Abstract
In order to manage electric vehicles (EVs) connected to charging grids, this paper presents an orderly charging approach based on the EVs’ optimal time-of-use pricing (OTOUP) demand response. Firstly, the Monte Carlo approach is employed to anticipate charging power by developing a probability distribution model of the charging behavior of EVs. Secondly, a scientific classification of the load period is performed using the fuzzy clustering approach. Then, a matrix of demand price elasticity is developed to measure the link between EV charging demand and charging price. Finally, the charging scheme is optimized by an adaptive genetic algorithm from the distribution network and EV user viewpoints. This paper describes how to implement the method presented in this paper in an IEEE-33-bus distribution network. The simulation results reveal that, when compared to fixed price and common time-of-use pricing (CTOUP), the OTOUP charging strategy bears a stronger impact on reducing peak–valley disparities, boosting operating voltage, and decreasing charging cost. Additionally, this paper studies the effect of varied degrees of responsiveness on charging strategies for EVs. The data imply that increased responsiveness enhances the likelihood of new load peak, and that additional countermeasures are required.
Highlights
Models of electric vehicles (EVs) charging load under various distribution network scenarios are established and analyzed, and the results indicate that increasing EV permeability will raise the peak load level [6]
The fuzzy clustering algorithm introduces a novel method for dividing load time scientifically; We propose an orderly charging strategy for EVs based on optimal time-of-use pricing (OTOUP) demand response, which has been shown to be effective in improving power grid stability and lowering charging costs for users; We propose an adaptive genetic algorithm to solve the EV charging scheduling that considers the benefits of power grids and consumers; The sensitivity analysis of an EV’s response to the charging effect is extended, discovering a high level of responsiveness results in an overresponse problem
The orderly charging strategy for EVs based on OTOUP demand response begins by determining the initial charging time and state of charge (SOC) of EVs using a probability distribution fitted to historical data and calculating the charging duration by combining the charging power
Summary
Environmental pollution, and energy depletion have increasingly grown as global concerns in recent years. According to the Ministry of Industry and Information Technology’s Development Strategy research report, China will have 60 million EVs on its roads by 2030 [3,4]. If large numbers of EVs are gathered for charging during peak electricity consumption, the stability of the grid will be compromised, resulting in increased line losses, lower power quality and lower transformer life. According to research on uncontrolled charging of EVs, this strategy results in the coexistence of charging and conventional loads, increasing peak load and network loss while decreasing distribution network voltage [8]. By examining the effect of various EV charging curves on the power grid, it was discovered that when the permeability of the EV exceeds a specific value, the voltage deviation increased, impairing the power grid’s stable operation [9]
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