论文标题
旨在改善单位承诺经济学：可再生能源和预测预测的附加量身定制者
Towards Improving Unit Commitment Economics: An Add-On Tailor for Renewable Energy and Reserve Predictions
论文作者
论文摘要
通常，日前的单位承诺（UC）是在预测的过程中进行的：它首先预测可再生能源（RES）可用性和系统储备要求；鉴于预测，然后对UC模型进行了优化以确定经济运营计划。实际上，该过程中的预测是原始的。换句话说，如果对预测进行进一步量身定制，以帮助UC制定经济运营计划，以应对RES和储备要求的实现，则UC经济学将大大受益。为此，本文介绍了对UC的Res and Reserve预测的量身定制的量身定制的，该量身定制为预测的过程中的附加组件。通过解决双层混合智能编程模型来训练Res and Reserve裁缝：高层根据其诱导的运营成本训练裁缝；较低级别的量身定制的预测，模仿系统操作过程，并将诱导的运营成本恢复到上层；最后，高层根据美联储的成本评估训练质量。通过这项培训，裁缝学会了将原始预测定制为面向成本的预测。此外，裁缝可以嵌入到现有的预测过程中，然后将其作为附加组件，从而改善UC经济学。最后，将提出的方法与传统的，二进制 - 放松，基于神经网络，随机和健壮方法进行了比较。
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.
