Ensemble Nonlinear Model Predictive Control for Residential Solar Battery Energy Management

In a dynamic distribution market environment, residential prosumers with solar power generation and battery energy storage devices can flexibly interact the grid via exchange. Providing schedule for this bidirectional dispatch facilitate operational planning operator bring additional benefits to some economic incentives. However, major obstacle achieving win–win situation is difficulty in: 1) predicting nonlinear behaviors of degradation under unknown operating conditions 2) addressing highly uncertain generation/load patterns, in computationally viable way. This article thus establishes robust short-term framework equipped rooftop photovoltaic (PV) panels household batteries. The objective achieve minimum-cost operation environment stipulated rules. A general optimization problem formulated, taking into consideration costs due electricity trading, degradation, various constraints. solved real-time using proposed ensemble model predictive control (EnNMPC)-based strategy, where uncertainty forecast has been addressed adequately albeit limited local data. effectiveness algorithm validated real-world prosumer datasets.