Experimental validation of scenario-based stochastic model predictive control of nanogrids

In microgrids and nanogrids, challenges arise from the inherent intermittency of renewable energy sources and the need to meet uncertain energy demand from users. To address these uncertainties, this paper investigates a two-layer, scenario-based stochastic Model Predictive Control (MPC) for a real lab-scale photovoltaic (PV)-based nanogrid. The high-level layer, which operates slowly and over longer time horizons, computes optimal reference values for the low-level layer based on predictions of uncertainty in PV generation and consumer load. The low-level layer, which operates on shorter time horizons and at higher frequencies, relies on scenario-based MPC. Scenario-based MPC has several advantages, such as not requiring prior knowledge of the underlying probability distribution. However, it can suffer from significant computational burdens, especially in real-time applications like nanogrid control. To overcome this challenge, this paper employs the Alternating Direction Method of Multipliers (ADMM) to efficiently solve the optimization problem. First, real PV and load data are used to characterize the scenarios. Then, the proposed scheme is experimentally validated on a PV-based nanogrid. The results show that the two-layer scenario-based MPC outperforms the two-layer chance-constrained MPC and significantly improves performance compared to a rule-based energy management system.