Generally, system operators conduct the economic operation of power systems in an open-loop predict-then-optimize process: the renewable energy source (RES) availability and system reserve requirements are first predicted; given the predictions, system operators solve optimization models such as unit commitment (UC) to determine the economical operation plans accordingly. However, such an open-loop process could essentially compromise the operation economics because its predictors myopically seek to improve the immediate statistical prediction errors instead of the ultimate operation cost. To this end, this paper presents a closed-loop predict-and-optimize framework, offering a prescriptive UC to improve the operation economics. First, a bilevel mixed-integer programming model is leveraged to train cost-oriented predictors tailored for optimal system operations: the upper level trains the RES and reserve predictors based on their induced operation cost; the lower level, with given predictions, mimics the system operation process and feeds the induced operation cost back to the upper level. Furthermore, the embeddability of the trained predictors grants a prescriptive UC model, which simultaneously provides RES-reserve predictions and UC decisions with enhanced operation economics. Finally, numerical case studies using real-world data illustrate the potential economic and practical advantages of prescriptive UC over deterministic, robust, and stochastic UC models.
翻译:一般来说,系统运营商通过开环的预测-优化过程来进行电力系统的经济运行:首先预测可再生能源 (RES) 的可用性和系统备用需求;在给定预测的情况下,系统运营商通过求解优化模型(如单位组合 (UC))来相应地确定经济运行计划。然而,这种开环过程可能会从根本上妥协运行经济性,因为其预测器随机地寻求改进即时的统计预测误差而非终极的运行成本。为此,本文提出了一种闭环预测优化框架,提供一种规定的UC,以改进运行经济性。首先,利用双层混合整数规划模型为最优系统运行量身定制成本导向的预测器:上层基于引发操作费用的RES和备用预测器进行训练;下层,在给定预测的情况下,模拟系统运行过程并将引发的操作费用反馈给上层。此外,受训预测器的嵌入性使其具备规定UC模型的能力,通过增强运行经济性同时提供RES-备用预测和UC决策。最后,使用真实世界数据进行的数值案例研究说明了规定UC相比确定性、鲁棒性和随机UC模型的潜在经济和实际优势。