Economic Analysis of Multi-type Energy Storages Considering the Deep Peak-Regulation of Thermal Power Units

doi:10.12204/j.issn.1000-7229.2022.01.015

Abstract

Abstract:

In the context of the large-scale access of clean energy and the increasing pressure of peak-shaving power grids, energy storage devices and deep peak-shaving thermal power units, as important adjustment resources to promote the absorption of new energy and suppress the peak-valley difference of the power grid, have been widely concerned by academia and industry. Considering the characteristics of multi-type energy storage devices and combined with the deep peak-shaving characteristics of thermal power units, this paper compares and analyzes the economic benefits of different types of energy storage devices assisting deep peak-shaving of thermal power units. Firstly, by fully considering the technical, economic characteristics and development prospects of multi-type energy storages, the most representative three kinds of energy storage systems: pumped storage, compressed-air energy storage and lithium-ion battery energy storage are selected, and the relevant system operation models are established. Secondly, considering the deep peak-shaving characteristics of thermal power units, the operation model and economic model of thermal power units are established. Thirdly, considering the life-cycle cost of multiple-type energy storage systems, a day-ahead economic dispatch model of multi-type energy storage systems considering the deep peak-shaving of thermal power units is constructed, and a comparative analysis of the economics of multi-type energy storage is carried out accordingly. Finally, on the basis of the typical daily data in a certain area, a simulation example is carried out to compare and analyze the economic benefits of multi-type energy storage power stations, and the recommendations of energy storage capacity configuration are put forward.

Key words: multi-type energy storage, thermal power unit, deep peak regulation, life cycle, economic analysis

CLC Number:

TM61

ZHANG Songyan, MIAO Shihong, YIN Binxin, YAO Fuxing, WANG Tingtao. Economic Analysis of Multi-type Energy Storages Considering the Deep Peak-Regulation of Thermal Power Units[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(1): 132-142.

Figures/Tables 13

Fig.1 Schematic diagram of advanced adiabatic compressed-air energy storage system

Table 1 Operating status of SCSM

Fig.2 Schematic diagram of peak-shaving process of thermal power units

Table 2 Cost characteristic parameters of different types of energy storage

Table 3 System operating costs in different typical days in scenarios 1 美元

Table 4 System operating costs in different scenarios 美元

Table 5 Economic benefit analysis of energy storage system in different scenarios

Fig.3 Winter operation of the system in scenario 1

Fig.4 Winter operation of the system in scenario 2

Fig.5 Winter operation of the system in scenario 3

Fig.6 Winter operation of the system in scenario 4

Table 6 Operation benefits of multi-type energy storage stations with different installed capacity

Fig.7 Variation trend of multi-type energy storage stations’ operation benefits with installed capacity

