Multi objective optimal configuration method for rural distribution networks with distributed photovoltaics and electric vehicle charging discharging

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Multi objective optimal configuration method for rural distribution networks with distributed photovoltaics and electric vehicle charging discharging

更新时间：2026-04-08

- Multi objective optimal configuration method for rural distribution networks with distributed photovoltaics and electric vehicle charging discharging
- Issue 2, (2026)
- 作者机构：
  
  国网江苏省电力有限公司新沂市供电分公司
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2026
- 稿件说明：
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liuzhichuang. Multi objective optimal configuration method for rural distribution networks with distributed photovoltaics and electric vehicle charging discharging[J]. 2026, (2).
DOI：

liuzhichuang. Multi objective optimal configuration method for rural distribution networks with distributed photovoltaics and electric vehicle charging discharging[J]. 2026, (2). DOI：

摘要

相较于城市地区，农村地区配电网相对薄弱，冗余度不高。近年来随着农村地区分布式光伏并网的增多和新能源汽车充电需求的增长，农村地区的配电网的建设亟待加强。针对农村地区的分布式光伏并网和电动汽车充放电共同作用引发的农村配电网配置问题，考虑到农村地区安装光伏板和充电桩的限制和配电网本身的约束，本文提出基于第二代非支配排序遗传算法（NSGA-II）的农村配电网多目标优化配置方法，求得Pareto解集，筛选出最优配置方案。在某地农村地区的实地验证表明：所提出的方法能够在满足分布式光伏“应并尽并”和充电桩报装需求的同时，满足农村配电网投资经济性的要求。

Abstract

The distribution networks in rural areas are relatively underdeveloped and have low redundancy. In recent years

with the increasing integration of distributed photovoltaics (DPV) into the grid and the growing demand for new energy vehicle (NEV) charging in rural areas

the construction of rural distribution networks is in urgent need of enhancement. To address the configuration issues of rural distribution networks caused by the combined effects of DPV grid integration and electric vehicle (EV) charging-discharging in rural areas

and taking into account the constraints on the installation of photovoltaic panels and charging piles as well as the inherent constraints of the distribution networks themselves in rural areas

this paper proposes a multi-objective optimal configuration method for rural distribution networks based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). This method obtains the Pareto solution set and screens out the optimal configuration scheme. Field verification in a rural area demonstrates that the proposed method can not only meet the requirements of "connecting all available DPV to the grid" and the application needs for charging pile installation

but also satisfy the investment economy requirements of rural distribution networks.

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