| 引用本文: | 康 童,朱吉然,冯楚瑞,等.面向光储充一体化社区的有序充电策略研究[J].电力系统保护与控制,2024,52(9):132-142.[点击复制] |
| KANG Tong,ZHU Jiran,FENG Churui,et al.An orderly charging strategy for a photovoltaic-storage-charging integrated community[J].Power System Protection and Control,2024,52(9):132-142[点击复制] |
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| 面向光储充一体化社区的有序充电策略研究 |
| 康童1,2,朱吉然1,2,冯楚瑞3,范敏3,任磊1,2,唐海国1,2 |
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| (1.国网湖南省电力有限公司电力科学研究院，湖南 长沙 410007;2.国网公司配电网智能化应用
技术实验室，湖南 长沙 410007;3.重庆大学自动化学院，重庆 400044) |
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| 摘要: |
| 针对当前有序充电策略优化目标单一且未考虑新能源出力的现状，提出了面向光储充一体化社区的有序充电策略。首先，将降低社区负荷峰谷差作为电网层优化目标，将减少用户充电费用作为用户层优化目标，完成双层多目标有序充电模型的设计。其次，设计基于云边协同的调度架构，将电网层优化模型部署在云端侧，用户层优化模型部署在边缘侧。该架构能有效利用边缘侧的计算资源，缓解云端侧面对电动汽车大规模接入时的计算压力。最后，以5种充电场景为例进行算例分析。实验表明，与无序充电相比，所提策略能够使社区负荷峰谷差减少40.47%，充电均价减少52.63%。与单层有序充电策略相比，该策略综合效果优势明显，在保障配电网安全稳定运行的同时，兼顾电动汽车用户的经济利益。 |
| 关键词: 光储充一体化社区 有序充电 双层多目标优化模型 云边协同 电动汽车 |
| DOI：10.19783/j.cnki.pspc.230998 |
| 投稿时间：2023-08-02修订日期：2023-12-01 |
| 基金项目:国家重点研发计划项目资助(2020YFB2009405)；国网湖南省电力有限公司科技项目资助(5216A5220010, 5216A522001Z) |
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| An orderly charging strategy for a photovoltaic-storage-charging integrated community |
| KANG Tong1,2,ZHU Jiran1,2,FENG Churui3,FAN Min3,REN Lei1,2,TANG Haiguo1,2 |
| (1. State Grid Hunan Electric Power Company Limited Research Institute, Changsha 410007, China; 2. State Grid Corporation
Laboratory of Intelligent Application Technology for Distribution Network, Changsha 410007, China;
3. College of Automation, Chongqing University, Chongqing 400044, China) |
| Abstract: |
| Currently the optimization objective of an orderly charging strategy has a single layer and new energy output is not considered. Thus an orderly charging strategy for a photovoltaic-storage-charging integrated community is proposed. First, the reduction of peak valley difference of community load is taken as the optimization goal of the power grid layer, and the reduction of user charging costs is taken as the optimization goal of the user layer to complete the design of a double-layer multi-objective orderly charging model. Secondly, a scheduling architecture based on cloud edge collaboration is designed, deploying the power grid layer optimization model on the cloud side and the user layer optimization model on the edge side. This architecture effectively uses the computing resources on the edge side and alleviates the computational pressure on the cloud side for large-scale access to electric vehicles. Finally, five charging scenarios are used as examples for simulation analysis. The experiment shows that compared to disorderly charging, the strategy proposed can reduce the peak valley difference of community load by 40.47% and the average charging price by 52.63%. Compared with the single-layer orderly charging strategies, this strategy has significant advantages in terms of overall effectiveness, ensuring the safe and stable operation of the distribution network while also taking into account the economic interests of electric vehicle users. |
| Key words: photovoltaic-storage-charging integrated community orderly charging double-layer multi-objective optimization model cloud edge collaboration electric vehicle |
