Integrating Renewable Energy Sources into Power Grids
ECE Professor Ali Abur was awarded a $350,000 grant from the NSF for “Robust Transient State Estimation for Three-Phase Power Systems.” The project aims to facilitate the efficient integration of inverter-based (IB) renewable energy sources into future generation of power systems.
Abstract Source: NSF
This NSF project aims to facilitate the efficient integration of inverter-based (IB)renewable energy sources into future generation of power systems. The project will bring transformative change by better modeling and estimation of the dynamic behavior of these sources so that they can be readily incorporated in the analysis and optimal control and coordination of all other sources and devices in power grids. The overall impact will be cleaner, less costly and more reliable energy delivery to the customers. It should also be noted that implementation and utilization of the project’s outcomes will provide job and training opportunities for the next generation of power engineers in the nation. The intellectual merits of the project include improved reliability of network applications that incorporate renewable energy sources, facilitating detection and correction of errors associated with IB source models and associated measurements and, incorporation of IB renewable energy sources in overall generation dispatch of large power grids. The broader impacts of the project include training and education of students from underrepresented and minority groups via REU programs in the summers and as student researchers during regular semesters.
The goal of this project is to develop a robust and efficient state estimator (SE) which can trace the fast dynamics of IB energy sources so that effective control feedback signals at time scales commensurate with such dynamics can be implemented. This goal will be accomplished by using fast data acquisition to capture voltage and current signals in discrete time. Such measurements will be used to incorporate discrete-time models of power system elements into state estimation formulation in a computationally robust and efficient manner; enabling fast and automatic rejection of errors in sampled measurements; exploitation of the special structure of discrete-time network model to develop a parallel computation framework facilitating scalability of the developed SE algorithm for very large scale systems including those operating under unbalanced three-phase operating conditions accounting for nonlinear elements and frequency dependencies of line/cable parameters.