PhD in AI-driven Fair Energy Curtailment Policies

## PhD in AI-driven Fair Energy Curtailment Policies

As a PhD candidate, you will help design AI-driven control algorithms that allow thousands of distributed energy resources to coordinate locally while ensuring the grid remains stable, efficient, and fair. You will explore probabilistic curtailment policies, develop scalable multi-agent decision-making methods, and work with real-world data to create solutions that can be deployed in tomorrow's electricity systems.

### Key Responsibilities

• Review literature on congestion management in modern distribution networks
• Analyze distributed optimization methods (consensus-based, ADMM-type, multi-agent MPC)
• Develop probabilistic curtailment policies grounded in fairness theory
• Design fairness-aware distributed controllers with scalability and robustness
• Model uncertainty in renewable generation (PV output, demand fluctuations)
• Evaluate robustness of control policies under forecasting errors and communication delays
• Implement multi-agent coordination mechanisms for local decision-making
• Simulate large-scale distribution networks using realistic feeder models
• Collaborate with energy system experts across departments
• Validate methods on real-world data from Dutch DSOs

### Requirements

• Master's degree in Electrical Engineering, Mechanical Engineering, Mathematics, or Physics
• Research-oriented attitude with curiosity
• Ability to work in interdisciplinary teams
• Motivated to develop teaching skills and coach students
• Fluent in spoken and written English (C1 level)