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.
Table of Contents
1. AI and Data-Driven Methods in Scenario Generation and Reduction2. 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
