Elsevier Stochastic Planning and Modeling for Energy Systems: Methods, Applications, and Developments

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. 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