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摘要
A new Multi-objective Fuzzy Adaptive Chaotic particle swarm optimization (MFACPSO) evolutionary algorithm for the Multi-objective daily Optimal Operation Management (MOOM) problem with regard to fuel cell power plants (FCPPs) in distribution system is presented in this paper. The purposes of the MOOM problem are to minimize the total electrical energy losses, the total electrical energy cost, and the total pollutant emission produced by sources. Conventional algorithms for solving the multi-objective optimization problems convert the multiple objectives into a single objective using a vector of the user-predefined weights. This conversion has several defects. For instance, the final solution of the algorithms greatly depends on the values of the weights and it also loses some information in the conversion and this strategy is not expected to provide a robust solution. The proposed algorithm maintains a finite-sized repository of non-dominated solutions which gets iteratively updated in the presence of new solutions. Since the objective functions are non-commensurable, a fuzzy clustering technique is used to control the size of the repository within the limits. The proposed algorithm is tested on two distribution test feeders and the results demonstrate the capabilities of the proposed approach to generate a set of well-distributed Pareto-optimal non-dominated solutions of the MOOM problem. The comparison with the different reported techniques demonstrates the superiority of the proposed MFACPSO in terms of the diversity of the Pareto-optimal solutions obtained. In addition, the results confirm the potential of the proposed technique to solve the MOOM problem and produce high-quality non-dominated solutions.
- 出版日期2011-10
