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Neural Network-Based Approximation of Continuous Control Set MPC for the Primary Control of DERs in AC Microgrids

Abstract:

This article presents the design and functional validation of deep neural network-based approximators for the control policy of constrained model predictive control applied to a distributed energy resource unit operating in grid-supporting mode within an AC microgrid. The control scheme follows a conventional cascaded architecture, consisting of a zero-level control loop that regulates the DER output voltage, and a primary control loop responsible for balancing power supply and demand. Simulations show that the control policy approximated using neural networks achieves functional performance equivalent to a conventional implicit formulation of MPC. Moreover, the execution time of neural networks is expected to scale better to highdimensional optimization problems compared to conventional iterative solvers. Experimental validation was carried out on a lab-scale plant prototype with controllers executed on dSPACE MicroLabBox platform. To meet execution-time requirements for a target control interval of 200μs, the conventional implicit formulation requires simplifying the constraints, which restrict the maximum actuation voltage, and adopting a short prediction horizon. In contrast, the neural-network-based controller enables the use of more complex constraint sets and a longer prediction horizon, thereby maximizing the utilization of the variable inverter voltage.