Generally, system operators conduct the economic operation of power systems in an open-loop predict-then-optimize process: the renewable energy source (RES) availability and system reserve requirements are first predicted; given the predictions, system operators solve optimization models such as unit commitment (UC) to determine the economical operation plans accordingly. However, such an open-loop process could essentially compromise the operation economics because its predictors myopically seek to improve the immediate statistical prediction errors instead of the ultimate operation cost. To this end, this paper presents a closed-loop predict-and-optimize framework, offering a prescriptive UC to improve the operation economics. First, a bilevel mixed-integer programming model is leveraged to train cost-oriented predictors tailored for optimal system operations: the upper level trains the RES and reserve predictors based on their induced operation cost; the lower level, with given predictions, mimics the system operation process and feeds the induced operation cost back to the upper level. Furthermore, the embeddability of the trained predictors grants a prescriptive UC model, which simultaneously provides RES-reserve predictions and UC decisions with enhanced operation economics. Finally, numerical case studies using real-world data illustrate the potential economic and practical advantages of prescriptive UC over deterministic, robust, and stochastic UC models.

翻译：通常，系统运营商以开环式的先预测后优化流程进行电力系统的经济运行：首先预测可再生能源（RES）可用性和系统备用需求；基于这些预测，系统运营商求解如机组组合（UC）等优化模型，以确定相应的经济运行计划。然而，这种开环流程本质上可能损害运行经济性，因为其预测器短视地追求改善即时的统计预测误差，而非最终运行成本。为此，本文提出一种闭环预测与优化框架，通过提供规范性机组组合（UC）来提升运行经济性。首先，利用双层混合整数规划模型训练面向最优系统运行的代价导向预测器：上层基于RES和备用预测诱发的运行成本进行训练；下层在给定预测条件下模拟系统运行过程，并将由此产生的运行成本反馈至上层。此外，训练所得预测器的可嵌入性赋予规范性UC模型以增强的运行经济性，使其能够同时提供RES-备用预测和UC决策。最后，基于真实数据的数值算例展示了规范性UC相较于确定性、鲁棒性和随机性UC模型潜在的经济与实际优势。