An Independent Energy Storage Electricity Insurance Service Model Based on Asymmetric Nash Bargaining Game

GONG Jinxia; DONG Jiangqun

doi:10.19725/j.cnki.1007-2322.2024.0065

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

An Independent Energy Storage Electricity Insurance Service Model Based on Asymmetric Nash Bargaining Game

更新时间：2026-06-02

- An Independent Energy Storage Electricity Insurance Service Model Based on Asymmetric Nash Bargaining Game
- Vol. 43, Issue 3, Pages: 578-588(2026)
- 作者机构：
  
  上海电力大学电气工程学院, 上海市杨浦区,200090
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2024.0065
  CLC： 3
- Published：2026
- 稿件说明：
移动端阅览
GONG Jinxia, DONG Jiangqun. An Independent Energy Storage Electricity Insurance Service Model Based on Asymmetric Nash Bargaining Game[J]. 2026, 43(3): 578-588.
DOI：

GONG Jinxia, DONG Jiangqun. An Independent Energy Storage Electricity Insurance Service Model Based on Asymmetric Nash Bargaining Game[J]. 2026, 43(3): 578-588. DOI： 10.19725/j.cnki.1007-2322.2024.0065.

摘要

为促进独立储能(independent energy storage

IES)参与电力市场，基于“收益堆叠”的思想，提出一种新商业模式：IES在电能量市场套利的同时，向微电网运营商、风力发电商等市场参与者出售电量保险服务，建立基于非对称纳什议价博弈的IES电量保险服务模型。首先，分别建立新商业模式下IES、风力发电商和微电网运营商的电量保险服务模型，以增加IES的收益流，降低风力发电商和微电网运营商的偏差惩罚成本。然后，采用非对称纳什议价博弈为IES的电量保险服务定价，将不考虑电量保险服务的传统商业模式下IES、风力发电商和微电网运营商的收益作为谈判破裂点，保证电量保险服务的可行性，并基于交易的保险电量量化各主体的议价能力，激发各主体参与电量保险服务的积极性。最后，通过算例验证模型的有效性。

Abstract

To promote the participation of independent energy storage (IES) in the electricity market

we put forward a new business model grounded in the idea of "revenue stacking". This model enalbes IES to engage in arbitrage within the electricity market and offer electricity insurance service to microgrid operators

wind power generators and other market participants. Additionally

an asymmetric Nash bargaining game-based model is established for IES electricity insurance service. First

under the new business model

the electricity insurance service models of IES

wind power producers and microgrid operators

are established aiming to increase the revenue stream of IES while reducing the deviation penalty cost of wind power producers and microgrid operators. Subsequently

the asymmetric Nash bargaining game is employed to determine the price of the electricity insurance service of IES. The revenues of IES

wind power producers and microgrid operators under the traditional business model are taken as the negotiation benchmark to ensure the feasibility of the electricity insurance service

which does not take into accout the power insurance service. Moreover

the bargaining power of each subject based on the traded insured power is quantified to stimulate the motivation of the subjects to participate in the electricity insurance service. Finally

the validity of the model is verified through examples.

关键词

Keywords

references

邹春蕾. 独立储能可参与电力市场试点[N]. 中国电力报, 2023-06-13(002).

XIAO Yunpeng, ZHANG Lan, ZHANG Xuan, et al . The coordinated market clearing mechanism for spot electric energy and regulating ancillary service incorporating independent energy storage resources[J ] . Proceedings of the CSEE, 2020, 40(S1): 167−180(in Chinese).

PRAKASH K, ALI M, SIDDIPUE M N I, et al . A review of battery energy storage systems for ancillary services in distribution grids: Current status, challenges and future directions[J ] . Frontiers in Energy Research, 2022, 10(3389): 1−32.

SEWARD W, QADRDAN M, JENKINS N. Revenue stacking for behind the meter battery storage in energy and ancillary services markets[J]. Electric Power Systems Research, 2022, 211(108292): 1−9.

WANG Kai, YAN Xiaohe, JIANG Kai, et al . Bidding strategy and regulation method for the unified wind/photovoltaic/energy storage power stations in electricity spot market considering carbon trading[J ] . Proceedings of the CSEE, 2023, 43(18): 7091−7102 (in Chinese).

CHEN Kemeng, XIAO Xi, TIAN Peigen, et al . A comprehensive optimization method for planning and operation of building integrated photovoltaic energy storage system[J ] . Proceedings of the CSEE, 2023, 43(13): 5001−5011(in Chinese).

苏南. 电力辅助服务将成储能主要盈利渠道[N]. 中国能源报, 2023-05-01(2).

