Solving economic emission load dispatch problems using particle swarm optimization with smart inertia factor

F. Shahir, M. Farsadi, Heydar Zafari, A. Sadighmanesh
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引用次数: 3

Abstract

This paper attempts to propose particle swarm optimization algorithm with smart inertia factor (PSO-SIF) to solve the problem of economic emission load dispatch (EELD) in thermal power plants. The aim of EELD solution is synchronous reduction of both fuel costs and emission level. EELD problem is a non-linear and non-convex problem which uses evolutionary algorithms as efficient to solve such problems due to its complicated characteristics. PSO-SIF algorithm is an efficient and robust algorithm to exploit universal optimal point of optimization problems. Tests implemented on 10-unite systems, considering valve-point loading effects, network losses with Multi-objective functions such as system fuel cost and emission level, to show PSO-SIF algorithm capabilities in solving EELD problems. Results obtained from present investigation indicated universal optimal point exploitation of the problems and superiority of the proposed algorithm compared to other new and efficient algorithms.
基于智能惯性因子的粒子群优化求解经济排放负荷调度问题
本文提出了一种基于智能惯性因子的粒子群优化算法(PSO-SIF)来解决火电厂经济排放负荷调度问题。EELD解决方案的目标是同时降低燃料成本和排放水平。EELD问题是一类非线性非凸问题,由于其复杂的特点,采用进化算法求解这类问题是非常有效的。PSO-SIF算法是一种高效、鲁棒的利用最优化问题普遍最优点的算法。在10单元系统上进行了测试,考虑了阀点负载效应、具有多目标函数(如系统燃料成本和排放水平)的网络损失,以显示PSO-SIF算法解决EELD问题的能力。研究结果表明,该算法具有通用性的最优点挖掘问题,与其他新型高效算法相比具有优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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