Large-Scale Linear Energy System Optimization: A Systematic Review on Parallelization Strategies via Decomposition

TL;DR

This paper systematically reviews parallelization strategies for large-scale linear energy system optimization models, highlighting the diversity of methods, lack of standard benchmarks, and providing recommendations for future research and software tools.

Contribution

It introduces a classification scheme for energy system models, reviews various parallel decomposition methods, and proposes standards for benchmarking and reporting in the field.

Findings

No single parallelization approach is universally best.

Standardized benchmarks are lacking, hindering method comparison.

Software tools for parallel decomposition vary in capabilities.

Abstract

As renewable energy integration, sector coupling, and spatiotemporal detail increase, energy system optimization models grow in size and complexity, often pushing solvers to their performance limits. This systematic review explores parallelization strategies that can address these challenges. We first propose a classification scheme for linear energy system optimization models, covering their analytical focus, mathematical structure, and scope. We then review parallel decomposition methods, finding that while many offer performance benefits, no single approach is universally superior. The lack of standardized benchmark suites further complicates comparison. To address this, we recommend essential criteria for future benchmarks and minimum reporting standards. We also survey available software tools for parallel decomposition, including modular frameworks and algorithmic abstractions. Though centered on energy system models, our insights extend to the broader operations research field.