Trading off robustness and performance in receding horizon control with uncertain energy resources

Increased utilization of residential and small commercial distributed energy resources (DERs) has led DER ag-gregators to develop concepts such as the virtual power plants (VPP). VPPs aggregate the energy resources and dispatch them akin to a conventional power plant or grid-scale battery to provide flexibility to the system operator. Since the level of flexibility from aggregated DERs is uncertain and time varying, the VPPs' dispatch can be challenging. To improve the system operation, flexible VPPs can be formulated probabilistically and can be realized with chance-constrained model predictive control (CCMPC). This can be solved using scenario-based methodology, which provides a-priori probabilistic guarantees on constraint. satisfaction. This paper focuses on understanding the robustness and performance trade offs in receding horizon control with uncertain energy resources. The CCMPC dispatches robustly the uncertain VPPs and conventional generators while taking into account economically optimal, secure reference trajectory for generating assets. Closed-loop performance is with respect to minimizing the deviation of conventional generators from their reference trajectory. To evaluate the trade off between robustness and system performance with uncertain energy resources, a simulation-based analysis is carried out on the modified IEEE 30-bus system.