 5.8. Linear plots of the regression models. 133
Fig. 5.9. Optimization process in a GA. 135
Fig. 5.10. Algorithm for operation strategy of DG_c1 and DG_c2. 138
Fig. 5.11. Algorithm for operation strategy of DG_n. 139
Fig. 5.12. LCOE and LCOS of energy sources. 143
Fig. 5.13. Correlation among the objective functions in DG_c1. 148
Fig. 5.14. Energy management through optimal dispatch in DG_c1 (Maximum electric demand: 30 MW, maximum thermal demand: 15 MW, RE load sharing... 151
Fig. 5.15. Energy management through optimal dispatch in DG_c1 (Maximum electric demand: 30 MW, maximum thermal demand: 15 MW, RE load sharing... 152
Fig. 5.16. Energy management through optimal dispatch in DG_c1 (Maximum electric demand: 60 MW, maximum thermal demand: 30 MW, RE load sharing... 155
Fig. 5.17. Energy management through optimal dispatch in DG_c1 (Maximum electric demand: 60 MW, maximum thermal demand: 30 MW, RE load sharing... 156
Fig. 5.18. Correlation among the objective functions in DG_c2. 157
Fig. 5.19. Energy management through optimal dispatch in DG_c2 (Maximum electric demand: 30 MW, maximum thermal demand: 15 MW, RE load sharing... 160
Fig. 5.20. Energy management through optimal dispatch in DG_c2 (Maximum electric demand: 30 MW, maximum thermal demand: 15 MW, RE load sharing... 161
Fig. 5.21. Energy management through optimal dispatch in DG_c2 (Maximum electric demand: 60 MW, maximum thermal demand: 30 MW, RE load sharing... 164
Fig. 5.22. Energy management through optimal dispatch in DG_c2 (Maximum electric demand: 60 MW, maximum thermal demand: 30 MW, RE load sharing... 165
Fig. 5.23. Correlation among the objective functions in DG_n. 166
Fig. 5.24. Energy management through optimal dispatch in DG_n (Maximum electric demand: 30 MW, maximum thermal demand: 15 MW, RE load sharing... 170
Fig. 5.25. Energy management through optimal dispatch in DG_n (Maximum electric demand: 30 MW, maximum thermal demand: 15 MW, RE load sharing... 171
Fig. 5.26. Energy management through optimal dispatch in DG_n (Maximum electric demand: 60 MW, maximum thermal demand: 30 MW, RE load sharing... 174
Fig. 5.27. Energy management through optimal dispatch in DG_n (Maximum electric demand: 60 MW, maximum thermal demand: 30 MW, RE load sharing... 175
Fig. 5.28. Results of the objective functions at a global optimum in DGs. 176
Fig. A.1. Configuration of a hydrogen fed conventional PAFC system. 184
Fig. A.2. Configuration of the hydrogen fed PAFC hybrid system. 186
Fig. A.3. HPE and LPE inlet temperature in the regenerative A-CAES-GT hybrid system based on the fuel types. 189
Fig. A.4. Water temperature after heating expanders in the regenerative A-CAES-GT hybrid system based on the fuel types. 190
Fig. A.5. Performance of the regenerative A-CAES-GT hybrid system based on the fuel types. 191
Fig. A.6. Energy management through optimal dispatch in hydrogen fed DG_n (Maximum electric demand: 30 MW, maximum thermal demand: 15 MW, RE... 194
Fig. A.7. Energy management through optimal dispatch in hydrogen fed DG_n (Maximum electric demand: 30 MW, maximum thermal demand: 15 MW, RE... 195
Fig. A.8. Energy management through optimal dispatch in hydrogen fed DG_n (Maximum electric demand: 60 MW, maximum thermal demand: 30 MW, RE... 197
Fig. A.9. Energy management through optimal dispatch in hydrogen fed DG_n (Maximum electric demand: 60 MW, maximum thermal demand: 30 MW, RE... 198