An Improved Particle Swarm Optimization for the Combined Heat and Power Dynamic Economic Dispatch Problem

Abstract

—This study presents a novel improved particle swarm optimization algorithm to solve the combined heat and power dynamic economic dispatch problem. This problem is formulated as a challenging non-convex and non-linear optimization problem considering practical characteristics, such as valve-point effects, transmission losses, ramp-rate limits, mutual dependency of power and heat, spinning reserve requirements, and transmission security constraints. The proposed method combines classical particle swarm optimization with a chaotic mechanism, time-variant acceleration coefficients, and a self-adaptive mutation scheme to prevent premature convergence and improve solution quality. Moreover, multiple efficient constraint handling strategies are employed to deal with complex constraints. The effectiveness of the proposed improved particle swarm optimization for solving the combined heat and power dynamic economic dispatch problem is validated on three different test systems, and the results are compared with those of other variants of particle swarm optimization as well as other methods reported in the literature. The numerical results demonstrate the superiority of improved particle swarm optimization in solving the combined heat and power dynamic economic dispatch problem while strictly satisfying all the constraints.

Keywords:

• dynamic economic dispatch
• combined heat and power
• spinning reserve requirement
• transmission security constraint
• chaotic descending inertia weight
• self-adaptive mutation
• multiple constraint handling strategies
• improved particle swarm optimization

Additional information

Funding

This work was supported by the National High Technology Research and Development Program of China (2012AA050215) and the National Natural Science Foundation of China (51374082).

Notes on contributors

Yujiao Zeng

Yujiao Zeng received her B.S. in 2007 from the Department of Information Science and Engineering, Central South University, Changsha, China, and her M.S. in 2010 from Automation Research and Design Institute of Metallurgical Industry, Beijing, China, where she is currently working toward her Ph.D. Her main research interests include distribution energy systems operation and planning, computational intelligence, and the operation of distribution energy systems and their application to power systems.

Yanguang Sun

Yanguang Sun received his B.S. in automation engineering from Hefei University of Technology, Hefei, China, in 1984; his M.S. in automation engineering from Harbin Industrial University, Harbin, in 1987; and his Ph.D. in automation engineering from Chinese Academy of Sciences, Beijing, in 1990. Since 1990, he has been with Automation Research and Design Institute of Metallurgical Industry, Beijing, where he is currently a professor of automation engineering. His current research includes distribution energy systems operation, planning, economics, and markets.