A metaheuristic is a higher-level procedure designed to find, generate, or select a heuristic or partial search algorithm that may provide a sufficiently good solution to an optimization problem with incomplete or imperfect information or limited computation capacity. Metaheuristics can often find good solutions with less computational effort than other algorithms. Modern and emerging power systems, with the growing complexity of distributed and intermittent generation and EV charging, are an application for such methods.

The new edition of Metaheuristic Optimization in Power Engineering in two volumes uses a MATLAB-based software package for testing and comparing methods, and includes several new and substantially revised and updated chapters.

Volume 1 covers principles and key algorithms, such as genetic and swarm algorithms, gravitational and metaheuristic algorithms, power flow and power dispatch under consideration of renewable generation.

Volume 2 focuses on power distribution networks, including power flow, voltage control and regulation, optimisation of generation placement and sizing, and state estimation analysis.

This reference for researchers and advanced students working on power system analysis and optimization offers an overview of metaheuristic optimization approaches to solving problems in modern power systems.

A metaheuristic is a higher-level procedure designed to find, generate, or select a heuristic or partial search algorithm that may provide a sufficiently good solution to an optimization problem with incomplete or imperfect information or limited computation capacity. Metaheuristics can often find good solutions with less computational effort than other algorithms. Modern and emerging power systems, with the growing complexity of distributed and intermittent generation and EV charging, are an application for such methods.

The new edition of Metaheuristic Optimization in Power Engineering in two volumes uses a MATLAB-based software package for testing and comparing methods, and includes several new and substantially revised and updated chapters.

Volume 1 covers principles and key algorithms, such as genetic and swarm algorithms, gravitational and metaheuristic algorithms, power flow and power dispatch under consideration of renewable generation.

Volume 2 focuses on power distribution networks, including power flow, voltage control and regulation, optimisation of generation placement and sizing, and state estimation analysis.

This reference for researchers and advanced students working on power system analysis and optimization offers an overview of metaheuristic optimization approaches to solving problems in modern power systems.