CAREER: Managing uncertainties in renewable powered grids

Clarkson University, Potsdam NY

Investigators

Abstract

This NSF CAREER project aims to develop algorithms to manage the inherent uncertainties of renewable generation in order to provide reliable and least cost operations of electric power systems. The project will bring transformative change to utility control rooms by fully incorporating renewable uncertainties into online assessment of instability risks and decision-making of resource dispatch. This will be achieved by developing a novel computational framework that combines physics-based power grid modeling, real-time sensor measurement, and pseudo measurement from numerical weather predictions with advanced machine learning and data analytics to achieve higher computation efficiency and accuracy required to manage renewable uncertainties in grid operations. The intellectual merits of the project include novel methodologies to enhance situational awareness of renewable generation, assess transient instability risks, and coordinate resource dispatch to mitigate the impact of renewable uncertainties. The broader impacts of the project include innovations to fundamental theories of uncertainty management in renewable energy powered grids and improvements to the workforce pipeline enabling the smooth transition to 100% decarbonized electricity systems. Current state of the art technologies, i.e., lack of situational awareness of offshore wind generation, determining transmission stability margin via off-line studies for online applications, and static/dynamic (capacity) reserve, do not adequately capture the new features of a renewable powered grid with high uncertainties. This project will bridge these gaps by 1) enhancing system operators' situational awareness through providing a new framework to fuse physics-based weather prediction and deep-learning methodologies for improved offshore wind generation forecasting; 2) enabling real-time transient instability risk assessment by developing a computational data analytic algorithm to approximate system dynamics; and 3) redesigning the operating reserve via the dynamic energy reserve technology to incorporate spatially and temporally correlated renewable uncertainties. These fundamental theories and technologies move the science of risk management in the utility control room from offline study with deterministic practices to data-driven, risk-aware online solutions, leading to informed decision-making and prompt mitigation actions by system operators to prevent cascading events. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.