Targeted demand response reduces price volatility of electric grid


Date:
March 23, 2022
Source:
Texas A&M University
Summary:
Demand response, a measure taken to reduce the energy load in
response to supply constraints, within the Texas electric grid
has been a topic of recent conversation after the wake of Winter
Storm Uri just one year ago.

Demand response can enhance the reliability of the grid through
renewable energy penetration and also significantly reduce price
volatility, or fluctuation, in the wholesale electricity market.



FULL STORY ========================================================================== Demand response, a measure taken to reduce the energy load in response
to supply constraints, within the Texas electric grid has been a topic
of recent conversation after the wake of Winter Storm Uri just one year
ago. Demand response can enhance the reliability of the grid through
renewable energy penetration and also significantly reduce price
volatility, or fluctuation, in the wholesale electricity market.


==========================================================================
To reduce the energy load across the entirety of the state's grid,
traditional demand response studies focus on reducing the energy load
in high population centers such as Houston and Dallas. However, Le Xie, professor in the Department of Electrical and Computer Engineering
at Texas A&M University, and his team found that focusing on a few
strategic locations across the state outside of those high-population
areas is much more cost-effective and can have a greater impact on the
price volatility of the grid. A machine learning algorithm is utilized to strategically select these demand response locations based on a synthetic
Texas grid model.

This research was published in the February issue of the journal iScience.

"Suppose today's electricity demand results in high prices and yesterday's electricity demand resulted in low prices," said postdoctoral researcher
Ki- Yeob Lee, who designed the algorithm used in the paper. "Can we move today's electricity demand closer to yesterday's electricity demand so
that this change can result in low prices? If this is not successful, can
we move today's electricity demand closer to the day before yesterday's electricity demand? Based on this simple idea, our machine-learning
algorithm searches for the day where electricity results in low prices
and the amount of demand response is minimal." Although previous studies
have demonstrated the benefits of demand response in mitigating price volatility, there is limited work considering the choice of locations
for maximal impact.

"We're taking a technology-agnostic approach," Xie said. "We're showing
the current market design and the consequences of this design. By pointing these things out, we can hopefully reduce the price volatility of the
grid, which we believe would be best for society." In addition to Xie
and Lee, contributors to the research include Xinbo Geng, Sivaranjani Seetharaman and Srinivas Shakkottai from the electrical and computer engineering department at Texas A&M; Bainan Xia from Breakthrough
Energy; and Hao Ming from Southeast University in China, who received
his doctorate from Texas A&M.


========================================================================== Story Source: Materials provided by Texas_A&M_University. Original
written by Rachel Rose.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Kiyeob Lee, Xinbo Geng, S. Sivaranjani, Bainan Xia, Hao Ming,
Srinivas
Shakkottai, Le Xie. Targeted demand response for mitigating
price volatility and enhancing grid reliability in synthetic
Texas electricity markets. iScience, 2022; 25 (2): 103723 DOI:
10.1016/j.isci.2021.103723 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220323150546.htm

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