A model predictive control-based energy management strategy for grid-connected nanogrids.

Nanogrids have emerged as a crucial solution for contemporary residential power systems, providing a robust framework for integrating distributed energy resources (DERs), comprising solar photovoltaics (PVs), wind turbines (WTs), energy storage systems (ESSs), and diesel generators (DGs). However, the uncertainty of consumer demand and the inherent intermittency of renewable energy present significant challenges to maintaining stability, power quality, and cost-effective operation. This study proposes an effective two-level energy management strategy (EMS) to reduce operating costs, considering system limitations and uncertainties in energy demand, grid costs, and renewable energy sources. In the first level, load demand forecasts, renewable energy forecasts, and grid tariffs are used to schedule generation units and storage systems one day ahead of time. The resulting constrained nonlinear optimization problem is solved using the Goat Optimization Algorithm (GOA), a bio-inspired metaheuristic optimization algorithm. At the second level of the proposed EMS, a Model Predictive Control (MPC) strategy repeatedly solves a receding-horizon optimization problem to update the power set-points of the available energy resources in real time, addressing forecast inaccuracies in solar irradiation, wind speed, and demand. The proposed GOA-MPC-EMS is assessed through two test case scenarios: individual nanogrid operation and an integrated multi-nanogrid cluster within a grid-connected environment. The results indicate that the proposed GOA-MPC-EMS achieves cost-effective and robust operation for a grid-tied nanogrid under uncertainties in electricity prices, weather conditions, and load demand. To evaluate the effectiveness of the GOA for the optimal day-ahead scheduling of diesel generators and batteries, a comparison with three other optimization techniques, namely Dream Optimization Algorithm (DOA), Harris Hawks Optimization (HHO), and Particle Swarm Optimization (PSO), is conducted. According to simulation results, the day-ahead operating cost is reduced by approximately $23.85 with GOA, $22.83 with DOA, $18.30 with HHO, and $16.88 with PSO compared with the scenario without demand-side management (DSM), corresponding to cost savings of approximately 8.16%, 7.81%, 6.26%, and 5.78%, respectively. Meanwhile, the real-time EMS achieves additional daily savings of $16.39, corresponding to a further reduction in operating cost of approximately 6.11%.