References 28

[1]	国家能源局. 国家能源局2021年一季度网上新闻发布会文字实录[EB/OL]. (2021-01-30)[2021-05-11]. http://www.nea.gov.cn/2021-01/30/c_139708580.htm.
[2]	赵福林, 俞啸玲, 杜诗嘉, 等. 计及需求响应的含大规模风电并网下电力系统灵活性评估[J]. 电力系统保护与控制, 2021, 49(1): 42-51.
	ZHAO Fulin, YU Xiaoling, DU Shijia, et al. Assessment on flexibility of a power grid with large-scale wind farm integration considering demand response[J]. Power System Protection and Control, 2021, 49(1): 42-51.
[3]	国家能源局. 国家能源局正式启动提升火电灵活性改造示范试点工作[EB/OL]. (2016-06-20)[2020-12-15]. http://www.gov.cn/xinwen/2016-06/20/content_5083818.htm.
[4]	李有亮, 谢小艳, 陈天宇, 等. 省级调峰辅助服务市场运营规则分析[J]. 中国电力企业管理, 2019, 22(8): 37-41.
	LI Youliang, XIE Xiaoyan, CHEN Tianyu, et al. Analysis of the operation rules of the provincial peak shaving auxiliary service market[J]. China Power Enterprise Management, 2019, 22(8): 37-41.
[5]	国家能源局东北电力监管局. 关于印发《东北电力辅助服务市场运营规则》的通知[EB/OL]. (2020-12-23)[2021-05-11]. http://dbj.nea.gov.cn/zwfw/zcfg/202012/t20201223_4055300.html.
[6]	傅旭, 李富春, 杨攀峰. 基于全生命周期的各类储能调峰效益比较[J]. 供用电, 2020, 37(7): 88-93, 43.
	FU Xu, LI Fuchun, YANG Panfeng. Benefits comparison of various energy storage equipment peak regulation based on the whole life cycle[J]. Distribution & Utilization, 2020, 37(7): 88-93, 43.
[7]	GONG Y, TAN C S, ZHANG Y N, et al. Peak shaving benefits assessment of renewable energy source considering joint operation of nuclear and pumped storage station[J]. Energy Procedia, 2018, 152: 953-958. doi: 10.1016/j.egypro.2018.09.099 URL
[8]	王海华, 陆冉, 曹炜, 等. 规模风电并网条件下储能系统参与辅助调峰服务容量配置优化研究[J]. 电工电能新技术, 2019, 38(6): 42-49.
	WANG Haihua, LU Ran, CAO Wei, et al. Optimal capacity allocation of energy storage system participating auxiliary peak regulation in large-scale wind power integration[J]. Advanced Technology of Electrical Engineering and Energy, 2019, 38(6): 42-49.
[9]	LI M Q, WANG Y T, TIAN Y F, et al. Deep peak shaving model of fire-storage combination under high permeability wind power conditions[C]// 2020 IEEE 3rd Student Conference on Electrical Machines and Systems (SCEMS). Jinan: IEEE, 2020: 674-679.
[10]	黎静华, 汪赛. 兼顾技术性和经济性的储能辅助调峰组合方案优化[J]. 电力系统自动化, 2017, 41(9): 44-50, 150.
	LI Jinghua, WANG Sai. Optimal combined peak-shaving scheme using energy storage for auxiliary considering both technology and economy[J]. Automation of Electric Power Systems, 2017, 41(9): 44-50, 150.
[11]	李军徽, 张嘉辉, 穆钢, 等. 储能辅助火电机组深度调峰的分层优化调度[J]. 电网技术, 2019, 43(11): 3961-3970.
	LI Junhui, ZHANG Jiahui, MU Gang, et al. Hierarchical optimization scheduling of deep peak shaving for energy-storage auxiliary thermal power generating units[J]. Power System Technology, 2019, 43(11): 3961-3970.
[12]	王静, 刘文霞, 李守强, 等. 计及机组降损收益的电源侧电池储能调频/调峰经济效益评价方法[J]. 电网技术, 2020, 44(11): 4236-4245.
	WANG Jing, LIU Wenxia, LI Shouqiang, et al. A method to evaluate economic benefits of power side battery energy storage frequency/peak regulation considering the benefits of reducing thermal power unit losses[J]. Power System Technology, 2020, 44(11): 4236-4245.
[13]	李军徽, 张嘉辉, 李翠萍, 等. 参与调峰的储能系统配置方案及经济性分析[J]. 电工技术学报, 2021, 36(19): 4148-4160.
	LI Junhui, ZHANG Jiahui, LI Cuiping, et al. Configuration scheme and economic analysis of energy storage system participating in grid peak shaving[J]. Transactions of China Electrotechnical Society, 2021, 36(19): 4148-4160.
[14]	赵书强, 吴杨, 李志伟, 等. 考虑风光出力不确定性的电力系统调峰能力及经济性分析[J/OL]. 电网技术, 10.13335/j.1000-3673.pst.2021.0814 doi: 10.13335/j.1000-3673.pst.2021.0814
	ZHAO Shuqiang, WU Yang, LI Zhiwei, et al. Analysis of power system peaking capacity and economy considering the uncertainty of wind and solar output[J/OL]. Power System Technology, 10.13335/j.1000-3673.pst.2021.0814 doi: 10.13335/j.1000-3673.pst.2021.0814
[15]	中国能源研究会储能专委会, 中关村储能产业技术联盟. 储能产业研究白皮书2021[R]. 北京: CNESA, 2021.
