基于分布鲁棒模型预测控制的微电网多时间尺度协同优化调度

源荷多元不确定性给“源荷储”一体化微电网优化调度带来了诸多挑战，但传统方案存在优化模型过于片面极端和时间尺度单一导致调度不合理的问题，无法兼顾可靠性和经济性，并且难以协调不确定性分析方法与不同时间尺度之间的配合关系。基于数据驱动的多离散场景分布鲁棒方法，提出了一种微电网两阶段分布鲁棒日前优化调度模型，使用列和约束生成算法进行求解。结合改进分布鲁棒优化的不确定方法、多时间尺度调度策略和模型预测控制理论，通过逐级细化调度时间尺度和减小预测周期的长度来提高精度，以最小化发电成本以及调节成本等为目标建立了日前-日内多时间尺度滚动优化调度模型，在较大程度上抵抗了系统不确定性因素的影响。结合算例仿真分析，进一步说明了所提模型能够有效消纳新能源、降低运行成本并且兼顾安全经济。

Multi-time scale cooperative optimal scheduling of microgrid based on distributed robust model predictive control

The multi-uncertainty of source and load poses significant challenges to the optimal scheduling of '' source-load-storage '' integrated microgrid. However, a limitation of the traditional optimization model is its one-sidedness and use of a single time scale, which can result in suboptimal scheduling outcomes. Striking a balance between reliability and economy presents a considerable obstacle, as does coordinating the relationship between uncertainty analysis methods and varying time scales. Based on the data-driven multi-discrete scene distribution robust method, a two-stage distributed robust day-ahead optimal scheduling model of microgrid is proposed, which is solved by column and constraint generation algorithm. By combining the improved distribut-ed robust optimization uncertainty method with a multi-time scale scheduling strategy and model predictive control theory, the accuracy of the scheduling can be enhanced. This is achieved through the gradual refinement of the scheduling time scale and reduction of the prediction period length. The day-ahead-day multi-time scale rolling optimization scheduling model is established to minimize the generation cost and adjustment cost, while also exhibiting a high degree of resilience to system uncertainties. Combined with the simulation analysis of the example, the proposed model has demonstrated advantages in incorporating new energy sources, reducing oper-ating costs, and balancing considerations of safety and economy.