Demand response of PEV networks by dynamic distributed pinning control scheme

Plug-in electric vehicles (PEVs) have emerged as pivotal response loads on the demand side in power grid, capable of providing essential services such as frequency regulation, load following, and other ancillary grid support. However, the efficient aggregation and control of multiple PEVs present significant challenges, primarily due to the inherent uncertainties in their availability and energy demands, as well as the need for real-time coordination across a distributed network. This paper addresses these complexities by introducing a novel two-layer interactive time-scale optimization and control structure for demand response load following services. The upper layer employs a stochastic chance-constrained optimization to deliver reliable and optimized power schedules for load utilities. Meanwhile, the lower layer leverages a distributed pinning control algorithm to equitably distribute and track the optimized power schedule among multiple PEV agents, ensuring network-wide stability and efficiency. The proposed framework was validated through simulations involving an twenty-PEV agent network, and the simulation results demonstrating notable improvements in response accuracy and robustness against variability, establishing the effectiveness of the distributed control strategy in real-world demand response scenarios.