A Novel Algorithm for Scheduling of Electric Vehicle Using Adaptive Load Forecasting with Vehicle-to-Grid Integration

Gautam, Akash; Verma, Arun Kumar; Srivastava, Manaswi

doi:10.1109/icps48983.2019.9067702

Cited by 15 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of WebThings, this system is scalable, open source, and interoperable, and it can also be implemented in production. Furthermore, because this research is scalable, it can be implemented in EV parking lots, connect several residences to form a microgrid, implement a V2X model, where cars are used to schedule loads [22,23], power microgrids or enterprises, or implement V2V charging [24,25].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A Solar Energy System with Vehicle-to-Home and Vehicle-to-Grid Option for Newfoundland/Canada Conditions through Mozilla IoT

Sundararajan¹,

Iqbal²

2022

JJEE

View full text Add to dashboard Cite

The hardware implementation of a solar energy system with vehicle-to-home (V2H) and vehicle-togrid (V2G) options for Newfoundland conditions through Mozilla IoT is discussed in this paper. To illustrate IoT, remote monitoring and control concepts, a prototype -is entirely a 12 V system -is created in the lab. To operate in multiple modes, the system checks the current and voltage parameters. The data is transmitted to the gateway using an ESP 32 microcontroller and the internet of things (IoT). Mozilla IoT is the platform that hosts the Raspberry Pi Things Gateway and serves as a dashboard to remotely control and monitor the system. The data that is transmitted is logged, and the logged data is shown as a graph. This paper presents the system design, details of demo experimental setup in addition to the test results which reveal that the successful implementation of proposed system -with V2H and V2G options -for Newfoundland/ Canada conditions.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Figs. 25, 26, 27 and 28 illustrate the test output for Nissan Leaf in charge mode. It can be seen from the graph that the output from PV is used for charging the in-house battery and simultaneously charging Nissan Leaf's battery.…”

Section: Nissan Leaf In Charging Modementioning

confidence: 99%

A Solar Energy System with Vehicle-to-Home and Vehicle-to-Grid Option for Newfoundland/Canada Conditions through Mozilla IoT

Sundararajan¹,

Iqbal²

2022

JJEE

View full text Add to dashboard Cite

show abstract

“…Therefore, there have been many research publications that come with various approaches to utilize the full benefit of EVs while mitigating the negative effects. Methods were introduced for increasing the potential of EVs as a resource [11][12][13], integrating with other systems such as renewable energy systems [14] or even gas storage systems [15]. The preparation to cope with high EV penetration in the future was suggested, such as optimization of allocation and size of charging stations [16][17][18], charging demand analysis [19,20], and charging management for reducing peak demand [21,22].…”

Section: Literature Reviewmentioning

confidence: 99%

Practical Grid-Based Spatial Estimation of Number of Electric Vehicles and Public Chargers for Country-Level Planning with Utilization of GIS Data

Prakobkaew

Sirisumrannukul

2022

Energies

View full text Add to dashboard Cite

This research proposes an approach to estimate the number of different types of electric vehicles for a vast area or an entire country, which can be divided into a large number of small areas such as a subdistrict scale. The estimation methodology extensively utilizes the vehicle registration data in conjunction with Thailand’s so-called EV30@30 campaign and GIS-based road infrastructure data. To facilitate the analysis, square grids are built to form cells representing the number of electric vehicles in any specific area of interest. This estimated number of electric vehicles is further analyzed to determine the energy consumption, calculate the recommended number of public chargers, and visualize an increase in the substation loads from those charging stations. The effectiveness of the proposed methods is demonstrated using the whole area of Thailand, consisting of five regions with a total area of 513,120 km2. The results show that the trucks contribute the most energy consumption while taxis rely on a lot of public chargers. The total energy consumption is about 79.4 GWh per day. A total of 12,565 public fast chargers are needed across the country to properly support daily travel, around half of them being located in the metropolitan area.

show abstract

“…The usage of electric vehicles (EVs) has grown significantly over the years due to the implementation of policies and the provision of incentives by governments worldwide. The market share of EVs is expected to reach 10.8 million units by 2026 [4]. This will increase the demand on the grid significantly and present other power quality issues, such as voltage imbalance, harmonic injections, voltage drops, etc., to the distribution [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Short-Term EV Charging Demand Forecast with Feedforward Artificial Neural Network

Effah,

Kwegyir,

Opoku

et al. 2023

JNTE

View full text Add to dashboard Cite

The global increase in greenhouse gas emissions from automobiles has brought about the manufacture and usage of large quantities of electric vehicles (EVs). However, to ensure proper integration of EVs into the grid, there is a need to forecast the charging demand of EVs accurately. This paper presents a short-term electric vehicle charging demand forecast using a feedforward artificial neural network optimized with a modified local leader phase spider monkey optimization (MLLP-SMO) algorithm, a proposed variant of spider monkey optimization. A proportionate fitness selection is employed to improve the update process of the local leader phase of the spider monkey optimization. The proposed algorithm trains a feedforward neural network to forecast electric vehicle charging demand. The effectiveness of the proposed forecasting model was tested and validated with electric vehicle public charging data from the United Kingdom Power Networks Low Carbon London Project. The model's performance was compared to a feedforward neural network trained with particle swarm optimization, genetic algorithm, classical spider monkey optimization, and two conventional forecasting models, multi-linear regression and Monte Carlo simulation. The performance of the proposed forecasting model was assessed using the mean absolute percentage error of forecast and forecasting accuracy. The model produced a forecast accuracy and mean absolute percentage error of 99.88% and 3.384%, respectively. The results show that MLLP-SMO as a trainer predicted better than the other forecasting models and met industry standard forecast accuracy.

show abstract

A Novel Algorithm for Scheduling of Electric Vehicle Using Adaptive Load Forecasting with Vehicle-to-Grid Integration

Cited by 15 publications

References 9 publications

A Solar Energy System with Vehicle-to-Home and Vehicle-to-Grid Option for Newfoundland/Canada Conditions through Mozilla IoT

A Solar Energy System with Vehicle-to-Home and Vehicle-to-Grid Option for Newfoundland/Canada Conditions through Mozilla IoT

Practical Grid-Based Spatial Estimation of Number of Electric Vehicles and Public Chargers for Country-Level Planning with Utilization of GIS Data

Short-Term EV Charging Demand Forecast with Feedforward Artificial Neural Network

Contact Info

Product

Resources

About