Review of learning-assisted power system optimization

With dramatic breakthroughs in recent years, machine learning is showing great potential to upgrade the toolbox for power system optimization. Understanding the strength and limitation of machine learning approaches is crucial to decide when and how to deploy them to boost the optimization performance. This paper pays special attention to the coordination between machine learning approaches and optimization models, and carefully evaluates how such data-driven analysis may improve the rule-based optimization. The typical references are selected and categorized into four groups: the boundary parameter improvement, the optimization option selection, the surrogate model, and the hybrid model. This taxonomy provides a novel perspective to elaborate the latest research progress and development. We further compare the design patterns of different categories, and discuss several key challenges and opportunities as well. Deep integration between machine learning approaches and optimization models is expected to become the most promising technical trend.

Automated summary

With recent dramatic breakthroughs, machine learning has shown great potential in upgrading the toolbox for power system optimization. Understanding the strengths and limitations of machine learning approaches is crucial in deciding when and how to deploy them to boost optimization performance. This paper focuses on the coordination between machine learning approaches and optimization models and evaluates how data-driven analysis can improve rule-based optimization.