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.