Analytics and Optimization for Renewable Energy Integration [Book News]

The book illustrates the application of stochastic mathematical methods and tools to power systems with renewable energy integration to improve analytics and decision making, benefiting both security and the economy. The book is divided in four parts. Part I presents the background of stochastic mathematical concepts. Part II focuses on modeling for longand short-term renewable energy uncertainties, dependencies among multiple renewable power plants, renewable energy generation, and the time series analysis. Part III deals with methods to handle the uncertainty of renewable energy in power system operation, including fast and efficient algorithms for probabilistic power flow, risk-based stochastic models to compute the optimal generation schedule under uncertainty, and efficient operation strategies for renewable power plants in electricity markets, handling tie-line scheduling under uncertainty. Part IV introduces computationally efficient approaches to simulate long-term power system operation; estimate the capacity credit of renewable energy; optimize the location, capacity, and sequence of renewable investments; and to optimize the long-term planning of generation and transmission in power systems with renewable energy penetration. Most of the sixteen chapters include case studies as well as summaries and references. The book is of paramount importance to graduate students, engineers, and researchers who are willing to increase the penetration of renewable energy in electrical networks. The book is also a valuable tool for graduate-level teachers and practicing power systems professionals who need to understand and master the planning and operation of future electrical power systems with increased renewable energy penetration.