A Reinforcement Learning Approach for Integrating an Intelligent Home Energy Management System with a Vehicle-to-Home Unit

• Published in: Applied sciences 2023-04, Vol.13 (9), p.5539
• Main Authors: Almughram, Ohoud, Abdullah ben Slama, Sami, Zafar, Bassam A.
• Format: Article
• Language: English
• Subjects: Algorithms; Alternative energy sources; Analysis; Automobiles; Blackouts; Buildings; Constraint modelling; Deep learning; Distributed generation; Electric power demand; Electric power generation; Electric utilities; Electric vehicles; Electricity; Electricity distribution; Electricity pricing; Emissions; Energy conservation; Energy consumption; Energy demand; Energy industry; Energy management; Energy management systems; Energy resources; Energy storage; Energy use; Fossil fuels; Green technology; Home appliances; home energy management system; Households; Optimization; Photovoltaics; Power consumption; Power supply; Prices; Productivity; Q-learning; Reinforcement; reinforcement learning solar photovoltaic; Renewable energy sources; Residential energy; Saudi Arabia; Smart houses; Solar energy; Solar power; Solar radiation; Systems stability; Technology; Turbines; Uncertainty; vehicle-to-home; Vehicles; Wind power
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13095539

These days, users consume more electricity during peak hours, and electricity prices are typically higher between 3:00 p.m. and 11:00 p.m. If electric vehicle (EV) charging occurs during the same hours, the impact on residential distribution networks increases. Thus, home energy management systems (HEMS) have been introduced to manage the energy demand among households and EVs in residential distribution networks, such as a smart micro-grid (MG). Moreover, HEMS can efficiently manage renewable energy sources, such as solar photovoltaic (PV) panels, wind turbines, and vehicle energy storage. Until now, no HEMS has intelligently coordinated the uncertainty of smart MG elements. This paper investigated the impact of PV solar power, MG storage, and EVs on the maximum solar radiation hours. Several deep learning (DL) algorithms were utilized to account for the uncertainties. A reinforcement learning home centralized photovoltaic (RL-HCPV) scheduling algorithm was developed to manage the energy demand between the smart MG elements. The RL-HCPV system was modelled according to several constraints to meet household electricity demands in sunny and cloudy weather. Additionally, simulations demonstrated how the proposed RL-HCPV system could incorporate uncertainty, and efficiently handle the demand response and how vehicle-to-home (V2H) can help to level the appliance load profile and reduce power consumption costs with sustainable power production. The results demonstrated the advantages of utilizing RL and V2H technology as potential smart building storage technology.