A novel generalized analytical reliability assessment method of smart grids including renewable and non-renewable distributed generations and plug-in hybrid electric vehicles

Hariri, Ali-Mohammad; Hashemi‐Dezaki, Hamed; Hejazi, Maryam A.

doi:10.1016/j.ress.2019.106746

Cited by 48 publications

(36 citation statements)

References 56 publications

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“…To consider the binary types of smart grids' uncertainties, for example, availability/unavailability of distribution lines, transformers, and nonrenewable DG units, the analytical state modeling is used, as shown in ( 1) and (2). 18,19,27 As seen, the zero and one values of the system state matrix's arrays denote that the corresponding subsystem is unavailable and available, respectively. The number of all system states is calculated, as mathematically expressed in (3).…”

Section: Proposed Methodsmentioning

confidence: 99%

Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties

Memari

Karimi

Hashemi‐Dezaki

2021

Int Trans Electr Energ Syst

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The reliability of the smart grid is adversely affected due to system uncertainties. Also, the steadily growing deployment of renewable distributed generation (DG) units increases the uncertainties of smart grids. Hence, it is essential to concern the uncertainties in the field of reliability evaluation of smart grids. Although the Monte Carlo simulation (MCS) has received a significant deal of consideration in the literature, there is a research gap in using the clustering algorithms to assess smart grids' reliability. This article aims to fill such a research gap by proposing a new reliability assessment method, using various clustering algorithms. The benefits from the proposed method's accuracy and fast computation are highlighted, while optimal operation, optimal short-term planning, and repetitive problems should be studied. In this paper,

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Section: Proposed Methodsmentioning

confidence: 99%

Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties

Memari

Karimi

Hashemi‐Dezaki

2021

Int Trans Electr Energ Syst

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“…37 The solar irradiance is normalized by dividing into the standard irradiance (G 0 ) according to (1), and the solar clearness (k t ) index is defined. 38 The Beta distribution is one of the most popular distributions for statistical modeling of the solar clearness index, [39][40][41] as shown in (2).…”

Section: Scenario Generation Using Mcsmentioning

confidence: 99%

“…The Beta distribution is one of the most popular distributions for statistical modeling of the solar clearness index, 39‐41 as shown in ().

f (k_{t} false| α, β) = \frac{1}{B (α, β)} k_{t}^{α ‐ 1} {()(, 1 ‐ k_{t})}^{β ‐ 1}

…”

Section: Ress'uncertainty Modelingmentioning

confidence: 99%

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Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources

Faraji

Hashemi‐Dezaki

Ketabi

2020

Energy Science & Engineering

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“…Nowadays, the transport is liable for 14% of GHG emissions [34]. The construction sector generates huge quantity of greenhouse gas emissions and is accountable for just about 35% of energy utilization owing to electric devices [3]. For energy domain, it is intended to reduce the energy and biological impact using proper strategies, depending on RES.…”

Section: Introductionmentioning

confidence: 99%

Integrating Renewable Energy Sources in Electric Vehicles via Optimization Assisted Model

Qianwen¹

2021

JCMPS

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Electric Vehicles (EVs) is found to be a capable method to enhance the transport systems. Nevertheless, higher supply of EVs leads to higher demand of electricity. An effectual technique to decrease this impact is to combine renewable energy sources (RESs) with charging infrastructure. This research aims to set up a dispatch policy using optimization theory for enhancing the economy of microgrid (MG) systems. The most significant intention is to lessen the functional cost of system while meeting system load requirements. Accordingly, the output constraints related to distributed power supply are optimized using Grasshopper Optimization with Genetic Algorithm (GOAGA). Further, the superiority of GOAGA is authenticated over existing works regarding wide-ranging measures. From the examination, the GOAGA model reveals a minimum cost value for C1, C2 and total cost over other techniques, therefore ensuring the superior economy of MG.

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A novel generalized analytical reliability assessment method of smart grids including renewable and non-renewable distributed generations and plug-in hybrid electric vehicles

Cited by 48 publications

References 56 publications

Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties

Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties

Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources

Integrating Renewable Energy Sources in Electric Vehicles via Optimization Assisted Model

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