MPC based frequency control of an autonomous microgrid integrated with electric vehicles

• Enhanced Frequency Stability in Two-Area Microgrid with Stochastic Behavior The study improves LFC performance in a two-area islanded microgrid by accounting for the stochastic behavior of EVs and renewable energy source fluctuations, achieving superior frequency stabilization. • Robust Finite Horizon MPC with Centralized Control Architecture The study proposes finite horizon MPC designed with centralized control architecture to ensure simultaneous and optimal control of both areas for improved system stability. • Assessment of Asymptotic Stability Using Local ISS Criterion The local input-to-state stability (ISS) criterion is applied to evaluate the asymptotic stability of the system, ensuring robust and reliable performance under dynamic operating conditions. • Integration of Battery Energy Storage System (BESS) for Continuous Stability The integration of BESS with the MPC enhances system resilience, ensuring continuous generation-demand balance, minimized tie-line power fluctuations, and improved performance under dynamic conditions. Although electric vehicles (EVs) offer bidirectional charging and can serve as mobile energy storage units, integrating them into microgrids and enabling them to participate in load frequency control (LFC) has proven to be a significant challenge. One of the crucial issues is the unpredictability of EV usage patterns, which can lead to sudden fluctuations in generation-demand balance. This unpredictability makes it difficult to coordinate EV charging and discharging with the grid's frequency control needs. Classical control techniques lack the capability to handle the EV's stochastic behavior. To address this, the study proposes the integration of a battery energy storage system to maintain continuous generation – demand balance. Then, a finite horizon model predictive control (MPC) is applied for the LFC of a two-area islanded MG integrated with the EV charging station. The MPC generates the optimal signals for the optimal adjustment of the power generation of the dispatchable sources as well as the EV aggregators. To evaluate system stability, the local input-to-state stability (ISS) criterion is employed. Simulation results demonstrate that the proposed MPC-based LFC outperforms conventional methods in terms of frequency nadir, settling time, and rate of change of frequency (RoCoF). Under a 2 % step load perturbation in one of the control areas (CAs), the proposed MPC reduces frequency nadir to −0.0306 Hz (a 12.9 % improvement over the best alternative), limits RoCoF to −0.0524 Hz/s (a 17.4 % enhancement), and achieves a minimum steady-state frequency deviation of 4.14 × 10⁻³ Hz (a 1.9 % reduction). Furthermore, in response to random load fluctuations and renewable energy source (RES) variability, the proposed controller ensures that frequency deviations remain within ±0.02 Hz, while RoCoF is constrained within ±0.02 Hz/s, demonstrating superior robustness, constraint handling and faster convergence.