基于缩减搜索空间的指数粒子群优化DG分配

S. R. Ghatak, P. Acharjee
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引用次数: 13

摘要

在当前放松管制的环境下,将分布式发电(DG)集成到径向配电网中是一种可靠、高效的方案,可以减少电力损耗,改善系统电压分布和稳定性。DG机组的优化配置是提高电网供电质量和可靠性的关键。利用电压稳定指标确定弱区和健康区。大小相同的DG分别置于弱区和健康区。弱区放置DG可以最大限度地改善电压分布,节约能源成本,减少损耗。为了减少DG优化分配所需的计算时间,建议仅在系统的弱母线位置进行DG性能分析。因此DG最优分配的搜索空间可以限制在系统的弱区。在考虑运行约束的基础上,建立了考虑电压曲线改善指数(VPII)和成本效益比(BCR)的目标函数。提出了一种考虑满搜索空间和缩减搜索空间的指数粒子群优化方法(Exponential Particle Swarm optimization, EPSO)。将该算法与其他类型的粒子群优化技术(如简单粒子群优化(SPSO)和自适应粒子群优化(APSO))进行了比较。所提出的EPSO方法在计算效率和解质量方面都取得了最好的性能。
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Optimal Allocation of DG Using Exponentential PSO with Reduced Search Space
In the current deregulating environment, integration of Distributed Generation (DG) in the radial distribution network is one of the reliable and efficient options which can be used for reduction of power loss, improving the voltage profile of the system and stability. Optimal allocation of DG units is essential for improving the quality of supply and reliability of the network. Using voltage stability index, weak and healthy zone are determined. DG with same size is placed in weak and healthy zone separately. The voltage profile improvement, cost of energy saving and reduction of losses can be maximized by placing DG in weak zone. To reduce the computational time required for optimal allocation of DG, it is proposed to conduct its performance analysis only at the weak bus locations of the system. Therefore, the search space for optimal allocation of DG can be restricted only to the weak zone of the system. Taking account of operational constraints, a new objective function is formulated considering Voltage Profile Improvement Index (VPII) and Benefit to Cost ratio (BCR). An Exponential Particle Swarm optimization (EPSO) method is proposed for optimal placement and sizing of DG considering both full and reduced search space. The proposed algorithm is compared with other types of Particle swarm optimization techniques (PSO) such as Simple Particle Swarm Optimization (SPSO) and Adaptive Particle Swarm Optimization (APSO). The best performance in terms of computational efficiency and solution quality is achieved for the proposed EPSO method.
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