Multi-objective power scheduling problem in smart homes using grey wolf optimiser

Smart Home Battery for the Multi-Objective Power Scheduling Problem in a Smart Home Using Grey Wolf Optimizer

“…These pricing schemes are generated to incentivize users to shift most of their home appliances operation time from peak periods to off-peak periods [6]. The most popular dynamic pricing schemes that can be generated by PSCs are real-time price (RTP), time-of-use price (TOU), critical period price (CPP), and inclining block rate (IBR) [9,[11][12][13][14].…”

“…The problem of scheduling the smart home appliance operation time at suitable periods in accordance with a dynamic pricing scheme(s) is called the power scheduling problem in a smart home (PSPSH). The primary objectives of addressing PSPSH are reducing EB of users, reducing power demand at peak periods by minimizing the ratio between highest power demand and average power demand (peak-to-average ratio (PAR)), and improving user comfort (UC) level [12,[14][15][16].…”

“…HEMS is mainly comprising hardware and software, which allows users to efficiently manage their power consumption by controlling and monitoring smart home appliances. HEMS enables users to set the parameters of their appliances such as appliances' allowable periods to be scheduled (operation time period (OTP)), the appliances' time required to finish their operation cycle (length of operation cycle (LOC)), and power needed by the appliances to be operated [12,[14][15][16].…”

“…The meta-heuristic optimization algorithms are widely used in addressing PSPSH due to their efficiency in exploring search spaces to find an optimal/near-optimal solution [40]. The most popular algorithms adapted for PSPSH are genetic algorithm (GA) [15], bacterial foraging optimization algorithm (BFOA) [41], harmony search algorithm (HSA) [42], particle swarm optimization (PSO) [43], and grey wolf optimizer (GWO) [12,44].…”

“…A new formulation for SHB is considered to reduce the amount of power consumed by smart home appliances at unsuitable periods that conflict with PSPSH objectives by storing power at suitable periods and discharging the stored power by appliances at the inappropriate periods. GWO is adapted to address PSPSH due to its high performance in exploring rugged and deep search spaces [12]. Besides, GWO efficiently maintains the balance between local and global optima in finding the optimal solution [12,53].…”