A team of researchers was awarded best paper at the Institute of Electrical and Electronics Engineers (IEEE) Power and Energy Society (PES) General Meeting 2020 for their paper titled, “A Holistic Framework for Parameter Coordination of Interconnected Microgrids Against Disaster,” which aims to enhance power grids’ resilience against natural disasters. IEEE PES The Power and Energy Society General Meeting is a flagship conference in the IEEE Power and Energy Society.
Over the past decade, extreme weather has exposed the fragility of the modern power grid with the inevitable blackouts that follow. But how inevitable are these blackouts and can they be avoided?
Microgrids – a small-scale yet autonomous power system that connects to the main power grid under normal operating conditions – allow for disconnection from the grid so that in the case of extreme weather, they can function properly and prevent major outages. In this paper, the research team addressed a stability issue in the operation of networked microgrids during natural disasters and extreme weather.
Authors of the paper include doctoral student Tong Huang and professor Dr. Le Xie from the Department of Electrical and Computer Engineering at Texas A&M University; and Drs. Hongbo Sun, Kyeong Jin Kim and Daniel Nikovski from Mitsubishi Electric Research Laboratories in Cambridge, Massachusetts.
They bring a new perspective to the community of grid resilience enhancement by focusing on the dynamic performance of microgrids – examining the system behaviors in the time scale of milliseconds/seconds – instead of the more common steady-state perspective in the time scale of minutes/hours. Looking into this finer time scale allows the research team to more accurately depict the state of the grid in the presence of disasters.
Additionally, the team proposes an innovative framework to coordinate all interface parameters of networked microgrids, which has the potential to serve as a critical building block in the energy management system for the next-generation power grid. The core of this framework is a novel stability assessment algorithm that is set apart from conventional stability assessment methods.