考虑电动汽车充电时序不确定性的共享储能系统双层优化配置

邓天祥; 窦银科; 高亮; 赵煜宁; 郝文辉

doi:10.19912/j.0254-0096.tynxb.2024-1360

PDF(1112 KB)

太阳能学报 ›› 2025, Vol. 46 ›› Issue (12) : 52-59. DOI: 10.19912/j.0254-0096.tynxb.2024-1360

考虑电动汽车充电时序不确定性的共享储能系统双层优化配置

邓天祥^1,2, 窦银科^1,2, 高亮³, 赵煜宁^1,2, 郝文辉^1,2

作者信息 +

DUAL-LAYER OPTIMIZATION CONFIGURATION OF SHARED ENERGY STORAGE SYSTEM CONSIDERING UNCERTAINTY OF CHARGING TIME SEQUENCE OF ELECTRIC VEHICLES

Deng Tianxiang^1,2, Dou Yinke^1,2, Gao Liang³, Zhao Yuning^1,2, Hao Wenhui^1,2

Author information +

文章历史 +

摘要

基于共享储能电站,考虑电动汽车充电时序不确定性,建立储能电站容量优化配置和系统优化运行的双层优化模型。首先对电动汽车充电时空分布提出预测并计算其功率,然后考虑运行和容量的最佳配置建立双层优化模型,通过Karush-Kuhn-Tucker（KKT）条件将双层模型转化为单层非线性优化问题,然后利用大M法将非线性问题变成线性问题进行求解。通过算例验证表明,在考虑电动汽车充电负荷下,使用共享储能模式可利用不同微电网的互补性,可降低系统运行成本,使共享储能电站的盈利能力提升,可实现用户与电站的双赢。

Abstract

Shared energy storage is a new type of energy storage business model, which can freely and flexibly choose energy storage capacity according to different scenarios. In this paper, based on shared energy storage power station, considering the uncertainty of electric rehicles（EV） charging timing, a two-layer optimization model of capacity optimization configuration and system optimization operation of energy storage power station is established. Firstly, a prediction of the spatiotemporal distribution of EV charging is proposed and its power is calculated, and then a two-layer optimization model is established considering the optimal configuration of operation and capacity. The two-layer optimization model is transformed into a single-layer nonlinear optimization problem by using Karush-Kuhn-Tucher（KKT） condition, and finally the nonlinear problem is transformed into a linear problem by using large M method. The calculation examples show that, considering the charging load of electric vehicles, the shared energy storage mode can take advantage of the complementarity of different micro-grids, reduce the system operation cost, improve the profitability of shared energy storage power stations, and achieve a win-win situation between users and power stations.

导出引用

邓天祥, 窦银科, 高亮, 赵煜宁, 郝文辉. 考虑电动汽车充电时序不确定性的共享储能系统双层优化配置[J]. 太阳能学报. 2025, 46(12): 52-59 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1360

Deng Tianxiang, Dou Yinke, Gao Liang, Zhao Yuning, Hao Wenhui. DUAL-LAYER OPTIMIZATION CONFIGURATION OF SHARED ENERGY STORAGE SYSTEM CONSIDERING UNCERTAINTY OF CHARGING TIME SEQUENCE OF ELECTRIC VEHICLES[J]. Acta Energiae Solaris Sinica. 2025, 46(12): 52-59 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1360

