A novel data-driven NLMPC strategy for techno-economic microgrid management with battery energy storage under uncertainty.

As renewable energy sources become more widespread and energy consumption continues to grow, there is an urgent requirement for smarter, more flexible control methods to manage microgrids (MGs) effectively. This study proposes a data-driven nonlinear model predictive control (NLMPC) framework for optimized MG operation, emphasizing energy storage system (ESS) integration. Effective MG management is crucial given increasing renewable penetration and energy demands. This framework coordinates distributed generation (DG) units, including rotating and non-rotating resources, with a battery ESS in a dynamic MG environment. Leveraging Gaussian Process Regression (GPR), the framework accurately models the complex dynamics of both DG units and the ESS. Unlike traditional model-based approaches, GPR learns system behavior from operational data, enabling precise performance prediction under varying conditions. This accuracy is crucial for optimized resource dispatch and efficient MG operation. GPR models capture ESS charging/discharging characteristics, efficiency, and state-of-charge (SOC) dynamics for informed ESS utilization. To address renewable energy uncertainties, Monte Carlo simulations are incorporated. This allows robust evaluation of the control strategy under various scenarios, ensuring MG stability and reliability despite fluctuating renewable generation. By considering these uncertainties, the NLMPC controller proactively manages DG and ESS dispatch, mitigating forecast errors and maximizing renewable energy use. The framework aims to achieve optimal power flow, balancing supply and demand while respecting operational constraints. This includes constraints on DG units, the ESS (SOC limits, charge/discharge rates), and overall MG operation (voltage and frequency stability). The NLMPC controller dynamically adjusts DG and ESS setpoints to minimize costs, maximize renewable energy use, and ensure MG stability and reliability. Simulation results demonstrate the framework's effectiveness. Significant cost savings (approximately 39.2% compared to Conventional MPC and 41.5% compared to Adaptive MPC) and voltage stability improvements (28.57% and 52.38% respectively) are achieved. These improvements stem from accurate system dynamics modeling, robust uncertainty handling, and coordinated DG and ESS control.