Optimal planning of PEVs Charging Stations and Demand Response programs considering distribution and traffic networks

Pazouki, Samaneh; Mohsenzadeh, Amin; Haghifam, Mahmoud‐Reza

doi:10.1109/sgc.2013.6733806

Cited by 20 publications

(12 citation statements)

References 11 publications

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“…Optimal planning of CSs with renewable resources and its impact on the connected distribution grid are emerging areas of research [7].Different methods have been adopted to determine optimal allocation of CSs to keep the voltage and line loading limits of distribution system within acceptable thresholds. Another factor which should be considered for EV is city traffic networks on their way to CSs for charging.…”

Section: Literature Surveymentioning

confidence: 99%

PV/BESS to support electric vehicle charging station integration in a capacity constrained power distribution grid using MCTLBO

Kasturi

Nayak

2020

Scientia Iranica

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Solar photovoltaic (PV) systems with backup battery energy storage system (BESS) mitigate power system related issues like ever-increasing load demand, power loss, voltage deviation and need for power system up-gradation as integration of electric vehicles (EVs)increases load while charging. This paper investigates the improvements of the system parameters like voltage, power loss and loading capabilities of IEEE-69 bus radial distribution system (RDS) with PV/BESS-powered EV charging stations (CSs). The RDS is divided into different zones depending on the total no. of EVs, EV charging time and available CS service time. One CS is assigned to each zone. An energy management strategy is developed to direct the power flow among the CS, PV panel, BESS, and the utility grid according to time of use of electricity price. The BESS is allowed to sell excess energy stored to the utility grid during peak hours.Multi-course teaching learning based multi-objective optimization (MCTLBO) is used to optimize the size of PV/BESS system and the locations of CSs in each zone in order to minimize both the annual CS operating cost and the system active power loss. The results validate the use of optimal PV/BESS to power CS for technoeconomic improvement of the system.

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Section: Literature Surveymentioning

confidence: 99%

PV/BESS to support electric vehicle charging station integration in a capacity constrained power distribution grid using MCTLBO

Kasturi

Nayak

2020

Scientia Iranica

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“…Pazouki, Mohsenzadeh, and Haghifam () reported optimal placement of charging station in IEEE 33 test network considering traffic constraint. Wang, Xu, Wen, and Wong () presented a novel methodology for the traffic‐constrained multi objective planning of EV charging stations considering superposition of IEEE 33 bus radial network and 25 node road network.…”

Section: Modeling Of Charging Infrastructure Planning Problemmentioning

confidence: 99%

Review of recent trends in charging infrastructure planning for electric vehicles

Deb

Tammi

Kalita

et al. 2018

WIREs Energy & Environment

129

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1. This work presents the scenario of charging infrastructure development across selected countries. An overview and importance of home charging is also presented in the work. 2. This paper also provides a deep insight of the complex and dynamic nature of the optimal placement of charging station problem, thereby considering the demand, logistics, and supply aspects related to the problem. 3. An overview and comparative analysis of different modeling approaches of charging station placement problem adopted by researchers is provided. 4. The different objective functions and constraints of the charging station placement problem are also discussed along with a brief understanding of the optimization algorithms adopted for the solution of this placement problem.The paper starts with an attempt to provide scenario of the charging infrastructure planning across selected countries, importance of home charging, and then presents a general overview and brief classification of the problem. The modeling approaches and the objective functions presented in different charging system studies are discussed in the work. Finally, the work concludes by identifying the future direction of work possible in this paradigm.

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“…These include e.g. the definition of the energy sources for the CS (Ebrahim, 2016;Meyer, Choi, & Wang, 2015;Nahrstedt & Chang, 2016;Sriboon, Sangsritorn, Tuohy, Sharma, & Leeprechanon, 2016), the integration of CS in smart grids (Liu, Kong, Liu, Peng & Wang, 2015;Moghbel, Masoum & Fereidouni, 2015;Nasrallah, Al-Anbagi, & Mouftah, 2016;Van Der Burgt, Sauba, Varvarigos, & Makris, 2015), energy demand management (Castello, LaClair, & Curt Maxey, 2014;Sehar, Pipattanasomporn, & Rahman, n.d.;Zhang, Ai, Gao, & Yan, 2014), power supply quality (Dharmakeerthi & Mithulananthan, 2015;Pinto, Pombo, Calado, & Mariano, 2015;Wang, Cui & Zhu, 2014), the planning of distribution networks (Pazouki, Mohsenzadeh, & Haghifam, 2013;, and the sizing and siting of CS with relevance for the power grid (Moradi, Abedini, & Hosseinian, 2015).…”

Section: Literature Without Location Modelingmentioning

confidence: 99%

A review of spatial localization methodologies for the electric vehicle charging infrastructure

Pagany

Camargo

Dorner

2018

International Journal of Sustainable Transportation

117

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With view to the high share of the transport sector in total energy consumption, e-mobility should play an important role within the transition of the energy systems. Policymakers in several countries consider electric vehicles (EV) as an alternative to fossil-fueled vehicles. In order to allow for the development of EV, the charging infrastructure has to be set up at locations with high charging potential, identified by means of various criteria such as demand density or trip length. Many methodologies for locating charging stations (CS) have been developed in the last few years. First, this paper presents a broad overview of publications in the domain of CS localization. A classification scheme is proposed regarding modeling theory and empirical application; further on, models are analyzed, distinguishing between users, route or destination centricity of the approaches and outcomes. In a second step, studies in the field of explicit spatial location planning are reviewed in more detail, that is, in terms of their target criteria and the specialization of underlying analytical processes. One divergence of these approaches lies in the varying level of spatial planning, which could be crucial depending on the planning requirements. It is striking that almost all CS locating concepts are proposed for urban areas. Other constraints, such as the lack of extensive empirical EV traffic data for a better understanding of the driving behavior, are identified. This paper provides an overview of the CS models, a classification approach especially considering the problem's spatial dimension, and derives perspectives for further research. ARTICLE HISTORY

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Optimal planning of PEVs Charging Stations and Demand Response programs considering distribution and traffic networks

Cited by 20 publications

References 11 publications

PV/BESS to support electric vehicle charging station integration in a capacity constrained power distribution grid using MCTLBO

PV/BESS to support electric vehicle charging station integration in a capacity constrained power distribution grid using MCTLBO

Review of recent trends in charging infrastructure planning for electric vehicles

A review of spatial localization methodologies for the electric vehicle charging infrastructure

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