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Generally, system operators conduct the economic operation of power systemsin an open-loop predict-then-optimize process: the renewable energy source(RES) availability and system reserve requirements are first predicted; giventhe predictions, system operators solve optimization models such as unitcommitment (UC) to determine the economical operation plans accordingly.However, such an open-loop process could essentially compromise the operationeconomics because its predictors myopically seek to improve the immediatestatistical prediction errors instead of the ultimate operation cost. To thisend, this paper presents a closed-loop predict-and-optimize framework, offeringa prescriptive UC to improve the operation economics. First, a bilevelmixed-integer programming model is leveraged to train cost-oriented predictorstailored for optimal system operations: the upper level trains the RES andreserve predictors based on their induced operation cost; the lower level, withgiven predictions, mimics the system operation process and feeds the inducedoperation cost back to the upper level. Furthermore, the embeddability of thetrained predictors grants a prescriptive UC model, which simultaneouslyprovides RES-reserve predictions and UC decisions with enhanced operationeconomics. Finally, numerical case studies using real-world data illustrate thepotential economic and practical advantages of prescriptive UC overdeterministic, robust, and stochastic UC models.

Towards Improving Operation Economics: A Bilevel MIP-Based Closed-Loop Predict-and-Optimize Framework for Prescribing Unit Commitment