Demand response potential in aggregated houses using GridLAB-D
Hanumantha Vajjala, Vivek Abhilash
AdvisorJewell, Ward T.
MetadataShow full item record
Electrical power consumption or demand varies very significantly from region to region. There are various factors which affect the demand for a particular location; higher the electrical demand higher is the wholesale electrical charge as well. These higher prices included the cost incurred to operate a peaking power plant; these power plants are commissioned to supply peak demands, and the capital cost to establish such a plant is very high; furthermore these plants are left idle for most part of the year. This is an inefficient process but it has to be commissioned to meet the basic criteria to supply uninterrupted power supply to the consumers. However, with the advance in technology, demand response is now progressing as a preferable alternative for peak load reductions, thereby reducing the dependencies on the peak load power plants. Some loads on the customer's side can be effectively controlled and shut off for short durations during the peak load times. These controllable devices can be used in such a way that there is no significant impact on the consumer's lifestyle or comfort. This research analyzes the potential of demand response for a small area or a community having residential houses in the United States. Different population sizes and properties of the residential sectors have been simulated with and without demand response for few days in the typical summer period June through September using GridLAB-D software. Potential savings in terms of demand reduction and energy consumption have been observed when demand response is coordinated with better thermal integration on residential houses. A correlated power consumption pattern has been observed for different population sizes, having same HVAC systems, thermal integration and different floor areas.
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science