| 含电动汽车的配电网双重不确定性网架规划方法 |
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| 引用本文： | 沈依婷,张菁,武鹏,柳璐,杨建林.含电动汽车的配电网双重不确定性网架规划方法[J].中国电力,2020,53(4):139-146. |
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| 作者姓名： | 沈依婷 张菁 武鹏 柳璐 杨建林 |
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| 作者单位： | 1. 上海工程技术大学 电子电气工程学院，上海 201620;2. 上海交通大学 电力传输与功率变换控制教育部重点实验室，上海 200240;3. 国网上海市电力公司经济技术研究院，上海 200120 |
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| 基金项目： | 国家自然科学基金青年科学基金项目（高比例可再生能源并网的输配电网协调规划模型研究，51807116） |
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| 摘 要： | 考虑配电系统中电动汽车(electric vehicle，EV)充电负荷的随机不确定性和一般负荷的模糊不确定性，基于不确定规划理论，建立同时含有模糊变量和随机变量的混合机会约束配电网架规划模型。模型以规划期内配电网固定投资和模糊随机网损费用之和最小为目标，引入混合机会约束，处理线路功率约束和节点电压约束，通过设置2种不同置信水平参数值，平衡投资费用和运行风险之间的关系。提出基于混合模拟的遗传算法，求解所建模型，采用25节点算例验证所提方法的正确性和有效性。
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| 关 键 词： | 电动汽车 双重不确定性 混合机会约束 混合模拟 配电网架规划 |
| 收稿时间： | 2019-05-27 |
| 修稿时间： | 2019-07-21 |
Bi-uncertainty Network Frame Planning Method for Distribution Network with Electric Vehicles |
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| SHEN Yiting,ZHANG Jing,WU Peng,LIU Lu,YANG Jianlin.Bi-uncertainty Network Frame Planning Method for Distribution Network with Electric Vehicles[J].Electric Power,2020,53(4):139-146. |
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| Authors: | SHEN Yiting ZHANG Jing WU Peng LIU Lu YANG Jianlin |
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| Affiliation: | 1. College of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China;2. Key Laboratory of Control of Power Transmission and Conversion of Ministry of Education, Shanghai Jiaotong University, Shanghai 200240, China;3. State Grid Shanghai Electric Power Corporation Economic and Technological Research Institute, Shanghai 200120, China |
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| Abstract: | Considering the stochastic uncertainty of charging load of electric vehicle (EV) and the fuzzy uncertainty of regular load in the distribution system, a hybrid chance-constrained distribution grid planning model including both fuzzy variables and random variables is established on the basis of the theory of uncertain planning. In this paper, the objective function is to minimize the total cost of fixed investment and fuzzy random network loss in the planning period, with the hybrid chance-constraint programming introduced to process the transmission power constraint and node voltage constraint. In addition, the balance can be well maintained between the investment cost and operation risk by setting two different confidence level parameter values. Finally, a genetic algorithm based on hybrid simulation is proposed to solve the model built in this paper, and a 25-node example is used to verify the correctness and validity of the proposed method. |
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| Keywords: | EV bi-uncertainty mixed chance-constrained hybrid simulation distribution network planning |
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