阶梯成本下考虑混合租建模式的云储能优化配置

栗然; 吕慧敏; 彭湘泽; 王炳乾; 祝晋尧

doi:10.19912/j.0254-0096.tynxb.2022-1612

PDF(1943 KB)

太阳能学报 ›› 2024, Vol. 45 ›› Issue (2) : 263-273. DOI: 10.19912/j.0254-0096.tynxb.2022-1612

阶梯成本下考虑混合租建模式的云储能优化配置

栗然¹, 吕慧敏¹, 彭湘泽¹, 王炳乾¹, 祝晋尧²

作者信息 +

OPTIMAL CONFIGURATION OF CLOUD ENERGY STORAGE CONSIDEING HYBRID SELF-BUILT AND LEASE MODE UNDER TIERED COST

Li Ran¹, Lyu Huimin¹, Peng Xiangze¹, Wang Bingqian¹, Zhu Jinyao²

Author information +

文章历史 +

摘要

为解决盲目投建云储能造成资源浪费、成本增加的问题,提出阶梯成本下“自建+租赁”混合模式的园区云储能优化配置方法。首先,分析云储能的特点,构建园区内有大量光伏用户参与的云储能服务模式。其次,建立不同时间尺度、双主体的双层优化模型,上层求解长时间尺度下云储能的规划问题,下层求解短时间尺度下用户群的运行问题。然后,通过Karush-Kuhn-Tucker（KKT）条件将双层模型转化为单层模型,再利用Big-M法对所得单层模型进行线性化处理。最后,在3个不同场景下进行算例分析,结果验证了所提云储能配置模型的有效性,在降低储能投资成本、提高储能资源利用率的同时可节省用户用电成本。

Abstract

In order to solve the problem of resource waste and cost increase caused by blind investment, the hybrid model of self-built and lease under tiered cost is applied to the cloud energy storage configuration in park. Firstly, the characteristics of cloud energy storage are analyzed, and service mode of cloud energy storage with a large number of photovoltaic users participating in the park is constructed. Secondly, a bi-level optimization model considering different time scales and two subjects is established, the upper layer solves the planning problem of cloud energy storage in long time scale, and the lower layer solves the operation problem of user group in a short time scale. Thirdly, the two-layer model is transformed into a single-layer model by Karush-Kuhn-Tucker（KKT） condition, and the resulting single-layer model is linearized by Big-M. Finally, with the analysis of three different scenarios, the results demonstrate the effectiveness of proposed cloud energy storage allocation model, it can reduce the investment costs of energy storage, improve the utilization rate of energy storage resources and save electricity cost of users.

导出引用

栗然, 吕慧敏, 彭湘泽, 王炳乾, 祝晋尧. 阶梯成本下考虑混合租建模式的云储能优化配置[J]. 太阳能学报. 2024, 45(2): 263-273 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1612

Li Ran, Lyu Huimin, Peng Xiangze, Wang Bingqian, Zhu Jinyao. OPTIMAL CONFIGURATION OF CLOUD ENERGY STORAGE CONSIDEING HYBRID SELF-BUILT AND LEASE MODE UNDER TIERED COST[J]. Acta Energiae Solaris Sinica. 2024, 45(2): 263-273 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1612

