scholarly journals Design and Sizing of Mobile Solar Photovoltaic Power Plant to Support Rapid Charging for Electric Vehicles

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3579 ◽  
Author(s):  
Kameswara Satya Prakash Oruganti ◽  
Chockalingam Aravind Vaithilingam ◽  
Gowthamraj Rajendran ◽  
Ramasamy A
Keyword(s):  
Power Plant ◽  
Solar Energy ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Solar Photovoltaic ◽  
Distribution Grid ◽  
Plug And Play ◽  
Photovoltaic Power ◽  
Fast Charging ◽  
Charging Stations

Existing DC fast-charging stations are experiencing power quality issues such as high harmonics in the line current, poor power factor in the input supply, and overloading of distribution transformers, due to the dynamic behavior of charging patterns when it is connected to the power grid. Most of the recent works involve the usage of renewable energy sources to mitigate the issues on the distribution grid. In order to design a mobile plug and play DC fast charging station, solar energy is the best and viable solution to carry out. In this paper, plug and play solar photovoltaic power plant to charge electric vehicles (EVs) is proposed and modelled using MATLAB/Simulink software. The proposed system can act as a mobile power plant. The controller allows the system to charge the battery, whenever there is abundant solar energy. Incoming EVs will be charged directly from the system battery where the charger acts as a rapid charging system. The proposed system can meet the concept of Solar Photovoltaic Rapid Charging Stations (SPRCS), which shows that 80% of charge can be fed to an EV in 10.25 s.

Machine learning roles in advancing the power network stability due to deployments of renewable energies and electric vehicles

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohamad Nassereddine
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Power Network ◽  
Network Stability ◽  
Fast Charging ◽  
Electrical Loads ◽  
Charging Stations

AbstractRenewable energy sources are widely installed across countries. In recent years, the capacity of the installed renewable network supports large percentage of the required electrical loads. The relying on renewable energy sources to support the required electrical loads could have a catastrophic impact on the network stability under sudden change in weather conditions. Also, the recent deployment of fast charging stations for electric vehicles adds additional load burden on the electrical work. The fast charging stations require large amount of power for short period. This major increase in power load with the presence of renewable energy generation, increases the risk of power failure/outage due to overload scenarios. To mitigate the issue, the paper introduces the machine learning roles to ensure network stability and reliability always maintained. The paper contains valuable information on the data collection devises within the power network, how these data can be used to ensure system stability. The paper introduces the architect for the machine learning algorithm to monitor and manage the installed renewable energy sources and fast charging stations for optimum power grid network stability. Case study is included.

scholarly journals Optimal Day-Time Charging Strategies for Electric Vehicles considering Photovoltaic Power System and Distribution Grid Constraints

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Weige Zhang ◽  
Wenjie Ge ◽  
Mei Huang ◽  
Jiuchun Jiang
Keyword(s):  
Electric Vehicles ◽  
Optimization Method ◽  
Programming Algorithm ◽  
Distribution Grid ◽  
Pv System ◽  
Load Curve ◽  
Electricity Cost ◽  
Photovoltaic Power ◽  
Coordinated Charging ◽  
Charging Stations

Electric vehicles (EVs) charging stations with a photovoltaic (PV) system for day-time charging have been studied. This paper investigates the issues such as how to coordinate the EVs customers for coordinated charging, maximize photovoltaic utilization, and reduce customers cost of EVs charging and operator electricity. Firstly, an ideal charging load curve was built through using the linear programming algorithm. This optimal curve, which realized maximum photovoltaic power and minimum electricity cost, was used as the objective curve. Secondly, a customer response model was utilized, to propose an optimization method and strategy for charging service tariffs. Particle swarm optimization algorithm was used for time-of-use tariffs and peak-flat-valley time division so that the charging load after price regulation was adjusted to best fit the objective curve, and both the EVs customers and the operator benefit from this. Finally, the proposed model and method have been verified by two cases.

scholarly journals Energy Management System Optimization of Drug Store Electric Vehicles Charging Station Operation

2021 ◽  
Vol 13 (11) ◽  
pp. 6163
Author(s):  
Yongyi Huang ◽  
Atsushi Yona ◽  
Hiroshi Takahashi ◽  
Ashraf Mohamed Hemeida ◽  
Paras Mandal ◽  
...  
Keyword(s):  
Power Generation ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
System Optimization ◽  
Mixed Integer ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Photovoltaic Power ◽  
Vehicle Charging ◽  
Charging Stations

Electric vehicle charging station have become an urgent need in many communities around the world, due to the increase of using electric vehicles over conventional vehicles. In addition, establishment of charging stations, and the grid impact of household photovoltaic power generation would reduce the feed-in tariff. These two factors are considered to propose setting up charging stations at convenience stores, which would enable the electric energy to be shared between locations. Charging stations could collect excess photovoltaic energy from homes and market it to electric vehicles. This article examines vehicle travel time, basic household energy demand, and the electricity consumption status of Okinawa city as a whole to model the operation of an electric vehicle charging station for a year. The entire program is optimized using MATLAB mixed integer linear programming (MILP) toolbox. The findings demonstrate that a profit could be achieved under the principle of ensuring the charging station’s stable service. Household photovoltaic power generation and electric vehicles are highly dependent on energy sharing between regions. The convenience store charging station service strategy suggested gives a solution to the future issues.

7E analysis of a conceptual utility-scale land-based solar photovoltaic power plant

Energy ◽  
2021 ◽  
Vol 219 ◽  
pp. 119610
Author(s):  
S. Sreenath ◽  
K. Sudhakar ◽  
Yusop AF
Keyword(s):  
Power Plant ◽  
Solar Photovoltaic ◽  
Photovoltaic Power ◽  
Utility Scale

Evaluation of energy generation in Iraqi territory by solar photovoltaic power plants with a capacity of 20 MW

2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Qusay Hassan ◽  
Saadoon Abdul Hafedh ◽  
Ali Hasan ◽  
Marek Jaszczur
Keyword(s):  
Experimental Data ◽  
Wind Speed ◽  
Power Plant ◽  
Ambient Temperature ◽  
Solar Irradiance ◽  
Electricity Generation ◽  
Power Plants ◽  
Solar Photovoltaic ◽  
Photovoltaic Power ◽  
Plant Capacity

Abstract The study evaluates the visibility of solar photovoltaic power plant construction for electricity generation based on a 20 MW capacity. The assessment was performed for four main cities in Iraq by using hourly experimental weather data (solar irradiance, wind speed, and ambient temperature). The experimental data was measured for the period from 1st January to 31st December of the year 2019, where the simulation process was performed at a 1 h time step resolution at the same resolution as the experimental data. There are two positionings considered for solar photovoltaic modules: (i) annual optimum tilt angle and (ii) two-axis tracking system. The effect of the ambient temperature and wind on the overall system energy generated was taken into consideration. The study is targeted at evaluating the potential solar energy in Iraq and the viability of electricity generation using a 20 MW solar photovoltaic power plant. The results showed that the overall performance of the suggested power plant capacity is highly dependent on the solar irradiance intensity and the ambient temperature with wind speed. The current 20 MW solar photovoltaic power plant capacity shows the highest energy that can be generated in the mid-western region and the lowest in the northeast regions. The greatest influence of the ambient temperature on the energy genrated by power plants is observed in the southern regions.

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