Comparison of optimization methods for model predictive control: An application to a compressed air energy storage system

This thesis compares different linear and nonlinear optimization methods for model predictive control of energy storage systems. The methods are applied to a compressed air energy storage system. Model predictive control is used to minimize operational costs covering a given 24-hour air demand using a time-sensitive electricity price as an incentive. The experiments are performed for several scenarios with variations in demand, electricity price, optimization timestep size and forecast quality.

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