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Integration of an Energy Management Tool and Digital Twin for Coordination and Control of Multi-vector Smart Energy Systems

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Document pages: 27 pages

Abstract: As Internet of Things (IoT) technologies enable greater communication betweenenergy assets in smart cities, the operational coordination of various energynetworks in a city or district becomes more viable. Suitable tools are neededthat can harness advanced control and machine learning techniques to achieveenvironmental, economic and resilience objectives. In this paper, an energymanagement tool is presented that can offer optimal control, scheduling,forecasting and coordination services to energy assets across a district,enabling optimal decisions under user-defined objectives. The tool presentedhere can coordinate different sub-systems in a district to avoid the violationof high-level system constraints and is designed in a generic fashion to enabletransferable use across different energy sectors. The work demonstrates thepotential for a single open-source optimisation framework to be applied acrossmultiple energy vectors, providing local government the opportunity to managedifferent assets in a coordinated fashion. This is shown through case studiesthat integrate low-carbon communal heating for social housing with electricvehicle charge-point management to achieve high-level system constraints andlocal government objectives in the borough of Greenwich, London. The paperillustrates the theoretical methodology, the software architecture and thedigital twin-based testing environment underpinning the proposed approach.

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