Optimal Accommodation of Renewable DGs in Distribution System Considering Plug-in Electric Vehicles Using Gorilla Troops Optimizer

M. M. Sankar, K. Chatterjee
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引用次数: 1

Abstract

Rapid adoption of plug-in electric vehicles (PEVs) can create a sizable burden on distribution networks. For proactive planning of the distribution network, it is vital to consider PEV loads while optimally allocating distributed generators (DGs). In this study, renewable wind turbines and solar photovoltaic based DG units are optimally accommodated in the distribution system while addressing the uncertainties in the wind and solar power generation. A realistic time-varying mixed load model is adopted, and the PEV loads are integrated considering different charging profiles. Gorilla troops optimizer (GTO) algorithm is employed for determining the best locations and ratings of renewable DGs with minimization of real power loss, bus voltage deviation and augmentation of voltage stability index as objectives. The methodology is tested on a 33-bus benchmark distribution network. The outcomes are objectively evaluated in terms of the optimization objectives, and a comparative analysis is presented to substantiate the potency of GTO algorithm.
考虑插电式电动汽车的配电网可再生dg优化调度
插电式电动汽车(pev)的迅速普及会给配电网带来相当大的负担。为了实现配电网的主动规划,在优化配置分布式发电机组时,考虑PEV负荷至关重要。在本研究中,可再生风力涡轮机和基于太阳能光伏发电的DG机组在解决风能和太阳能发电的不确定性的同时,在配电系统中得到了最佳容纳。采用现实时变混合负荷模型,综合考虑不同充电方式的电动汽车负荷。采用大猩猩部队优化算法(Gorilla troops optimizer, GTO),以实际损耗最小、母线电压偏差最小和电压稳定指数增大为目标,确定可再生dg的最佳位置和额定值。该方法在一个33总线基准配电网上进行了测试。根据优化目标对优化结果进行了客观评价,并通过对比分析验证了GTO算法的有效性。
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