Abstract
The K-Medoids algorithm is adopted along with the proposed elephant herd optimization (EHO) algorithm in this peculiar work to cluster the consumers according to their required load consumption patterns and for price-based demand response (DR) program implementation. Consumer baseline load (CBL) estimation is done using the clustered load data. The novel approach of clustering the data analysis in this proposed work is done by collecting the load data from the transformer centers under the control of the 33/11 kV substation in the state of Goa, India, as well as the price data from the state load dispatch center and Indian energy exchange. Further the aggregated load data obtained for the 11 kV feeder is used to estimate the reference consumer load consumption by using regression and an artificial neural network model. Consumer response towards load change with price is modeled based on price elasticity and the consumer benefit function. An EHO algorithm is proposed for implementing the time of use (TOU) DR program for domestic consumers. The algorithm’s effectiveness and robustness is demonstrated by comparing the results with those of the genetic algorithm and particle swarm optimization technique. The proposed work of CBL estimation, consumer segregation, and optimum evaluation of the TOU DR program using the EHO algorithm assists in the implementation of DR programs. The proposed research can be used for analyzing the demand reduction potential, deployment of alternative energy sources, reduction in the use of conventional energy sources, promotion of energy efficiency, and the reduction of CO2 emissions.
Published Version
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