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.
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