Authors: N. Verba; P. Rodolfo Baldivieso Monasterios; E. Morris; G. Konstantopoulos; E. Gaura; and S. McArthur
Published in: 15th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES) At: Cologne, Germany - Link
Date Published: September 2020
Abstract:
With an increase in the adoption of renewable energy sources and storage, microgrids and smart local energy systems have made considerable headway in recent years. The unreliable nature of renewable resources and the high costs of storage infrastructure, however, pose challenges to designers. Research in distributed control, multi-agent systems and advanced analytics can aid in improving the efficiency and reliability of microgrids. However, the integration of these services and technologies into a single system is difficult due to their isolated nature and varying preference in protocols and platforms. This paper proposes a flexible fog computing-based, distributed deployment and virtualisation architecture that solves some of the integration challenges while offering increased flexibility and scalability. This architecture is implemented and deployed on an existing UKRI-funded microgrid demonstrator and evaluated on its ability to integrate the control, energy pricing, and analytics elements as well as on the extended features it offers to the microgrid.
Keywords: Smart local energy systems, fog computing, integration, flexibility, scalability, distributed control, multi-agent systems, data to knowledge chains
Insights for EnergyREV:
Open technical specifications for the frameworks, technologies and protocols used in SLES are required to support and speed up innovation. Traditional solutions for managing operational data will not be able to cope with the velocity, volume and variety of data generated by modern energy systems that require cleaning, processing, storing and curation to support on-line decision making, whole systems modelling, forecasting and reporting. Digital Platforms that enable companies to deploy fast, large scale interlinked processing, with fit-for-purpose model training and validation elements are needed to ensure that SLES pipelines are monitored, robust, understandable and can adapt to changes in requirements and setup.