Simultaneous planning of plug-in hybrid electric vehicle charging stations and wind power generation in distribution networks considering uncertainties

Shojaabadi, Saeed; Abapour, Saeed; Abapour, Mehdi; Nahavandi, Ali

doi:10.1016/j.renene.2016.06.032

Cited by 88 publications

(45 citation statements)

References 34 publications

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“…In this article, the IHPSO algorithm and the PSO algorithm are tested by the test functions f 1 (x)~f 4 (x). (2).33 (5).38 (10).21 (4).50 (10).66 (10) (2).38 (10).21 (2).50 (9).66 (9) (12).10(15).11 (15).12 (10).13 (11).14 (15).23 (8).24 (12).25 (11).26 (11).27 (14).28 (15).29 ( (15).9 (15). 10(15).11 (12).12 (7).13 (13).14 (15).23 (8).24 (12).25 (1).27 (7).28 (12 (14).9 (14).…”

Section: Performance Comparison Of Algorithmsmentioning

confidence: 99%

“…Based on these considerations, the work by Liu et al 10 builds a joint planning model of EVCSs and renewable energy with minimum voltage fluctuations, minimum load fluctuations and maximum capacity for EVCS energy storage connections, and the genetic particle swarm algorithm is used to determine the optimal construction scheme. Other works 11,12 consider the impacts of increasing the load demand, dynamic electricity pricing and DG power intermittence on the EVCS and DG coordination planning problem.…”

Section: Introductionmentioning

confidence: 99%

“…ADNs coordinate and control DG, the energy storage system, controllable loads and other power equipment in the distribution network by optimising the system management mode to promote the source absorption of green and clean energy. The planning models in [10][11][12] do not consider the adjustment ability of ADNs when considering the planning problem, and could not make full use of the positive effects of DG and EVCs on reducing network loss and improving system power flow distribution, which limited the development of DG and EVCS. The work by Zare et al 13 takes active management measures for DG connected to distribution network and establishes a distributed planning model of DG based on multiobjective two-layer distribution under the active management model.…”

Section: Introductionmentioning

confidence: 99%

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Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming

Liu

Yan

et al. 2020

Int Trans Electr Energ Syst

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To facilitate the development of active distribution networks with high penetration of large‐scale distributed generation (DG) and electric vehicles (EVs), active management strategies should be considered at the planning stage to implement the coordinated optimal allocations of DG and electric vehicle charging stations (EVCSs). In this article, EV charging load curves are obtained by the Monte Carlo simulation method. This article reduces the number of photovoltaic outputs and load scenarios by the K‐means++ clustering algorithm to obtain a typical scenario set. Additionally, we propose a bi‐level programming model for the coordinated DG and EVCSs planning problem. The maximisation of annual overall profit for the power supply company is taken as the objective function for the upper planning level. Then, each scenario is optimised at the lower level by using active management strategies. The improved harmonic particle swarm optimisation algorithm is used to solve the bi‐level model. The validation results for the IEEE‐33 node, PG&E‐69 node test system and an actual regional 30‐node distribution network show that the bi‐level programming model proposed in this article can improve the planning capacity of DG and EVCSs, and effectively increase the annual overall profit of the power supply company, while improving environmental and social welfare, and reducing system power losses and voltage shifts. The study provides a new perspective on the distribution network planning problem.

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Section: Performance Comparison Of Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming

Liu

Yan

et al. 2020

Int Trans Electr Energ Syst

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“…Sachan and Kishor () presented a methodology for optimal placement of centralized EV charging station in IEEE 34 distribution network considering a 24‐hr period variable load demand. Shojaabadi, Abapour, Abapour, and Nahavandi () presented a new strategic approach for the simultaneous planning of PHEV charging stations and wind power generation station in distribution network considering uncertainties in load demand and wind speed. Pan and Zhang () provided a planning scheme for the centralized charging station in IEEE 123 bus test 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

134

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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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“…In other words, allocation of charging stations for PEVs can be used as an effective tool in order to solve network problems such as peak load, load growth, etc. Also, various demand response programs (DRPs) could be applied in the mentioned networks [15].…”

Section: Introductionmentioning

confidence: 99%

Combined solar charging stations and energy storage units allocation for electric vehicles by considering uncertainties

Khanghah

Anvari‐Moghaddam

Vasquez

2017

2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Syst

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Abstract-Plug-in electric vehicles (PEVs) are becoming a key feature of smart grids. PEVs will be embedded in the network as a mobile load-storage with probabilistic behavior. In order to manage PEVs as flexible loads, charging stations (CSs) have essential roles. In this paper, a new method for optimal sitting and sizing of solar CSs using energy storage (ES) options is presented. Also, behavior of PEVs in the presence of other loads, electricity price and solar power generation uncertainties are considered. The proposed optimization model maximizes the distribution company (DisCo) benefit by appropriate use of CSs, maximizes the benefit of CSs owners and minimizes the power loss, load demand and voltage sags during peak times considering different technical constraints. The optimization variables are the location and capacity of CSs (consists of solar units and energy storage systems). In this paper, charge-discharge actions of PEVs are regulated based on time-of-use demand response programs. In order to solve the optimization problem considering uncertainty of load growth, number of EVs, electricity price, initial state of charge in PEV batteries and solar power generation, genetic algorithm method using Monte-Carlo simulation is used. The simulation results show that the proposed method has several advantages for DisCo and owners of CSs.

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Simultaneous planning of plug-in hybrid electric vehicle charging stations and wind power generation in distribution networks considering uncertainties

Cited by 88 publications

References 34 publications

Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming

Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming

Review of recent trends in charging infrastructure planning for electric vehicles

Combined solar charging stations and energy storage units allocation for electric vehicles by considering uncertainties

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