Multi‐objective synergistic planning of EV fast‐charging stations in the distribution system coupled with the transportation network

Shukla, Akanksha; Verma, Kusum; Kumar, Rajesh

doi:10.1049/iet-gtd.2019.0486

Cited by 94 publications

(52 citation statements)

References 30 publications

(42 reference statements)

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“…Eqs. (19) and (20) are related to the safety limit of the active and reactive power, respectively. The amount of the generated power at all the buses must satisfy the load demand and losses.…”

Section: ) Vrp Indexmentioning

confidence: 99%

“…In [19], the authors presented a scenario based planning model of charging station placement with the network reconfiguration being taken into account, and solved this problem using a coevolutionary approach. In [20], the authors proposed a multiobjective framework of charging station placement with the voltage deviation, power losses, and EV flow. The Multiobjective Grey Wolf Optimization (MOGWA) was further developed in their work.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement Problem

et al. 2020

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A new hybrid multi-objective evolutionary algorithm is developed and deployed in the present work for the optimal allocation of Electric Vehicle (EV) charging stations. The charging stations must be positioned on the road in such a way that they are easily accessible to the EV drivers and the electric power grid is not overloaded. The optimization framework aims at simultaneously reducing the cost, guaranteeing sufficient grid stability and feasible charging station accessibility. The grid stability is measured by a composite index consisting of Voltage stability, Reliability, and Power loss (VRP index). A Pareto dominance based hybrid Chicken Swarm Optimization and Teaching Learning Based Optimization (CSO TLBO) algorithm is utilized to obtain the Pareto optimal solution. It amalgamates swarm intelligence with teachinglearning process and inherits the strengths of CSO and TLBO. The two level algorithm has been validated on the multi-objective benchmark problems as well as EV charging station placement. The performance of the Pareto dominance based CSO TLBO is compared with that of other state-of-the-art algorithms. Furthermore, a fuzzy decision making is used to extract the best solution from the non dominated set of solutions. The combination of CSO and TLBO can yield promising results, which is found to be efficient in dealing with the practical charging station placement problem. INDEX TERMS Accessibility index, charging station, chicken swarm optimization, teaching learning optimization, cost, electric vehicle.

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Section: ) Vrp Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement Problem

et al. 2020

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“…As a result, the grid receives full impact from the irregular charging/discharging loads of EVs, thus increasing the level of power quality (PQ) problems in the power distribution system. In [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42], ECSs not using RE and ESSs were investigated. Based on the results presented in the reviewed papers, in some cases, only limited EVs can possibly be connected.…”

Section: Ecss Without Res and Esssmentioning

confidence: 99%

“…A more than 500% higher profit was observed using the proposed method compared to a common benchmark method. The design and configuration of ECSs were explored in [30][31][32][33][34][35][36][37][38]. I. S. Bayram et al [30] proposed two frameworks for the capacity planning of ECS networks with various charging technologies to satisfy multiclass customers with various charging needs.…”

Section: Ecss Without Res and Esssmentioning

confidence: 99%

“…Then, Pareto dominance, chicken swarm optimization (CSO), and teaching-learning-based optimization (TLBO) were used to solve the planning problem. An ECS planning model with minimized losses and a maximized EV flow was proposed in [38]. A multi-objective grey wolf optimizer (MOGWO) algorithm and fuzzy satisfaction-based decision-making method were used to obtain the final result, which was tested on the IEEE 123-bus distribution system connected to a transportation network.…”

Section: Ecss Without Res and Esssmentioning

confidence: 99%

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System Planning of Grid-Connected Electric Vehicle Charging Stations and Key Technologies: A Review

2019

Energies

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The optimal planning of electric vehicle (EV) charging stations (ECSs) with advanced control algorithms is very important to accelerate the development of EVs, which is a promising solution to reduce carbon emissions of conventional internal combustion engine vehicles (ICEVs). The large and fluctuant load currents of ECSs can bring negative impacts to both EV-related power converters and power distribution systems if the energy flow is not regulated properly. Recent review papers related to EVs found in open literature have mainly focused on the design of power converter-based chargers and power interfaces, analyses of power quality (PQ) issues, the development of wireless charging techniques, etc. There is currently no review paper that focuses on key technologies in various system configurations, optimal energy management and advanced control issues in practical applications. To compensate for this insufficiency and provide timely research directions, this paper reviews 143 previously published papers related to the aforementioned topics in recent literature including 17 EV-related review papers found in Institute of Electrical and Electronics Engineers (IEEE)/Institution of Engineering and Technology (IET) (IEEE/IET) Electronic Library (IEL) and ScienceDirect OnSite (SDOS) databases. In this paper, existing system configurations, related design methods, algorithms and key technologies for ECSs are systematically reviewed. Based on discussions given in the reviewed papers, the most popular ECS configuration is a hybrid system design that integrates renewable energy (RE)-based power generation (REBPG), various energy storage systems (ESSs), and utility grids. It is noteworthy that the addition of an ESS with properly designed control algorithms can simultaneously buffer the fast, fluctuant power demand during charging, smooth the intermittent power generation of REBPG, and increase the overall efficiency and operating flexibility of ECSs. In addition, verifying the significance of the flexibility and possible profits that portable ESSs provide in ECS networks is a potential research theme in ECS fields, in which the potential applications of portable ESSs in the grid-tied ECSs are numerous and could cover a full technical spectrum.

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Optimal Location of EV Charging Stations by Modified Direct Search Algorithm

Deb

Sachan

Zhimomi

2022

Smart Charging Solutions for Hybrid and Electric Vehicles

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Multi‐objective synergistic planning of EV fast‐charging stations in the distribution system coupled with the transportation network

Cited by 94 publications

References 30 publications

A Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement Problem

A Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement Problem

System Planning of Grid-Connected Electric Vehicle Charging Stations and Key Technologies: A Review

Optimal Location of EV Charging Stations by Modified Direct Search Algorithm

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