Optimal location of an electrical vehicle charging station in a local microgrid using an embedded hybrid optimizer

Suresh, Vishnu; Bazmohammadi, Najmeh; Janík, P.; Guerrero, Josep M.; Kaczorowska, Dominika; Rezmer, J.; Jasiński, Michał; Leonowicz, Zbigniew

doi:10.1016/j.ijepes.2021.106979

Cited by 25 publications

(10 citation statements)

References 32 publications

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“…It can be seen that the switching function S K is contained in Equation (15). Obviously, the DC bus current I dc-bus flowing into the converter contains high-frequency components related to the switching frequency.…”

Section: Three-phase Ac Charging Modementioning

confidence: 99%

“…With the continuous increase in FC power, many researchers are studying the application of FC in EVs [8][9][10][11]. The main research directions of fast charging are efficient power output, reasonable power scheduling, big data analysis of charging stations, and so on [12][13][14][15]. However, fast charging requires high battery performance and battery management systems, while slow charging has lower damage to the battery.…”

Section: Introductionmentioning

confidence: 99%

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An Optimal Control Algorithm with Reduced DC-Bus Current Fluctuation for Multiple Charging Modes of Electric Vehicle Charging Station

Chen

et al. 2021

WEVJ

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This paper presents a systematic structure and a control strategy for the electric vehicle charging station. The system uses a three-phase three-level neutral point clamped (NPC) rectifier to drive multiple three-phase three-level NPC converters to provide electric energy for electric vehicles. This topology can realize the single-phase AC mode, three-phase AC mode, and DC mode by adding some switches to meet different charging requirements. In the case of multiple electric vehicles charging simultaneously, a system optimization control algorithm is adopted to minimize DC-bus current fluctuation by analyzing and reconstructing the DC-bus current in various charging modes. This algorithm uses the genetic algorithm (ga) as the core of computing and reduces the number of change parameter variables within a limited range. The DC-bus current fluctuation is still minimal. The charging station system structure and the proposed system-level optimization control algorithm can improve the DC-side current stability through model calculation and simulation verification.

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Section: Three-phase Ac Charging Modementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Optimal Control Algorithm with Reduced DC-Bus Current Fluctuation for Multiple Charging Modes of Electric Vehicle Charging Station

Chen

et al. 2021

WEVJ

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“…This study integrates two research perspectives to provide theoretical and practical implications of the studied statistics and calculations in cross-country comparisons anchored in Industry 4.0. The Industry 4.0 influence on electromobility development is visible especially in the integration of communications [53,54], modern power system management [55], EVs control management [56,57], and computing technologies for IoV [58], which are based on grid stability and large-scale EV charging networks [59,60]. The importance of those Industry 4.0 characteristics is visible also in the whole energy sector [17,61].…”

Section: Introductionmentioning

confidence: 99%

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

2022

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The Industry 4.0 idea influences the development of both charging stations and electromobility development, due to its emphasis on device communication, cooperation, and proximity. Therefore, in electromobility development, growing attention is paid to chargers’ infrastructure density and automotive electric vehicles’ accessibility. The main goal of this scientific paper was to present the electromobility development represented in the number of charging stations and its infrastructure development calculations. In this study, the sequence of methods was used to indicate and explore the research gap. The first was the Structured Literature Review (SLR) variation method. The second method was the classical tabular comparison of gathered results. The third research method was a cluster analysis based on secondary data with cross-country comparisons of the number of charging stations and electric cars. Therefore, this paper presents a theoretical discussion and practical business implications based on the achieved results of clusters and rankings. The main finding of this paper is that charging stations play a pivotal role in electromobility development in countries with already developed road infrastructure and maritime transportation. The charging stations can support energetic infrastructure, especially in countries with vast geographical distances. The charging stations and electric vehicles statistics presented in ratios and ranks proved similarities in the electromobility development patterns in the analyzed countries. This paper also presents the limitations of the performed study and identifies possible future research avenues.

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“…Parastvand et al [33] proposed a graph-based method for the placement and sizing of EV charging stations considering traffic for limiting the vehicles' waiting time at the charging station. Surest et al [34] developed a hybrid optimizer based on ant colony optimization to allocate EV charging stations in a microgrid. Richardson et al [35] analyzed the impact of local versus centralized controlled charging strategies of EV batteries on low-voltage distribution networks.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Charging Load Allocation at Residential Locations Utilizing the Energy Savings Gained by Optimal Network Reconductoring

Goli

Jasthi²,

Gampa³

et al. 2022

Smart Cities

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In this study, a two-stage methodology based on the energy savings gained by optimal network reconductoring was developed for the sizing and allocation of electric vehicle (EV) charging load at the residential locations in urban distribution systems. During the first stage, the Flower Pollination Algorithm (FPA) was applied to minimize the annual energy losses of the radial distribution system through optimum network reconductoring. A multi-objective function was formulated to minimize investment, peak loss, and annual energy loss costs at different load factors. The results obtained with the flower pollination algorithm were compared with the particle swarm optimization algorithm. In the second stage, a simple heuristic procedure was developed for the sizing and allocation of EV charging load at every node of the distribution system utilizing part of the annual energy savings obtained by optimal network reconductoring. The number of electric cars, electric bikes, and electric scooters that can be charged at every node was computed while maintaining the voltage and branch current constraints. The simulation results were demonstrated on 123 bus and 51 bus radial distribution networks to validate the effectiveness of the proposed methodology.

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Optimal location of an electrical vehicle charging station in a local microgrid using an embedded hybrid optimizer

Cited by 25 publications

References 32 publications

An Optimal Control Algorithm with Reduced DC-Bus Current Fluctuation for Multiple Charging Modes of Electric Vehicle Charging Station

An Optimal Control Algorithm with Reduced DC-Bus Current Fluctuation for Multiple Charging Modes of Electric Vehicle Charging Station

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

Electric Vehicle Charging Load Allocation at Residential Locations Utilizing the Energy Savings Gained by Optimal Network Reconductoring

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