Stochastic Planning and Modeling for Energy Systems: Methods, Applications, and Developments acts as a comprehensive resource on both modeling and planning techniques for stochastic methods in power systems, spanning from scenario generation and reduction to investment and operational planning under uncertainty. Chapters demonstrate modeling systems with multiple, interacting uncertainties, load, renewables, network constraints, prices, and how to use these models for robust investment and operational planning. Methods, applications, and the latest developments, including stochastic methods to generation, distribution, capacity investment, DER siting, and demand-side flexibility, especially under high shares of renewables and EVs are presented.
Additionally, real-world planning challenges, including capacity expansion, microgrid design, and integration of new technologies like hydrogen, batteries, and supercapacitors are examined. Real-world case studies and algorithms are included to demonstrate stochastic workflows and methods. This is a valuable reference for transmission and distribution operators, system planners, market designers, power-system engineers, energy analysts, and MSc-level graduate students in power systems engineering.
1. Cover image - p. c 2. Title page - p. i 3. Copyright - p. iv 4. Contents - p. v 5. Contributors - p. xiii 6. About the Editor - p. xvii 7. Acknowledgments - p. xix 8. Chapter 1 AI and data-driven methods in scenario generation and reduction - p. 1 9. Chapter 2 Scenario generation techniques: From Monte Carlo, Latin hypercube, and beyond - p. 25 10. Chapter 3 Scenario reduction methods: Clustering, fast forward selection and distance metrics - p. 49 11. Chapter 4 A synergistic framework for efficient and uncertainty-calibrated solar irradiance forecasting using data compression and optimized neural networks - p. 79 12. Chapter 5 Resilient microgrid operation under uncertainty - p. 109 13. Chapter 6 Case studies in renewable-dominant and islanded microgrids - p. 139 14. Chapter 7 Modeling electric vehicle uncertainty: Charging behavior & grid impact - p. 207 15. Chapter 8 Demand-side uncertainty and planning for flexibility provision - p. 293 16. Chapter 9 Navigating competition in retailing layer: A risk-averse decision-making model for electricity market retailers - p. 331 17. Chapter 10 Stochastic reinforcement learning for uncertainty-aware power converter controlusing digital twin - p. 351 18. Chapter 11 Planning for distributed energy resources and microgrids—Scope: Stochastic siting, sizing, and control of DER clusters in diverse contexts - p. 377 19. Chapter 12 AI-driven energy management for renewable-dominated isolated microgrid under uncertainty - p. 399 20. Chapter 13 Microgrid and power network state estimation with the open-source tool GridCal (aPAC) - p. 419 21. Chapter 14 AI-driven scenario generation and reduction for renewable-rich energy systems: RNN-WGAN synthesis and deep clustering - p. 465 22. Chapter 15 Intelligent energy management for renewable energy communities and microgrids: Models, algorithms, and practical constraints - p. 491 23. Chapter 16 DER clusters in diverse contexts: Stochastic siting, sizing, and control for distributed energy resources and microgrids planning - p. 533 24. Chapter 17 Stochastic modeling for energy storage and hydrogen systems in hybrid electric platforms - p. 565 25. Chapter 18 A stochastic, Adaptive, and nature-inspired electric distribution grids architecture: Data-driven futuristic power grids through emergent intelligence-based operational mechanism - p. 609 26. Index - p. 649
Stochastic Planning and Modeling for Energy Systems: Methods, Applications, and Developments acts as a comprehensive resource on both modeling and planning techniques for stochastic methods in power systems, spanning from scenario generation and reduction to investment and operational planning under uncertainty. Chapters demonstrate modeling systems with multiple, interacting uncertainties, load, renewables, network constraints, prices, and how to use these models for robust investment and operational planning. Methods, applications, and the latest developments, including stochastic methods to generation, distribution, capacity investment, DER siting, and demand-side flexibility, especially under high shares of renewables and EVs are presented.
Additionally, real-world planning challenges, including capacity expansion, microgrid design, and integration of new technologies like hydrogen, batteries, and supercapacitors are examined. Real-world case studies and algorithms are included to demonstrate stochastic workflows and methods. This is a valuable reference for transmission and distribution operators, system planners, market designers, power-system engineers, energy analysts, and MSc-level graduate students in power systems engineering.
1. Cover image - p. c 2. Title page - p. i 3. Copyright - p. iv 4. Contents - p. v 5. Contributors - p. xiii 6. About the Editor - p. xvii 7. Acknowledgments - p. xix 8. Chapter 1 AI and data-driven methods in scenario generation and reduction - p. 1 9. Chapter 2 Scenario generation techniques: From Monte Carlo, Latin hypercube, and beyond - p. 25 10. Chapter 3 Scenario reduction methods: Clustering, fast forward selection and distance metrics - p. 49 11. Chapter 4 A synergistic framework for efficient and uncertainty-calibrated solar irradiance forecasting using data compression and optimized neural networks - p. 79 12. Chapter 5 Resilient microgrid operation under uncertainty - p. 109 13. Chapter 6 Case studies in renewable-dominant and islanded microgrids - p. 139 14. Chapter 7 Modeling electric vehicle uncertainty: Charging behavior & grid impact - p. 207 15. Chapter 8 Demand-side uncertainty and planning for flexibility provision - p. 293 16. Chapter 9 Navigating competition in retailing layer: A risk-averse decision-making model for electricity market retailers - p. 331 17. Chapter 10 Stochastic reinforcement learning for uncertainty-aware power converter controlusing digital twin - p. 351 18. Chapter 11 Planning for distributed energy resources and microgrids—Scope: Stochastic siting, sizing, and control of DER clusters in diverse contexts - p. 377 19. Chapter 12 AI-driven energy management for renewable-dominated isolated microgrid under uncertainty - p. 399 20. Chapter 13 Microgrid and power network state estimation with the open-source tool GridCal (aPAC) - p. 419 21. Chapter 14 AI-driven scenario generation and reduction for renewable-rich energy systems: RNN-WGAN synthesis and deep clustering - p. 465 22. Chapter 15 Intelligent energy management for renewable energy communities and microgrids: Models, algorithms, and practical constraints - p. 491 23. Chapter 16 DER clusters in diverse contexts: Stochastic siting, sizing, and control for distributed energy resources and microgrids planning - p. 533 24. Chapter 17 Stochastic modeling for energy storage and hydrogen systems in hybrid electric platforms - p. 565 25. Chapter 18 A stochastic, Adaptive, and nature-inspired electric distribution grids architecture: Data-driven futuristic power grids through emergent intelligence-based operational mechanism - p. 609 26. Index - p. 649