CUI Hantao, LI Fangxing, FANG Xin, et al . Bilevel arbitrage potential evaluation for grid-scale energy storage considering wind power and LMP smoothing effect[J ] . IEEE Transactions on Sustainable Energy, 2018, 9(2): 707−718.

ZHAO Dongwei, JAFARi M, BOTTERUD A, et al . Strategic energy storage investments: A case study of the CAISO electricity market[J ] . Applied Energy, 2022, 325(119909): 1−16.

DONG Jun, LÜ Shiyao, ZHU Yuan, et al . Research on wind power energy storage joint optimization operation under the double detailed rules assessment taking into account the benefits of green certificate[J ] . Sustainability, 2023, 15(1): 431.

WANG Qian, ZHANG Xueguang, XU Dianguo. A joint planning method of wind-storage system considering endogenous and exogenous uncertain[J]. Proceedings of the CSEE, 2023, 43(1): 169−180(in Chinese).

DENG Jiaojiao, LI Xuxia, WANG Ziyu, et al . An energy storage allocation method for renewable energy stations based on standardized supply curve[J ] . Energy Reports, 2023, 9(1): 973−982.

MIAO Miao, LOU Suhua, ZHANG Yuanxin, et al . Research on the optimized operation of hybrid wind and battery energy storage system based on peak-valley electricity price[J ] . Energies, 2021, 14(12): 3707.

VAKILINIA I, SENGUPTA S. A coalitional cyber-insurance framework for a common platform[J]. IEEE Transactions on Information Forensics and Security, 14(6): 1526-1538.

JIA Qiangang, CHEN Sijie, LI Yiyan, et al . Deviation insurance for risk-averse wind power producers in the NORDIC power market[J ] . International Journal of Electrical Power Energy Systems, 2022, 134(107431): 1−4.

LI Guanguan, LI Qiqiang, SONG Wen, et al . Incentivizing distributed energy trading among prosumers: A general Nash bargaining approach[J ] . International Journal of Electrical Power Energy Systems, 2021, 131(107100).

GUAN Li, ZHOU Lei, LIU Hanghang, et al . Analysis of independent energy storage power station participating in power spot market mechanism and trial operation[J ] . Electric Power, 2022, 55(10): 185−190(in Chinese).

AGUIAR N, GUPTA V. An insurance contract design to boost storage participation in the electricity market[J]. IEEE Transactions on Sustainable Energy, 2021, 12(1): 543−552.

DU Jia’nan, HAN Xiaoqing, LI Tingjun, et al . Optimization strategy of multi-microgrid electric energy cooperative operation considering electricity price uncertainty and game cheating behaviors[J ] . P ower System Technology, 2022, 46(11): 4217−4227(in Chinese).

XU Yangbing, YE Shengxuan, QIN Zhiqi, et al . A coordinated optimal scheduling model with Nash bargaining for shared energy storage and multi-microgrids based on two-layer ADMM[J ] . Sustainable Energy Technologies and Assessments, 2023(56): 102996.

KIM H, LEE J, BAHRAMI S, et al . Direct energy trading of microgrids in distribution energy market[J ] . IEEE Transactions on Power Systems, 2019, 35(1): 639−651.

ZHU Zongyao, WANG Xiuli, WU Xiong, et al . Investment and operation analysis of shared energy storage considering multi-scenario planning[J ] . Automation of Electric Power Systems, 2023, 47(7): 23−31(in Chinese).

ZHAO Dongwei, WANG Hao, HUANG Jianwei, et al . Virtual energy storage sharing and capacity allocation[J ] . IEEE Transactions on Smart Grid, 2020, 11(2): 1112−1123.

WANG Kui, YU Yang, WU Chenye. Optimal electricity storage sharing mechanism for single peaked time-of-use pricing scheme[J]. Electric Power Systems Research, 2021, 195(107176): 1−13.

Views

161

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Characterization and Suppression of Sub-synchronous Oscillations in Flexible Grid-connected Offshore Wind Farm Systems Equipped With SVGs Based Improved Artificial Bee Colony Algorithm

Primary Frequency Regulation Strategy for a Wind-storage-joint System Based on LSTM Ultra-short-term Power Generation Prediction

An Optimal Scheduling Method for Source-load-storage Systems Considering Carbon Trading Cost and Forecast Uncertainty

An Optimization Method of Communication Device Operation and Maintenance Management for Low-carbon Smart Parks

A Resilience-enhanced Multi-agent Framework for Power Systems Based on Proximal Policy Optimization

Related Author

No data

Related Institution

No data

AI问答

⁰