AI-Enabled Digital Twin for Local Energy Systems and Markets

We want to develop an AI-enabled Digital Twin for local energy systems and markets to improve the design, operation, and participation of distributed energy resources in future energy systems.

The Digital Twin will combine real-world energy data, market rules, and system constraints to simulate local energy flows, flexibility mechanisms, and interactions between producers, consumers, and prosumers. The objective is to support evidence-based decision-making for local energy markets, energy communities, aggregators, and public authorities.

The concept builds on practical experience with local energy markets, demand-side flexibility, and socio-technical energy systems, translating these into dynamic, explainable AI models rather than static analyses. The Digital Twin is envisioned as a tool for testing scenarios related to market design, flexibility incentives, renewable integration, and system resilience at local and regional levels.

We are seeking collaboration with research organisations, universities, SMEs, technology providers, DSOs, aggregators, municipalities, and energy communities with expertise in areas such as:

Artificial intelligence and data analytics for energy systems

Digital twins and simulation of energy markets

Local energy markets, flexibility, and demand response

Energy communities and citizen participation

Regulatory, market, and governance aspects of energy transition