Digital Twin Architecture and Energy Management Strategies for Microgrid-Based Energy Communities

Abstract

The research develops a conceptual design, software architecture, and rule-based control algorithms for a cloud-based digital twin system applied to a microgrid-based renewable energy community. Operating as a tertiary control layer, the proposed cloud-based platform is designed to support Level 5 autonomous operation within the digital twin maturity framework by enabling automated real-time data integration and system optimization.

The system mirrors the physical infrastructure of a seven-house microgrid within a multi-layered digital domain. Utilizing the C4 model abstraction framework, the software architecture defines interactions between core Community Sizing, Forecast, Market, and Energy Management System (EMS) platforms. Building Information Modeling (BIM) principles provide the visual foundation via Autodesk Revit, integrated seamlessly through the Autodesk Viewer. Functional data pipelines are mapped to circulate continuous sensor telemetry and external information across Apache Kafka, Spark, and Microsoft Azure cloud PaaS ecosystems.

The operational logic governing the energy community is executed via sequential Python-based algorithm flows. The UML abstraction framework is used to formalize operational states, process sequences, and execution conditions within the software architecture. Real-time rule-based dispatch strategies regulate self-consumption, surplus distribution, load supply, Battery Energy Storage System (BESS) management, and grid interactions, combined with day-ahead energy arbitrage planning. The system dynamically adjusts energy distribution flows in 15-minute time slots to correct discrepancies between forecasted and actual demand.