Upcoming Event: PhD Dissertation Defense

Demand Bidding Models for Industrial Electrical Loads

Xin Tang, Ph.D. Candidate, Oden Institute

Abstract

The increasing penetration of renewable energy in power grids introduces variability that makes balancing supply and demand more complex. While demand response programs help mitigate this challenge, their impact is often unpredictable, limiting their usefulness to grid operators. Demand bidding offers a more structured alternative by allowing industrial electricity consumers to actively participate in electricity markets, adjusting consumption based on price signals.
   
This work proposes a novel demand bidding model tailored to industrial loads, incorporating both economic objectives and operational constraints specific to manufacturing processes. The model is designed for day-ahead market participation and is computationally efficient for real-world implementation. We validate its effectiveness through simulations on both small- and large-scale power systems, demonstrating its scalability. Additionally, we extend the framework to a multi-product industrial facility, capturing the interplay between different energy-dependent production processes. Results show that demand bidding can reduce electricity costs, improve grid flexibility, and integrate seamlessly into market operations. These findings highlight demand bidding as a viable tool for enhancing grid reliability and enabling deeper renewable energy integration.

Biography

 Xin Tang started the CSEM program in Fall 2020. She works with Dr. Michael Baldea on the optimization of power grids. She is the CSEM student Recruitment Chair and a volunteer for UT's Victim Advocate Network (VAN). Before joining CSEM, she graduated with honors from Rose-Hulman Institute of Technology with a B.S. in chemical engineering and computational science and a minor in Japanese.

BS, Chemical Engineering and Computational Science, Rose-Hulman Institute of Technology, Indiana 
MS, CSEM, UT Austin