Adaptive Control Strategy for Real-Time Regulation of PEV Charging in Response to Fluctuating Renewable Energy Supply

Nemala, Jayasri; Anusuya, Devi V.S.; Preeti, Tewari; Sorabh, Lakhanpal; Ulabbas, Abedi Tamam Ali Abd; Kapil, Bodha · 2025 · DOAJ

DOI: 10.1051/e3sconf/202561601006

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Summary

This paper addresses the challenge of managing Plug-in Electric Vehicle (PEV) charging loads to match the intermittent and fluctuating supply of renewable energy, specifically wind power. The authors identify that while PEVs can serve as programmable loads to enhance grid stability, their unpredictable arrival times and varying battery states create significant uncertainties. To mitigate these issues, the study proposes a novel adaptive control strategy that coordinates PEV charging in real-time, treating the aggregate PEV load as a controllable resource to balance renewable generation without requiring direct state estimation of individual vehicles. The methodology combines a theoretical load modeling framework with a robust control design. The authors model PEV charging behavior using a first-order bilinear partial differential equation (PDE) based on the concept of "load transit," which describes the diffusion of vehicles from low to high states of charge. This PDE is discretized into a finite-dimensional state-space model. To validate this theoretical approach, the authors compare it against a Monte Carlo simulation benchmark derived from real-world driving data from the US National Household Travel Survey (NHTS). The control mechanism employs Sliding Mode Control (SMC) to synthesize a robust output feedback controller. This controller relies solely on the observable instantaneous mismatch between renewable supply and customer demand, avoiding the need for complex state error calculations. A projection operator is incorporated to prevent integrator windup during control saturation events. Numerical simulations demonstrate the effectiveness of the proposed strategy in tracking wind power trajectories. The results show that the aggregate PEV charging power closely follows the available wind generation, effectively utilizing the renewable supply. The study highlights that tracking performance remains robust even in the presence of feedback delays of 10 and 20 seconds, with tracking errors remaining bounded and significantly lower than in delayed scenarios without the proposed control adjustments. However, the simulations also reveal a limitation: when wind power generation exceeds the total charging capacity of available PEVs, the excess energy cannot be absorbed by the vehicles alone, leading to unused renewable power. The significance of this work lies in providing a stable, mathematically rigorous blueprint for demand-side management in grids with high renewable penetration. By proving that SMC can robustly handle uncertainties and time delays, the study supports the integration of PEVs as virtual storage solutions. The authors conclude that while the strategy is effective for tracking renewable supply, it should be combined with other controllable loads, such as thermostatic HVAC systems, to fully utilize excess renewable energy during periods when PEV availability is insufficient. This hybrid approach offers a pathway to enhance grid reliability and maximize renewable energy consumption.

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• adaptive driving beam