A Multi-Grade Pricing Strategy for Distributed Energy Storage Considering Default Risks of Customized Power Services

doi:10.16183/j.cnki.jsjtu.2023.481

Abstract

Abstract:

To address the problems in the profit model and transaction pricing of distributed energy storage providing multiple customized power services for sensitive customers, a multi-grade pricing strategy for distributed energy storage to provide various customized power services is proposed, including reactive power compensation, voltage sag control, and harmonic control. First, based on the four-quadrant operation characteristics of energy storage converter, a multi-grade evaluation indicator system of customized power services is established considering the differentiated user demands for power quality. Then, a cost-to-capacity model is developed for energy storage to provide customized power services in different power quality standards. Next, by taking economic loss of power quality into account, a user customized power utility function is established with individual rational constraint. Afterwards, considering power quality default risk and investment cost constraints, a customized power revenue model of distributed energy storage is constructed. Furthermore, a multi-grade trading framework for distributed energy storage to provide differentiated customized power services and its multi-grade pricing optimization strategy are proposed. Finally, in order to obtain the optimal additional tariff and user purchase package for premium power, the nonlinear multi-grade pricing model is transformed into a mixed integer linear programming model for optimization by using the big M method and transforming the user utility function into a constraint. The comparative analysis of the algorithm demonstrates that the proposed strategy can reduce the annual cost of customized power services for users while simultaneously enhancing the energy storage revenue.

Key words: distributed energy storage, customized power, conditional value-at-risk, multi-grade pricing, mixed integer programming

CLC Number:

TM714

FANG Jun, HE De, PEI Zhigang, PENG Zhihui, BAO Jieying, LIU Weikang, ZHOU Bin. A Multi-Grade Pricing Strategy for Distributed Energy Storage Considering Default Risks of Customized Power Services[J]. Journal of Shanghai Jiao Tong University, 2025, 59(9): 1359-1369.

