**Job Description**
This PhD position aims to design next-generation decision support tools for battery operators to optimize participation in multiple short-term electricity markets and congestion management. The research involves developing state-of-the-art model predictive control tools, focusing on hedging decisions against battery model uncertainty through distributionally robust chance constrained optimization. The role also requires integrating data-driven models for state-of-health and degradation, leveraging experimental data, and exploring the integration of local connection capacity markets and novel connection agreements to assess their impact on the battery owner’s business case. This project is funded under the NWO-funded BATT-AI project.
**Skills & Abilities**
• Strong quantitative and analytical skills.
• Very good written and spoken communication skills in English.
• Programming experience in Julia, Python or a similar language (Bonus).
• Taken courses in Operations Research or Model Predictive Control (Bonus).
• Prior research experience, especially in energy system/market modelling or other energy-related research (Bonus).
**Qualifications**
Required Degree(s) in:
• Science
• Engineering
• Economics
(Masters degree must have been awarded by the agreed-upon starting date of the PhD)
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