Multi-interval programming based scheduling of appliances with user preferences and dynamic pricing in residential area

Abstract

In industrial and commercial sectors, numerous countries had successfully implemented the dynamic pricing as a solution to the problem of high power demand in peak hours. But, an extensive use of real-time pricing in the residential electricity sector is hugely missing. In order to boost the efficiency of electricity market by demand response, real-time pricing needs to be implemented into residential sector also. In this paper the proposed algorithm is implemented for residential consumers of different categories with real time pricing data of ComEd, Northern Illinois Power Company, and Alactra Utilities Corporation. The proposed algorithm incorporates single interval and multi interval programming for different power pricing schemes. The proposed algorithm is suggested using metaheuristic optimization techniques viz. cuckoo search (CS), adaptive cuckoo search (ACS) and Hybrid GA–PSO for the optimum scheduling of residential appliances. The objective of this paper is to minimize the monthly electricity bill cost as well as peak demand under uncertain electricity prices. The comparative analysis of optimal solutions obtained by various artificial intelligence techniques validates the high performance of proposed algorithm. It facilitates both the residential consumer and utilities with benefits.