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Total Optimization of Smart Community by Differential Evolutionary Particle Swarm Optimization

机译:基于差分进化粒子群算法的智能社区整体优化

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摘要

This paper proposes a total optimization method of a smart community (SC) by Differential evolutionary particle swarm optimization (DEEPSO). Various sectors of SC models such as an electric utility model, an industry model, and a building model are utilized in this paper. Whole of a SC is optimized in order to minimize energy costs, shift electric power load peak at high load hours, and minimize total CO 2 emission of the SC using the models. The simulation results by the proposed method are compared with those by Particle swarm optimization (PSO), Differential Evolution (DE), and Evolutionary particle swarm optimization (EPSO) based methods.
机译:提出了一种基于差分进化粒子群算法(DEEPSO)的智能社区(SC)总体优化方法。本文利用了SC模型的各个领域,例如电力实用模型,行业模型和建筑模型。为了使能源成本最小化,在高负载小时内改变电力负荷峰值并使用模型将SC的总CO 2排放最小化,对整个SC进行了优化。将该方法的仿真结果与基于粒子群优化(PSO),差分进化(DE)和基于进化粒子群优化(EPSO)的方法进行了比较。

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