Abstract
The Coronavirus herd immunity optimizer (CHIO) is a new human-based optimization algorithm that imitates the herd immunity strategy to eliminate of the COVID-19 disease. In this paper, the coronavirus herd immunity optimizer (CHIO) is modified to tackle a discrete power scheduling problem in a smart home (PSPSH). PSPSH is a combinatorial optimization problem with NP-hard features. It is a highly constrained discrete scheduling problem concerned with assigning the operation time for smart home appliances based on a dynamic pricing scheme(s) and several other constraints. The primary objective when solving PSPSH is to maintain the stability of the power system by reducing the ratio between average and highest power demand (peak-to-average ratio (PAR)) and reducing electricity bill (EB) with considering the comfort level of users (UC). This paper modifies and adapts the CHIO algorithm to deal with such discrete optimization problems, particularly PSPSH. The adaptation and modification include embedding PSPSH problem-specific operators to CHIO operations to meet the discrete search space requirements. PSPSH is modeled as a multi-objective problem considering all objectives, including PAR, EB, and UC. The proposed method is examined using a dataset that contains 36 home appliances and seven consumption scenarios. The main CHIO parameters are tuned to find their best values. These best values are used to evaluate the proposed method by comparing its results with comparative five metaheuristic algorithms. The proposed method shows encouraging results and almost obtains the best results in all consumption scenarios.
Highlights
The traditional grids cannot fulfill the rapid growth of users’ power demand because of their primitive equipment and distribution systems, which can lead to blackouts in residential areas
The results demonstrated that genetic algorithm (GA) excels ant colony optimization and binary particle swarm optimization (PSO) concerning electricity bill (EB)
The performance of PSO was compared against GA and the results showed that PSO surpasses GA in terms of trade-offs between power scheduling problem in a smart home (PSPSH) objectives
Summary
Sharif Naser Makhadmeh 1 , Mohammed Azmi Al-Betar 1,2 , Mohammed A.
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