Dynamic communication topologies for distributed heuristics in energy system optimization algorithms

TL;DR

This paper introduces a dynamic communication topology method for distributed energy system optimization heuristics, adapting during runtime using simulated annealing to improve solution quality and convergence.

Contribution

It proposes a novel adaptive topology approach based on simulated annealing and compares it to static topologies in energy system optimization contexts.

Findings

Adaptive topology improves optimization performance

Correlations found between landscape properties and performance

Dynamic approach outperforms static configurations

Abstract

The communication topology is an essential aspect in designing distributed optimization heuristics. It can influence the exploration and exploitation of the search space and thus the optimization performance in terms of solution quality, convergence speed and collaboration costs, all relevant aspects for applications operating critical infrastructure in energy systems. In this work, we present an approach for adapting the communication topology during runtime, based on the principles of simulated annealing. We compare the approach to common static topologies regarding the performance of an exemplary distributed optimization heuristic. Finally, we investigate the correlations between fitness landscape properties and defined performance metrics.