Funplex: A Modified Simplex Algorithm to Efficiently Explore Near-Optimal Spaces

TL;DR

Funplex is a novel multi-objective Simplex-based algorithm that efficiently explores near-optimal solution spaces in energy system optimization, significantly reducing computational effort and improving solution quality.

Contribution

Introduces Funplex, a new Simplex-inspired algorithm that enhances MGA by minimizing redundant calculations, enabling faster and higher-quality near-optimal space exploration.

Findings

Funplex is five times faster than existing MGA methods.

It yields higher-quality near-optimal spaces.

Scales well with the number of investment variables.

Abstract

Modeling to generate alternatives (MGA) is an increasingly popular method in energy system optimization. MGA explores the near-optimal space, namely, system alternatives whose costs are within a certain fraction of the globally optimal cost. Real-world stakeholders may prefer these alternatives due to intangible factors. Nonetheless, widespread MGA adoption is hampered by its additional computational burden. Current MGA methods identify boundary points of the near-optimal space through repeated, independent optimization problems. Hundreds of model runs are usually required, and such individual runs are often inefficient because they repeat calculations or retrace previous trajectories. In this study, we transcend such limitations by introducing a novel algorithm called Funplex, which uses methods from multi-objective Simplex to optimize many MGA objectives with minimal computational redundancy. For a simple linear-programming energy hub case study, we show that Funplex is five times faster than existing methods and yields higher-quality near-optimal spaces. Furthermore, sensitivity analyses suggest that Funplex scales well with the number of investment variables, making it promising for capacity planning models. The current proof-of-concept implementation based on a full multi-objective tableau may face memory and stability limitations for large models. Nonetheless, future developments based on more advanced versions of Simplex may overcome such barriers, thereby making MGA more accessible and standard among modeling teams.