Integration of Electric Vehicles and Energy Storage System in Home Energy Management System with Home to Grid Capability

Abstract

In this paper, we proposed a home energy management system (HEMS) that includes photovoltaic (PV), electric vehicle (EV), and energy storage systems (ESS). The proposed HEMS fully utilizes the PV power in operating domestic appliances and charging EV/ESS. The surplus power is fed back to the grid to achieve economic benefits. A novel charging and discharging scheme of EV/ESS is presented to minimize the energy cost, control the maximum load demand, increase the battery life, and satisfy the user’s-traveling needs. The EV/ESS charges during low pricing periods and discharges in high pricing periods. In the proposed method, a multi-objective problem is formulated, which simultaneously minimizes the energy cost, peak to average ratio (PAR), and customer dissatisfaction. The multi-objective optimization is solved using binary particle swarm optimization (BPSO). The results clearly show that it minimizes the operating cost from 402.89 cents to 191.46 cents, so that a reduction of 52.47% is obtained. Moreover, it reduces the PAR and discomfort index by 15.11% and 16.67%, respectively, in a 24 h time span. Furthermore, the home has home to grid (H2G) capability as it sells the surplus energy, and the total cost is further reduced by 29.41%.

Hardware Implementation of a Home Energy Management System Using Remodeled Sperm Swarm Optimization (RMSSO) Algorithm

“…These convergence curves were plotted against the number of iterations (50 counts). From the figure, it is proven that the RMSSO algorithm attained energy consumption costs lower than those with the other algorithms in all five climate conditions, which was because of the effective modification of Equation (32). Further, the curves prove that RMSSO was capable of exhaustively exploring and exploiting the search space in order to determine the best optimal cost with the lowest response time.…”

“…In the proposed remodeled sperm swarm optimization algorithm, the constriction mechanism allows the algorithm to find the best solution by continuously avoiding the trapping of local solutions. The mathematical model of sperm's helical shape movement is given in Equation (32). This equation represents the nature of helical movement of the sperm swarm.…”

“…. (32) where m and M are the current and maximum number of iterations, respectively. Thus, the inertia parameter is adaptively decreased from 1 to 0.…”

“…As a part of the smart home/smart grid, electric vehicles (EV) can be used to balance the demand and supply. Mohammad et al [32] have proposed an energy management unit for residential buildings with local PV power generation to maximize the user comfort, including the availability of EVs, PAR reduction, and minimize the energy consumption costs.…”

Deep learning based optimal energy management for photovoltaic and battery energy storage integrated home micro-grid system

“…In 23 , the authors propose a HEMS based on binary particle swarm optimization that uses PV power to operate residential appliances and charge/discharge the EV/ESS during low/high tariffs. Similarly, the grey wolf optimization algorithm is designed to schedule charging and discharging periods by considering low/high electricity pricing time in a RES-ESS integrated system 24 .…”