A Real-Time EV Charging Scheduling for Parking Lots With PV System and Energy Store System

The problem of electric vehicle (EV) charging scheduling in commercial parking lots has become a meaningful study in recent years, especially for the parking lots near the workplace that serve fixed users. This paper focuses on the optimization of the EV charging in the parking lot integrating energ...

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Main Authors: Wei Jiang, Yongqi Zhen
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8750840/
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spelling doaj-31e9cf1f926343259eaa0d297ae41ccf2021-03-29T23:22:26ZengIEEEIEEE Access2169-35362019-01-017861848619310.1109/ACCESS.2019.29255598750840A Real-Time EV Charging Scheduling for Parking Lots With PV System and Energy Store SystemWei Jiang0Yongqi Zhen1https://orcid.org/0000-0001-9675-4998College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, ChinaCollege of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, ChinaThe problem of electric vehicle (EV) charging scheduling in commercial parking lots has become a meaningful study in recent years, especially for the parking lots near the workplace that serve fixed users. This paper focuses on the optimization of the EV charging in the parking lot integrating energy storage system (ESS) and photovoltaic (PV) system. A smart charging management system is first established. The charging optimization problem is formulated as a cost minimization problem. Then, grey wolf optimizer (GWO) is introduced as a method to find the optimal solution. Considering the constraint conditions in the optimization problem, an improved binary grey wolf optimizer (IBGWO) is proposed, which can improve the convergence speed and optimization accuracy. Finally, a real-time EV charging scheduling strategy based on short-term PV power prediction and IBGWO is proposed. Several cases are simulated to analyze the performance of the proposed strategy. The experimental results show that the proposed IBGWO is superior in solving the proposed charging scheduling problem compared with other meta-heuristic algorithms. Moreover, the proposed strategy can effectively improve the utilization rate of the PV power and reduce the electricity cost of operators.https://ieeexplore.ieee.org/document/8750840/Electric vehiclecharging managementreal-time strategygrey wolf optimizer
collection DOAJ
language English
format Article
sources DOAJ
author Wei Jiang
Yongqi Zhen
spellingShingle Wei Jiang
Yongqi Zhen
A Real-Time EV Charging Scheduling for Parking Lots With PV System and Energy Store System
IEEE Access
Electric vehicle
charging management
real-time strategy
grey wolf optimizer
author_facet Wei Jiang
Yongqi Zhen
author_sort Wei Jiang
title A Real-Time EV Charging Scheduling for Parking Lots With PV System and Energy Store System
title_short A Real-Time EV Charging Scheduling for Parking Lots With PV System and Energy Store System
title_full A Real-Time EV Charging Scheduling for Parking Lots With PV System and Energy Store System
title_fullStr A Real-Time EV Charging Scheduling for Parking Lots With PV System and Energy Store System
title_full_unstemmed A Real-Time EV Charging Scheduling for Parking Lots With PV System and Energy Store System
title_sort real-time ev charging scheduling for parking lots with pv system and energy store system
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description The problem of electric vehicle (EV) charging scheduling in commercial parking lots has become a meaningful study in recent years, especially for the parking lots near the workplace that serve fixed users. This paper focuses on the optimization of the EV charging in the parking lot integrating energy storage system (ESS) and photovoltaic (PV) system. A smart charging management system is first established. The charging optimization problem is formulated as a cost minimization problem. Then, grey wolf optimizer (GWO) is introduced as a method to find the optimal solution. Considering the constraint conditions in the optimization problem, an improved binary grey wolf optimizer (IBGWO) is proposed, which can improve the convergence speed and optimization accuracy. Finally, a real-time EV charging scheduling strategy based on short-term PV power prediction and IBGWO is proposed. Several cases are simulated to analyze the performance of the proposed strategy. The experimental results show that the proposed IBGWO is superior in solving the proposed charging scheduling problem compared with other meta-heuristic algorithms. Moreover, the proposed strategy can effectively improve the utilization rate of the PV power and reduce the electricity cost of operators.
topic Electric vehicle
charging management
real-time strategy
grey wolf optimizer
url https://ieeexplore.ieee.org/document/8750840/
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