EnergyTwin: A Multi-Agent System for Simulating and Coordinating Energy Microgrids

TL;DR

EnergyTwin is an innovative multi-agent simulation platform that integrates physical models with forecast-based planning and negotiations to improve microgrid management and resilience.

Contribution

It introduces a novel agent-based microgrid simulation environment coupling physical models with forecast-informed planning and negotiations.

Findings

Forecast-driven planning increases local energy self-sufficiency.

Higher battery reserves are maintained with the new approach.

Reduces exposure to low-resilience operating states.

Abstract

Microgrids are deployed to reduce purchased grid energy, limit exposure to volatile tariffs, and ensure service continuity during disturbances. This requires coordinating heterogeneous distributed energy resources across multiple time scales and under variable conditions. Among existing tools, typically, power-system simulators capture physical behaviour but assume centralized control, while multi-agent frameworks model decentralized decision-making but represent energy with no physical grounding. In this context, the EnergyTwin is introduced, an agent-based microgrid simulation environment that couples physically grounded models with forecast-informed, rolling-horizon planning, and negotiations. Each asset is modeled as an agent, interacting with a central agent that obtains forecasts, formulates predictions, and allocates energy through contract-based interactions. EnergyTwin targets tertiary-layer decision making and is extensible for digital-twin use. Its feasibility was evaluated in a university campus microgrid scenario where multiple planning strategies were compared. Achieved results show that forecast-driven rolling-horizon planning increases local energy self-sufficiency, maintains higher battery reserves, and reduces exposure to low-resilience operating states. They demonstrate also potential of EnergyTwin as platform supporting research on resilient, negotiation-driven microgrids.