中图分类号： TM73

参考文献

[1] 周孝信, 陈树勇, 鲁宗相, 等. 能源转型中我国新一代电力系统的技术特征[J]. 中国电机工程学报, 2018, 38(7): 1893-1904.
ZHOU X X, CHEN S Y, LU Z X, et al.Technology features of the new generation power system in China[J]. Proceedings of the CSEE, 2018, 38(7): 1893-1904.
[2] 李建林, 马会萌, 袁晓冬, 等. 规模化分布式储能的关键应用技术研究综述[J]. 电网技术, 2017, 41(10):3365-3375.
LI J L, MA H M, YUAN X D, et al.Overview on key applied technologies of large-scale distributed energy storage[J]. Power system technology, 2017, 41(10):3365-3375.
[3] 任大伟, 侯金鸣, 肖晋宇, 等. 能源电力清洁化转型中的储能关键技术探讨[J]. 高电压技术, 2021, 47(8):2751-2759.
REN D W, HOU J M, XIAO J Y, et al.Exploration of key technologies for energy storage in the cleansing transformation of energy and power[J]. High voltage engineering, 2021, 47(8): 2751-2759.
[4] 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 Y, LIU F, WEI W, et al.Energy sharing at demand side: concept, mechanism and prospect[J]. Automation of electric power systems, 2021, 45(2): 1-11.
[5] LIU J K, ZHANG N, KANG C Q, et al.Cloud energy storage for residential and small commercial consumers: a business case study[J]. Applied energy, 2017, 188: 226-236.
[6] 刘静琨, 张宁, 康重庆. 电力系统云储能研究框架与基础模型[J]. 中国电机工程学报, 2017, 37(12): 3361-3371.
LIU J K, ZHANG N, KANG C Q.Research framework and basic models for cloud energy storage in power system[J]. Proceedings of the CSEE, 2017, 37(12): 3361-3371.
[7] 康重庆, 刘静琨, 张宁. 未来电力系统储能的新形态: 云储能[J]. 电力系统自动化, 2017, 41(21): 2-8, 16.
KANG C Q, LIU J K, ZHANG N.A new form of energy storage in future power system:cloud energy storage[J]. Automation of electric power systems, 2017, 41(21): 2-8, 16.
[8] 李越. 基于用户侧储能系统的电力负荷优化调度研究[D]. 杭州: 浙江大学, 2018.
LI Y.Research on optimal dispatching of power load based on user-side energy storage system[D]. Hangzhou: Zhejiang University, 2018.
[9] 吴盛军, 刘建坤, 周前, 等. 考虑储能电站服务的冷热电多微网系统优化经济调度[J]. 电力系统自动化, 2019, 43(10): 10-18.
WU S J, LIU J K, ZHOU Q, et al.Optimal economic scheduling for multi-microgrid system with combined cooling, heating and power considering service of energy storage station[J]. Automation of electric power systems, 2019, 43(10): 10-18.
[10] 陈昌铭, 张群, 黄亦昕, 等. 考虑最优建设时序和云储能的园区综合能源系统优化配置方法[J]. 电力系统自动化, 2022, 46(2): 24-32.
CHEN C M, ZHANG Q, HUANG Y X, et al.Optimal configuration method of park-level integrated energy system considering optimal construction time sequence and cloud energy storage[J]. Automation of electric power systems, 2022, 46(2): 24-32.
[11] 吴献祥. 云储能在需求侧的运行优化及面向风电场的容量配置[D]. 长沙: 长沙理工大学, 2019.
WU X X.Operation optimization of cloud energy storage on demand side and capacity allocation for wind farms[D]. Changsha:Changsha University of Science & Technology, 2019.
[12] 蒋从伟, 欧庆和, 吴仲超, 等. 基于联盟博弈的多微网共享储能联合配置与优化[J]. 中国电力, 2022, 55(12): 11-21.
JIANG C W, OU Q H, WU Z C, et al.Joint configuration and optimization of multi-microgrid shared energy storage based on coalition game[J]. Electric power, 2022, 55(12): 11-21.
[13] 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): 571-585.
LIU Y H, XU Q S, YANG Y B, 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): 571-585.
[14] LIU J K, ZHANG N, KANG C Q, et al.Decision-making models for the participants in cloud energy storage[J]. IEEE transactions on smart grid, 2018, 9(6): 5512-5521.
[15] 郭亦宗, 王楚通, 施云辉, 等. 区域综合能源系统电/热云储能综合优化配置[J]. 电网技术, 2020, 44(5):1611-1623.
GUO Y Z, WANG C T, SHI Y H, et al.Comprehensive optimization configuration of electric and thermal cloud energy storage in regional integrated energy system[J]. Power system technology, 2020, 44(5): 1611-1623.
[16] 丁曦, 姜威, 郭创新, 等. 考虑需求响应的电/气/热云储能优化配置策略[J]. 电力建设, 2022, 43(3): 83-99.
DING X, JIANG W, GUO C X, et al.Optimal configuration of electricity-heat-gas cloud energy storage considering demand response[J]. Electric construction, 2022, 43(3): 83-99.
[17] JIANG L L, ZHOU R J, ZHU J S, et al.Electricity charge saved for industrial and commercial utilizing cloud energy storage services[C]//2019 IEEE 3rd Conference on Energy Internet and Energy System Integration(EI2). Changsha, China, 2019: 1113-1116.
[18] 刘继春, 陈雪, 向月. 考虑共享模式的市场机制下售电公司储能优化配置及投资效益分析[J]. 电网技术, 2020, 44(5): 1740-1750.
LIU J C, CHEN X, XIANG Y.Optimal sizing and investment benefit analysis for energy storage of electricity retailers under market mechanisms considering shared mode[J]. Power system technology, 2020, 44(5): 1740-1750.
[19] 吴盛军, 李群, 刘建坤, 等. 基于储能电站服务的冷热电多微网系统双层优化配置[J]. 电网技术, 2021, 45(10): 3822-3829.
WU S J, LI Q, LIU J K, et al.Bi-level optimal configuration for combined cooling heating and power multi-microgrids based on energy storage station service[J]. Power system technology, 2021, 45(10): 3822-3829.
[20] HE H J, CHENG L, ZHU H, et al.Optimal capacity pricing and sizing approach of cloud energy storage: a bi-level model[C]//2019 IEEE Power & Energy Society General Meeting (PESGM). Atlanta, GA, USA, 2019: 1-5.
[21] 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.
[22] 舒康安, 艾小猛, 方家琨, 等. 基于价格引导的气电联合系统双层优化模型[J]. 电网技术, 2019, 43(1): 100-107.
SHU K A, AI X M, FANG J K, et al.Bi-level optimal model for integrated power and gas system based on price signals[J]. Power system technology, 2019, 43(1): 100-107.
[23] 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): 4403-4412.
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): 4403-4412.
[24] 陈厚合, 何旭, 姜涛, 等. 计及可中断负荷的电力系统可用输电能力计算[J]. 电力系统自动化, 2017, 41(15): 81-87, 106.
CHEN H H, HE X, JIANG T, et al.Available transfer capability calculation of power systems considering interruptible load[J]. Automation of electric power systems, 2017, 41(15): 81-87, 106.
[25] 白浩, 袁智勇, 周长城, 等. 计及新能源波动与相关性的配电网最大供电能力调度方法[J]. 电力系统保护与控制, 2021, 49(8):66-73.
BAI H, YUAN Z Y, ZHOU C C, et al.Dispatching method of maximum power supply capacity of a power distributed network considering fluctuation and correlation of renewable energy[J]. Power system protection and control, 2021, 49(8): 66-73.