Abstract
The increasing penetration of distributed generations (DGs) and electric vehicles (EVs) offers not only several opportunities but also introduces many challenges for the distribution system operators (DSOs) regarding power quality. This article investigates the network performances due to uncoordinated DG and EV distribution. It also considers power quality-related performances such as the neutral current, energy loss, voltage imbalance, and bus voltage as a multiobjective optimization problem. The differential evolution optimization algorithm is employed to solve the multiobjective optimization problem to coordinate EV and DG in a distribution grid. This article proposed a method to coordinate EV and DG distribution. The proposed method allows DSOs to jointly optimize the phase sequence and optimal dispatch of DGs to improve the network's performance. If the network requires further improvement, the EV charging or discharging rate is coordinated for a particular location. The efficacy of the proposed method is tested in an Australian low-voltage distribution grid considering the amount of imbalance due to higher penetration of DG and EV. It is observed that the proposed method reduces voltage unbalance factor by up to 98.24%, neutral current up to 94%, and energy loss by 59.45%, and improve bus voltage by 10.42%.
Highlights
D UE to the increasing pressures of greenhouse gas emission and climate change, electric vehicles (EVs) have been growing as a popular choice for daily transportation
Our aim is to improve the network performance which does not depend on the EV charging cost [22]
In order to evaluate the effectiveness of the proposed method, we implemented it in both test systems
Summary
D UE to the increasing pressures of greenhouse gas emission and climate change, electric vehicles (EVs) have been growing as a popular choice for daily transportation. It has been reported in [1] that 12 million EVs have been sold already. Manuscript received April 24, 2019; revised September 26, 2019 and January 15, 2020; accepted January 15, 2020. Date of publication February 4, 2020; date of current version September 2, 2020.
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