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Implementation of hybrid particle swarm optimization for combined Economic-Emission Load Dispatch Problem

机译:混合粒子群算法在经济负荷联合分配中的实现

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This paper presents the implementation of hybrid particle swarm optimization for solving Economic-Emission Load Dispatch Problem (EELD). Due to environmental issues, the environmental pollution releases by thermal power generation should be considered in power dispatch planning instead of minimizing the total fuel cost only. Significant emission reduction can be achieved by performing the emission power dispatch. In this study, the hybrid Evolutionary programming (EP) and Particle Swarm Optimization (PSO) named Evolutionary Particle Swarm Optimization (EPSO) is proposed. The effectiveness of the EPSO algorithm has been tested on the IEEE 30 bus system and the results obtained are compared with the other reported algorithms. The results also reveal the capability of the proposed EPSO for obtaining the best fuel cost and emission amount at shorter time compared to PSO.
机译:本文提出了混合粒子群优化算法来解决经济排放负荷分配问题(EELD)的实现。由于环境问题,在电力调度计划中应考虑由火力发电释放的环境污染,而不是仅使总燃料成本最小化。通过执行发射功率分配,可以实现显着的减排。在这项研究中,提出了混合进化规划(EP)和粒子群优化(PSO),称为进化粒子群优化(EPSO)。 EPSO算法的有效性已在IEEE 30总线系统上进行了测试,并将获得的结果与其他已报告的算法进行了比较。结果还表明,与PSO相比,拟议的EPSO具有在更短的时间内获得最佳燃料成本和排放量的能力。

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