Transmission Topology Optimization using accelerated MapElites

TL;DR

This paper introduces a GPU-accelerated, end-to-end transmission topology optimization method using MapElites, achieving rapid Pareto front generation and open-sourcing the code for operational planning.

Contribution

It presents a novel GPU-based optimization approach with a fully GPU-native DC loadflow solver and MapElites, enabling fast, scalable transmission topology optimization.

Findings

Optimization runs in under 15 minutes.

The approach is under evaluation by European TSOs.

Code is open-source at github.com/eliagroup/ToOp.

Abstract

Transmission Topology Optimization has great potential to improve efficiency and flexibility of grid operations through non-costly switching actions, but previous approaches struggle with runtime performance and scalability. In this work, we present an optimization approach that leverages GPU acceleration to speed up computations. In a genetic algorithm setting, topologies are randomly mutated and evaluated in parallel for multiple optimization criteria. Combined with a fully GPU-native DC loadflow solver, there is no CPU-GPU data transfer required in the DC optimization loop. Using a variant of the illumination algorithm MapElites, we efficiently generate a set of diverse candidate solutions on the pareto front. Together with an importing and AC validation step, we present an end-to-end optimization solution that runs in under 15 minutes. The approach is currently under evaluation by operational planning operators in two European TSOs. We furthermore open-source our code at github.com/eliagroup/ToOp.