上海交通大学学报

上海交通大学学报 >

2025 , Vol. 59 >Issue 7: 877 - 888

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2023.407

新型电力系统与综合能源

用户-基站-充电站能量互动和储能共享优化方法

  • 胡龙 ,
  • 方八零 ,
  • 樊飞龙 ,
  • 陈达伟 ,
  • 李新喜 ,
  • 曾润
展开
  • 1.上海交通大学 国家电投智慧能源创新学院,上海 200240
    2.湖南工业大学 电气与信息工程学院,湖南 株洲 412007
    3.上海交通大学 电子信息与电气工程学院,上海 200240
    4.广东工业大学 材料与能源学院,广州 510006
    5.上海采日能源科技有限公司,上海 200240
胡 龙(1997—),硕士生,从事储能运行优化和电力系统调度与分析研究.
方八零,讲师;E-mail:14118@hut.edu.cn.

收稿日期: 2023-08-21

  修回日期: 2023-09-26

  录用日期: 2023-10-23

  网络出版日期: 2023-11-08

基金资助

国家自然科学基金资助项目(51977062)

收起

Energy Interaction and Energy Storage Sharing Optimization Method for Users, Base Stations, and Charging Stations

  • HU Long ,
  • FANG Baling ,
  • FAN Feilong ,
  • CHEN Dawei ,
  • LI Xinxi ,
  • ZENG Run
Expand
  • 1. College of Smart Energy, Shanghai Jiao Tong University, Shanghai 200240, China
    2. College of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, Hunan, China
    3. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    4. School of Materials and Energy, Guangdong University of Technology, Guangzhou 510006, China
    5. Shanghai Sermatec Energy Technology Co., Ltd., Shanghai 200240, China

Received date: 2023-08-21

  Revised date: 2023-09-26

  Accepted date: 2023-10-23

  Online published: 2023-11-08

Fold

摘要

园区工业用户、基站和充电站单一主体内部能量优化时,受其本地功率供给和需求限制,储能等灵活性资源利用率低,能量利用效率不足.基于工业用户、基站和充电站互补性量化,提出一种基于博弈定价激励机制的工业用户、基站和充电站能量共享、互动优化方法.通过工业用户、基站和充电站特性分析,构建以系统净负荷标准差为互补性指数的量化模型;考虑空调和电动汽车的可调节能力,以及工业用户、充电站与基站的主动决策能力,建立了激励工业用户、充电站和基站储能共享及能量互动的主从博弈定价模型;针对模型中存在0-1整数变量的特点,提出基于自适应差分进化算法和混合整数优化理论的求解方法.基于园区实际数据构建算例,对所提模型与方法进行验证,算例分析表明,优化工业用户、基站和充电站各时段储能和能源调度可有效利用其互补性,提高各主体的经济效益和闲置灵活性资源的利用率,同时提升系统整体的能源自洽能力.

关键词： 共享储能; 主从博弈; 灵活性资源; 基站; 电动汽车

本文引用格式

胡龙 , 方八零 , 樊飞龙 , 陈达伟 , 李新喜 , 曾润 . 用户-基站-充电站能量互动和储能共享优化方法[J]. 上海交通大学学报, 2025 , 59(7) : 877 -888 . DOI: 10.16183/j.cnki.jsjtu.2023.407

Abstract

The internal energy optimization within a single entity of industrial users, base stations, and charging stations is constrained by local power supply and demand limitations, resulting in low utilization of flexible resources such as energy storage and insufficient energy utilization efficiency. To address these issues, an energy sharing and interactive optimization method is proposed for industrial users, base stations, and charging stations based on the quantification of their complementarity and a game-based pricing incentive mechanism. First, a complementary quantification model is developed based on the analysis of the characteristics of industrial users, base stations, and charging stations, using the standard deviation of net load as a complementary indicator. Then, considering the adjustable capabilities of air conditioning and electric vehicles, as well as the proactive decision-making abilities of industrial users, charging stations, and base stations, a master-slave game-based pricing model is established to incentivize the sharing of energy storage and energy interaction among these entities. Next, incorporating 0-1 integer variables, a solution method utilizes the adaptive differential evolution algorithm combined with the mixed-integer optimization theory. Finally, case studies validate that optimizing the energy storage and energy dispatch of industrial users, base stations, and charging stations in different time periods can effectively leverage their complementarity, enhance the economic benefits of each entity, improve the utilization of idle flexible resources, and enhance the overall energy self-consistency of the system.