中图分类号： TM76

参考文献

[1] 张志, 邵尹池, 伦涛, 等. 电化学储能系统参与调峰调频政策综述与补偿机制探究[J]. 电力工程技术, 2020, 39(5): 71-77, 84.
ZHANG Z, SHAO Y C, LUN T, et al.Review on the policies and compensation mechanism of BESS participation in the auxiliary service of frequency and peak modulation[J]. Electric power engineering technology, 2020, 39(5): 71-77, 84.
[2] China News Service.Shared energy storage in China increased new energy generation by over 100 million kWh for the first time[EB/OL]. https://www.chinanews.com.cn/ny/2022/03-11/9699559. shtml.
[3] 刘敦楠, 赵宁宁, 李鹏飞, 等. 基于“共享储能-需求侧资源” 联合跟踪可再生能源发电曲线的市场化消纳模式[J]. 电网技术, 2021, 45(7): 2791-2802.
LIU D N, ZHAO N N, LI P F, et al.Market-oriented consumption model based on the joint tracking of renewable energy generation curve of “shared energy storage & demand side resources”[J]. Power system technology, 2021, 45(7): 2791-2802.
[4] 谢雨龙, 罗逸飏, 李智威, 等. 考虑微网新能源经济消纳的共享储能优化配置[J]. 高电压技术, 2022, 48(11): 4403-4413.
XIE Y L, LUO Y Y, LI Z W, et al.Optimal allocation of shared energy storage considering the economic consumption of microgrid new energy[J]. High voltage engineering, 2022, 48(11): 4403-4413.
[5] 李建林, 崔宜琳, 马速良, 等. 需求侧共享储能的运营模式优化及其经济效益分析研究[J]. 电网技术, 2022, 46(12): 4954-4969.
LI J L, CUI Y L, MA S L, et al.Operation mode optimization and economic benefit analysis of demand-side shared energy storage[J]. Power system technology, 2022, 46(12): 4954-4969.
[6] 吴玮坪, 胡泽春, 宋永华. 结合随机规划和序贯蒙特卡洛模拟的风电场储能优化配置方法[J]. 电网技术, 2018, 42(4): 1055-1062.
WU W P, HU Z C, SONG Y H.Optimal sizing of energy storage system for wind farms combining stochastic programming and sequential Monte Carlo simulation[J]. Power system technology, 2018, 42(4): 1055-1062.
[7] 喻颖倩, 何川, 刘天琪, 等. 计及储能调频与寿命损耗的多套餐共享储能主从博弈分布鲁棒定价方法研究[J]. 中国电机工程学报, 2025, 45(4): 1450-1465.
YU Y Q, HE C, LIU T Q, et al.Research on robust pricing method of shared energy storage multi-package master-slave game distributed robust considering energy storage frequency modulation and life loss[J]. Proceedings of the CSEE, 2025, 45(4): 1450-1465.
[8] 臧云帆, 夏晟, 李嘉文, 等. 含共享储能的微电网群分布鲁棒博弈优化调度方法[J]. 电力系统保护与控制, 2023, 51(24): 90-101.
ZANG Y F, XIA S, LI J W, et al.A robust game optimization scheduling method for shared energy storage micro electric network group distribution[J]. Power system protection and control, 2023, 51(24): 90-101.
[9] 孙英聪, 陈来军, 李笑竹, 等. 考虑资源相关性的新能源场站群集中式共享储能优化配置[J]. 太阳能学报, 2024, 45(7): 2-9.
SUN Y C, CHEN L J, LI X Z, et al.Optimal allocation of centralized shared energy storage for renewable energy station clusters with consideration of resource correlation[J]. Acta energiae solaris sinica, 2024, 45(7): 2-9.
[10] 杨健维, 李爱, 廖凯. 城际高速路网中光储充电站的定容规划[J]. 电网技术, 2020, 44(3): 934-943.
YANG J W, LI A, LIAO K.Capacity planning of light storage charging station for intercity highways based on charging guidance[J]. Power system technology, 2020, 44(3): 934-943.
[11] 邵尹池, 穆云飞, 余晓丹, 等. “车-路-网” 模式下电动汽车充电负荷时空预测及其对配电网潮流的影响[J]. 中国电机工程学报, 2017, 37(18): 5207-5219, 5519.
SHAO Y C, MU Y F, YU X D, et al.A spatial-temporal charging load forecast and impact analysis method for distribution network using EVs-traffic-distribution model[J]. Proceedings of the CSEE, 2017, 37(18): 5207-5219, 5519.
[12] 闫丽梅, 曾家威, 徐建军, 等. 含电动汽车和光伏的配电网演化动态博弈调度策略[J]. 太阳能学报, 2024, 45(5): 316-323.
YAN L M, ZENG J W, XU J J, et al.Scheduling strategy of distribution grid with electric vehicles and photolatic based on evolutionary dynamics game[J]. Acta energiae solaris sinica, 2024, 45(5): 316-323.
[13] 陆燕娟, 潘庭龙, 杨朝辉. 计及电动汽车的社区微网储能容量配置[J]. 太阳能学报, 2021, 42(12): 362-367.
LU Y J, PAN T L, YANG C H.Energy storage capacity configuration in community microgrid considering electric vehicles[J]. Acta energiae solaris sinica, 2021, 42(12): 362-367.
[14] 王成山, 洪博文, 郭力, 等. 冷热电联供微网优化调度通用建模方法[J]. 中国电机工程学报, 2013, 33(31): 26-33, 3.
WANG C S, HONG B W, GUO L, et al.A general modeling method for optimal dispatch of combined cooling, heating and power microgrid[J]. Proceedings of the CSEE, 2013, 33(31): 26-33, 3.
[15] 闫东翔, 陈玥. 共享储能商业模式和定价机制研究综述[J]. 电力系统自动化, 2022, 46(23): 178-191.
YAN D X, CHEN Y.Review on business model and pricing mechanism for shared energy storage[J]. Automation of electric power systems, 2022, 46(23): 178-191.
[16] 徐青山, 李淋, 盛业宏, 等. 冷热电联供型多微网主动配电系统日前优化经济调度[J]. 电网技术, 2018, 42(6): 1726-1735.
XU Q S, LI L, SHENG Y H, et al.Day-ahead optimized economic dispatch of active distribution power system with combined cooling, heating and power-based microgrids[J]. Power system technology, 2018, 42(6): 1726-1735.
[17] 欧阳明高. 我国节能与新能源汽车发展战略与对策[J]. 汽车工程, 2006, 28(4): 317-321.
OUYANG M G.Development strategy and countermeasures of energy-saving and new energy vehicles in China[J]. Automotive engineering, 2006, 28(4): 317-321.
[18] DOUCETTE R T, MCCULLOCH M D.Modeling the prospects of plug-in hybrid electric vehicles to reduce CO₂ emissions[J]. Applied energy, 2011, 88(7): 2315-2323.
[19] 董晓红, 孔华志, 丁飞, 等. 考虑电池老化的电动汽车中长期充电负荷预测方法[J]. 电力系统自动化, 2024, 48(13): 109-119.
DONG X H, KONG H Z, DING F, et al.Medium-and long-term charging load forecasting method for electric vehicles considering battery aging[J]. Automation of electric power systems, 2024, 48(13): 109-119.
[20] 田立亭, 史双龙, 贾卓. 电动汽车充电功率需求的统计学建模方法[J]. 电网技术, 2010, 34(11): 126-130.
TIAN L T, SHI S L, JIA Z.A statistical model for charging power demand of electric vehicles[J]. Power system technology, 2010, 34(11): 126-130.
[21] ZHANG S Y, ZHANG K S, ZHANG G, et al.The bi-level optimization model research for energy-intensive load and energy storage system considering congested wind power consumption[J]. Processes, 2022, 10(1): 51.
[22] SINHA A, SOUN T, DEB K.Using Karush-Kuhn-Tucker proximity measure for solving bilevel optimization problems[J]. Swarm and evolutionary computation, 2019, 44: 496-510.
[23] 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 L, XU Q S, WANG X Q, 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.