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.

翻译：通常，日前机组组合（UC）采用“预测-优化”流程：首先预测可再生能源（RES）可用性与系统备用需求；基于这些预测，优化UC模型以确定经济运行方案。实际上，该流程中的预测数据是原始未加工的。换言之，若对预测结果进一步定制化处理，使其能辅助UC针对RES与备用需求的实际情况制定经济运行方案，UC的经济性将获得显著提升。为此，本文提出一种面向UC的、以成本为导向的RES-备用预测定制器，作为“预测-优化”流程的增强模块部署。该RES-备用定制器通过求解双层混合整数规划模型进行训练：上层根据定制器引发的运行成本训练定制器；下层在给定定制化预测后，模拟系统运行过程并将引发的运行成本反馈至上层；最终，上层依据反馈成本评估训练质量。通过此训练过程，定制器学会将原始预测转化为面向成本的预测。此外，该定制器可作为增强模块嵌入现有“预测-优化”流程，从而提升UC经济性。最后，本文所提方法与传统的、二进制松弛的、基于神经网络的、随机化及鲁棒化方法进行了对比分析。