Defence Azin Khaleghi: "Cooperative Decision-Making under Uncertainty: Applications to Energy Markets and Railway Systems"
Promotors: First promotor Prof Claudio De Persis, second promotor: Dr Ashish Cherukuri
Abstract: This dissertation develops mathematical models for decision-making under uncertainty, with applications in renewable energy markets, cooperative decision-making, and railway systems. It investigates how effective decisions can be designed when future outcomes are uncertain, information is incomplete, or system parameters cannot be known precisely.
For wind power producers, uncertain electricity generation creates a direct financial challenge because market commitments must be made before actual production is known. This dissertation develops stochastic models and multi-period bidding strategies to examine how producers can balance expected profit against exposure to imbalance risk. The results show that incorporating storage information and accounting for decisions across multiple market periods can improve economic performance while reducing financial risk.
The research then develops cooperative models for situations in which multiple decision-makers form coalitions and coordinate their decisions under uncertainty. These models quantify how cooperation and aggregation can reduce variability and generate additional economic value. Allocation mechanisms are designed to distribute these benefits in a fair and stable manner. Moreover, learning-based models are developed that allow joint decisions and profit allocations to adapt progressively as new information becomes available.
Finally, this dissertation develops control models for virtually coupled rail vehicles, in which vehicles coordinate their speeds and relative distances through communication and feedback. The analysis accounts for uncertainty in physical factors such as train mass, track slopes, and aerodynamic forces, and establishes conditions under which coordinated operation remains stable and reliable.
Overall, this dissertation advances mathematical methods for decision-making under uncertainty through optimisation, cooperative game theory, learning, and control.