Almost 10 % of the global population are living on islands. More than other areas, islands are exposed to extreme weather and thus the direct consequences of climate change. In addition, islanders often suffer from high energy costs due to the costly import of fossil fuels. This is driving the pressure for change towards an energy system with a high proportion of locally generated renewable energy.
To operate the island’s decentralized microgrid with both volatile renewable energy and stochastic loads safely and efficiently, an energy management system is required.
In this thesis, we consider an energy management system based on optimal control. The core of the control strategy is a system model that represents the energy system of an exemplary island, consisting of renewable energy generation (i.e., Wind and PV) battery storage and diesel generators as backups. The optimal control strategy relies on the forecasts of renewable energy production and electric load. These forecasts are either based on physical models or machine learning methods.
Both the system model and the forecasts are integrated into the formulation of a mathematical optimization problem. This enables the optimal controller to act predictively and provide an optimal charging schedule for the controllable energy storages.
A major target of this project is to investigate on how sector-coupling can improve the overall energy efficiency by considering electric vehicles as shiftable loads or even as additional electric energy storages that can be operated grid supportive.
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