Intelligent Scheduling of Smart Home Appliances Based on Demand Response Considering the Cost and Peak-to-Average Ratio in Residential Homes

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.

Download Full-text

Appliance scheduling optimization with micro-grid in smart home network

10.32920/ryerson.14668311 ◽

2021 ◽

Author(s):

Fatima Abdul Qayyum

Keyword(s):

Smart Grid ◽

Smart Home ◽

Mixed Integer ◽

Energy Sources ◽

Time Range ◽

Electricity Cost ◽

Home Appliances ◽

Smart Appliances ◽

Micro Grid ◽

Smart Home Appliances

The fast emerging smart grid technology provides greater information flow, flexibility and control to both electricity consumers and electricity suppliers. Of these benefits, the two way flow of information between consumer and electricity producer in smart grid opened new vistas of applications. Smart home appliances are connected to home area network (HAN) to co-ordinate power usage demanded for the home under control. We are, therefore, witnessing an increasing interest in smart homes from the point of view of optimal energy management, renewable green energy sources and smart appliances. Hence, the problem of scheduling of smart appliances operations in a given time range with set of energy sources like national grid and local generation micro-grid is investigated in this thesis. Renewable energy source that is adopted in this thesis is a photovoltaic panel as a power producing appliance. Appliance operation is modeled in terms of un-interruptible sequence phases, given in load demand profile with a goal of minimizing electricity cost fulfilling duration, energy requirement, and user preference constraints. An optimization algorithm which can provide a schedule plan for smart home appliances usage is proposed based on the mixed integer linear programming technique. The effect of adding a photovoltaic system in the home results in reduction of electricity bill and the peak demand of the home and export of energy to the national grid in times when solar energy production is more than the demand of the home. The situation is modeled using Matlab with Yalmip library to exploit the state-of-the-art Gurobi solver for obtaining the timing of appliance scheduling in the smart home in comparable time to be true as real time process for demand side management.

Download Full-text