Figures/Tables 11

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References 30

[1]	国家能源局. 2022年光伏发电建设运行情况[EB/OL]. (2023-02-17)[2023-09-18]. http://www.nea.gov.cn/2023-02/17/c1310698128.htm?eqid=94f1dd940000b94a00000003643f4c9b.
	National Energy Administration. Construction and operation of photovoltaic power generation in 2022[EB/OL]. (2023-02-17)[2023-09-18]. http://www.nea.gov.cn/2023-02/17/c1310698128.htm?eqid=94f1dd940000b94a00000003643f4c9b.
[2]	国家发展改革委,国家能源局. 关于印发《“十四五”新型储能发展实施方案》的通知[EB/OL]. (2022-01-29)[2023-09-18]. https://www.gov.cn/zhengce/zhengceku/2022-03/22/content_5680417.htm.
	National Development and Reform Commission, National Energy Administration. Notice on the issuance of the “14th Five-Year Plan” new energy storage development implementation plan[EB/OL]. (2022-01-29)[2023-09-18]. https://www.gov.cn/zhengce/zhengceku/2022-03/22/content_5680417.htm.
[3]	李迁, 姜欣, 张钧钊, 等. 规模化储能参与电力现货市场的商业模式[J]. 上海交通大学学报, 2022, 57(12): 1543-1558.
	LI Qian, JIANG Xin, ZHANG Junzhao, et al. Business models of large-scale energy storage system to participate in electricity spot market[J]. Journal of Shanghai Jiao Tong University, 2022, 57(12): 1543-1558.
[4]	SANG L W, XU Y L, LONG H, et al. Electricity price prediction for energy storage system arbitrage: A decision-focused approach[J]. IEEE Transactions on Smart Grid, 2022, 13(4): 2822-2832.
[5]	OLSEN D J, KIRSCHEN D S. Profitable emissions-reducing energy storage[J]. IEEE Transactions on Power Systems, 2020, 35(2): 1509-1519.
[6]	陈丽娟, 吴甜恬, 柳惠波, 等. 基于需量管理的两阶段大用户储能优化模型[J]. 电力系统自动化, 2019, 43(1): 194-200.
	CHEN Lijuan, WU Tiantian, LIU Huibo, et al. Demand management based two-stage optimal storage model for large users[J]. Automation of Electric Power Systems, 2019, 43(1): 194-200.
[7]	严干贵, 刘莹, 段双明, 等. 电池储能单元群参与电力系统二次调频的功率分配策略[J]. 电力系统自动化, 2020, 44(14): 26-34.
	YAN Gangui, LIU Ying, DUAN Shuangming, et al. Power distribution strategy for battery energy storage unit group participating in secondary frequency regulation of power system[J]. Automation of Electric Power Systems, 2020, 44(14): 26-34.
[8]	CAO Y, ZHOU B, CHUNG C Y, et al. Dynamic modelling and mutual coordination of electricity and watershed networks for spatio-temporal operational flexibility enhancement under rainy climates[J]. IEEE Transactions on Smart Grid, 2023, 14(5): 3450-3464.
[9]	CHEN C M, LI Y, QIU W Q, et al. Cooperative game based day-ahead scheduling for local integrated energy systems with shared energy storage[J]. IEEE Transactions on Sustainable Energy, 2022, 13(4): 1994-2011.
[10]	吕祥梅, 刘天琪, 刘绚, 等. 考虑高比例新能源消纳的多能源园区日前低碳经济调度[J]. 上海交通大学学报, 2021, 55(12): 1586-1597. doi: 10.16183/j.cnki.jsjtu.2021.339
	LÜ Xiangmei, LIU Tianqi, LIU Xuan, et al. Low-carbon economic dispatch of multi-energy park considering high proportion of renewable energy[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1586-1597.
[11]	刘晓忠, 杨鹏, 杨哲, 等. 基于用户需求层级的增量配电网电能质量定价方法[J]. 电气工程学报, 2020, 15(3): 135-142.
	LIU Xiaozhong, YANG Peng, YANG Zhe, et al. Pricing method of power quality based on user demand levels in incremental distribution networks[J]. Journal of Electrical Engineering, 2020, 15(3): 135-142.
[12]	张青苗, 陈来军, 马恒瑞, 等. 基于主从博弈的共享储能分时电价策略[J]. 智慧电力, 2022, 50(7): 82-88.
	ZHANG Qingmiao, CHEN Laijun, MA Hengrui, et al. Time-of-use price strategy for shared energy storage based on Stackelberg game[J]. Smart Power, 2022, 50(7): 82-88.
[13]	刘娜, 武占军, 郭灵杰, 等. 引入电能质量保险机制的日前电能量市场打分出清决策[J]. 电网技术, 2022, 46(6): 2151-2164.
	LIU Na, WU Zhanjun, GUO Lingjie, et al. Day-ahead electricity energy market score clearing decision introducing power quality insurance mechanism[J]. Power System Technology, 2022, 46(6): 2151-2164.
[14]	LI J Y, XU D S, WANG J H, et al. P2P multigrade energy trading for heterogeneous distributed energy resources and flexible demand[J]. IEEE Transactions on Smart Grid, 2023, 14(2): 1577-1589.
[15]	LEE T J, HENRIQUEZ AUBA R, POOLLA B K, et al. Pricing and energy trading in peer-to-peer zero marginal-cost microgrids[J]. IEEE Transactions on Smart Grid, 2022, 13(1): 702-714.
[16]	SABER H, HEIDARABADI H, MOEINI AGHTAIE M, et al. Expansion planning studies of independent-locally operated battery energy storage systems (BESSs): A CVaR-based study[J]. IEEE Transactions on Sustainable Energy, 2020, 11(4): 2109-2118.
[17]	KISHIDA M, CETINKAYA A, Risk-aware linear quadratic control using conditional value-at-risk[J]. IEEE Transactions on Automatic Control, 2022, 68(1): 416-423.
[18]	水利电力部,国家物价局. 关于颁布《功率因数调整电费办法》的通知[S]. 北京: 水利电力部, 国家物价局, 1983.
	Ministry of Water Conservancy and Electric Power, State Administration for Commodity Prices. Notice on promulgating the “measures for power factor adjustment of electricity charges”[S]. Beijing: Ministry of Water Conservancy and Electric Power, State Administration for Commodity Prices, 1983.
[19]	中国国家标准化管理委员会. 电能质量公用电网谐波: GB/T 14549—1993[S]. 北京: 中国标准出版社, 1993.
	Standardization Administration of China. Harmonic standard for power quality public power grid: GB/T 14549—1993[S]. Beijing: Standards Press of China, 1993.
[20]	中国国家标准化管理委员会. 电能质量电压暂降与短时中断: GB/T 30137—2013[S]. 北京: 中国标准出版社, 2013.
	Standardization Administration of China. Power quality voltage sags and short interruptions: GB/T 30137—2013[S]. Beijing: Standards Press of China, 2013.
[21]	王俊, 曹建军. 优质电力市场中的特征定价方法研究[J]. 中国市场, 2022 (3): 14-16.
	WANG Jun, CAO Jianjun. Research on hedonic price method in high-quality electricity market[J]. China Market, 2022 (3): 14-16.
[22]	杨博, 王俊婷, 俞磊, 等. 基于孔雀优化算法的配电网储能系统双层多目标优化配置[J]. 上海交通大学学报, 2022, 56(10): 1294-1307. doi: 10.16183/j.cnki.jsjtu.2021.371
	YANG Bo, WANG Junting, YU Lei, et al. Peafowl optimization algorithm based bi-level multi-objective optimal allocation of energy storage systems in distribution network[J]. Journal of Shanghai Jiao Tong University, 2022, 56(10): 1294-1307.
[23]	李璟. 谐波治理措施及谐波电流计算的经验公式[J]. 价值工程, 2013, 32(11): 26-27.
	LI Jing. Harmonics control measures and emprical formula for harmonics current calculation[J]. Value Engineering, 2013, 32(11): 26-27.
[24]	吴晓飞, 戴晖, 黄晓剑, 等. 挖掘光伏无功能力的配电网无功电压协调控制策略[J]. 电力建设, 2019, 40(5): 78-89. doi: 10.3969/j.issn.1000-7229.2019.05.010
	WU Xiaofei, DAI Hui, HUANG Xiaojian, et al. Coordinated voltage control strategy of distribution network considering PV’s reactive power[J]. Electric Power Construction, 2019, 40(5): 78-89. doi: 10.3969/j.issn.1000-7229.2019.05.010
[25]	FAN L L, WANG Z Y, MIAO Z X. Large angle deviation in grid-following IBRs upon grid voltage dip[J]. IEEE Transactions on Energy Conversion, 2023, 39(1): 1-10.
[26]	ZHONG J J, LI Y, WU Y, et al. Optimal operation of energy hub: An integrated model combined distributionally robust optimization method with Stackelberg game[J]. IEEE Transactions on Sustainable Energy, 2023, 14(3): 1835-1848.
[27]	董海艳, 陈杰, 贾清泉, 等. 考虑敏感用户需求的电能质量分级与购售电策略[J]. 电力自动化设备, 2022, 42(2): 201-209.
	DONG Haiyan, CHEN Jie, JIA Qingquan, et al. Power quality gradation and power purchase and sale strategy considering sensitive users’ demand[J]. Electric Power Automation Equipment, 2022, 42(2): 201-209.
[28]	MORADIPARI A, TUCKER N, ALIZADEH M. Mobility-aware electric vehicle fast charging load models with geographical price variations[J]. IEEE Transactions on Transportation Electrification, 2021, 7(2): 554-565.
[29]	WANG Y, DENG L F, BOLLEN M H J, et al. Calculation of the point-on-wave for voltage dips in three-phase systems[J]. IEEE Transactions on Power Delivery, 2020, 35(4): 2068-2079.
[30]	张逸, 吴逸帆, 陈晶腾. 新型电力系统背景下电压暂降风险评估技术挑战与展望[J]. 电力建设, 2023, 44(2): 15-24. doi: 10.12204/j.issn.1000-7229.2023.02.002
	ZHANG Yi, WU Yifan, CHEN Jingteng. Research status of voltage sag risk assessment and prospect under the background of new power system[J]. Electric Power Construction, 2023, 44(2): 15-24. doi: 10.12204/j.issn.1000-7229.2023.02.002

