考虑不确定性的风-光-储合作联盟参与含需求响应的主辅联合市场的竞价交易模型

清洁能源逐渐成为电力市场的竞争主体，考虑风光出力不确定性和探究高效的市场运行机制成为电力现货市场建设进程中的关键问题。采用非参数核密度估计法和多元Gaussian-Copula函数生成典型出力场景来描述风光出力的不确定性和时空相关性。上层模型基于纳什谈判理论最大化风-光-储合作联盟收益，采用麻雀搜索算法优化风光发电商的报价策略。下层模型以购电总成本最小为目标，设计了多主体参与含价格型需求响应的主辅联合市场的出清机制。最后分析了风光出力的典型场景和市场出清结果的合理性。通过算例验证了该优化模型能够提升合作个体的收益，降低了购电总成本和负荷的峰谷差。

Bidding transaction model of wind-solar-storage cooperative alliance participating in the main and auxiliary joint market with demand response considering uncertainty

Clean energy has gradually become the main body of competition in the electricity market. Considering the uncertainty of the wind and photovoltaic output and exploring an efficient market operation mechanism become the key issues in the construction process of the electricity spot market. To describe the uncertainty and spatiotemporal correlation of wind and photovoltaic output, the nonparametric kernel density estimation method and multivariate Gaussian Copula function are used to generate typical output scenarios. The upper model is based on the Nash negotiation theory to maximize the revenue of a wind-solar-storage cooperative alliance. The sparrow search algorithm is adopted to optimize the bidding strategy of the wind and photovoltaic producers. The lower model aims to minimize the total cost of electricity purchase. A clearing mechanism for multi-agent participation in the main and auxiliary joint market with the price-based demand response is designed. Finally, the rationality of a typical scenario of the wind and photovoltaic output and the market clearing results are analyzed. The simulation results demonstrate that the optimization model can improve the income of cooperative individuals, and reduce the total power purchase cost and the peak valley difference of load.