Model Predictive Control for Offset-free Tracking: a new data-driven application for Secondary Voltage Regulation on Transmission Network

This paper investigates the evolution of Secondary Voltage Regulation (SVR) in response to the increasing penetration of enewable energy sources (RES) within power systems. Traditional SVR, historically reliant on fossil fuel-based plants, faces challenges stemming from their under-utilization and intermittent operation. As conventional power plants decline, emerging resources such as synchronous compensators and STATCOMS offer voltage control capabilities without compromising RES integration, prompting the need for a redesigned control system to effectively harness their capabilities and optimize voltage regulation performance in an increasingly dynamic network environment. In this evolving scenario, a review of SVR literature reveals a shift towards data-driven methodologies, leveraging real-world data for improved control strategies. To address these challenges, a new Secondary Voltage Regulator is proposed based on a data-driven Model Predictive Control (MPC) approach, designed for offset-free tracking. The suggested approach, known for its proficiency in tracking, has been adjusted to provide an implementation suitable for the Italian transmission system. Field tests conducted on Sicilian transmission network validate the effectiveness of the MPC-based controller under real-world conditions, filling an important gap in understanding its performance and applicability in transmission systems.