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Improved Particle Swarm Optimization for Multi-area Economic Dispatch with Reserve Sharing Scheme

机译:具有储备共享方案的多区域经济调度的改进粒子群算法

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This paper presents an improved particle swarm optimization (IPSO) to solve Multi-area Economic Dispatch (MAED) problem. The objective of MAED problem is to determine the optimal generating schedule of thermal units and inter-area power transactions in such a way that total fuel cost is optimized while satisfying tie-line, spinning reserve and other operational constraints. The spinning reserve requirements for reserve sharing provisions are investigated by considering contingency and pooling spinning reserves. The control equation of IPSO is modified by suggesting improved cognitive component of the particle’s velocity by suggesting preceding experience. The operators of IPSO are also modified to maintain a proper balance between cognitive and social behavior of the swarm. The effectiveness of the proposed method has been tested on four areas, 16 generators and 40 generators test systems. The application results show that IPSO is very promising to solve MAED problem.
机译:本文提出了一种改进的粒子群算法(IPSO)来解决多区域经济调度(MAED)问题。 MAED问题的目的是确定热力单元和区域间电力交易的最佳发电时间表,以使总燃料成本得到优化,同时满足联络线,旋转储备和其他操作约束。通过考虑应急准备金和汇总纺丝储备,研究了储备共享准备金的纺丝储备要求。通过建议先前的经验来建议改善粒子速度的认知分量,从而修改IPSO的控制方程。还对IPSO的运​​营商进行了修改,以在群体的认知和社会行为之间保持适当的平衡。该方法的有效性已经在四个领域进行了测试,分别是16个发电机和40个发电机测试系统。应用结果表明,IPSO在解决MAED问题方面非常有前途。

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