PhD Candidate in Multimarket Bidding Decision Support in Nordic Power Markets

About this job

The Department of Electric Energy (IEL) at NTNU is seeking a highly motivated candidate for a full-time (100%) PhD position for 3 years as part of the Norwegian Centre on AI for Decisions ( aiD ). You will join the Electricity Markets and Energy System Planning (EMESP) research group at IEL, where we foster an open, inclusive, and collaborative working environment. Our work environment is defined by its friendly and supportive atmosphere, with regular gatherings such as professional meetings within the research group, weekly colloquia, shared lunches, and “Friday coffee” sessions to end the week. These formal and informal events offer opportunities to share ideas, celebrate milestones, and build relationships. PhD candidates also organize social activities open to everyone interested, fostering a welcoming and inclusive community. Your immediate Line Manager will be the Head of Department. About the project The position will be part of aiD, the Norwegian Centre on AI for Decisions, an interdisciplinary national AI center led by NTNU and SINTEF. AID brings together academic institutions, research organizations, and more than 50 professional organizations. Its primary objective is to advance AI for decision-making through fundamental research and real-world use cases, ensuring that AI-enhanced human decisions and autonomous systems are effective, safe, and trustworthy in sectors critical to society. This PhD project will contribute to aiD by developing trustworthy AI-supported methods for multimarket bidding and decision support in Nordic power markets. The rapid integration of wind power, battery storage, and other flexible resources is creating new opportunities for market participation, but also more complex decision-making problems. Energy producers increasingly need to coordinate decisions across day-ahead, balancing, and other electricity markets while dealing with uncertain renewable generation, activation needs, regulation and imbalance prices, and rapidly changing market conditions. Recent developments such as 15-minute market time units, automated mFRR energy activation, and flow-based market coupling further increase the need for decision-support methods that are fast, robust, risk-aware, and suitable for real-time operation. The project will focus on how AI and mathematical optimization can be combined to support sequential bidding decisions under uncertainty. The initial use case will consider a wind power operator with battery storage participating in the day-ahead market and the mFRR capacity and energy activation markets. The research will investigate how probabilistic forecasts, market-state and regime information, and the future value of battery flexibility can be incorporated into bidding decisions. A central scientific question will be to determine which parts of the decision problem are best handled by data-driven learning and which should remain within structured optimization. In line with AID’s research areas, the project will emphasize knowledge embedding, uncertainty representation, risk-aware decision-making, computational efficiency, generalization under changing market conditions, and safe constraint handling. The PhD candidate will develop and validate decision-support methods based on deep reinforcement learning, stochastic optimization, and hybrid combinations of learning and optimization. Learning-based policies will be compared with equivalent rolling stochastic optimization benchmarks operating with the same information and operational constraints. The project will also explore how AI can be used to approximate, accelerate, contextualize, or enhance optimization, for example by learning future flexibility value, selecting relevant uncertainty scenarios, or reducing computational complexity. The methods will initially be tested for coordinated day-ahead and mFRR market participation and may later be extended to more strongly coupled market combinations and other flexible energy resources. The project therefore offers the opportunity to work at the intersection of artificial inte

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