Review of Learning-Assisted Power System Optimization

Abstract

Machine learning, with a dramatic breakthrough in recent years, is showing great potential to upgrade the power system optimization toolbox. Understanding the strength and limitation of machine learning approaches is crucial to answer when and how to integrate them in various power system optimization tasks. This paper pays special attention to the coordination between machine learning approaches and optimization models, and carefully evaluates to what extent such data-driven analysis may benefit the rule-based optimization. A series of typical references are selected and categorized into four kinds: the boundary parameter improvement, the optimization option selection, the surrogate model and the hybrid model. This taxonomy provides a novel perspective to understand the latest research progress and achievements. We further discuss several key challenges and provide an in-depth comparison on the features and designs of different categories. Deep integration of machine learning approaches and optimization models is expected to become the most promising technical trend.

Source-Grid-Load Cross-Area Coordinated Optimization Model Based on IGDT and Wind-Photovoltaic-Photothermal System

“…Zhang Junbo et al [10] proposed a data-driven reactive power and voltage sequence control optimization method. Ruan Guangchun et al [11] paid special attention to the coordination between machine learning methods and optimization models and carefully evaluated how this data-driven analysis can improve rule-based optimization. Srivastava Abhishek et al [12] proposed a new meta-heuristic optimization technology based on AI: enhanced top-of-class optimization.…”