Physics-Informed Digital Twin and Safe Reinforcement Learning for Rural Microgrid Energy Management
Physics-Informed Digital Twin and Safe Reinforcement Learning for Rural Microgrid Energy Management
Description
Details
Context and Problem Statement
Rural microgrids combine intermittent solar generation, limited storage, and uncertain loads. Rule-based control is often suboptimal, while unconstrained reinforcement learning may violate voltage or battery-aging limits.
Research Question
How can strict electrical and battery-aging constraints be guaranteed throughout training and operation of a learned energy-management policy?
Proposed Approach
Formulate a constrained MDP, develop a digital twin combining power-flow and battery-degradation models, apply safe reinforcement learning with infeasible-action projection, and validate through hardware-in-the-loop simulation.
Expected Contribution
A comparison among rule-based control, model predictive control, and safe RL in terms of operating cost, load shedding, battery degradation, and constraint violations.
Expected Prototype
An embedded or PLC-deployable controller connected to the digital twin with a supervision interface for state of charge, forecasts, and safety margins.
Datasets
PVGIS, NASA POWER, open load profiles, and laboratory microgrid measurements.
Challenges
Simulation-to-hardware mismatch, battery-aging modeling, limited local load profiles, and sensor-fault robustness.
Research Question
Innovation
Expected Deliverable
Technologies
Required Skills
- Electrical Engineering and Power Systems
- Reinforcement Learning
- Numerical Simulation and Modeling
- Embedded Programming
Datasets
- PVGIS (European Commission)
- NASA POWER
- Measurements from a laboratory microgrid testbed