Data-Driven Stochastic Model Predictive Control for DC-Coupled Residential PV-Storage Systems
Ashwin Shirsat, Wenyuan Tang
Abstract
This paper develops a stochastic model predictive control (SMPC) based framework for the real-time operation of residential-scale DC-coupled PV-storage systems. The proposed framework combines bivariate Markov chains to build the uncertainty model of PV generation and residential load, a Bayesian approach based recursive learning of the Markov model, and a scenario-based formulation for the SMPC problem. This approach operates in real-time, thus minimizing the impact of the mismatch between the forecasted data and the actual observation on the system performance by updating the control decisions with the realization of the stochastic parameters at each time step. Load and PV generation are jointly modeled, and the interdependence between them is accounted for through bivariate Markov chains. The use of recursive online learning guarantees that the uncertainty model is continuously updated to enhance its prediction capabilities for scenario generation. The numerical simulations using real-world data demonstrate the enhanced performance of the proposed approach over the conventional approaches, on a par with model predictive control with complete knowledge of the uncertainties.