Development of Advanced Methods to Increase the Accommodation Capacity of Inverter-Based Generation in Transmission Networks

• Grant number: 25/12222-5
• Support Opportunities: Regular Research Grants
• Start date: November 01, 2025
• End date: October 31, 2028
• Field of knowledge: Engineering - Electrical Engineering - Power Systems
• Principal Investigator: Daniel Dotta
• Grantee: Daniel Dotta
• Host Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• City of the host institution: Campinas

Abstract

The increasing penetration of renewable energy sources connected via inverters-such as solar photovoltaic and wind power-has significantly altered the dynamics of Power Systems (PS). The replacement of synchronous generators with non-synchronous units has the potential to reduce the system's ability to control key variables such as frequency and voltage, thereby increasing its vulnerability to contingencies, as observed in recent blackouts. This project proposes the development of advanced methodologies, based on data analytics and computational modeling, aimed at enhancing the secure accommodation capacity of these new generation sources in Brazilian and global transmission systems.The proposal is structured around three interrelated research lines: (i) the application of machine learning techniques and deep neural networks for real-time disturbance detection and classification; (ii) validation of dynamic models of generating units using synchrophasor (PMU) data, allowing improved alignment between simulation and actual system behavior; and (iii) assessment of the locational robustness of frequency response, based on nodal inertia metrics and the formation of coherent regions using graph theory.The project builds upon existing infrastructure at FEEC/UNICAMP, with access to PMU data from the Brazilian system and international networks, and is supported by an active research group composed of PhD candidates and undergraduate researchers. The expected outcomes include improved operation of power systems under high penetration of inverter-based generation, the development of scalable computational tools, and the training of highly qualified human resources. The proposed methodologies also aim to provide technical support to system operators and regulators within the broader context of the energy transition. (AU)