An optimal home energy management system with integration of renewable energy and energy storage with home to grid capability

Abstract

Summary Residential building consumes a significant amount of energy. To address the issue, these structures have been supplied with renewable energy sources (RES), an energy storage system (ESS), and an electric vehicle (EV). In a home, a home energy management system (HEMS) has been implemented to schedule and regulate domestic appliances. Many studies in HEMS have been conducted in order to reduce the cost of power and the peak to average ratio (PAR). However, there is insufficient use of RES, ESS, EV, and excess domestic energy. As a result, this research presents a HEMS architecture that is integrated with RES and ESS and includes home‐to‐grid (H2G) power flow functionality. The RES electricity is initially utilized to power domestic appliances and charge ESS before being transferred back into the main grid to reap economic rewards. During the low‐price period, the ESS and EV are charged and discharged during the high‐price period. The paper built a multi‐objective optimization problem using the provided approach that integrates electricity cost and system PAR. The grey wolf optimization algorithm is used to tackle the multi‐objective optimization problem. The results clearly show that the proposed technique decreases expenditures by 45.80% and PAR by 28.44%, compared to the baseline timetable in 24 h. The H2G power flow feature allows the home to send back excess energy to the grid, cutting electricity expenditures by 70% and achieving additional economic benefits.

Critical success factors for the implementation and management of energy cloud environments

“…17 The main drivers for EC are the massive input of energy from distributed sources, electric vehicles, and energy storage systems, 38 which, integrated into the users' energy systems, reduce their dependence on the distribution network. 39 Besides, technological evolution, combined with a favorable regulatory context, can have positive impacts on the dissemination of EC, 40 which is an energy management model capable of reducing infrastructure costs for decision-making, in addition to increasing efficiency, reliability, and scalability, proposing a virtual renewable energy trading system. 22 In this model, stakeholders can interact directly, without centralized oversight or third-party intervention, and sellers and buyers will trade energy freely through an application platform.…”

Efficient Scheduling of Home Energy Management Controller (HEMC) Using Heuristic Optimization Techniques

“…On the other hand, introducing the different pricing schemes at the retailer level allows an opportunity for the electricity consumer to minimize his electricity bills by shifting from peak hour to shoulder [14]. As we do not have a large-scale energy storage system, a balanced mechanism for energy generation and consumption must be implemented to avoid complete shutter-down and load-shedding problems [15]. In this article, we propose a mathematical model and implement a controlling mechanism:…”

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 . By adjusting energy demand during low/high tariffs, the optimal scheduling of interconnected multi-energy hubs can be achieved, minimizing total operational costs and carbon emissions 25 .…”

“…Existing studies 6 – 8 , 25 , 28 focused on shifting the operational time of appliances and RES by considering tariff settings and neglecting the generation and consumption profile. Day and day-ahead scheduling 13 , 14 based on single-objective 26 and multi-objective 11 , 24 optimization functions were developed without considering the appliances’ predicted operational restraints. Furthermore 30 – 34 , did not explore a DL-based day-ahead prediction scheme, whereas the system proposed in this study implements a new, highly accurate prediction model (i.e., Bi-LSTM) for power generation and consumption forecasts.…”