A Day-ahead Market Bidding Strategy for Independent Energy Storage Systems Considering Conditional Value at Risk

TIAN Zihao; FENG Donghan; ZHOU Yun; LIU Qiyuan; LAN Li; FU Chen

doi:10.19725/j.cnki.1007-2322.2024.0128

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A Day-ahead Market Bidding Strategy for Independent Energy Storage Systems Considering Conditional Value at Risk

更新时间：2026-10-19

- A Day-ahead Market Bidding Strategy for Independent Energy Storage Systems Considering Conditional Value at Risk
- Vol. 43, Issue 5, Pages: 919-930(2026)
- 作者机构：
  
  1. 电力传输与功率变换控制教育部重点实验室(上海交通大学), 上海市闵行区,200240
  2. 国网上海市电力公司经济技术研究院, 上海市徐汇区,200233
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2024.0128
  CLC： 5
- Published：2026
- 稿件说明：
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TIAN Zihao, FENG Donghan, ZHOU Yun, et al. A Day-ahead Market Bidding Strategy for Independent Energy Storage Systems Considering Conditional Value at Risk[J]. 2026, 43(5): 919-930.
DOI：

TIAN Zihao, FENG Donghan, ZHOU Yun, et al. A Day-ahead Market Bidding Strategy for Independent Energy Storage Systems Considering Conditional Value at Risk[J]. 2026, 43(5): 919-930. DOI： 10.19725/j.cnki.1007-2322.2024.0128.

摘要

随着新能源高速发展与储能市场化进程不断推进，为了应对新能源发电与负荷的不确定性给储能市场交易带来的风险，保障储能市场收益，提出一种考虑条件风险价值的独立储能日前市场申报策略。首先，构建独立储能参与日前电能量与调频辅助服务市场的模式。其次，构建储能市场申报双层优化模型，上层以最大化储能运营商市场收益为目标，引入条件风险价值(conditional value-at-risk，CVaR)作为风险管理手段，满足不同风险偏好储能运营商的决策需求，下层以市场购电成本最小化为目标进行联合市场出清。然后，利用对偶理论与卡罗需–库恩–塔克(Karush-Kuhn-Tucker，KKT)最优性条件对模型进行转化并求解。最后，采用某地区实际数据，结合改进 IEEE 30节点对模型进行验证，通过仿真结果验证所提方法的有效性与实用性。

Abstract

With the rapid development of new energy and the continuous advancement of energy storage (ES) marketization

an independent energy storage day-ahead market bidding strategy considering conditional value-at-risk (CVaR) is proposed. This approach aims to address the risks arising from the uncertainties in new energy power generation and load on the energy storage market transactions

thereby ensuring the profitability of energy storage. Firstly

the framework is established for independent energy storage to participate in the day-ahead energy and frequency regulation markets. Secondly

a dual-layer optimization model for the day-ahead market bidding strategy is constructed. The upper layer aims to maximize the market revenue of ES operators

incorporating CVaR as a risk management method to meet the decision-making needs of ES operators with varying risk preferences. The lower layer aims to minimize power purchase cost for joint market clearing. Subsequently

the dual theory and the Karush-Kuhn-Tucker (KKT) optimality conditions are utilized to solve the model. Finally

the actual data from a certain region are used to verify the model with the improved IEEE 30-bus system

and the effectiveness and practicality of the proposed method are confirmed by simulation results.

关键词

Keywords

references

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