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

Source: arXiv - 2511.20590v1

Overview

The paper introduces EnergyTwin, a novel multi‑agent simulation platform that blends physically accurate power‑system models with AI‑driven, forecast‑based planning and negotiation. By treating every distributed resource (PV panels, batteries, loads, etc.) as an autonomous agent that talks to a central coordinator, EnergyTwin enables realistic “what‑if” studies of microgrid operation across multiple time horizons—something that traditional simulators or pure agent‑based tools struggle to do.

Key Contributions

• Hybrid simulation engine that couples detailed physical grid models (e.g., power flow, state‑of‑charge dynamics) with a multi‑agent decision layer.
• Rolling‑horizon planning: agents receive short‑term forecasts (weather, demand, market prices) and continuously re‑optimize their schedules.
• Contract‑based negotiation protocol allowing agents to request, offer, and trade energy in a decentralized fashion while a central “market” agent enforces feasibility.
• Extensible digital‑twin architecture: plug‑in new asset models, forecasting services, or optimization algorithms without rewriting the core engine.
• Empirical validation on a realistic university campus microgrid, comparing several planning strategies (baseline, forecast‑only, negotiation‑enhanced) and quantifying gains in self‑sufficiency and resilience.

Methodology

1. Agent Modeling – Each physical component (solar inverter, battery storage, flexible load, diesel generator, etc.) is wrapped in an autonomous software agent exposing its capabilities (charging limits, generation forecasts, flexibility windows).
2. Central Coordinator – A “grid‑operator” agent aggregates external forecasts (weather, load, market tariffs) and runs a rolling‑horizon optimizer that produces tentative energy allocations for the next planning window (e.g., 15 min steps over 24 h).
3. Negotiation Loop – Agents receive the provisional contracts, evaluate them against their internal constraints, and can propose counter‑offers (e.g., ask for more charge, sell excess PV). The coordinator iterates until a feasible agreement is reached or a timeout occurs.
4. Physical Simulation – The agreed schedules are fed into a power‑system simulator (based on standard load‑flow equations) that updates voltages, line flows, and battery SOC, ensuring that the “digital twin” respects real physics.
5. Evaluation Scenario – The authors built a campus‑scale microgrid model (≈ 2 MW peak) with realistic PV generation, battery capacity, and stochastic demand. They ran three experiments: (a) no forecasting, (b) forecast‑driven planning only, and (c) full forecast + negotiation. Metrics such as self‑consumption ratio, battery reserve margin, and occurrence of low‑resilience states were recorded.

Results & Findings

Key takeaways

• Rolling‑horizon forecasts alone already boost self‑consumption and keep batteries healthier.
• Adding a lightweight negotiation step yields a further ~7 % jump in self‑sufficiency and cuts risky low‑reserve periods by more than 80 %.
• The platform can evaluate “what‑if” policies (e.g., aggressive demand response) without sacrificing physical realism.

Practical Implications

• Microgrid developers can prototype control strategies (price‑responsive loads, peer‑to‑peer energy trading) in a sandbox that respects real power‑flow constraints, shortening the gap between simulation and field trials.
• Utility planners gain a tool to assess how distributed storage and renewable forecasts affect grid import peaks, helping design tariff structures or incentive programs.
• Edge‑computing & IoT teams can integrate their own forecasting services (ML models on weather stations, demand predictors) via the open API, turning EnergyTwin into a living digital twin for ongoing operations.
• Software vendors can build plug‑ins (e.g., blockchain‑based contract settlement, reinforcement‑learning agents) on top of the existing negotiation framework, accelerating innovation in decentralized energy markets.
• Resilience engineering: By quantifying low‑resilience states, operators can set safety thresholds and trigger automated fallback actions (e.g., load shedding) before a blackout occurs.

Limitations & Future Work

• Scalability: The current prototype was tested on a ~2 MW campus; performance on city‑scale or national‑level microgrids (thousands of agents) remains to be demonstrated.
• Forecast quality dependency: Gains hinge on reasonably accurate weather/demand forecasts; the authors note degradation when forecast errors exceed 15 %.
• Simplified market model: Contracts are bilateral and assume perfect compliance; future work could incorporate stochastic participant behavior, penalties, or blockchain‑based settlement.
• Hardware‑in‑the‑loop validation: The study is purely simulation‑based; integrating real hardware controllers (e.g., actual BMS units) would strengthen confidence for field deployment.

Authors

• Jakub Muszyński
• Ignacy Walużenicz
• Patryk Zan
• Zofia Wrona
• Maria Ganzha
• Marcin Paprzycki
• Costin Bădică

Paper Information

• arXiv ID: 2511.20590v1