[16]	李姚旺, 苗世洪, 尹斌鑫, 等. 考虑先进绝热压缩空气储能电站备用特性的电力系统优化调度策略[J]. 中国电机工程学报, 2018, 38(18): 5392-5404.
	LI Yaowang, MIAO Shihong, YIN Binxin, et al. Power system optimal scheduling strategy considering reserve characteristics of advanced adiabatic compressed air energy storage plant[J]. Proceedings of the CSEE, 2018, 38(18): 5392-5404.
[17]	MONGIRD K, FOTEDAR V, VISWANATHAN V, et al. Energy storage technology and cost characterization[EB/OL].[2020-12-11]. https://www.energy.gov/sites/prod/files/2019/07/f65/Storage%20Cost%20and%20Performance%20Characterization%20Report_Final.pdf
[18]	胡泽春, 丁华杰, 孔涛. 风电-抽水蓄能联合日运行优化调度模型[J]. 电力系统自动化, 2012, 36(2): 36-41, 57.
	HU Zechun, DING Huajie, KONG Tao. A joint daily operational optimization model for wind power and pumped-storage plant[J]. Automation of Electric Power Systems, 2012, 36(2): 36-41, 57.
[19]	KOCAMAN A S, MODI V. Value of pumped hydro storage in a hybrid energy generation and allocation system[J]. Applied Energy, 2017, 205: 1202-1215. doi: 10.1016/j.apenergy.2017.08.129 URL
[20]	荆朝霞, 胡荣兴, 袁灼新, 等. 含风/光/抽水蓄能并计及负荷响应的海岛微网优化配置[J]. 电力系统自动化, 2017, 41(1): 65-72, 116.
	JING Zhaoxia, HU Rongxing, YUAN Zhuoxin, et al. Capacity configuration optimization for island microgrid with wind/solar/pumped storage considering demand response[J]. Automation of Electric Power Systems, 2017, 41(1): 65-72, 116.
[21]	蔡杰, 张松岩, 杜治, 等. 含光热集热模块的先进绝热压缩空气储能系统容量配置策略[J]. 电力自动化设备, 2020, 40(7): 165-176.
	CAI Jie, ZHANG Songyan, DU Zhi, et al. Capacity allocation strategy of advanced adiabatic compressed air energy storage system with solar thermal collector module[J]. Electric Power Automation Equipment, 2020, 40(7): 165-176.
[22]	王荔妍, 陈启鑫, 何冠楠, 等. 考虑电池储能寿命模型的发电计划优化[J]. 电力系统自动化, 2019, 43(8): 93-100.
	WANG Liyan, CHEN Qixin, HE Guannan, et al. Optimization of generation scheduling considering battery energy storage life model[J]. Automation of Electric Power Systems, 2019, 43(8): 93-100.
[23]	宾洋, 于静美, 朱英凯, 等. 实时雨流计数法及其在钴酸锂电池健康状态建模中的应用[J]. 中国电机工程学报, 2017, 37(12): 3627-3635.
	BIN Yang, YU Jingmei, ZHU Yingkai, et al. A real-time rain flow algorithm and its application to state of health modeling for LiCoO₂ lithium-ion batteries[J]. Proceedings of the CSEE, 2017, 37(12): 3627-3635..
[24]	林俐, 田欣雨. 基于火电机组分级深度调峰的电力系统经济调度及效益分析[J]. 电网技术, 2017, 41(7): 2255-2263.
	LIN Li, TIAN Xinyu. Analysis of deep peak regulation and its benefit of thermal units in power system with large scale wind power integrated[J]. Power System Technology, 2017, 41(7): 2255-2263.
[25]	林俐, 邹兰青, 周鹏, 等. 规模风电并网条件下火电机组深度调峰的多角度经济性分析[J]. 电力系统自动化, 2017, 41(7): 21-27.
	LIN Li, ZOU Lanqing, ZHOU Peng, et al. Multi-angle economic analysis on deep peak regulation of thermal power units with large-scale wind power integration[J]. Automation of Electric Power Systems, 2017, 41(7): 21-27.
[26]	尹斌鑫, 苗世洪, 李姚旺, 等. 先进绝热压缩空气储能在综合能源系统中的经济性分析方法[J]. 电工技术学报, 2020, 35(19): 4062-4075.
	YIN Binxin, MIAO Shihong, LI Yaowang, et al. Study on the economic analysis method of advanced adiabatic compressed air energy storage in integrated energy system[J]. Transactions of China Electrotechnical Society, 2020, 35(19): 4062-4075.
[27]	丁逸行, 徐青山, 吕亚娟, 等. 考虑需量管理的用户侧储能优化配置[J]. 电网技术, 2019, 43(4): 1179-1186.
	DING Yixing, XU Qingshan, LÜ Yajuan, et al. Optimal configuration of user-side energy storage considering power demand management[J]. Power System Technology, 2019, 43(4): 1179-1186.
[28]	尹斌鑫, 苗世洪, 李姚旺, 等. 考虑变寿命特性的先进绝热压缩空气储能电站容量规划模型[J]. 电工技术学报, 2020, 35(3): 612-622.
	YIN Binxin, MIAO Shihong, LI Yaowang, et al. A capacity planning model of advanced adiabatic compressed air energy storage plant considering lifetime varying characteristic[J]. Transactions of China Electrotechnical Society, 2020, 35(3): 612-622.