论文标题
可行性层协助机器学习方法
Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operations
论文作者
论文摘要
预先完成的操作涉及一个复杂且计算密集的优化过程，以确定发电机的承诺时间表和派遣。优化过程是一个混合企业线性程序（MILP），也称为安全受限的单位承诺（SCUC）。独立的系统操作员（ISOS）每天运行SCUC，需要最新的算法来加快流程。可以利用历史信息中的现有模式来减少SCUC的模型，这可以节省大量时间。在本文中，研究了基于机器学习（ML）的分类方法，即逻辑回归，神经网络，随机森林和K-Nearest邻居，以减少SCUC模型。然后，使用可行性层（FL）和后处理技术来帮助ML，以确保高质量的解决方案。提出的方法在多个测试系统上进行了验证，即IEEE 24总线系统，IEEE-73总线系统，IEEE 118-BUS系统，500个总线系统和波兰2383-BUS系统。此外，使用可再生生成的改良IEEE 24总线系统，证明了随机SCUC（SSCUC）的模型降低。仿真结果表明，高训练精度可以识别承诺时间表，而FL和后处理确保ML预测不会导致溶液质量损失最小的不可行的解决方案。
Day-ahead operations involves a complex and computationally intensive optimization process to determine the generator commitment schedule and dispatch. The optimization process is a mixed-integer linear program (MILP) also known as security-constrained unit commitment (SCUC). Independent system operators (ISOs) run SCUC daily and require state-of-the-art algorithms to speed up the process. Existing patterns in historical information can be leveraged for model reduction of SCUC, which can provide significant time savings. In this paper, machine learning (ML) based classification approaches, namely logistic regression, neural networks, random forest and K-nearest neighbor, were studied for model reduction of SCUC. The ML was then aided with a feasibility layer (FL) and post-process technique to ensure high-quality solutions. The proposed approach is validated on several test systems namely, IEEE 24-Bus system, IEEE-73 Bus system, IEEE 118-Bus system, 500-Bus system, and Polish 2383-Bus system. Moreover, model reduction of a stochastic SCUC (SSCUC) was demonstrated utilizing a modified IEEE 24-Bus system with renewable generation. Simulation results demonstrate a high training accuracy to identify commitment schedule while FL and post-process ensure ML predictions do not lead to infeasible solutions with minimal loss in solution quality.
