A differential evolution based Memetic Algorithm for workload optimization in power generation plants

Work load optimization in power generation plants is of practical importance in carbon constrained power industry. The main objective of the coal-fired power generation workload optimization is to minimize fuel consumption while maintaining the desired output and to maintain NOx emission within the environmental license limit. In this article, we represent an efficient Memetic Algorithm (MA) with a constraint handling method for the power generation loading optimization. This MA is developed by combining a competitive variant of Deferential Evolution (DE) and Simplex method. The proposed approach incorporates the constraint handling method to modify the selection rule which guides the search process in better direction. The simulation results based on a coal-fired power plant clearly indicate that our proposed method is very effective and it shows great computational efficiency in power generation workload optimization.

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