电力现货市场和需求响应市场中具有储能系统的负荷集成商的风险意识协调交易策略

iEnergy Pub Date : 2025-03-19 DOI:10.23919/IEN.2025.0004
Ziyang Xiang;Chunyi Huang;Kangping Li;Chengmin Wang;Pierluigi Siano
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引用次数: 0

摘要

需求响应(DR)市场作为电力现货市场的重要补充,在唤起用户侧调节能力以缓解极端事件期间系统级供需失衡方面发挥着关键作用。虽然DR市场为负荷聚合商(LA)提供了电力现货市场之外的额外盈利机会,但由于用户行为的重大不确定性,它也引入了新的交易风险。调度储能系统是提高电网风险管理能力的有效手段;然而,协调ESS业务与双市场交易战略仍然是一个紧迫的挑战。为此,本文提出了一种新颖的LA同时参与日前电力现货市场和DR市场的系统风险意识协同交易模型,该模型将ESS基于市场清算规则的容量分配机制纳入到双市场中,共同制定竞价和定价决策。首先,分析了洛杉矶参与双重市场的内在耦合特征,并提出了一个整合ESS设施的竞价定价联合优化框架。其次,基于价格反应机制建立了不确定用户反应模型,并采用考虑反应不足和反应过度的实际市场结算规则来计算交易收入,其中可能的收入损失通过条件风险值来量化。第三,通过引入这些条款和ESS的容量分配机制,构建了LA的风险感知随机协调交易模型,并推导了风险与收益权衡的二元模型中的竞价和定价策略。案例仿真结果验证了所提出的交易策略在控制交易风险和提高交易收益方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A risk-aware coordinated trading strategy for load aggregators with energy storage systems in the electricity spot market and demand response market
The demand response (DR) market, as a vital complement to the electricity spot market, plays a key role in evoking user-side regulation capability to mitigate system-level supply-demand imbalances during extreme events. While the DR market offers the load aggregator (LA) additional profitable opportunities beyond the electricity spot market, it also introduces new trading risks due to the significant uncertainty in users' behaviors. Dispatching energy storage systems (ESSs) is an effective means to enhance the risk management capabilities of LAs; however, coordinating ESS operations with dual-market trading strategies remains an urgent challenge. To this end, this paper proposes a novel systematic risk-aware coordinated trading model for the LA in concurrently participating in the day-ahead electricity spot market and DR market, which incorporates the capacity allocation mechanism of ESS based on market clearing rules to jointly formulate bidding and pricing decisions for the dual market. First, the intrinsic coupling characteristics of the LA participating in the dual market are analyzed, and a joint optimization framework for formulating bidding and pricing strategies that integrates ESS facilities is proposed. Second, an uncertain user response model is developed based on price-response mechanisms, and actual market settlement rules accounting for under- and over-responses are employed to calculate trading revenues, where possible revenue losses are quantified via conditional value at risk. Third, by imposing these terms and the capacity allocation mechanism of ESS, the risk-aware stochastic coordinated trading model of the LA is built, where the bidding and pricing strategies in the dual model that trade off risk and profit are derived. The simulation results of a case study validate the effectiveness of the proposed trading strategy in controlling trading risk and improving the trading income of the LA.
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