article · Applied Sciences
Peak electricity consumption and higher tariffs between 3:00 p.m. and 11:00 p.m., combined with electric vehicle charging, increase the burden on residential distribution networks. To address this, an automated approach coordinates household demand, rooftop solar photovoltaics, microgrid storage, and vehicle-to-home units. Deep learning algorithms account for environmental and operational uncertainties, while a reinforcement learning home centralised photovoltaic scheduling algorithm manages power allocation. The model handles constraints across both sunny and cloudy conditions. Simulation results show that integrating vehicle-to-home capabilities with reinforcement learning flattens household appliance load profiles, coordinates demand response effectively, and lowers power costs. The approach demonstrates the utility of using electric vehicle batteries as dynamic storage options within smart buildings to support sustainable local energy generation.
Rising electric vehicle adoption and peak-hour energy prices place increasing stress on neighbourhood electricity grids. Integrating electric car batteries with home solar systems offers a way to balance local supply and demand. By managing weather uncertainty and power costs automatically, such systems can lower household electricity bills while supporting the wider transition to renewable residential energy networks.
The simulated algorithm could enable software solutions for smart building energy management, microgrid operations, and vehicle-to-home integration. The primary target users are smart home technology developers, microgrid managers, and utility providers seeking to stabilise distribution networks. As the findings are based solely on simulation models evaluating load profiles and pricing constraints, the technology sits at an applied research stage and requires real-world hardware validation before commercial deployment.
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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.
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DOI: 10.3390/app13095539
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