A Fuzzy Evaluation and AHP based Method for the Energy Efficiency Evaluation of EV Charging Station

Luo, Hao; Ruan, Jiangjun; Li, Fang

doi:10.4304/jcp.9.5.1185-1192

Cited by 11 publications

(3 citation statements)

References 29 publications

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“…Some decision-making research has applied AHP and fuzzy logic theory to energy and electricity planning. Luo et al (2014) used a method based on AHP and fuzzy logic to evaluate the energy efficiency of EV charging stations. Le et al (2016) proposed the Fuzzy Analytic Hierarchy Process algorithm (Fuzzy-AHP) approach to determine the weight of the load nodes of a system and select the control strategy when the system operates at various load levels.…”

Section: Benefits Analysis With Thementioning

confidence: 99%

Benefits Analysis of Smart Grid Projects. White paper, 2014-2016

Marnay

Liu

et al. 2016

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Introduction Smart grids are rolling out internationally, with the United States (U.S.) nearing completion of a significant USD4-plus-billion federal program funded under the American Recovery and Reinvestment Act (ARRA-2009). The emergence of smart grids is widespread across developed countries. Multiple approaches to analyzing the benefits of smart grids have emerged. The goals of this white paper are to review these approaches and analyze examples of each to highlight their differences, advantages, and disadvantages. This work was conducted under the auspices of a joint U.S.-China research effort, the Climate Change Working Group (CCWG) Implementation Plan, Smart Grid. We present comparative benefits assessments (BAs) of smart grid demonstrations in the U.

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Section: Benefits Analysis With Thementioning

confidence: 99%

Benefits Analysis of Smart Grid Projects. White paper, 2014-2016

Marnay

Liu

et al. 2016

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“…Addressing the impact of uncertainty in electric vehicle charging loads on the grid, a method based on an improved Bass model (Ren et al, 2023) has been proposed to predict EV ownership, and a Monte Carlo-based model is constructed for urban EV charging load prediction. Firstly, a combination prediction model is proposed to analyze the number of conventional vehicles and establish the connection between conventional vehicles and electric vehicles using Analytic Hierarchy Process and Delphi method (Luo et al, 2014) to predict EV ownership. Secondly, the Monte Carlo method (Liu and Qi, 2014) is used to simulate the travel habits of urban EV users and predict the unordered charging behavior of EVs, thus generating the daily load curve of EVs.…”

Section: Introductionmentioning

confidence: 99%

Research on electric vehicle charging load prediction method based on spectral clustering and deep learning network

Xin,

Yang,

Beibei

et al. 2024

Front. Energy Res.

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With the increasing prominence of environmental and energy issues, electric vehicles (EVs) as representatives of clean energy vehicles have experienced rapid development in recent years, and the charging load has also exhibited statistical characteristics. Accurate prediction of EV charging load is crucial to improve grid load dispatch and intelligent level. However, current research on EV charging load prediction still faces challenges such as data reliability, complexity and variability of charging behavior, uncertainty, and lack of standardization methods. Therefore, this paper proposes an electric vehicle charging load prediction method based on spectral clustering and deep learning network (SC-CNN-LSTM). Firstly, to address the insufficient amount of EV charging load data, this paper proposes to use Monte Carlo simulation to sample and simulate historical load data. Then, in order to identify the internal structure and patterns of charging load, the sampled and simulated dataset is clustered using spectral clustering, dividing the data into different clusters, where each cluster represents samples with similar charging load characteristics. Finally, based on the different sample features of each cluster, corresponding CNN-LSTM models are constructed and trained and predict using the respective data. By modifying the model parameters, the prediction accuracy of the model is improved. Through comparative experiments, the proposed method in this paper has significantly improved prediction accuracy compared to traditional prediction methods without clustering, thus validating the effectiveness and practicality of the method.

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“…The interference of clean energy to the power system mainly depends on the randomness of the system net load (power load minus wind and optical power generation output), which can provide the required flexibility for the system through a variety of flexibility resources. From the aspect of power supply, the flexibility of power system mainly comes from power supply side, power grid side, demand side, dispatching transaction and so on [10][11]. From the perspective of flexible resource subjects, it mainly includes adjustable traditional generator sets, energy storage systems, interconnected power grids, complementary clean energy and demand response.…”

Section: Introductionmentioning

confidence: 99%

Selection of Electrochemical Energy Storage Types Based on Renewable Energy Storage Technology

Jiang

2022

J. Phys.: Conf. Ser.

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With the strong support of the national new energy policy, higher requirements are put forward for the flexible regulation ability base on the power system. It is the key factor of the flexible regulation ability of the system. How to achieve better new energy consumption through reasonable selection of energy storage types has become an urgent problem to be solved. In view of this, this paper establishes an energy storage type selection model and analyzes a numerical example. The conclusion is that lead-carbon battery and lithium-ion battery have different advantages: lead battery is more suitable for small-scale new energy consumption. Although lithium-ion battery is superior to lead-carbon battery in construction, operation and maintenance, it has more cycles, avoids frequent replacement, and has high battery conversion efficiency. With the further decline of battery manufacturing cost, the benefit of investing in energy storage system will be further improved. The conclusion can provide a basis for formulating relevant policies

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A Fuzzy Evaluation and AHP based Method for the Energy Efficiency Evaluation of EV Charging Station

Cited by 11 publications

References 29 publications

Benefits Analysis of Smart Grid Projects. White paper, 2014-2016

Benefits Analysis of Smart Grid Projects. White paper, 2014-2016

Research on electric vehicle charging load prediction method based on spectral clustering and deep learning network

Selection of Electrochemical Energy Storage Types Based on Renewable Energy Storage Technology

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