电力系统保护与控制2026，Vol.54Issue(15)：83-94,12.DOI:10.19783/j.cnki.pspc.251176
考虑承载能力影响的高渗透率光伏配电网多目标规划方法研究
Multi-objective planning method for high-penetration photovoltaic distribution networks considering hosting capacity constraints
摘要
Abstract
Traditional distribution network planning often fails to adequately consider the hosting capacity limitations caused by high penetration of distributed photovoltaic(PV)generation and large-scale electric vehicle(EV)integration,leading to issues such as deteriorated power quality,increased risks of voltage violations,and higher network losses during practical system operation.To address these challenges,this paper proposes a multi-objective hierarchical distribution network planning method that explicitly considers hosting capacity constraints.First,typical daily profiles for distribution network load and PV output,representing annual fluctuation characteristics,are selected using the k-means clustering method,and Monte Carlo simulation is employed to generate EV charging demand.Then,a multi-objective hierarchical planning model considering hosting capacity limitations is established.The upper-level planning model aims to minimize the total investment cost while maximizing the distribution network hosting capacity score,thereby determining the optimal capacity configuration of distributed PV and energy storage systems.The lower-level operational optimization model minimizes the operating cost over representative days to achieve optimal distribution network dispatch.Finally,case studies are conducted to verify the feasibility and effectiveness of the proposed model and to quantitatively analyze the impacts of high-penetration distributed PV integration and large-scale EV charging on distribution network hosting capacity.关键词
分布式光伏/承载能力/配电网规划/储能系统Key words
distributed photovoltaic/hosting capacity/distribution network planning/energy storage system引用本文复制引用
李春霖,刘通,原吕泽芮,喻磊..考虑承载能力影响的高渗透率光伏配电网多目标规划方法研究[J].电力系统保护与控制,2026,54(15):83-94,12.基金项目
This work is supported by the National Natural Science Foundation of China(No.52377123). 国家自然科学基金项目资助(52377123) （No.52377123）
南方电网公司"数字电网"开放基金项目资助(DPGCSG-2024-KF-30) （DPGCSG-2024-KF-30）
