UT Power Engineering Laboratory - "Powering the Future"

Demand Response Potential Assessment Tool

Team

Kevin Dowling
Brandon Johnson
Saqib Khan
Michael Starke
Nasr Alkadi

Description

Many industrial manufacturing plants have electrical loads that can be varied in such a way as to support grid stability and capacity. In a previous study conducted by ORNL a large scale aluminum smelting plant was successfully modified to sell regulation services to the local ISO at a profit. Despite this, very few plant operators have taken advantage of this potential revenue stream. This project is based on the assumption that plant operators do not invest in providing demand response due to a lack of knowledge about what machinery can participate and what sort of return on investment can be expected.

The demand response potential assessment tool generates a regression relationship between sales and electricity consumption for each industry classification code obtained from the Industrial Assessment Center database. These relationships are then applied to a complete database of industrial manufacturing listings with approximate sales figures to approximate electricity consumption.

A general load curve and the demand response potential for each industry is developed by aggregating example operating characteristics of industrial process steps which are selected by research, field measurements, and engineering judgment. The type of demand response services loads are capable of providing are evaluated as percentages of electricity being used which are also selected by research, field measurements, and engineering judgment.

All of this data is combined into approximate load curves for WECC balancing areas as well as a large collection of maps to display demand response potential with respect to geographic location. Maps include demand response potential for each balancing area, demand response potential for each zip code, as well as maps for each industry classification. The results of the electricity estimations were validated against MECS and EIA studies and matched very closely while providing resolution levels down to the individual plant.

Although only 36 industries were analyzed in the course of this project, the tool is scalable to include all manufacturing industries and can be updated as the databases expand.






Contact Information
Contact Information

Min H. Kao Engineering Building
http://power.eecs.utk.edu/