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Generally, day-ahead unit commitment (UC) is conducted in apredict-then-optimize process: it starts by predicting the renewable energysource (RES) availability and system reserve requirements; given thepredictions, the UC model is then optimized to determine the economic operationplans. In fact, predictions within the process are raw. In other words, if thepredictions are further tailored to assist UC in making the economic operationplans against realizations of the RES and reserve requirements, UC economicswill benefit significantly. To this end, this paper presents a cost-orientedtailor of RES-and-reserve predictions for UC, deployed as an add-on to thepredict-then-optimize process. The RES-and-reserve tailor is trained by solvinga bi-level mixed-integer programming model: the upper level trains the tailorbased on its induced operating cost; the lower level, given tailoredpredictions, mimics the system operation process and feeds the inducedoperating cost back to the upper level; finally, the upper level evaluates thetraining quality according to the fed-back cost. Through this training, thetailor learns to customize the raw predictions into cost-oriented predictions.Moreover, the tailor can be embedded into the existing predict-then-optimizeprocess as an add-on, improving the UC economics. Lastly, the presented methodis compared to traditional, binary-relaxation, neural network-based,stochastic, and robust methods.

Towards Improving Unit Commitment Economics: An Add-On Tailor for Renewable Energy and Reserve Predictions