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

• URL: http://arxiv.org/abs/2208.13065v4
• Date: Sun, 7 Jul 2024 22:00:33 GMT
• Title: Towards Improving Unit Commitment Economics: An Add-On Tailor for Renewable Energy and Reserve Predictions
• Authors: Xianbang Chen, Yikui Liu, Lei Wu,
• Abstract summary: Day-ahead unit commitment (UC) is conducted in a predictthenoptimize process. This paper presents a cost-oriented tailor of RES-and-reserve predictions for UC, deployed as an add-on to the predictthenoptimize process.
• Score: 3.1512621877369433
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generally, day-ahead unit commitment (UC) is conducted in a predict-then-optimize process: it starts by predicting the renewable energy source (RES) availability and system reserve requirements; given the predictions, the UC model is then optimized to determine the economic operation plans. In fact, predictions within the process are raw. In other words, if the predictions are further tailored to assist UC in making the economic operation plans against realizations of the RES and reserve requirements, UC economics will benefit significantly. To this end, this paper presents a cost-oriented tailor of RES-and-reserve predictions for UC, deployed as an add-on to the predict-then-optimize process. The RES-and-reserve tailor is trained by solving a bi-level mixed-integer programming model: the upper level trains the tailor based on its induced operating cost; the lower level, given tailored predictions, mimics the system operation process and feeds the induced operating cost back to the upper level; finally, the upper level evaluates the training quality according to the fed-back cost. Through this training, the tailor learns to customize the raw predictions into cost-oriented predictions. Moreover, the tailor can be embedded into the existing predict-then-optimize process as an add-on, improving the UC economics. Lastly, the presented method is compared to traditional, binary-relaxation, neural network-based, stochastic, and robust methods.

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