Transmission Topology Optimization using accelerated MapElites
For power system operators, this work addresses the runtime and scalability bottlenecks of transmission topology optimization, enabling practical deployment.
The paper presents a GPU-accelerated MapElites algorithm for transmission topology optimization that runs in under 15 minutes, generating diverse candidate solutions on the Pareto front. The approach is being evaluated by two European TSOs and the code is open-sourced.
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.