面向电力市场的分布式储能聚合参与电网调峰

doi:10.12028/j.issn.2095-4239.2018.0227

储能科学与技术 ›› 2019, Vol. 8 ›› Issue (2): 276-283.doi: 10.12028/j.issn.2095-4239.2018.0227

面向电力市场的分布式储能聚合参与电网调峰

林立乾¹, 米增强¹, 贾雨龙¹, 范辉², 杜鹏¹

1 华北电力大学新能源电力系统国家重点实验室, 河北保定 071003;
2 国网河北省电力有限公司, 河北石家庄 050000

收稿日期:2018-11-20 修回日期:2018-12-20 出版日期:2019-03-01 发布日期:2018-12-20
作者简介:林立乾(1995-),男,硕士研究生,研究方向为分布式储能及系统调度运行,E-mail:1021912400@qq.com
基金资助:
国家电网公司科技项目项目（KJGW2018-014）；中央高校基本科研业务费专项资金项目（2018QN075）

Distributed energy storage aggregation for power grid peak shaving in a power market

LIN Liqian¹, MI Zengqiang¹, JIA Yulong¹, FAN Hui², DU Peng¹

1 State Key Laboratory of Alternate Electrical Power System with Renewable Energy Source, North China Electric Power University, Baoding 071003, Hebei, China;
2 State Grid Hebei Electric Power Company, Shijiazhuang 050000, Hebei, China

Received:2018-11-20 Revised:2018-12-20 Online:2019-03-01 Published:2018-12-20

摘要/Abstract

摘要： 随着新一轮电力市场化改革的深入，为了促进分布式储能有效参与电网调峰，提出了对其规模化聚合管理的思路；建立了分布式储能聚合商以竞价形式参与电网调峰调度的优化模型。在日前调度中，聚合商根据电力交易中心公布的次日调峰需求，通过对分布式储能行为特性预测，并按阶梯报价策略参与竞价；电力交易中心以最小化调峰调度成本为目标优化调度计划。在实时调度中，考虑了储能行为特性日前预测误差和电池损耗，聚合商以最大化自身利益为目标优化充放电出力，使得聚合商在满足日前中标出力的同时，利用市场电价差获利。仿真算例表明，分布式储能聚合商以竞价形式参与电网调度既能减少电网调峰调度成本，还可以达到削峰填谷的效果，储能设备的损耗成本是影响充放电出力的关键因素。

关键词: 电力市场, 分布式储能, 聚合商, 调峰, 调度模型

Abstract: With the new round of power market in-depth reform, we propose an concept of large-scale aggregation management and establish an optimization model for distributed energy storage aggregation providers to participate in power grid peaking scheduling in the form of bidding. In the day-to-day scheduling, the aggregation providers participate in the bidding by predicting the characteristics of distributed energy storage system behavior according to the next-day peaking demand announced by the power trading center; the power trading center optimizes the dispatch plan with a goal of minimizing the peaking scheduling cost. In the real-time scheduling, the aggregation providers optimize the charge and discharge outputs with a goal of maximizing its own interests considering the energy storage system characteristics of the previous prediction error and losses so that the aggregation providers could profit from the market electricity price while satisfying the previous successful bid. Simulation examples show that distributed energy storage aggregation providers participating in the grid dispatching could reduce the cost of peak shaving scheduling and achieve the effect of peak shaving in the form of bidding. The loss cost of energy storage system is the key factor affecting the outputs of charge and discharge.

Key words: electricity market, distributed energy storage, aggregation provider, peak shaving, scheduling model

中图分类号:

TM73

林立乾, 米增强, 贾雨龙, 范辉, 杜鹏. 面向电力市场的分布式储能聚合参与电网调峰[J]. 储能科学与技术, 2019, 8(2): 276-283.

LIN Liqian, MI Zengqiang, JIA Yulong, FAN Hui, DU Peng. Distributed energy storage aggregation for power grid peak shaving in a power market[J]. Energy Storage Science and Technology, 2019, 8(2): 276-283.

