Description:

The optimisation of power systems has recently seen the application of numerous metaheuristic methods. While there are benefits and drawbacks associated with various optimisation techniques, their efficiency may fluctuate depending on whether the problem is discrete or continuous. To improve performance in terms of convergence rates and solution accuracy, a wide array of variants and combinations of this metaheuristic optimisation strategy have been proposed. Consequently, solving real-sized power system engineering problems with metaheuristic algorithms can sometimes require significant computing power; however, the potential rewards justify the investment.

This edition of the book provides a systematic discussion on the application of soft computation approaches in power systems. It examines how these methods can address complex challenges faced by fossil fuel plants, including the operation of valves under varying loads, limitations on power fluctuations, and restricted operational areas—all of which are pertinent to the economic dispatch problem. The book discusses significant topics, such as the use of biogeography-based optimisation for managing power allocation in constrained regions as well as controlling the rate of power changes. It also explores the artificial bee colony algorithm for the management of multiple fuel sources and a teacher learning-based method for combined heat and power management. Furthermore, the book introduces concepts like short-range variable heads and multi-chain hydrothermal generation scheduling that employ real-coded genetic algorithms and predator-prey optimisation techniques.

Anyone interested in learning more about optimising power system operations will find valuable insights in this book. Only a basic understanding of numerical methods and matrix operations can grasp most of the content.

KEY FEATURES

• Load flow techniques and economic dispatch—both classical and rigorous.

• Economic dispatch considering valve-point loading, ramp-rate limits and prohibited operating zones.

• Real-coded genetic algorithm, evolutionary programming, particle swarm optimization and differential evolution algorithm for economic dispatch.

• Stochastic multiobjective thermal power dispatch with security.

• Stochastic multiobjective hydrothermal generation scheduling.

• Multiobjective thermal power dispatch using artificial neural networks.

• Fuzzy multiobjective generation scheduling.

• Multiobjective generation scheduling by searching weight pattern.

• Biogeography-based optimisation for economic dispatch.

• Artificial bee colony algorithm for multi-fuel economic dispatch.

• Combined heat and power economic dispatch using teacher-based learning optimisation.

• Short-range variable head and multi-chain hydrothermal generation scheduling using a real-coded genetic algorithm and predator-prey optimisation.

• The enumerated method, priority list method, dynamic programming, Lagrange relaxation, and heuristic approaches for unit commitment.

TARGET AUDIENCE

• B.Tech Electrical Engineering

• M. Tech Electrical Engineering

• M.Tech Power Systems Engineering

• M.Tech Power System and Control