考虑可再生能源不确定性的风-光-储-蓄多时间尺度联合优化调度

提出一种计及可再生能源不确定性的风电、光伏、电化学储能和变速抽水蓄能电站多能互补协同的优化调度方法。考虑电化学储能和变速抽水蓄能电站的互补调节特性，建立风-光-储-蓄联合运行的多时间尺度调度架构。在日前调度阶段，考虑风电和光伏出力的相关性，生成基于生成式对抗网络和峰值密度聚类算法的日前风光联合出力典型场景，综合考虑风光出力的不确定性和抽水蓄能电站与电化学储能的容量特性，建立面向调峰需求的随机优化日前调度模型；在日内优化阶段，以最小化弃风弃光与电网备用电能为目标，构建变速抽水蓄能电站出力修正方法，制定电化学储能日内调度计划；在实时校正阶段，基于模型预测控制方法，以日内优化调度结果为参考，对电化学储能出力进行精确控制，最小化风光预测误差的影响。算例分析结果表明，所提方法可以有效减小电网负荷峰谷差，平抑风光联合出力波动，提升可再生能源消纳率。

Multi-time scale joint optimal scheduling for wind-photovoltaic-electrochemical energy storage-pumped storage considering renewable energy uncertainty

An optimal scheduling method for multi-energy complementary collaboration of wind power, photovoltaic, electrochemical energy storage and variable-speed pumped storage power station is proposed considering the uncertainty of renewable energy. Considering the complementary adjustment characteristic of electrochemical energy storage and variable-speed pumped storage power station, a multi-time scale scheduling framework for wind-photovoltaic-electrochemical energy storage-pumped storage joint operation is proposed. In the day-ahead scheduling stage, considering the correlation between wind power and photovoltaic outputs, typical scenarios of day-ahead wind power and photovoltaic joint output are generated based on generative adversarial network and density peak clustering algorithm, a peak-shaving oriented stochastic optimization day-ahead scheduling model is built comprehensively considering the uncertainty of wind power and photovoltaic outputs and capacity characteristic of pumped storage power station and electrochemical energy storage. In the intra-day optimization stage, a correction method of variable-speed pumped storage power station output is constructed with the minimum wind power and photovoltaic curtailment as its object, and an intra-day scheduling strategy of electrochemical energy storage is built. In the real-time correction stage, based on model predictive control method, the electrochemical energy storage output is accurately controlled taking the intra-day optimal scheduling results as the reference, which minimizes the impact of wind power and photovoltaic forecasting errors. The case analysis results show that the proposed method can effectively decrease the peak-valley load difference of power grid, stabilize the fluctuation of wind power and photovoltaic joint output, and promote the accommodation rate of renewable energy.