The next generation electric grid faces the challenge of integrating a growing amount of renewable energy generation such as wind while maintaining reliability. This research is focused on developing a fundamental approach to the optimal design and control of a smart electric microgrid, and to develop an open-source set of tools to facilitate the development of new smart grid applications. Intellectual Merit: Renewable energy generators differ in several key ways from conventional coal or oil-based generators, in that they are intermittent, cannot be accurately forecast and are not dispatchable. This research will develop statistical methods to investigate the fundamental limits of grid reliability because of generation uncertainty, with the ultimate aim of developing new understanding of the smart electric grid. Furthermore, the proposed research will develop optimal control algorithms for voltage and frequency regulation and economic dispatch explicitly taking into account virtual and real energy storage capabilities and active management of renewable generators. These control algorithms will be developed from a first principles basis, rather than simply tweaking existing control procedures and will be designed to be implemented in a distributed way to the extent possible to minimize the need for an excessively expensive communication infrastructure supporting the grid.

Broader Impacts: This research will precisely quantify the effects of storage and demand response capabilities in mitigating the effects of generation uncertainty and would be highly beneficial in designing and optimizing smart metering and load-management technologies. One of the aims of this project is to develop an open-source set of tools such as smart sensors and controller-actuators for small wind turbines and batteries, to facilitate small-scale experimentation with smart grid and distributed generation. This project will also involve extensive undergraduate research activity and results will be widely disseminated in technical publications and conferences.

Project Start
Project End
Budget Start
2012-06-01
Budget End
2018-09-30
Support Year
Fiscal Year
2011
Total Cost
$400,000
Indirect Cost
Name
University of Iowa
Department
Type
DUNS #
City
Iowa City
State
IA
Country
United States
Zip Code
52242