Texas A&M researchers found that reducing the energy load at strategic locations when supply is strained allows for a reduction in cost for the wholesale market.
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.
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