Economic planning approach for electric vehicle charging stations integrating traffic and power grid constraints

Huang, Xiaoqing; Chen, Jie; Yang, Hang; Cao, Yijia; Guan, Weide; Huang, Bicheng

doi:10.1049/iet-gtd.2018.5456

Cited by 60 publications

(31 citation statements)

References 29 publications

(7 reference statements)

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“…The BGA is a stochastic search algorithm based on the concept of natural selection and genetics [32]. In the past, many authors have applied BGA for optimal sitting and sizing of EV CSs because of its ability to deal with both continuous and discrete variables and with linear and nonlinear objective functions compared with other evolutionary techniques [33], [34], [35]. For this reason, the proposed CS planning model chose BGA to solve the multiobjective optimization problem.…”

Section: A Optimization Toolsmentioning

confidence: 99%

Analysis of an Optimal Planning Model for Electric Vehicle Fast-Charging Stations in Al Ain City, United Arab Emirates

et al. 2021

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For the large-scale promotion of electric vehicles (EV), reliable fast-charging stations (FCS) demand high priority among EV users. However, unplanned locations of charging stations (CSs) and station capacity determination have adverse effects on the operation and the performance of powerdistribution network. In this study, we developed an optimal FCS-planning model considering the aspects of EV users' convenience, station economic benefits, the impact on distribution systems and the effect on environment. A queuing-theory-based CS sizing algorithm that benefits EV users as well as improves CS capacity utilization was proposed. The proposed planning model was verified through a case study using real road network data by employing multi-objective binary and non-dominated sorting genetic algorithm. In addition, to evaluate the efficiency of the proposed sizing algorithm, sensitivity analyses for different EV penetration levels and station utilization were conducted. The simulation results show that the proposed CSallocation model is beneficial in terms of achieving the satisfaction of EV users, cost savings, better station utilization, and less impact on power grids and the environment. Finally, to validate the effectiveness of the proposed planning model, a comparative study with one of the previous work on CS planning is also performed. The results demonstrate that the proposed charging station sizing method is highly efficient in optimizing EV users' satisfaction and for better station utilization.

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Section: A Optimization Toolsmentioning

confidence: 99%

Analysis of an Optimal Planning Model for Electric Vehicle Fast-Charging Stations in Al Ain City, United Arab Emirates

et al. 2021

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“…Voronoi edge Voronoi vertex grid constraints [17]. In those cases, Voronoi decomposition helped to find an optimised spatial division based on the considered planning zones.…”

Section: Voronoi Polygon Of P Imentioning

confidence: 99%

Spatial Aggregation of Small-Scale Photovoltaic Generation Using Voronoi Decomposition

Lopez‐Lorente

Liu

Morrow

2020

IEEE Trans. Sustain. Energy

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In this paper, a methodology based in Voronoi decomposition is proposed to spatially aggregate small-scale solar generation. The locations of relevant electrical infrastructure are used to manage the uncertainty on locating solar photovoltaic installations. The known coordinates of step-down high-to medium-voltage electrical substations (i.e. bulk supply points) are used to divide the territory and find multiple representative locations where solar resource can be assessed. Modelling solar photovoltaic generation from global solar radiation observations permits the estimation of power output and degradation factor due to age for the entire small-scale photovoltaic fleet. The results are validated against multiple solar installations across the region of study (Northern Ireland, UK) and show a relatively low root mean square error with monthly values ranging from 0.036 to 0.123 kW/kWp. The proposed method is scalable to larger and smaller geographical areas and transferable to other categories of solar photovoltaic installations. This methodology can serve as a basis for multiple applications, such as solar generation forecasting. System operators could utilise this method to improve knowledge of when, where and in what amount additional resources would be required to manage solar penetration in favour of a robust, low-carbon and efficient power network.

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“…LOLP indicates the probability that the available power, at any time, is less than the load demand. LOLP is defined by (18) as the ratio of time intervals in which the generation is less than the demand to total time horizon. According to (19), LOLP is constrained by an upper limit:…”

Section: Reliability Constraintmentioning

confidence: 99%

“…Considering the increased tendency to integrate the PEVs on the MGs, optimal planning of production and consumption of electrical energy in an MG in the presence of PEVs is very important. It should be noted that most of the studies reported on the topic have focused on the effect of PEVs on MG operation, whereas only a few of them focused on MG planning for decreasing the operational cost [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Exact approach for charging of PEVs with V2G capability to improve micro‐grid reliability

Shams

Sadeghfam

Rostami

et al. 2019

IET Generation, Transmission & Distribution

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Economic planning approach for electric vehicle charging stations integrating traffic and power grid constraints

Cited by 60 publications

References 29 publications

Analysis of an Optimal Planning Model for Electric Vehicle Fast-Charging Stations in Al Ain City, United Arab Emirates

Analysis of an Optimal Planning Model for Electric Vehicle Fast-Charging Stations in Al Ain City, United Arab Emirates

Spatial Aggregation of Small-Scale Photovoltaic Generation Using Voronoi Decomposition

Exact approach for charging of PEVs with V2G capability to improve micro‐grid reliability

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