Allocation of RESs and PEV Fast-Charging Station on Coupled Transportation and Distribution Networks

Saadati, Reza; Jafari‐Nokandi, Meysam; Saebi, Javad

doi:10.1016/j.scs.2020.102527

Cited by 30 publications

(14 citation statements)

References 20 publications

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“…The proposed model was implemented in the IEEE 37-bus radial network coupled with a 25node traffic network demonstrated by Figure 7 [26]. The peak load of the distribution network is 2.5 MW.…”

Section: Numerical Resultsmentioning

confidence: 99%

Optimal Allocation of Plug-in Electric Vehicle Parking Lots for Maximum Serviceability and Profit in the coupled distribution and transportation networks

2022

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Optimal planning and management of electric vehicle parking lots (EVPLs) can be an effective approach for improving the operation of both the distribution system and traffic networks. However, the limited land areas of cities can be an obstacle for constructing a large number of parking lots (PLs). This paper proposed a model for optimal siting and sizing of EVPLs as well as their charging schedule to maximize the total profit of their owners, while maximum parking demand of plug-in electric vehicles (PEVs) can be satisfied. In the proposed model, the purpose of trips, number of PEVs, plus their arrival and departure time in different urban areas are considered. Distribution network constraints are also taken into account using linearized load flow equations. The proposed model is implemented in a 37-bus distribution system coupled with a 25-node transportation network which includes four different areas in terms of PEV travel type. The simulation results show the effectiveness of the proposed model to cover the parking demand of PEVs with a limited number of PLs.

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Section: Numerical Resultsmentioning

confidence: 99%

Optimal Allocation of Plug-in Electric Vehicle Parking Lots for Maximum Serviceability and Profit in the coupled distribution and transportation networks

2022

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“…Zhang et al, 39 have elucidated a joint planning model to optimize locations with capacities of EVCSs and DPVSs simultaneously to lessen energy losses in the distribution systems. Sa'adati et al, 40 have introduced an optimal location with capacity of FCSs and renewable energy sources were concurrently determined, while deviation paths and uncertainties of RESs. Bakhshinejad et al, 41 have introduced a method not only deal the challenges but also create an opportunity to enhance the parameters of network.…”

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

Simultaneous allocation ofEVcharging stations and renewable energy sources: An EliteRERNN‐m2MPAapproach

Vijayalakshmi

Arumugam²,

Christy

et al. 2022

Intl J of Energy Research

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This article proposes a hybrid optimization technique for optimal location and sizing of electric vehicle fast charging stations (EVFCSs) and renewable energy sources (RESs). The proposed hybrid optimization technique is the consolidation of recalling-enhanced recurrent neural network (RERNN) and Marine Predators Algorithm (MPA), hence it is called RERNN-m2MPA technique.Here, an enhanced MPA (m2MPA) is proposed. The major objective of this article is to energy loss reduction, voltage deviation of the power system network and minimization of the land cost with maximum weightage to serve maximum EV with minimum installation cost. The lessening of voltage Abbreviations: F l , load force; F r , rolling resistance force; F acc , acceleration force; S, area; m ev , m ess , mass of EV and ESS; ψ, solar irradiance random variable; μ, σ, mean and SD of solar irradiance; F f , fill factor; i z , output of solar module temperature current; i MPP , V module current at the maximum power point; i SC , short circuit current; t, solar module temperature; t a , ambient temperature; i SC , short circuit current of the module; S I , scale index; T, temperature of electrolyte; T f , electrolyte freezing temperature; i b , battery current; i dis , average current of the discharge battery module; D D , Deviation distance; i B , branch current; n B , total branch present in the network; V Bus T ð Þ, bus voltage at time period T; ABC, phases; W g , weightage of the location of EVCS; L c , total cost of all the EVCS; EV De Ve , required energy of EV at Vth vehicle; C Bat Ve , battery capacity of EV at Vth vehicle; SOC Arrival Ve , SOC Depart Ve , arrival SOC and departure SOC of Vth vehicle; E Ra Ve , electric range of Vth vehicle; D ET , each trip distance; D Rate , discharging rate in G2V mode; l EV Bus , load of EVCS; EV Arrival Ve

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“…Zhang et al 44 presented a combined mode to optimize EVCSs locations to reduce loss of energy at the distribution systems. Sa'adati et al 45 portrayed the ideal location and capacity of fast charging stations and RES were simultaneously defined. Bakhshinejad et al 46 introduced a solution that not only face the challenges but also to make an opportunity to upgrade the network parameters.…”

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

Simultaneously allocating electric vehicle charging stations ( EVCS ) and photovoltaic ( PV ) energy resources in smart grid considering uncertainties: A hybrid technique

Alphonse

Raj

Arumugam

2022

Intl J of Energy Research

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Summary This paper proposes a simultaneously allocating electric vehicle charging station (EVCS) and photovoltaic (PV) energy resources in smart grid considering uncertainties using a hybrid technique. The proposed hybrid technique is the combination of capuchin search algorithm (CapSA) and wild horse optimizer (WHO), and hence it is named as ECapSA technique. The dynamic behaviour of capuchin monkeys is enhanced by the wild horse's social life behaviour. That's why, it is called as enhanced CapSA (ECapSA) technique. In the proposed work, the following four main objectives are considered and formulated by the ECapSA technique: (a) minimization of energy loss, (b) minimization of 24‐h voltage deviation, (c) maximization of weightage to offer electric vehicle charging service for maximal users, and (d) minimization of land cost. The locations of electric vehicle charging station and distributed generation (DG) affect the power loss along voltage at the distribution network. Besides, the locations on the road network determine the land cost together with weightage. The proposed formulation solution is carried out in two layers: upper and lower layers. The electric vehicle charging station allocation is modelled in upper layer and distributed generation of the optimization in lower layer. Here, 24 h slots are considered for load variation and photovoltaic generation variation at the charging station (CS). The energy loss reduction is an objective of problem formulations instead of power loss reduction. The ECapSA technique is implemented in the MATLAB platform, its performance is analyzed with existing techniques.

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Allocation of RESs and PEV Fast-Charging Station on Coupled Transportation and Distribution Networks

Cited by 30 publications

References 20 publications

Optimal Allocation of Plug-in Electric Vehicle Parking Lots for Maximum Serviceability and Profit in the coupled distribution and transportation networks

Optimal Allocation of Plug-in Electric Vehicle Parking Lots for Maximum Serviceability and Profit in the coupled distribution and transportation networks

Simultaneous allocation ofEVcharging stations and renewable energy sources: An EliteRERNN‐m2MPAapproach

Simultaneously allocating electric vehicle charging stations ( EVCS ) and photovoltaic ( PV ) energy resources in smart grid considering uncertainties: A hybrid technique

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