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Particle swarm optimization with smart inertia factor for combined heat and power economic dispatch

机译:粒子群优化智能惯性因素,用于综合热电经济调度

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In this research, particle swarm optimization with smart inertia factor (PSO-SIF) algorithm is suggested to solve combined heat and power economic dispatch (CHPED) problem. The CHPED problem is one of the most important problems in power systems and a challenging non-convex and non-linear optimization problem. The aim of solving the CHPED problem is to determine optimal heat and power of generating units with the minimized cost of total system and satisfied constraints of the problem. In the proposed algorithm, inertia coefficients are controlled regarding cost function in each population. Thus, each population has unique inertia coefficient and as a result unique velocity in convergent direction for the best group solution. In order to examine the proposed algorithm's capabilities and find optimum solution for CHPED problem, two test systems regarding valve-point effect, system power loss and system constraints are optimized. The obtained results demonstrate the superiority of the proposed method in solving non-convex CHPED problem over compared to the other new and efficient algorithms.
机译:在本研究中,建议使用智能惯量因子(PSO-SIF)算法进行粒子群优化,以解决综合热量和电力经济调度(CHPED)问题。 CHPED问题是电力系统中最重要的问题之一,以及挑战的非凸和非线性优化问题。解决CHPED问题的目的是确定具有最小化整体系统成本的最佳热量和功率,以及对问题的满意约束。在所提出的算法中,惯性系数被控制在每个群体中的成本函数。因此,每种群体具有独特的惯性系数,并且结果是最佳组溶液的会聚方向的独特速度。为了检查所提出的算法的能力并找到酸的最佳解决方案,优化了有关阀点效应,系统功率损耗和系统约束的两个测试系统。所获得的结果表明,与其他新的和高效算法相比,求解非凸酸的问题的方法的优越性。

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