Large-scale Grid Optimization: The Workhorse of Future Grid Computations

TL;DR

This paper reviews recent advancements in large-scale power grid optimization methods, highlighting the dominance of physics-based approaches and emerging data-driven techniques, and identifies existing gaps in the field.

Contribution

It systematically analyzes recent developments in transmission and distribution grid optimization, emphasizing the shift towards data-driven methods and identifying research gaps.

Findings

Physics-based methods dominate large-scale grid optimization

Data-driven, especially physics-constrained, methods are emerging alternatives

Identified gaps in current research and industry applications

Abstract

Purpose: The computation methods for modeling, controlling and optimizing the transforming grid are evolving rapidly. We review and systemize knowledge for a special class of computation methods that solve large-scale power grid optimization problems. Summary: Large-scale grid optimizations are pertinent for, amongst other things, hedging against risk due to resource stochasticity, evaluating aggregated DERs' impact on grid operation and design, and improving the overall efficiency of grid operation in terms of cost, reliability, and carbon footprint. We attribute the continual growth in scale and complexity of grid optimizations to a large influx of new spatial and temporal features in both transmission (T) and distribution (D) networks. Therefore, to systemize knowledge in the field, we discuss the recent advancements in T and D systems from the viewpoint of mechanistic physics-based and emerging data-driven methods. Findings: We find that while mechanistic physics-based methods are leading the science in solving large-scale grid optimizations, data-driven techniques, especially physics-constrained ones, are emerging as an alternative to solve otherwise intractable problems. We also find observable gaps in the field and ascertain these gaps from the paper's literature review and by collecting and synthesizing feedback from industry experts.