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Solving Multi-Area Environmental/Economic Dispatch by Pareto-Based Chemical-Reaction Optimization Algorithm

IEEE/CAA Journal of Automatica Sinica 2019年第5期6卷 1240-1250页

作者： Junqing Li Quanke Pan Peiyong Duan Hongyan Sang Kaizhou Gao the School of Computer Science Liaocheng UniversityLiaocheng 252059China the School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200072China the School of Information Science and Engineering Shandong Normal UniversityJinan 250014China the Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

In this study, we present a Pareto-based chemicalreaction optimization(PCRO) algorithm for solving the multiarea environmental/economic dispatch optimization *** objectives are minimized simultaneously, i.e., total fuel cost and emission. In the proposed algorithm, each solution is represented by a chemical molecule. A novel encoding mechanism for solving the multi-area environmental/economic dispatch optimization problems is designed to dynamically enhance the performance of the proposed algorithm. Then, an ensemble of effective neighborhood approaches is developed, and a selfadaptive neighborhood structure selection mechanism is also embedded in PCRO to increase the search ability while maintaining population diversity. In addition, a grid-based crowding distance strategy is introduced, which can obviously enable the algorithm to easily converge near the Pareto front. Furthermore,a kinetic-energy-based search procedure is developed to enhance the global search ability. Finally, the proposed algorithm is tested on sets of the instances that are generated based on realistic production. Through the analysis of experimental results, the highly effective performance of the proposed PCRO algorithm is favorably compared with several algorithms, with regards to both solution quality and diversity.

关键词： Chemical-reaction optimization algorithm gridbased crowding distance multi-area environmental/economic dispatch (MAEED) problem multi-objective optimization

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