Institute for Automation and Applied Informatics

Stochastic Optimal Power Flow using Polynomial Chaos Expansion

The increasing amount of distributed generation from renewable energy sources, the availability of always cheaper technologies to store and convert electric energy and the growth of efficient electric devices (from vehicles to heaters) opened new possibilities in the design and operation of electric power systems. This increased number of control options has the potential to greatly improve the efficiency of the system. However, it also requires non-trivial automatic decisions on the best inputs that should be applied, decisions that should account for the uncertainty affecting uncontrolled generation and demand. Appropriate optimization algorithms are therefore required for an effective operation of the future power systems. The purpose of the proposed thesis is to develop such an algorithm for the case of radial power grids.
Objectives of the thesis include:
reviewing and comparing different formulations of the stochastic optimal power flow problem;
learning what the polynomial chaos expansion is and how it could be used in optimization;
designing a method to solve stochastic optimal power flow applying polynomial chaos expansion to the forward backward sweep optimal power flow method
simulate and analyze the effects of the designed algorithm.
We are looking for motivated students interested in power systems control and optimization. A basic knowledge in power systems is required. A basic knowledge in optimization is desirable but not necessary.

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