【摘要】 高比例新能源系统面临多时间尺度功率广域失衡问题，储能凭借其快速功率响应与能量时移能力，已逐渐成为电网调节的重要资源。但储能参与电能量与调频市场的联合机制尚不健全，其跨市场协同决策交易方法仍显不足。提出了储能在电能量-调频市场联合下的协同决策交易模型。首先，梳理了储能参与电能量与调频市场的交易模式；其次，构建了上层通过优化报价决策，实现储能电站收益最大化、下层实现电能量市场与调频市场联合出清的交易模型；然后，给出了决策交易模型的求解方法：上层采用遗传算法进行报价策略寻优，下层基于线性规划实现市场出清，并通过迭代反馈机制实现整体优化；最后，通过算例验证所提模型与方法，结果表明，所提模型通过电能量市场与调频市场的联合出清机制，为储能提供了跨市场协同的决策交易框架，实现了容量资源在两类市场间的优化配置与收益最大化。更多还原

【Abstract】 High-proportion renewable energy systems face the challenge of wide-area power imbalances across multiple time scales. Energy storage, with its fast power response and energy time-shifting capability, has gradually become an important resource for grid regulation. However, the joint mechanism for energy storage to participate in the energy market and the frequency regulation market is still not well established, and the cross-market collaborative decision-making trading method remains insufficient. This paper proposes a collaborative decision-making and trading model for energy storage in the joint electricity energy and frequency regulation market. Firstly, the trading modes for energy storage participating in the energy market and the frequency regulation market are analyzed. Secondly, a trading model is constructed, where the upper level maximizes the revenue of energy storage stations through optimal bidding decisions, and the lower level achieves joint clearing of the energy market and the frequency regulation market. Then, the solution method for the decision-making and trading model is given: the upper level uses a genetic algorithm for bidding strategy optimization, the lower level uses linear programming for market clearing, and overall optimization is achieved through an iterative feedback mechanism. Finally, a case study verifies the proposed model and method. The results show that the proposed model, through the joint clearing mechanism of the energy market and the frequency regulation market, provides a cross-market collaborative decision-making trading framework for energy storage, achieving optimal allocation of capacity resources between the two markets and maximizing revenue.更多还原