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PSO Based Fuzzy Stochastic Long-Term Model for Deployment of Distributed Energy Resources in Distribution Systems With Several Objectives

机译:基于PSO的配电系统分布式能源配置的模糊随机长期模型

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摘要

This paper presents a particle swarm optimization (PSO) based fuzzy stochastic long term approach for determining optimum location and size of distributed energy resources (DERs). The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting. A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduction in peak load level, and reduction in voltage deviation are simultaneously considered as the objective functions. At first these objectives are fuzzified and designed to be comparable with each other, then they are introduced to a PSO algorithm in order to obtain the solution which maximizes the value of integrated objective function. The output power of DERs is scheduled for each load level. An enhanced economic model is also proposed to justify investment on DER. IEEE 30-bus radial distribution test system is used as an illustrative example to show the effectiveness of the proposed method.
机译:本文提出了一种基于粒子群优化(PSO)的模糊随机长期方法,用于确定分布式能源(DER)的最佳位置和大小。蒙特卡罗模拟方法用于对与长期负荷预测相关的不确定性进行建模。目标函数中考虑了多个目标的适当组合。从电力市场购买的损耗和功率的减少,峰值负载水平的损耗的减少以及电压偏差的减少被同时视为目标函数。首先,将这些目标模糊化并设计为可以相互比较,然后将它们引入PSO算法中,以获得使集成目标函数的价值最大化的解决方案。针对每个负载级别计划了DER的输出功率。还提出了一种增强的经济模型,以证明对DER的投资是合理的。以IEEE 30总线径向分布测试系统为例,说明了该方法的有效性。

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