Key words： shared energy storage; Stackelberg game; flexible resources; base station; electric vehicle

参考文献

[1] DOROSTKAR-GHAMSARI M R, FOTUHI-FIRUZABAD M, LEHTONEN M, et al. Value of distribution network reconfiguration in presence of renewable energy resources[J]. IEEE Transactions on Power Systems, 2016, 31(3): 1879-1888.
[2] 熊宇峰, 司杨, 郑天文, 等. 基于主从博弈的工业园区综合能源系统氢储能优化配置[J]. 电工技术学报, 2021, 36(3): 507-516.
  XIONG Yufeng, SI Yang, ZHENG Tianwen, et al. Optimal configuration of hydrogen storage in industrial park integrated energy system based on Stackelberg game[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 507-516.
[3] ISRAR A, YANG Q, LI W, et al. Renewable energy powered sustainable 5G network infrastructure: Opportunities, challenges and perspectives[J]. Journal of Network and Computer Applications, 2021, 175: 102910.
[4] 高昇宇, 柳志航, 卫志农, 等. 城市智能光储充电塔自适应鲁棒日前优化调度[J]. 电力系统自动化, 2019, 43(20): 39-48.
  GAO Shengyu, LIU Zhihang, WEI Zhinong, et al. Adaptive robust day-ahead optimal dispatch for urban smart photovoltaic storage and charging tower[J]. Automation of Electric Power Systems, 2019, 43(20): 39-48.
[5] TUSHAR W, SAHA T K, YUEN C, et al. Peer-to-peer trading in electricity networks: An overview[J]. IEEE Transactions on Smart Grid, 2020, 11(4): 3185-3200.
[6] 陈玥, 刘锋, 魏韡, 等. 需求侧能量共享:概念、机制与展望[J]. 电力系统自动化, 2021, 45(2): 1-11.
  CHEN Yue, LIU Feng, WEI Wei, et al. Energy sharing at demand-side: Concept, mechanism and prospect[J]. Automation of Electric Power Systems, 2021, 45(2): 1-11.
[7] 李淋, 徐青山, 王晓晴, 等. 基于共享储能电站的工业用户日前优化经济调度[J]. 电力建设, 2020, 41(5): 100-107.
  LI Lin, XU Qingshan, WANG Xiaoqing, et al. Optimal economic scheduling of industrial customers on the basis of sharing energy-storage station[J]. Electric Power Construction, 2020, 41(5): 100-107.
[8] 刘轶涵, 徐青山, 杨永标, 等. 计及配电网潮流约束下基于广义纳什议价理论的工业用户共享储能配置[J]. 电网技术, 2023, 47(2): 571-583.
  LIU Yihan, XU Qingshan, YANG Yongbiao, et al. Distribution network power flow constrained shared energy storage configuration for industrial consumers based on generalized Nash bargaining theory[J]. Power System Technology, 2023, 47(2): 571-583.
[9] AHMED F, NAEEM M, EJAZ W, et al. Renewable energy assisted traffic aware cellular base station energy cooperation[J]. Energies, 2018, 11(1): 99.
[10] 周宸宇, 冯成, 王毅. 基于移动用户接入控制的5G通信基站需求响应[J]. 中国电机工程学报, 2021, 41(16): 5452-5461.
  ZHOU Chenyu, FENG Cheng, WANG Yi. Demand response of 5G communication base stations based on admission control of mobile users[J]. Proceedings of the CSEE, 2021, 41(16): 5452-5461.
[11] SHENG M, ZHAI D S, WANG X J, et al. Intelligent energy and traffic coordination for green cellular networks with hybrid energy supply[J]. IEEE Transactions on Vehicular Technology, 2017, 66(2): 1631-1646.
[12] 李俊双, 胡炎, 邰能灵. 计及通信负载与供电可靠性的5G基站储能与配电网协同优化调度[J]. 上海交通大学学报, 2023, 57(7): 791-802.
  LI Junshuang, HU Yan, TAI Nengling. Collaborative optimization scheduling of 5G base station energy storage and distribution network considering communication load and power supply reliability[J]. Journal of Shanghai Jiao Tong University, 2023, 57(7): 791-802.
[13] 麻秀范, 刘子豪, 王颖, 等. 考虑通信负载迁移及储能动态备电的5G基站光伏消纳能力研究[J]. 电工技术学报, 2023, 38(21): 5832-5845.
  MA Xiufan, LIU Zihao, WANG Ying, et al. Research on photovoltaic absorption capacity of 5G base station considering communication load migration and energy storage dynamic backup[J]. Transactions of China Electrotechnical Society, 2023, 38(21): 5832-5845.