等级	功率因数	3次谐波电流允许值/ [A·(MV·A)^-1]	年电压暂降事故次数
H1	≥0.85	≤0.20	≤10
H2	≥0.90	≤0.18	≤10
H3	≥0.90	≤0.16	≤5
H4	≥0.95	≤0.14	≤5
H5	≥0.95	≤0.12	≤2
H6	≥0.95	≤0.10	0

用户	年用电量/ (MW·h)	电压暂降敏感负荷/kW	功率因数	3次谐波电流/A	经济损失函数参数
用户	年用电量/ (MW·h)	电压暂降敏感负荷/kW	功率因数	3次谐波电流/A	a_j_,2	a_j_,1	L_j_,0
1	265	51	0.76	2.52	0.010 5	-0.085	0.5
2	230	31	0.73	1.39	0.012 1	-0.100	0.5
3	230	30	0.73	2.21	0.014 0	-0.135	1.05
4	270	82	0.85	1.17	0.015 1	-0.140	0.5
5	198	25	0.73	1.04	0.018 5	-0.166	0.5
6	440	115	0.85	8.98	0.018 1	-0.170	0.5
7	245	33	0.75	0.74	0.011 0	-0.095	1.05
8	189	29	0.76	1.11	0.012 0	-0.140	0.5
9	248	65	0.84	4.01	0.009 0	-0.075	0.5
10	290	81	0.84	1.90	0.009 6	-0.080	0.5

等级	附加电价/[元·(kW·h)^-1]
H1	0.048 8
H2	0.057 3
H3	0.058 7
H4	0.059 0
H5	0.061 0
H6	0.062 2

用户	购电套餐	定制电力服务费/ (元·a^-1)	减少的用电经济损失/(元·a^-1)	总效用/ (元·a^-1)
1	H4	15 635.00	45 580.00	29 944.00
2	H6	14 299.85	37 977.60	23 677.75
3	H5	14 028.69	74 750.00	60 721.31
4	H3	15 854.74	76 707.00	60 852.26
5	H5	12 076.87	73 223.37	61 146.50
6	H5	26 837.49	175 120.00	148 282.51
7	H3	14 386.71	45 570.00	31 183.29
8	H6	11 750.75	77 112.00	65 361.25
9	H3	14 562.87	35 712.00	21 149.13
10	H4	17 110.00	48 218.88	31 108.88

经济指标	金额/(元·a^-1)	收益(成本)与总收益(总成本) 之比/%
电能质量治理收益	156 542.97	41.10
峰谷套利收益	214 263.25	56.26
新能源消纳收益	10 037.46	2.64
储能定制电力总成本	136 628.56	96.13
违约风险成本	5 506.69	3.87
储能总收益	238 708.43	—