A Simulation Tool for V2G Enabled Demand Response Based on Model Predictive Control
<|reference_start|>A Simulation Tool for V2G Enabled Demand Response Based on Model Predictive Control: Integrating electric vehicles (EVs) into the power grid can revolutionize energy management strategies, offering both challenges and opportunities for creating a more sustainable and resilient grid. In this context, model predictive control (MPC) emerges as a powerful tool for addressing the complexities of Grid-to-vehicle (G2V) and vehicle-to-grid (V2G) enabled demand response management. By leveraging advanced optimization techniques, MPC algorithms can anticipate future grid conditions and dynamically adjust EV charging and discharging schedules to balance supply and demand while minimizing operational costs and maximizing flexibility. However, no standard tools exist to evaluate novel energy management strategies based on MPC approaches. Our research focuses on harnessing the potential of MPC in G2V and V2G applications, by providing a simulation tool that allows to maximize EV flexibility and support demand response initiatives while mitigating the impact on EV battery health. In this paper, we propose an open-source MPC controller for G2V and V2G-enabled demand response management. The proposed approach is capable of tackling the uncertainties inherent in demand response operations. Through extensive simulation and analysis, we demonstrate the efficacy of our approach in maximizing the benefits of G2V and V2G while assessing the impact on the longevity and reliability of EV batteries. Specifically, our controller enables Charge Point Operators (CPOs) to optimize EV charging and discharging schedules in real-time, taking into account fluctuating energy prices, grid constraints, and EV user preferences.<|reference_end|>
@article{diaz-londono2024a,
    title={A Simulation Tool for V2G Enabled Demand Response Based on Model
  Predictive Control},
    author={Cesar Diaz-Londono, Stavros Orfanoudakis, Pedro P. Vergara, Peter
  Palensky, Fredy Ruiz, Giambattista Gruosso},
    journal={arXiv preprint arXiv:2405.11963},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11963},
    primaryClass={eess.SY cs.SY}
}