Electric vehicle charging current scenario generation based on generative adversarial network combined with clustering algorithm

Yang, Fan; Yin, Shoupeng; Zhou, Suyang; Li, Dongdong; Fang, Chen; Lin, Shunfu

doi:10.1002/2050-7038.12971

Cited by 9 publications

(4 citation statements)

References 29 publications

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“…The objective was to enhance the electricity market within microgrids. Alternatively, this algorithm has been employed by [287] for generating electric vehicle charging scenarios to understand the distribution of EV charging current.…”

Section: J Electric Vehiclesmentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

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As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies.INDEX TERMS Clustering algorithms, K-means clustering, power systems. FIGURE 3. K-means clustering and power system bibliometric trend per country.

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Section: J Electric Vehiclesmentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

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“…Cluster analysis can be combined with neural networks to generate synthetic data for different regions and train the model using historical and synthetic data. Such an approach is proposed in [82] by dividing the distribution circuits into four categories (clusters). The authors note that the proposed method can produce accurate time series scenarios, under different EV penetration levels, to ensure stable power system operation.…”

Section: Model-free Analysismentioning

confidence: 99%

Applications of Clustering Methods for Different Aspects of Electric Vehicles

2023

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The growing penetration of electric vehicles can pose several challenges for power systems, especially distribution systems, due to the introduction of significant uncertain load. Analysis of these challenges becomes computationally expensive with higher penetration of electric vehicles due to various preferences, travel behavior, and the battery size of electric vehicles. This problem can be addressed using clustering methods which have been successfully used in many other sectors. Recently, there have been several studies published on applying clustering methods for various aspects of electric vehicles. To summarize the existing efforts and provide future research directions, this contribution presents a three-step analysis. First, the existing clustering methods, including hard and soft clustering, are discussed. Then, the recent literature on the application of clustering methods for different aspects of electric vehicles is reviewed. The review concentrates on four major aspects of electric vehicles: the behavior of the user, driving cycle, used batteries, and charging stations. Then, several representative studies are selected from each category and their merits and demerits are summarized. Finally, gaps in the existing literature are identified and directions for future research are presented. They indicate the need for further research on the impact on distribution circuits, charging infrastructure during emergencies, equity and disparity in rebate allocations, and the use of big data with cluster analysis to assist transportation network management.

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“…Equations ( 27) to (30) represent active and reactive power limits of load shedding. Equations (31) and (32) give AC security constraints; Equation (32) represents bus voltage limits, and constraints (34) and (35) indicate active and reactive power transmission line flow.…”

Section: Stochastic Scuc Modelmentioning

confidence: 99%

Optimal electric vehicles charging scheduling for energy and reserve markets considering wind uncertainty and generator contingency

Gupta

Kalkhambkar

Sharma

et al. 2021

Intl J of Energy Research

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Decarburization of electrical systems encourage high wind power into electric power systems and the electrification of transport sectors through electric vehicles (EVs). The increasing penetration of uncertain wind power generation and transportation networks via EV charging stations has introduced challenges for system operators to manage power systems and market operations. In this context, this paper presents a stochastic AC security-constrained unit commitment (SCUC) model to clear day-ahead energy and reserve markets considering transportationelectricity networks through EV charging stations in the presence of uncertain wind power and generator contingency. Pre-contingency ranking is a common strategy for reducing the time of the SCUC problem, but it provides high-impact outages. To address this issue, generator outages ranked first are identified using the postcontingency generator response ranking approach. The main contribution of this

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Electric vehicle charging current scenario generation based on generative adversarial network combined with clustering algorithm

Cited by 9 publications

References 29 publications

K-Means and Alternative Clustering Methods in Modern Power Systems

K-Means and Alternative Clustering Methods in Modern Power Systems

Applications of Clustering Methods for Different Aspects of Electric Vehicles

Optimal electric vehicles charging scheduling for energy and reserve markets considering wind uncertainty and generator contingency

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