中国电力2025，Vol.58Issue(10)：82-96,15.DOI:10.11930/j.issn.1004-9649.202507034
考虑电动汽车需求响应的交直流混合配电网智能软开关与储能装置鲁棒联合规划方法
A Robust Joint Planning Method for Soft Open Points and Energy Storage Systems in AC/DC Hybrid Distribution Networks Considering Electric Vehicle Demand Response
摘要
Abstract
To meet the new demands of high-quality development of distribution networks and enhance their capacity to accommodate large-scale distributed generation and electric vehicle(EV)loads,this paper proposes a robust joint planning method for soft open points(SOP)and distributed energy storage systems(DESS)in AC/DC hybrid distribution networks,with consideration of EV demand response.Firstly,to address source-load uncertainty,typical and extreme daily operation scenarios are extracted using K-means clustering,and a scenario probability uncertainty set is constructed with l1-norm and infinity-norm constraints to adjust the model's conservativeness.And then,the response behaviors of EV users to real-time price are characterized by a demand price elasticity coefficient.A two-stage robust optimization model is formulated to minimize the annual total cost,and the second-order cone relaxation and McCormick envelopes are used to convexify the model.Scenario probability variables are expanded in binary form to enable worst-case scenario search within the uncertainty set.Candidate SOP locations are extended based on network partitioning.The model is solved efficiently by applying duality theory and the inexact column-and-constraint generation(i-C&CG)algorithm.Finally,the effectiveness of the proposed model in supporting voltage,ensuring renewable energy accommodation,and reducing losses is verified in a 69-bus system.关键词
交直流混合配电网/电动汽车/智能软开关/储能装置/两阶段鲁棒Key words
AC/DC hybrid distribution network/electric vehicle/soft open point/energy storage system/two-stage robust optimization引用本文复制引用
廖建,张耀,张贝西,董浩淼,李嘉兴,孙乾皓..考虑电动汽车需求响应的交直流混合配电网智能软开关与储能装置鲁棒联合规划方法[J].中国电力,2025,58(10):82-96,15.基金项目
国家重点研发计划资助项目(2022YFB2403500) （2022YFB2403500）
陕西省自然科学基础研究计划资助项目(2025JC-YBMS-441). This work is supported by National Key Research and Development Program of China(No.2022YFB2403500),Natural Science Basic Research Program of Shaanxi Province(No.2025JC-YBMS-441). （2025JC-YBMS-441）
