An optimization-based EVA-VCAS for effective voltage sag management

• EVs as grid support: This research explores the potential of Electric Vehicles (EVs) to provide ancillary services to the power grid, particularly for mitigating voltage sags. • New Voltage Sag Score (VSS): A novel metric, the VSS, is introduced to comprehensively evaluate the severity of voltage sags. • Optimization Algorithms: Five optimization algorithms are employed to determine the optimal EV charging/discharging strategies for minimizing voltage sags. • Power injection modes: Different power injection modes (active-reactive and reactive-only) are compared to assess their effectiveness. • PSO algorithm superiority: The Particle Swarm Optimization (PSO) algorithm, when combined with active-reactive power injection and the integral of the Squared Error (ISE) objective function, demonstrates superior performance in mitigating voltage sags. • Improved system stability: The EV-based voltage control system (EV-VCAS) significantly reduces the duration and depth of voltage sags, enhancing overall system stability. • Feasibility of EV-VCAS: The study demonstrates the feasibility of the EV-VCAS, even considering the mobile nature of EVs. Voltage sags, which are temporary drops in voltage levels, pose significant challenges to power system stability and reliability, necessitating effective mitigation strategies. However, there is an opportunity to leverage energy stored in Electric Vehicles (EVs) to support the grid during disturbances through ancillary services. The entities that manage the energy flow from electric vehicles (EVs) to the grid are EV aggregators (EVAs). To measure the effectiveness of this approach, the Voltage Sag Score (VSS), a new single score designed to address the shortcomings of traditional metrics, is introduced. This score aims to complement aspects of the Rate of Change of Voltage (RoCoV) in the voltage disturbances. The proposed VSS integrates magnitude, duration, and minimum RoCoV with customizable weights depending on the element of importance. Five optimization algorithms—Particle Swarm Optimization (PSO), Nelder-Mead, Dividing Rectangles (DIRECT), Broyden-Fletcher-Goldfarb-Shanno (BFGS), and Legacy algorithms—are applied with two objective functions, Integral of the Squared Error (ISE) and Integral of the Time-weighted Squared Error (ITSE), across different power injection modes (active-reactive and reactive-only). These algorithms are implemented and compared using the System Parameter Identification toolbox in DIgSILENT PowerFactory.The results demonstrate that the PSO technique, combined with active and reactive power injection and the ISE performance measure, is the most effective solution for mitigating voltage sags, achieving a 63 % reduction in VSS. This approach significantly decreases the depth and duration of voltage drops, enhancing grid stability. Moreover, the method accounts for the mobility of EVs, proving that EVAs can effectively manage voltage issues even when EVs are not physically close to the affected areas. In such remote scenarios, the VSS was further reduced by 16.48 %, showcasing the robustness and adaptability of the proposed solution. When tested on a larger and more realistic grid, such as the IEEE 39-bus system, the VSS was reduced by 34.65 %, further validating the practical effectiveness and scalability of the approach in real-world grid environments.