Co-Optimization of EV Charging Control and Incentivization for Enhanced Power System Stability
<|reference_start|>Co-Optimization of EV Charging Control and Incentivization for Enhanced Power System Stability: We study how high charging rate demands from electric vehicles (EVs) in a power distribution grid may collectively cause its dynamic instability, and, accordingly, how a price incentivization strategy can be used to steer customers to settle for lesser charging rate demands so that these instabilities can be avoided. We pose the problem as a joint optimization and optimal control formulation. The optimization determines the optimal charging setpoints for EVs to minimize the $\mathcal{H}_2$-norm of the transfer function of the grid model, while the optimal control simultaneously develops a linear quadratic regulator (LQR) based state-feedback control signal for the battery-currents of those EVs to jointly minimize the risk of grid instability. A subsequent algorithm is developed to determine how much customers may be willing to sacrifice their intended charging rate demands in return for financial incentives. Results are derived for both unidirectional and bidirectional charging, and validated using numerical simulations of multiple EV charging stations in the IEEE 33-bus power distribution model.<|reference_end|>
@article{podder2024co-optimization,
    title={Co-Optimization of EV Charging Control and Incentivization for Enhanced
  Power System Stability},
    author={Amit Kumer Podder, Tomonori Sadamoto, and Aranya Chakrabortty},
    journal={arXiv preprint arXiv:2405.00947},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.00947},
    primaryClass={math.OC cs.SY eess.SY}
}