EN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
- 中国电力 2025年58卷第10期 页码：82-96
- 作者机构：
西安交通大学 电气工程学院,陕西,西安,710049
- 作者简介：
- 基金信息：国家重点研发计划资助项目（2022YFB2403500）;陕西省自然科学基础研究计划资助项目（2025JC-YBMS-441）。
DOI：10.11930/j.issn.1004-9649.202507034
中图分类号：-
网络出版：2025-10-23，
纸质出版：2025
- 稿件说明：
移动端阅览
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.
LIAO Jian, ZHANG Yao, ZHANG Beixi, et al. A Robust Joint Planning Method for Soft Open Points and Energy Storage Systems in AC/DC Hybrid Distribution Networks Considering Electric Vehicle Demand Response[J]. 2025, 58(10): 82-96.
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. DOI： 10.11930/j.issn.1004-9649.202507034.
LIAO Jian, ZHANG Yao, ZHANG Beixi, et al. A Robust Joint Planning Method for Soft Open Points and Energy Storage Systems in AC/DC Hybrid Distribution Networks Considering Electric Vehicle Demand Response[J]. 2025, 58(10): 82-96. DOI： 10.11930/j.issn.1004-9649.202507034.
摘要
为适应配电网高质量发展的新需求，保障系统对大规模分布式电源与电动汽车（electric vehicle，EV）的承载能力，提出考虑电动汽车需求响应的交直流混合配电网中智能软开关（soft open point，SOP）与分布式储能装置（distributed energy storage system，DESS）鲁棒联合规划方法。首先，针对源荷不确定性，采用K-means提取典型与极端的日运行场景，并引入范数约束构造场景概率不确定集，以调控模型保守性。随后，通过需求价格弹性系数刻画EV用户对实时电价的响应行为，构建以年综合成本最低为目标的两阶段鲁棒规划模型，并通过二阶锥松弛与McCormick包络等方法进行凸转化。模型引入场景概率的二进制展开，实现在不确定集区间内的最恶劣场景搜索，并结合电网分区拓展SOP安装待选位置，采用对偶理论和非精确列与约束生成算法（inexact column constraint generation，i-CCG）实现高效求解。最后在69节点系统中验证了模型对支撑电压、保障消纳、减少损耗的有效性。
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 sce
nario probability uncertainty set is constructed with l
1
-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-CCG) 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
Keywords
references
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