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Defense of a Ph.D. Dissertation- Qingxin Shi

Major Professor:

Dr. Fangxing "Fran" Li


Demand side control for power system frequency regulation


     The frequent variation of wind or solar power output causes a short-term mismatch between generation and demand and system frequency fluctuation. The traditional approach to deal with this problem is to increase the amount of system spinning reserve, which requires addition cost. In recent years, researchers are actively exploring the utilization of residential and commercial loads in frequency regulation without affecting customers’ life quality. It is named as dynamic demand control (DDC). This dissertation conducted an in-depth study on DDC for bulk power system frequency regulation, in both technical and economic perspective.
     First, an analytical method was proposed for aggregating multi-machine system frequency response (SFR) model. The method is a fast tool for simulating the system frequency response after a disturbance. Therefore, it has wide applications in power system dynamic studies and acts as a solid theoretical foundation for the DDC studies.
     Second, DDC strategies for both primary and secondary frequency regulation were studied. The control strategy has the following features. 1) The target load reduction amount can be achieved in a decentralized manner, while the control parameters are updated by the control center. 2) The daily demand profiles of thermostatic loads are modeled. 3) The load recovery process is considered in the control strategy. Consequently, the aggregate loads can provide flexible frequency control capability without causing significant power rebound. Therefore, demand side control is an essential compensation for traditional frequency regulation approaches and can improve the frequency response of the bulk power system.
     Furthermore, this dissertation also conducts an economic analysis on demand response. Based on the large-scale customer survey, we estimate the expense of frequency regulation and peak load reduction via incentive-based demand response (IBDR). The results provide useful suggestions for utility companies when implementing IBDR programs.

Friday, July 5 at 10:00am to 12:00pm

Min H. Kao Electrical Engineering and Computer Science, 639
1520 Middle Drive, Knoxville, TN 37996

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