[14] HAN J P, LIU N, HUANG Y J, et al. Collaborative optimization of distribution network and 5G mobile network with renewable energy sources in smart grid[J]. International Journal of Electrical Power & Energy Systems, 2021, 130: 107027.
[15] 李高俊杰, 杨军, 朱旭, 等. 计及电动汽车用户响应特性的充电站实时电能共享机制[J]. 电力系统自动化, 2022, 46(12): 56-66.
  LI Gaojunjie, YANG Jun, ZHU Xu, et al. Real-time energy sharing mechanism of charging stations considering user response characteristics of electric vehicles[J]. Automation of Electric Power Systems, 2022, 46(12): 56-66.
[16] YAN D X, CHEN Y. A distributed online algorithm for promoting energy sharing between EV charging stations[J]. IEEE Transactions on Smart Grid, 2023, 14(2): 1158-1172.
[17] HE Y D, ZHOU Y K, LIU J, et al. An inter-city energy migration framework for regional energy balance through daily commuting fuel-cell vehicles[J]. Applied Energy, 2022, 324: 119714.
[18] ZHAO Y X, LIN J, SONG Y H, et al. A hierarchical strategy for restorative self-healing of hydrogen-penetrated distribution systems considering energy sharing via mobile resources[J]. IEEE Transactions on Power Systems, 2023, 38(2): 1388-1404.
[19] ZOU Y, WANG Q G, CHI Y, et al. Electric load profile of 5G base station in distribution systems based on data flow analysis[J]. IEEE Transactions on Smart Grid, 2022, 13(3): 2452-2466.
[20] 曾博, 穆宏伟, 董厚琦, 等. 考虑5G基站低碳赋能的主动配电网优化运行[J]. 上海交通大学学报, 2022, 56(3): 279-292.
  ZENG Bo, MU Hongwei, DONG Houqi, et al. Optimization of active distribution network operation considering decarbonization endowment from 5G base stations[J]. Journal of Shanghai Jiao Tong University, 2022, 56(3): 279-292.
[21] 麻秀范, 冯晓瑜. 考虑5G网络用电需求及可靠性的变电站双Q规划法[J]. 电工技术学报, 2023, 38(11): 2962-2976.
  MA Xiufan, FENG Xiaoyu. Double Q planning method for substation considering power demand of 5G network and reliability[J]. Transactions of China Electrotechnical Society, 2023, 38(11): 2962-2976.
[22] 龚诚嘉锐, 林顺富, 边晓燕, 等. 基于多主体主从博弈的负荷聚合商经济优化模型[J]. 电力系统保护与控制, 2022, 50(2): 30-40.
  GONG Chengjiarui, LIN Shunfu, BIAN Xiaoyan, et al. Economic optimization model of load aggregator basedon multi-agent Stackelberg game[J]. Protection and Control of Electric Power System, 2022, 50(2): 30-40.
[23] 刘志伟, 苗世洪, 杨炜晨, 等. 计及温度不确定性的配电网广义储能分层调控策略[J]. 电工技术学报, 2023, 38(21): 5794-5807.
  LIU Zhiwei, MIAO Shihong, YANG Weichen, et al. Generalized energy storage hierarchical regulation strategy for distribution network considering temperature uncertainty[J]. Transactions of China Electrotechnical Society, 2023, 38(21): 5794-5807.
[24] NAGPAL H, AVRAMIDIS I I, CAPITANESCU F, et al. Local energy communities in service of sustainability and grid flexibility provision: Hierarchical management of shared energy storage[J]. IEEE Transactions on Sustainable Energy, 2022, 13(3): 1523-1535.
[25] 卢强, 陈来军, 梅生伟. 博弈论在电力系统中典型应用及若干展望[J]. 中国电机工程学报, 2014, 34(29): 5009-5017.
  LU Qiang, CHEN Laijun, MEI Shengwei. Typical applications and prospects of game theory in power system[J]. Proceedings of the CSEE, 2014, 34(29): 5009-5017.
[26] 张衡, 张沈习, 程浩忠, 等. Stackelberg博弈在电力市场中的应用研究综述[J]. 电工技术学报, 2022, 37(13): 3250-3262.
  ZHANG Heng, ZHANG Shenxi, CHENG Hao-zhong, et al. A state-of-the-art review on Stackelberg game and its applications in power market[J]. Transactions of China Electrotechnical Society, 2022, 37(13): 3250-3262.
[27] 潘虹锦, 高红均, 杨艳红, 等. 基于主从博弈的售电商多元零售套餐设计与多级市场购电策略[J]. 中国电机工程学报, 2022, 42(13): 4785-4799.
  PAN Hongjin, GAO Hongjun, YANG Yanhong, et al. Multi-type retail packages design and multi-level market power purchase strategy for electricity retailers based on master-slave game[J]. Proceedings of the CSEE, 2022, 42(13): 4785-4799.
Options
文章导航

/

〈 〉