Mathematical Modelling of Contemporary Electricity Markets reviews major methodologies and tools to accurately analyze and forecast contemporary electricity markets in a ways that is ideal for practitioner and academic audiences. Approaches include optimization, neural networks, genetic algorithms, co-optimization, econometrics, E3 models and energy system models. The work examines how new challenges affect power market modeling, including discussions of stochastic renewables, price volatility, dynamic participation of demand, integration of storage and electric vehicles, interdependence with other commodity markets and the evolution of policy developments (market coupling processes, security of supply). Coverage addresses all major forms of electricity markets: day-ahead, forward, intraday, balancing, and capacity.
1. Introduction - p. xv 2. Part I: Modelling market fundamentals of electricity markets - p. xxii 3. Chapter 1. Forecasting energy demand with econometrics - p. 3 4. Chapter 2. An econometric approach for Germany’s short-term energy demand forecasting - p. 17 5. Chapter 3. A novel adaptive day-ahead load forecast method, incorporating non-metered distributed generation: A comparison of selected European countries - p. 40 6. Chapter 4. Forecasting week-ahead hourly electricity prices in Belgium with statistical and machine learning methods - p. 59 7. Chapter 5. Use probabilistic forecasting to model uncertainties in electricity markets—A wind power example - p. 75 8. Chapter 6. Modelling interlinked commodities' prices: The case of natural gas - p. 95 9. Part II: Modelling electricity markets - p. 108 10. Chapter 7. An optimization model for the economic dispatch problem in power exchanges - p. 111 11. Chapter 8. Power system flexibility: A methodological analytical framework based on unit commitment and economic dispatch modelling - p. 141 12. Chapter 9. Retailer profit maximization with the assistance of price and load forecasting processes - p. 157 13. Chapter 10. Modelling cross-border interactions of EU balancing markets: A focus on scarcity pricing - p. 173 14. Chapter 11. Electricity portfolio optimization: Cost minimization using MILP - p. 191 15. Part III: Modelling technology challenges in electricity market - p. 206 16. Chapter 12. Business opportunities in the day ahead markets by storage integration: An application to the German case - p. 220 17. Chapter 13. The integration of dynamic demand in electricity markets: Blockchain 3.0 as an enabler of microgrid energy exchange, demand response and storage - p. 225 18. Chapter 14. Optimizing CHP operational planning for participating in day-ahead power markets: The case of a coal-fired CHP system with thermal energy storage - p. 237 19. Chapter 15. Statistical analysis of power flows based on system marginal price differentials between two power systems - p. 269 20. Chapter 16. EW Flex: A decentralized flexibility marketplace fostering TSO-DSO cooperation - p. 279 21. Part IV: Modelling policy challenges in electricity markets - p. 285 22. Chapter 17. Forecasting electricity supply shocks: A Bayesian panel VAR analysis - p. 289 23. Chapter 18. Formulating and estimating an energy security index: A geopolitical review of quantitative approaches - p. 295 24. Chapter 19. Assessing the Western Balkans power systems: A case study of Serbia - p. 317 25. Chapter 20. Evaluation of capacity expansion scenarios for the Hellenic electric sector - p. 333 26. Chapter 21. An ex-ante market monitoring and regulation mechanism for market concentration in electricity and natural gas markets - p. 380 27. Index - p. 413
Mathematical Modelling of Contemporary Electricity Markets reviews major methodologies and tools to accurately analyze and forecast contemporary electricity markets in a ways that is ideal for practitioner and academic audiences. Approaches include optimization, neural networks, genetic algorithms, co-optimization, econometrics, E3 models and energy system models. The work examines how new challenges affect power market modeling, including discussions of stochastic renewables, price volatility, dynamic participation of demand, integration of storage and electric vehicles, interdependence with other commodity markets and the evolution of policy developments (market coupling processes, security of supply). Coverage addresses all major forms of electricity markets: day-ahead, forward, intraday, balancing, and capacity.
1. Introduction - p. xv 2. Part I: Modelling market fundamentals of electricity markets - p. xxii 3. Chapter 1. Forecasting energy demand with econometrics - p. 3 4. Chapter 2. An econometric approach for Germany’s short-term energy demand forecasting - p. 17 5. Chapter 3. A novel adaptive day-ahead load forecast method, incorporating non-metered distributed generation: A comparison of selected European countries - p. 40 6. Chapter 4. Forecasting week-ahead hourly electricity prices in Belgium with statistical and machine learning methods - p. 59 7. Chapter 5. Use probabilistic forecasting to model uncertainties in electricity markets—A wind power example - p. 75 8. Chapter 6. Modelling interlinked commodities' prices: The case of natural gas - p. 95 9. Part II: Modelling electricity markets - p. 108 10. Chapter 7. An optimization model for the economic dispatch problem in power exchanges - p. 111 11. Chapter 8. Power system flexibility: A methodological analytical framework based on unit commitment and economic dispatch modelling - p. 141 12. Chapter 9. Retailer profit maximization with the assistance of price and load forecasting processes - p. 157 13. Chapter 10. Modelling cross-border interactions of EU balancing markets: A focus on scarcity pricing - p. 173 14. Chapter 11. Electricity portfolio optimization: Cost minimization using MILP - p. 191 15. Part III: Modelling technology challenges in electricity market - p. 206 16. Chapter 12. Business opportunities in the day ahead markets by storage integration: An application to the German case - p. 220 17. Chapter 13. The integration of dynamic demand in electricity markets: Blockchain 3.0 as an enabler of microgrid energy exchange, demand response and storage - p. 225 18. Chapter 14. Optimizing CHP operational planning for participating in day-ahead power markets: The case of a coal-fired CHP system with thermal energy storage - p. 237 19. Chapter 15. Statistical analysis of power flows based on system marginal price differentials between two power systems - p. 269 20. Chapter 16. EW Flex: A decentralized flexibility marketplace fostering TSO-DSO cooperation - p. 279 21. Part IV: Modelling policy challenges in electricity markets - p. 285 22. Chapter 17. Forecasting electricity supply shocks: A Bayesian panel VAR analysis - p. 289 23. Chapter 18. Formulating and estimating an energy security index: A geopolitical review of quantitative approaches - p. 295 24. Chapter 19. Assessing the Western Balkans power systems: A case study of Serbia - p. 317 25. Chapter 20. Evaluation of capacity expansion scenarios for the Hellenic electric sector - p. 333 26. Chapter 21. An ex-ante market monitoring and regulation mechanism for market concentration in electricity and natural gas markets - p. 380 27. Index - p. 413
