基于自适应混沌粒子群算法的分布式电源优化

目前我国是以＂大机组、大电网、高电压＂为主要特征的单一供电系统,这种集中发电、远距离传输、大电网互联的方式虽然有效减少了系统的备用容量以及加强了各网络间的同步,但很难快速追踪远距离的末端负荷变化,负荷峰谷差增大将导致电荒出现,线路距离太远也加大了系统的网损。基于此,本文考虑在配电网中合理接入分布式电源进行优化,为了确定接入位置和容量,本文以分布式电源安装年费用、配电网年有功损耗及分布式电源环境效益为多目标函数;为了避免粒子群算法（PSO）容易陷入局部最优,文中采用计算速度快、收敛可靠的自适应混沌算法（ACPSO）进行计算,设计了基本计算流程;最后采用IEEE14节点系统进行算例分析,接入分布式电源后配电网网损有效降低,且能减少配电网运行总费用,同时证明ACPSO算法在解决分布式电源优化问题上具有一定优越性。

Base on the Adaptive Chaotic Particle Swarm Optimization of Distributed Source Location and Constant Volume

At present, China is based on a large unit, large grid, high voltage single supply power system as the main feature, this centralized power generation, long-distance transmission, grid interconnection effectively reduce the system＇s spare capacity and strengthening of network synchronization, but difficult to fast-track the end of the long-range changes in load, peak load difference increases will lead to electricity shortage, the line is too far away also increased the net loss. Based on this, we consider reasonable access distributed generation distribution network optimization, in order to determine the location and capacity of access to distributed generation installed annual cost, with the grid years active loss and distributed generation environmental benefits, this paper take the adaptive chaotic optimization （ACPSO） caculate the objective function, it avoid particle swarm optimization （PSO） is easy to fall into local optimum,then design a basic calculation process. Finally take IEEE14 node system as example to analyse, access to distributed generation distribution network loss effectively reduce, and can reduce the running total cost of the distribution network, and at the same time prove ACPSO algorithm in solving the distributed generation optimization problem with a certain superiority.