Voltage control in modern power systems has become increasingly complex due to the high penetration of renewable generation. Numerous solutions have been proposed from both the transmission and distribution sides, involving generators and system operators. However, the contribution of loads has remained limited, mainly to demand shifting and basic demand response strategies. This work introduces a novel approach that leverages digital twins to enhance the active participation of loads in supporting voltage control. Unlike traditional methods, the proposed framework builds digital twins exclusively from measurable data, enabling virtually any converter-interfaced load connected to a grid, regardless of whether the network is fully known or not, to contribute effectively to voltage regulation. The methodology is first demonstrated through a parametric study, which evaluates the impact of different load behaviors and control strategies on network voltage stability. To further validate the approach, hardware-in-the-loop (HIL) experiments are conducted, confirming the feasibility of real-time implementation. Four voltage control use-cases are developed and tested for a controllable thermal load, showing that even individual loads can provide meaningful support to grid voltage regulation. The results highlight the potential of data-driven digital twins to unlock new, scalable, and flexible contributions from loads, reinforcing the stability of future power systems with high renewable penetration.
Araúz et al. (Thu,) studied this question.