Renewable energy sources as a catalyst for energy transition: Technological innovations and an example of the energy transition in France

Lamnatou, Chr.; Cristofari, C.; Chemisana, D.

doi:10.1016/j.renene.2023.119600

Cited by 10 publications

(2 citation statements)

References 70 publications

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“…The transition from conventional distribution systems to active distribution networks (ADNs), microgrids (MGs), and smart grids (SGs) is steadily growing [1,2]. This is mainly because of recent developments in cost-effective solutions for renewable energy resources (RERs) and electric vehicles (EVs) [3,4].…”

Section: Motivation and Incitementmentioning

confidence: 99%

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Optimized allocation of microgrids’ distributed generations and electric vehicle charging stations considering system uncertainties by clustering algorithms

Yaghoubi‐Nia,

Hashemi‐Dezaki,

Niasar

2024

IET Renewable Power Gen

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The reliability‐oriented optimized sizing and placement of electric vehicle (EV) charging stations (EVCSs) has received less attention. In addition, the literature review shows that a research gap exists regarding a clustering‐based method to optimize the allocation of DGs and EVCSs, considering the system uncertainties. This article tries to fill such a knowledge gap by proposing a new clustering‐based method to optimize the allocation of DGs and EVs simultaneously, considering the uncertainties of EV behaviours and stochastic behaviours of renewable DGs. Developing a new stochastic model for EVs using the clustering algorithm is one of the essential contributions. The uncertain parameters, e.g. EV charging loads based on EV owners’ behaviours (arrival time, departure time, and driving distance) and renewable DGs, would be clustered. The proposed method could solve the execution time challenges of Monte Carlo simulation‐based approaches to concern the stochastic behaviours of smart grids. The simultaneously reliability‐oriented optimal allocation of EVCSs, DGs, and protection equipment, using the proposed clustering‐based algorithm is another main contribution. The IEEE 33‐bus test system is studied to examine the introduced method. Simulation results imply that a 1.45% accuracy improvement could be obtained compared to available analytical ones, while its execution time is appropriate.

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Section: Motivation and Incitementmentioning

confidence: 99%

“…After defining the states of elements and sub-systems, the system state can be determined according to Equation ( 2). The number of system states (I ) is a function of sub-systems and elements' number (E), as mathematically expressed in Equation (3).…”

Section: Proposed Clustering-based Reliability Evaluation Based On Th...mentioning

confidence: 99%

Optimized allocation of microgrids’ distributed generations and electric vehicle charging stations considering system uncertainties by clustering algorithms

Yaghoubi‐Nia,

Hashemi‐Dezaki,

Niasar

2024

IET Renewable Power Gen

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Day-ahead optimal scheduling for integrated energy system considering dynamic pipe network delay

Wu,

Li,

Yang

2024

Electr Eng

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Correlation between hardness and SEM-EDS characterization of palm oil waste based biocoke

Gani,

Erdiwansyah,

Desvita

et al. 2024

Energy Geoscience

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Renewable energy sources as a catalyst for energy transition: Technological innovations and an example of the energy transition in France

Cited by 10 publications

References 70 publications

Optimized allocation of microgrids’ distributed generations and electric vehicle charging stations considering system uncertainties by clustering algorithms

Optimized allocation of microgrids’ distributed generations and electric vehicle charging stations considering system uncertainties by clustering algorithms

Day-ahead optimal scheduling for integrated energy system considering dynamic pipe network delay

Correlation between hardness and SEM-EDS characterization of palm oil waste based biocoke

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