Stochastic Planning and Modeling for Energy Systems
Methods, Applications, and Developments
- 1st Edition - August 1, 2026
- Latest edition
- Editor: Miadreza Shafie-Khah
- Language: English
Stochastic Planning and Modeling for Energy Systems: Methods, Applications, and Developments acts as a comprehensive resource on both modeling and planning techniques for stocha… Read more
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. Ensuring reliability and resilience in today’s rapid energy transition landscape requires rigorous uncertainty management. Chapters demonstrate modeling systems with multiple, interacting uncertainties, load, renewables, network constraints, prices, and using those 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. Stochastic Planning and Modeling for Energy Systems: Methods, Applications, and Developments is valuable to transmission and distribution operators, system planners, market designers, power-system engineers, energy analysts, and MSc-level graduate students in power systems engineering.
- 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
Utilities, consultancies, transmission and distribution operators, system planners, market designers, Power-system engineers, energy analysts, graduate students (MSc-level), policy advisors, R&D professionals
1. Introduction to Stochastic Planning and Modeling in Energy Systems Scope: Concepts, motivations, structure; modern uncertainty sources
2. Characterizing Uncertainty in Energy Systems: Data and Statistical Foundations Scope: Load, renewable, price, and demand-side statistical models; data preprocessing
3. Scenario Generation Techniques: Monte Carlo, Latin Hypercube & Beyond Scope: Sampling-based and quasi-Monte Carlo methods; trade-offs in accuracy vs. cost
4. Scenario Reduction Methods: Clustering, Fast Forward Selection & Distance Metrics Scope: Backward/forward reduction, moment-matching, distance measures
5. Stochastic Investment Planning: Generation, Transmission & Distribution Scope: Multistage programming for capacity expansion; CVaR, robust optimization
6. Operational Planning under Uncertainty: Unit Commitment and Economic Dispatch Scope: Day-ahead and real-time dispatch; renewables, storage, demand response
7. Case Studies in Renewable-Dominant and Islanded Microgrids Scope: High-penetration PV/wind and off-grid microgrids; performance metrics
8. Modeling Electric Vehicle Uncertainty: Charging Behavior & Grid Impact Scope: Aggregate EV load scenarios and distribution impacts
9. Demand-Side Uncertainty and Planning for Flexibility Provision Scope: End-use variability, demand-response design, flexibility markets
10. Stochastic Modeling for Energy Storage and Hydrogen Systems Scope: Battery degradation, supercapacitors, electrolyzer scenarios, flexibility roles
11. Software Tools and Simulation Frameworks for Stochastic Planning Scope: Pyomo, Pandapower, GAMS, PLEXOS tutorials for scenario modeling
12. AI and Data-Driven Methods in Scenario Generation and Reduction Scope: GANs, deep clustering, and other ML techniques for efficient scenarios
13. Market Design, Policy and Regulatory Implications of Stochastic Planning Scope: Tariff structures, capacity markets, and regulatory frameworks under uncertainty
14. Strategic Capacity Expansion Planning under Uncertainty Scope: High-level investment strategies balancing cost, risk, and flexibility
15. Planning for Distributed Energy Resources and Microgrids Scope: Stochastic siting, sizing, and control of DER clusters in diverse contexts
2. Characterizing Uncertainty in Energy Systems: Data and Statistical Foundations Scope: Load, renewable, price, and demand-side statistical models; data preprocessing
3. Scenario Generation Techniques: Monte Carlo, Latin Hypercube & Beyond Scope: Sampling-based and quasi-Monte Carlo methods; trade-offs in accuracy vs. cost
4. Scenario Reduction Methods: Clustering, Fast Forward Selection & Distance Metrics Scope: Backward/forward reduction, moment-matching, distance measures
5. Stochastic Investment Planning: Generation, Transmission & Distribution Scope: Multistage programming for capacity expansion; CVaR, robust optimization
6. Operational Planning under Uncertainty: Unit Commitment and Economic Dispatch Scope: Day-ahead and real-time dispatch; renewables, storage, demand response
7. Case Studies in Renewable-Dominant and Islanded Microgrids Scope: High-penetration PV/wind and off-grid microgrids; performance metrics
8. Modeling Electric Vehicle Uncertainty: Charging Behavior & Grid Impact Scope: Aggregate EV load scenarios and distribution impacts
9. Demand-Side Uncertainty and Planning for Flexibility Provision Scope: End-use variability, demand-response design, flexibility markets
10. Stochastic Modeling for Energy Storage and Hydrogen Systems Scope: Battery degradation, supercapacitors, electrolyzer scenarios, flexibility roles
11. Software Tools and Simulation Frameworks for Stochastic Planning Scope: Pyomo, Pandapower, GAMS, PLEXOS tutorials for scenario modeling
12. AI and Data-Driven Methods in Scenario Generation and Reduction Scope: GANs, deep clustering, and other ML techniques for efficient scenarios
13. Market Design, Policy and Regulatory Implications of Stochastic Planning Scope: Tariff structures, capacity markets, and regulatory frameworks under uncertainty
14. Strategic Capacity Expansion Planning under Uncertainty Scope: High-level investment strategies balancing cost, risk, and flexibility
15. Planning for Distributed Energy Resources and Microgrids Scope: Stochastic siting, sizing, and control of DER clusters in diverse contexts
- Edition: 1
- Latest edition
- Published: August 1, 2026
- Language: English
MS
Miadreza Shafie-Khah
Miadreza Shafie-khah received his first PhD in electrical engineering from Tarbiat Modares University, Tehran, Iran. He received his second PhD in electromechanical engineering and first postdoc from the University of Beira Interior (UBI), Covilha, Portugal. He received his second postdoc from the University of Salerno, Salerno, Italy. He was a tenure-track professor at the University of Vaasa, Vaasa, Finland. Currently, he is a Full Professor and Scientific Director at the University of Vaasa Executive Education, Vaasa, Finland, and Head of Research and Innovation Division in Nowocert, Dublin, Ireland. He is an Associate Editor of the IEEE Transactions on Sustainable Energy, IEEE Transactions on Intelligent Transportation Systems, IEEE Systems Journal, IEEE Access, IEEE Open Access Journal of Power and Energy, IEEE Power Engineering Letters, and IET Generation, Transmission and Distribution. He is an IEEE Senior Member since 2017. He has co-authored numerous papers and acted as editor for numerous books from international publishers of repute.
Affiliations and expertise
Professor and Scientific Director, Energy Business eMBA, University of Vaasa, Finland