Stochastic Planning and Modeling for Energy Systems
Methods, Applications, and Developments
- 1 Edición - 1 de agosto de 2026
- Última edición
- Editor: Miadreza Shafie-khah
- Idioma: Inglés
Stochastic Planning and Modeling for Energy Systems: Methods, Applications, and Developments acts as a comprehensive resource on both modeling and planning techniques for stocha… Leer más
Descripción
Descripción
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.
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.
Puntos claves
Puntos claves
- Demonstrates end-to-end stochastic workflows using detailed case studies, including islanded microgrids and high-EV scenarios
- Presents step-by-step treatment of sampling methods, reduction techniques, multistage programming, and risk-measure incorporation through proven algorithms
- Provides software tutorials on implementing Pyomo, Pandapower, GAMS, and PLEXOS
De interès para
De interès para
Utilities, consultancies, transmission and distribution operators, system planners, market designers, Power-system engineers, energy analysts, graduate students (MSc-level), policy advisors, R&D professionals
Índice
Índice
1. AI and Data-Driven Methods in Scenario Generation and Reduction
2. Scenario Generation Techniques: Monte Carlo, Latin Hypercube & Beyond
3. Scenario Reduction Methods: Clustering, Fast Forward Selection & Distance Metrics
4. A Synergistic Framework for Efficient and Uncertainty-Calibrated Solar Irradiance Forecasting using Data Compression and Optimized Neural Networks
5. Resilient Microgrid Operation under Uncertainty
6. Case Studies in Renewable-Dominant and Islanded Microgrids
7. Modeling Electric Vehicle Uncertainty: Charging Behavior & Grid Impact
8. Demand-Side Uncertainty and Planning for Flexibility Provision
9. Navigating Competition in Retailing Layer: A Risk-Averse Decision Making Model for Electricity Markets Retailers
10. Stochastic Reinforcement Learning for Uncertainty-Aware Power Converter Control using Digital Twin
11. Planning for Distributed Energy Resources and Microgrids
12. AI-Driven Energy Management for Renewable-Dominated Isolated Microgrid Under Uncertainty
13. Microgrid and Power Network State Estimation with the Open-Source Tool GridCal (aPAC)
14. AI-Driven Scenario Generation and Reduction for Renewable-Rich Energy Systems: RNN-WGAN Synthesis and Deep Clustering
15. Intelligent Energy Management for Renewable Energy Communities and Microgrids: Models, Algorithms, and Practical Constraints
16. DER Clusters in Diverse Contexts: Stochastic Siting, Sizing, and Control for Distributed Energy Resources and Microgrids Planning
17. Stochastic Modeling for Energy Storage and Hydrogen Systems in Hybrid Electric Platforms
18. A Stochastic and Nature-Inspired Electric Distribution Grids Architecture: Data-Driven Futuristic Power Grids through Emergent Intelligence-based Operational Mechanism
2. Scenario Generation Techniques: Monte Carlo, Latin Hypercube & Beyond
3. Scenario Reduction Methods: Clustering, Fast Forward Selection & Distance Metrics
4. A Synergistic Framework for Efficient and Uncertainty-Calibrated Solar Irradiance Forecasting using Data Compression and Optimized Neural Networks
5. Resilient Microgrid Operation under Uncertainty
6. Case Studies in Renewable-Dominant and Islanded Microgrids
7. Modeling Electric Vehicle Uncertainty: Charging Behavior & Grid Impact
8. Demand-Side Uncertainty and Planning for Flexibility Provision
9. Navigating Competition in Retailing Layer: A Risk-Averse Decision Making Model for Electricity Markets Retailers
10. Stochastic Reinforcement Learning for Uncertainty-Aware Power Converter Control using Digital Twin
11. Planning for Distributed Energy Resources and Microgrids
12. AI-Driven Energy Management for Renewable-Dominated Isolated Microgrid Under Uncertainty
13. Microgrid and Power Network State Estimation with the Open-Source Tool GridCal (aPAC)
14. AI-Driven Scenario Generation and Reduction for Renewable-Rich Energy Systems: RNN-WGAN Synthesis and Deep Clustering
15. Intelligent Energy Management for Renewable Energy Communities and Microgrids: Models, Algorithms, and Practical Constraints
16. DER Clusters in Diverse Contexts: Stochastic Siting, Sizing, and Control for Distributed Energy Resources and Microgrids Planning
17. Stochastic Modeling for Energy Storage and Hydrogen Systems in Hybrid Electric Platforms
18. A Stochastic and Nature-Inspired Electric Distribution Grids Architecture: Data-Driven Futuristic Power Grids through Emergent Intelligence-based Operational Mechanism
Detalles del producto
Detalles del producto
- Edición: 1
- Última edición
- Publicado: 1 de agosto de 2026
- Idioma: Inglés
Sobre el editor
Sobre el editor
MS
Miadreza Shafie-khah
Miadreza Shafie-khah is the Head of Research and Innovation Division at Nowocert, Dublin, Ireland, and a visiting professor at the Royal Melbourne Institute of Technology, Australia. He is the editor-in-chief or associate editor of several prestigious journals including the IEEE Transactions on Sustainable Energy and the IEEE Transactions on Intelligent Transportation Systems. His main research interest is in demand response, decentralized electricity markets, and electric vehicles.
Afiliaciones y experiencia
Professor and Scientific Director, Energy Business eMBA, University of Vaasa, Finland