PhD in deploying local flexibility for collaborative congestion management

Addressing shortages in transport capacity to accommodate new renewable energy projects and large customers presents a significant challenge for Distribution System Operators (DSOs). Large-scale network reinforcement is often constrained by excessive costs, logistical complexities (e.g., limited materials and manpower), and lengthy spatial planning procedures. Therefore, optimizing the use of existing infrastructure through congestion management to increase hosting capacity, defer network reinforcement, and distribute investments more efficiently over time is crucial.

This project focuses on leveraging flexible assets connected to low-voltage grids—such as hybrid heat pumps, electric vehicles, and distributed energy generation—using a non-discriminatory, market-based mechanism to resolve congestion in medium-voltage distribution grids. Small-scale resources are typically represented by aggregators, who serve as proxies to provide congestion management services to the DSO through well-defined products and procedures.

Despite extensive research in this area, two key challenges remain:

• Individual aggregators may not control sufficient resources in a given network area to meet congestion management product specifications (e.g., capacity, duration).
• The reliability of responses may be limited due to factors like customer overrides or forecasting errors, leading to financial and technical risks for both aggregators and DSOs.

To address these challenges, this project proposes the collaborative offering of congestion management services from multiple aggregators managing small-scale flexibility both within and across congestion areas, in both pre-emptive and real-time scenarios. A suitable market-based mechanism must be developed, characterized, and compared to alternative solutions to evaluate its effectiveness.

As part of this PhD project, you will:

• Develop a novel market-based mechanism to enable collaboration between multiple aggregators without compromising system balance.
• Investigate the impact of this mechanism on the decision-making processes of the DSO.
• Compare various technical and market-based congestion management strategies.
• Analyze the reliability and effectiveness of the proposed mechanism under conditions of uncertainty and quantify the technical and financial risks to stakeholders.

From a technical standpoint, your work will involve developing and solving large-scale, multi-objective, multi-level optimization problems under uncertainty, accounting for both network operations and customer behavioral models. You will employ both mathematical programming and machine learning techniques to create scalable decision-support methodologies.

This PhD position is part of the EuroTech PhD program at TU/e in collaboration with the Technical University of Denmark (DTU), which you are expected to visit for an extended research stay. The PhD position is co-financed by the Eindhoven Institute for Renewable Energy Systems under the focus area 'System Transition and Scenarios'. You will primarily contribute to Intelligent Energy Systems research activities of the Electrical Energy Systems group at the Department of Electrical Engineering under the supervision of dr. Nikolaos Paterakis. Besides research, you will also have the opportunity to contribute to education within the department. Apart from supervising BSc and MSc students in their research projects, other assistance in education, e.g. in bachelor courses, is usually limited to around 5% of your contract time. The co-supervisors at DTU are dr. Charalampos Ziras and dr. Tilman Weckesser at the Department of Wind and Energy Systems in the Section of Distributed Energy Systems.

Requirements:

• A master's degree (or an equivalent university degree) in Electrical Engineering, Operations Research, Applied Mathematics, or a similar field. You can also apply before completing your program.
• Good programming skills are necessary. Experience in developing optimization models and algorithms in Python or Julia is an advantage.
• A research-oriented attitude.
• Fluent in spoken and written English.

You must possess a comprehensive knowledge of power systems and electricity markets. You must have a proven ability to develop and apply quantitative decision-making methods (e.g., mathematical programming or data-driven approaches), as evidenced by a relevant MSc thesis or publications. Furthermore, familiarity with topics such as congestion management, flexibility provision, energy management, energy communities, mathematical decomposition methods, distributed optimization techniques is highly desirable.

Salary Benefits:

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,872 max. €3,670).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.

38 hours per week

De Rondom 70