Electric vehicles management enabling G2V and V2G in smart distribution system for maximizing profits using MOMVO

Kasturi, Kumari; Nayak, Chinmay Kumar; Nayak, Manas Ranjan

doi:10.1002/2050-7038.12013

Cited by 40 publications

(19 citation statements)

References 23 publications

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“…With the advent of smart grids and the possibility of online communication, the two-way power exchange capability of EVs can be used to charge and discharge vehicles at the appropriate time [4,36]. In this way, the parking lots of EV (here EVCS) could be used as a new example of distributed energy resources, which will be able to correct and improve the load characteristic of the network with a smart and optimal planning.…”

Section: Proposed Methodology For Smart Evs Charging/dischargingmentioning

confidence: 99%

“…In other words, it would store the energy produced while demand is lower, and feeding it back into the grid when demand is higher. This concept is known as Vehicle To Grid (V2G) and it's easy to imagine the advantages of this technology as EVs become more widespread [4,5]. The V2G concept, as well as paying attention to the behavior of EVs owners to park their vehicles most of the time, has raised the idea of constructing and planning EV Charging Stations (EVCS).…”

Section: Introductionmentioning

confidence: 99%

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Optimal Allocation of Electric Vehicle Charging Stations With Adopted Smart Charging/Discharging Schedule

et al. 2020

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Modeling and allocation of the Electric vehicles Charging Stations (EVCS) within the distribution network, as per the growing use of Electric vehicles (EVs) is a challenging task. In this paper, to manage the EVCS, first, with the aim of peak shaving, valley filling, and flattening the load curve of the network, the optimal planning of EV's charging/discharging is devised. In this regard, after modeling involving random variables, a novel hybrid method, based on the Multi Objective Particle Swarm Optimization (MOPSO) optimization algorithm and sequential Monte Carlo simulation is presented. The purpose of the presented optimal charge/discharge schedule is to control the rate and time of charging/ discharging of EVs. In the proposed model, various battery operation strategies, including Uncontrolled Charging Mode (UCM), Controlled Charging Mode (CCM), and Smart Charge/Discharge Mode (SCDM) are also considered. In the next step, in order to implement the charge/discharge schedule of the EVCS profitably, a new formulation is presented for the allocation of two EVCSs (administrative and residential EVCS). In the proposed formulation, various objective functions such as power loss reduction, reducing power purchases from the upstream network, reducing voltage deviation in buses, improving reliability is addressed. Moreover, to incentivize the owner to construct the EVCS with adopted charging/discharging schedule, the optimal profits sharing between Distribution System Operator (DSO) and EVCS owner is also performed. The proposed formulation is applied to a standard network (IEEE 69 buses) and encouraging results are achieved.

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Section: Proposed Methodology For Smart Evs Charging/dischargingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Electric Vehicle Charging Stations With Adopted Smart Charging/Discharging Schedule

et al. 2020

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“…Moreover, demand side management (DSM) in MG operation is related to demand response (DR) programs. PHEVs, if properly managed, as a new technology can result in more economical MG operation with charging/discharging electrical energy stored in their batteries at the appropriate times . Therefore, by considering the above‐mentioned features, this paper is faced with a complex multi‐objective problem (MOP) problem which includes an optimal simultaneous DFR and OGS of generation units in an MG with considering PHEVs, DR programs, and the uncertainties of non‐dispatchable DG units.…”

Section: Introductionmentioning

confidence: 99%

A new optimal operation framework for balanced microgrids considering reconfiguration and generation scheduling simultaneously

Sedighizadeh

Shaghaghi‐shahr

Aghamohammadi

et al. 2020

Int Trans Electr Energ Syst

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Summary In this article, the optimal distribution feeder reconfiguration (DFR) and optimal generation scheduling (OGS) are simultaneously taken into account to enhance operational flexibility and efficiency in microgrids (MGs). The proposed model considers different features of MGs, including distributed generation (DG) units, plug in hybrid electric vehicles (PHEVs), and demand response (DR) programs. Three DR programs are modeled including, load shifting, load curtailing, and time of use (TOU) programs. Uncertainties of renewable generations and loads are modeled using a computationally competitive approach. Both controlled and uncontrolled charging/discharging patterns of PHEVs are investigated. To enhance tractability, the proposed model is formulated as a mixed‐integer second‐order cone programing (MISOCP) problem to minimize operational cost subject to technical and financial constraints. To evaluate the performance of the proposed model, it is examined on the balanced 33‐bus test systems in different case studies of DFR and OGS. The optimization problem is solved using the GAMS software package. It is found that PHEVs and DR not only enhances MG operation flexibility, but they also reduce the total operation cost and smooth load profile.

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“…1 Plug-in electric vehicle (PEV) that can be recharged from an external source of electricity is a kind of EV. 3,4 Moreover, it will explicitly increase the risk of substation overloads. 3,4 Moreover, it will explicitly increase the risk of substation overloads.…”

Section: Introductionmentioning

confidence: 99%

“…2 It is noticeable that the uncoordinated charging of large-scale PEVs would lead to serious impacts such as elevating load peaks, increasing energy losses, and decreasing node voltage and so on. 3,4 Moreover, it will explicitly increase the risk of substation overloads. 5 Reinforcing the grid is a potential way to mitigate the negative impacts, but it might involve a huge amount of investment.…”

Section: Introductionmentioning

confidence: 99%

Computational performance analysis for centralized coordinated charging methods of plug‐in electric vehicles: From the grid operator perspective

Shang

Zheng

Shao

et al. 2019

Int Trans Electr Energ Syst

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With an ever‐increasing number of plug‐in electric vehicles (PEVs), there is a fast‐growing interest in PEVs' charging impact on the stability and the operating cost of power grid as well as the ecological environment. The centralized coordinated charging method is one of the promising solutions to mitigate such undesired impacts as elevating load peaks, increasing energy losses, and decreasing node voltage. However, the computational complexity is a critical issue to obtain the coordinated charging strategies especially for a large number of PEVs. In this context, it is very essential to analyze the computational performance of the centralized coordinated charging methods. In this paper, a paradigm for analyzing the computational performance is provided. Three centralized methods with different standpoint, viz., to minimize carbon emissions, to minimize load variance, and to minimize generation cost, are investigated to conduct a computational performance analysis from the grid operator perspective. First, the optimization theory is employed to transform the three engineering problems into the mathematical programming models. Then, whether the mathematical programming models are convex or nonconvex is analyzed. The results show that the first two mathematical programming models are convex, and the third mathematical programming model is nonconvex. And it demonstrates that the centralized scheduling model that is convex programming has a better computational performance theoretically. At last, simulations are carried out to verify the theoretical computational performance for different types of centralized coordinated charging methods.

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Electric vehicles management enabling G2V and V2G in smart distribution system for maximizing profits using MOMVO

Cited by 40 publications

References 23 publications

Optimal Allocation of Electric Vehicle Charging Stations With Adopted Smart Charging/Discharging Schedule

Optimal Allocation of Electric Vehicle Charging Stations With Adopted Smart Charging/Discharging Schedule

A new optimal operation framework for balanced microgrids considering reconfiguration and generation scheduling simultaneously

Computational performance analysis for centralized coordinated charging methods of plug‐in electric vehicles: From the grid operator perspective

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