Abstract
With recent developments, smart grids assured for residential customers the opportunity to schedule smart home appliances’ operation times to simultaneously reduce both the electricity bill and the PAR based on demand response, as well as increasing user comfort. It is clear that the multi-objective combinatorial optimization problem involves constraints and the consumer’s preferences, and the solution to the problem is a difficult task. There have been a limited number of investigations carried out so far to solve the indicated problems using metaheuristic techniques like particle swarm optimization, mixed-integer linear programming, and the grey wolf and crow search optimization algorithms, etc. Due to the on/off control of smart home appliances, binary-coded genetic algorithms seem to be a well-fitted approach to obtain an optimal solution. It can be said that the novelty of this work is to represent the on/off state of the smart home appliance with a binary string which undergoes crossover and mutation operations during the genetic process. Because special binary numbers represent interruptible and uninterruptible smart home appliances, new types of crossover and mutation were developed to find the most convenient solutions to the problem. Although there are a few works which were carried out using the genetic algorithms, the proposed approach is rather distinct from those employed in their work. The designed genetic software runs at least ten times, and the most fitting result is taken as the optimal solution to the indicated problem; in order to ensure the optimal result, the fitness against the generation is plotted in each run, whether it is converged or not. The simulation results are significantly encouraging and meaningful to residential customers and utilities for the achievement of the goal, and they are feasible for a wide-range applications of home energy management systems.
Highlights
There are a limited number of studies that have been conducted on this subject, and some of these studies are listed in the subsection below
Paterakis et al conducted another study on the home energy management system (EMS) to define the optimal schedule of appliances for the day of a smart household under hourly pricing, and to reduce peak power based on demand response (DR) strategies [5]
It was seen that [17] is an exemplary study to test the validity of the approach and the scheme we propose compared to the studies given above; the results were produced considering the data used here, and necessary comparisons were made to obtain a better improvement in the problem of home energy management
Summary
Technological developments in this field have brought new features to both the distribution of electrical energy and the use of electrical household appliances This has made it possible to control electrical household appliances over the internet and to change the unit price of electricity for every hour, or even for every 15 minutes by means of a smart grid. With these developments, taking into account the usage characteristics of electrical household appliances and compromising daily usage habits, a significant reduction in daily energy costs can be achieved under the predefined constraints and conditions. There are a limited number of studies that have been conducted on this subject, and some of these studies are listed in the subsection below
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