NURAN CIHANGIR MARTIN
Data: 11/04/2024, 9h
Sala Zoom: https://puc-rio.zoom.us/j/94154576603?pwd=RmN4elJxRDQraWhFVFhTRzNDRVdpdz09 & Sala 950L – localizada no 9ª do Edifício Cardeal Leme.
Orientadores: Bruno Fânzeres dos Santos
Resumo:
In response to climate change, modern power systems are undergoing a decarbonisation-based transition involving vast deployment of renewable energy sources. For the success of this transition, various challenges need to be addressed in power system operations stemming from the high output variability along with limited predictability and controllability, leading to flexibility needs in power system operations. Power flow computation – and specifically, optimal power flow and unit commitment – is one of the most important computational tools for system operators to determine the state of
the power system. This computation is performed for various decisions on the grid, to dispatch the components in the network, to reconfigure them as well as price the services provided by generators and consumers on the grid. Various simplifications are made in power flow computation to tackle the computational burden of the models, which tend to be high for realistic systems. Model accuracy is increasingly causing high costs for system operations, since the real situation is deviating from the forecast leading to a need for costly actions by system operators in real-time. This thesis focuses on challenges in modern power system operations and pricing. Firstly, the thesis constructs methods and algorithms to enhance computational capability and model accuracy for AC Network-Constrained Unit Commitment and Optimal Power Flow problems through devising an improved approximation of the physical laws governing power flows. Secondly, it applies these methods and algorithms to the coordination problem between Distribution System Operators (DSOs) and Transmission System Operators (TSOs), introducing novel distributed optimisation techniques for managing congestion and voltage problems as well as addressing network information exchange aspects. Finally, the thesis proposes a new pricing mechanism endogenously addressing the non-convex operational decisions for energy and reserve scheduling for day-ahead planning, considering stochasticity of renewable energy generation. Computational and accuracy benefits are illustrated in case studies, by employing various metrics developed.