Evdokia Nikolova Receives NSF Grant to Improve Power Grid Efficiency
Evdokia Nikolova, Assistant Professor in Texas ECE, has received a National Science Foundation (NSF) grant for her work on "AitF: Collaborative Research: Algorithms and Mechanisms for the Distribution Grid.” The goal of this project is to “help the distribution grid and its participants transition from its current functionality of serving mostly traditional consumers, to the future grid that needs to sustainably integrate prosumers, renewables and distributed energy resources.”
Traditionally, power in distribution grids has flown one way: from the substation to the end consumer. In the new world of prosumers, distribution grids need to accommodate flow in both directions. This is challenging the existing wire and transformer abilities to serve load at acceptable quality levels. Moreover, the increasing number of prosumers is resulting in dramatically higher uncertainty in demand forecasting, which, with further prosumer increase, may prove unsustainable and ultimately threaten the utility companies' operation and business viability.
Prof. Nikolova will develop simplified mathematical models to solve problems that will enable the future power grid to sustain massive growth in renewables and distributed energy resources. These solutions can reduce congestion and improve efficiency of the grid while further enhancing its reliability.
This research has the potential to increase integration of renewable energy resources, mitigate the risks associated with variability of renewables, and better manage congestion thus providing more reliable, cost effective and efficient operation of the grid.
Evdokia Nikolova is an Assistant Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin. Previously she was an Assistant Professor at the Computer Science & Engineering Department at Texas A&M University and a postdoctoral associate in the Computer Science and Artificial Intelligence Laboratory at MIT. Her research focuses on risk analysis from an algorithmic perspective arising in stochastic optimization, networks, economics and complex systems.