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
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Description
Description
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.
Key features
Key features
- 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
Readership
Readership
Utilities, consultancies, transmission and distribution operators, system planners, market designers, Power-system engineers, energy analysts, graduate students (MSc-level), policy advisors, R&D professionals
Table of contents
Table of contents
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
Product details
Product details
- Edition: 1
- Latest edition
- Published: August 1, 2026
- Language: English
About the editor
About the 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.
Affiliations and expertise
Professor and Scientific Director, Energy Business eMBA, University of Vaasa, Finland