A community energy management system for smart microgrids

Abstract

Community micro-grid energy projects are needed to drive de-carbonisation and increase equity of energy systems among displaced communities. However, micro-grid solutions are often inflexible and lack functionality to respond to displaced community energy needs and ensure the long-term sustainability of interventions. This paper explores the use of fog-computing retrofit architectures deployed on community micro-grid infrastructures to enable flexible demand management to improve service delivery and longevity. A micro-services solution is proposed that decouples components increasing resilience and testability while allowing hybrid edge-cloud deployments. The architecture is outlined and demonstrated for a micro-grid providing energy to two nurseries and a playground in Kigeme refugee camp, Rwanda. To enact the community priorities within the demand management system, modified Genetic Algorithm (GA) methods are outlined and tested for different use-case scenarios. The performance of the GA methods are then compared with a pre-existing battery protect controller and an alternative deterministic (space-shared) energy manager model. To achieve a high utility function in almost every use-case, a search space GA method was required for GA to outperform both the existing battery protect controller and proposed deterministic method. The results show how simple community priorities can be set and used to enact control on the system in 24h timeframes that are in line with the local decision-making context.