参考文献

[1] 向红吉, 戴朝华, 明杰, 等. 考虑低谷时刻负调峰能力及风电预测区间的多目标机组组合优化研究[J]. 电网技术, 2017, 41(6):1912-1918. XIANG Hongji, DAI Chaohua, MING Jie, et al. Research on multi-objective optimization of unit commitment considering negative peak load regulation ability in valley load period and wind power prediction interval[J]. Power System Technology, 2017, 41(6):1912-1918.
[2] 刘英军, 刘畅, 王伟, 等. 储能发展现状与趋势分析[J]. 中外能源, 2017, 22(4):80-88. LIU Yingjun, LIU Chang, WANG Wei, et al. Analysis of development status and trend of energy storage technology[J]. Sino-Global Energy, 2017, 22(4):80-88.
[3] 陈海生, 刘畅, 齐智平. 分布式储能的发展现状与趋势[J]. 中国科学院院刊, 2016, 31(2):224-231. CHEN Haisheng, LIU Chang, QI Zhiping. Developing trend and present status of distributed energy storage[J]. Bulletin of Chinese Academy of Sciences, 2016, 31(2):224-231.
[4] 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 Jianlin, MA Huimeng, YUAN Xiaodong, et al. overview on key applied technologies of large-scale distributed energy storage[J]. Power System Technology, 2017, 41(10):3365-3375.
[5] 孙玲玲, 高赐威, 谈健, 等. 负荷聚合技术及其应用[J]. 电力系统自动化, 2017, 41(6):159-167. SUN Lingling, GAO Ciwei, TAN Jian, et al. Load aggregation technology and its applications[J]. Automation of Electric Power Systems, 2017, 41(6):159-167.
[6] 杨永标, 颜庆国, 徐石明, 等. 公共楼宇空调负荷参与电网虚拟调峰的思考[J]. 电力系统自动化, 2015, 39(17):103-107. YANG Yongbiao, YAN Qingguo, XU Shiming, et al. Thinking of public building air-conditioning load participating in grid with virtual peak clipping[J]. Automation of Electric Power Systems, 2015, 39(17):103-107.
[7] 刘萌, 梁雯, 张岩, 等. 计及空调负荷群控制的源-荷协同优化调度模型[J]. 电网技术, 2017, 41(4):1230-1236. LIU Meng, LIANG Wen, ZHANG Yan, et al. Cooperative generation load optimal dispatching model considering air-conditioning load group control[J]. Power System Technology, 2017, 41(4):1230-1236.
[8] 李作锋, 范洁, 杨永标, 等. 面向电网调峰辅助服务的规模化空调负荷优化调度研究[J]. 电器与能效管理技术, 2018(2):64-72. LI Zuofeng, FAN Jie, YANG Yongbiao, et al. Research on large-scale air-conditioning load optimal dispatch for power grid peak clipping[J]. Electrical & Energy Management Technology, 2018(2):64-72.
[9] LIU H, GE S. Optimization of TOU price of electricity based on Electric Vehicle orderly charge[C]//Power and Energy Society General Meeting, IEEE, 2013:1-5.
[10] 沈瑜, 岳园园, 闫华光, 等. 地区电网需求响应资源聚合与调控策略研究[J]. 电网技术, 2017, 41(10):3341-3347.SHEN Yu, YUE Yuanyuan, YAN Huaguang, et al. Research on aggregation and optimization strategies of demand response resources for district power grid[J]. Power System Technology, 2017, 41(10):3341-3347.
[11] 杨铮, 彭思成, 廖清芬, 等. 面向综合能源楼宇的电动汽车辅助服务方案[J]. 电网技术, 2017, 41(9):2831-2839. YANG Zheng, PENG Sicheng, LIAO Qingfen, et al. ancillary services provided by electric vehicles for building integrated energy system[J]. Power System Technology, 2017, 41(9):2831-2839.
[12] 高赐威, 李倩玉, 李扬. 基于DLC的空调负荷双层优化调度和控制策略[J]. 中国电机工程学报, 2014, 34(10):1546-1555. GAO Ciwei, LI Qianyu, LI Yang. Bi-level optimal dispatch and control strategy for air-conditioning load based on direct load control[J]. Proceedings of the CSEE, 2014, 34(10):1546-1555.
[13] KARA E C, MACDONALD J S, BLACK D, et al. Estimating the benefits of electric vehicle smart charging at non-residential locations:A data-driven approach[J]. Applied Energy, 2015, 155:515-525.
[14] 曹瑛, 刘建锋, 龚锦霞. 面向负荷聚合商的风光消纳调度优化模型[J]. 可再生能源, 2018, 36(4):563-567. CAO Ying, LIU Jianfeng, GONG Jinxia. Optimization model for wind and solar power accommodation based on load aggregator[J]. Renewable Energy Resources, 2018, 36(4):563-567.
[15] HE H, SANANDAJI B M, POOLLA K, et al. Aggregate flexibility of thermostatically controlled loads[J]. IEEE Transactions on Power Systems, 2015, 30(1):189-198.
[16] LIU B, WANG L, JIN Y H, et al. Improved particle swarm optimization combined with chaos[J]. Chaos Solitons & Fractals, 2005, 25(5):1261-1271.
[17] 郭玉天. 含分布式电源的配电网重构研究[D]. 保定:华北电力大学, 2013. GUO Tianyu. Research on the reconfiguration in distribution network with distributed generations[D]. Baoding:North China Electric Power University, 2013.
[18] PJM energy market data[EB/OL].[2018-11-12]. https://www.pjm.com/markets-and-operations/energy.aspx.
[19] BENINI M, CANEVESE S, CIRIO D, et al. Battery energy storage systems for the provision of primary and secondary frequency regulation in Italy[C]//IEEE International Conference on Environment & Electrical Engineering, IEEE, 2016:1-6.
[20] WHITE C D, ZHANG K M. Using vehicle-to-grid technology for frequency regulation and peak-load reduction[J]. Journal of Power Sources, 2011, 196(8):3972-3980.

面向电力市场的分布式储能聚合参与电网调峰

Distributed energy storage aggregation for power grid peak shaving in a power market

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