Enhanced macro model for packetized control of thermostatic loads
Abstract
Demand response has become a critical tool for enhancing power grid resilience amid increasing renewable generation variability. Ensemble control of residential thermostatic loads shows signif- icant promise as a scalable demand response strategy. However, co-optimizing customer comfort with grid objectives remains a key challenge. Effective strategies require accurate aggregate load modeling to co-optimize grid and consumer needs. This work enhances a state-of-the-art Pack- etized Energy Management model, improving flexibility for thermostatically controlled loads. The extended framework achieves superior reference tracking and higher PJM performance scores and better customer quality of service, enabling more reliable demand response.
Primary Faculty Mentor Name
Jackson Anderson
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Electrical Engineering
Primary Research Category
Engineering and Math Science
Enhanced macro model for packetized control of thermostatic loads
Demand response has become a critical tool for enhancing power grid resilience amid increasing renewable generation variability. Ensemble control of residential thermostatic loads shows signif- icant promise as a scalable demand response strategy. However, co-optimizing customer comfort with grid objectives remains a key challenge. Effective strategies require accurate aggregate load modeling to co-optimize grid and consumer needs. This work enhances a state-of-the-art Pack- etized Energy Management model, improving flexibility for thermostatically controlled loads. The extended framework achieves superior reference tracking and higher PJM performance scores and better customer quality of service, enabling more reliable demand response.
