Residential token system based demand response using differential evolution algorithm
Demand response programs are highly beneficial for the consumers, system operators as well as the environment and are one of the most price effective methods for ancillary service support, integration of renewable energy and peak - load reduction. About 33% of all electricity is used for residential use, which means there is huge potential for DR applications. Usage of this concept reduces the stress of power generation on the utility and gives opportunity to the consumers to participate in the electricity market. The main purpose of demand response can be justified by applying it to the residential sector as they are more flexible to shift their load consumption. This thesis proposes implementing a token system-based network from computer networking for the selection of residents to receive incentives from the utility. Further, a differential evolution algorithm is used to optimize and reschedule the hours of power usage for residential loads of a day based on the real time pricing scheme. The proposed algorithm was tested on MATLAB and the results are presented in chapter 5.