Outage management in a smart distribution grid integrated with PEV and its V2B applications

Solomon, Endeshaw; Hagos, Issaias Gidey; Khan, B. Zorina

doi:10.1016/b978-0-323-85169-5.00013-7

Cited by 2 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main drawback of V2H is that it is only suitable for single-family homes in residential blocks. Some examples of V2H pilot projects are Leaf to Home [28], Toyota Smart Home [29], and Honda Smart Home [30]. So far, Leaf to Home is the only commercially available pilot project among the three.…”

Section: Introductionmentioning

confidence: 99%

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

2023

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

2023

View full text Add to dashboard Cite

show abstract

Energy Sector Growth and Sustainability

Musti

2023

Leadership and Governance for Sustainability

View full text Add to dashboard Cite

The energy sector in any country requires a lot of natural resources and logistical support. These resources can be effectively controlled by the state when compared to non-state actors. This chapter first identifies various key, macro-level issues in the energy sector, such as appropriate policy framework, energy mix, duck curve problem, geo-location of power plants, and energy prices. The chapter then discusses each of these along with their internal relationships due to the natural cash flows and then examines the need for the active intervention of the government. Then various factors associated with those macro-level issues will be illustrated with both quantitative and qualitative examples.

show abstract

Outage management in a smart distribution grid integrated with PEV and its V2B applications

Cited by 2 publications

References 15 publications

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

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

Energy Sector Growth and Sustainability

Contact Info

Product

Resources

About