Topology Aware Resource Optimization and Uncertainty Quantification Energy Models for the Power Grid

University Of North Dakota Main Campus, Grand Forks ND

Investigators

Abstract

The project focuses on the development, implementation, and evaluation of new and effective policies for topology aware resource allocation of energy resources under uncertainty. When a malfunction occurs in an electricity provisioning system, it is vitally important to quickly diagnose the problem and take corrective action to prevent outages. This project will support fundamental research to enhance both the proactive and reactive reliable operation of the smart grid without costly infrastructure investments. Specifically, this research project will show that controlling the grid's topology can enhance the grid's reliability and better manage resources. In addition, this research will develop the procedures required to find the most reliable grid topology in response to changes in energy demand. Thus, the primary societal impact of this research is to increase the capability to prevent and resolve unexpected blackouts, which account for approximately $90 billion in losses each year for U.S. businesses and consumers. This research involves several disciplines including power systems, parallel computing and optimization. Integer Linear Programming models can overcome several limitations in the current topological aware models such as capacity planning, re-allocation and scheduling of resources. The research team will study a collection of mixed integer linear programming models designed to identify optimal combinations of supply sources, demand sites to serve, and the pathways along which the reallocated power should flow. The models explicitly support the uncertainty associated with alternative sources such as wind power. A simulator configured with multiple intelligent distributed software agents will be developed to support the evaluation of the model solutions. Applications of interest include (but are not restricted to) generator and load scheduling applications in energy management and service systems; pricing and revenue management problems; and inventory control.