Simulation of the Process of Implementation of an Intelligent Electric Power Metering System

In’kov, Yu. M.; Розенберг, Е. Н.; Maron, A. I.

doi:10.3103/s1068371220010046

Cited by 8 publications

(5 citation statements)

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“…When the residents in the urban community use the fuel car or electric vehicle, it has little influence on the trip characteristics. This article is based on the 2009 U.S. household car travel survey [17][18][19][20] and the following is the probability density function of when electric cars go home and leave home.Assuming that the owner participates in the dispatching plan immediately after returning home, that is, the ev is immediately connected to the power grid, the time when EV i starts to connect to the power grid is tg,i~N(17.9,3.42),and the time when EV I is expected to leave the power grid is tl,i~N(9.24,3.162).…”

Section: Charging and Discharging Load Analysis Of Electric Vehiclesmentioning

confidence: 99%

“…In order to solve the problem of power load mutation and multi-dimension treatment of centralized dispatching mode caused by large-scale electric vehicles (evs) accessing to power grid, regional hierarchical control is an effective means to guide large-scale electric vehicles (evs) to control [5][6][7]. The model established in the above-mentioned literatures is mainly aimed at the benefit of power grid measurement, and the ultimate goal is to smooth the load fluctuation, but it does not take into account the response of users and the ability of regional electric vehicles to participate in dispatching.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Optimization Model Based on V2G Technology

Luo

Zhu

2021

Proceedings of CECNet 2021

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Large-scale electric vehicle (EV) random access to the power grid, the load peak-valley difference will become larger, seriously affect the stable operation of the power grid. In this paper, a V2G based bi-level optimal scheduling model for EV charging and discharging is proposed. The upper model takes the minimum variance of the total load as the objective function, and the lower model takes the increase of user participation and the maximization of user revenue as the objective function. The multi-population genetic algorithm is used to analyze the model, and the results show that the model can not only smooth the load fluctuation, effectively reduce the load peak-valley difference, but also maximize the economic benefits of users participating in V2G service.

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Section: Charging and Discharging Load Analysis Of Electric Vehiclesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hierarchical Optimization Model Based on V2G Technology

Luo

Zhu

2021

Proceedings of CECNet 2021

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“…The reliability of manual meter reading data is not high. It can not meet the real-time requirements of data information in power system, and also can not meet the accuracy of loss data, the reliability of data is not high [3] . In order to improve the accuracy of power metering data acquisition, improve the accuracy of identifying abnormal data.…”

Section: Introductionmentioning

confidence: 99%

Identification method of abnormal data of electric energy measurement based on BP neural network algorithm

Liu¹,

Ye²,

Liu

et al. 2022

J. Phys.: Conf. Ser.

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The problem of unclear power load type exists in the conventional abnormal data identification method of electric energy metering, which leads to a high error percentage. A method of abnormal data identification of electric energy metering based on BP neural network algorithm is designed. In this paper, the data of electric energy measurement are obtained and used to calculate the active power loss. It adjusts the connection weights and thresholds between neurons according to the error function of sample size. In this paper, BP neural network algorithm is used to identify the type of power load, and the time balance data set is constructed. According to the data set discriminant analysis of key behavior pattern characteristics, the abnormal data recognition of power measurement is completed. The experimental results show that the average error percentage of the abnormal data identification method of electric energy measurement in this paper is 0.0233, 0.0530, 0.0539 and 0.0524, respectively, compared with the other three abnormal data identification methods of electric energy measurement. The abnormal data identification method of electric energy measurement in a plain text has higher reliability.

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“…If there is no approximate value determined by analysis of the number of people required, they will not provide a solution to the problem. This article considers the solution to the problem of determining the number of personnel required for the replacement program 3 . Yli‐Huumo proposed that blockchain is the first decentralized transaction and data management technology developed for Bitcoin cryptocurrency.…”

Section: Introductionmentioning

confidence: 99%

“…This article considers the solution to the problem of determining the number of personnel required for the replacement program. 3 Yli-Huumo proposed that blockchain is the first decentralized transaction and data management technology developed for Bitcoin cryptocurrency. Since this idea was proposed in 2008, people's interest in blockchain technology has been increasing.…”

Section: Introductionmentioning

confidence: 99%

Intelligent control of electric automation instrumentation based on blockchain

Hao

2021

Security and Privacy

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With the development of society and the continuous improvement of residents' living standards, residents have higher and higher requirements for the technical level of power system. This paper mainly studies how to apply intelligent technology to the research of instrument electrical control. Nowadays, the traditional electrical instruments cannot meet the needs of the public, so the research on intelligent instruments is the most important problem at present. This article uses the RSA public key encryption algorithm based on the public blockchain. In the experimental results of this article, big data shows that from 2016 to mid-2017, there were 50 merchants using non-smart technologies in electrified dashboards in 2016. There are 40 merchants using smart technology in electrified dashboards, which is lower than the number of non-smart control technologies. However, in 2017, the number of merchants using non-intelligent control was 35, and by November 2017, it was 120, a decrease of 5% from 2016. The number of merchants using smart technology in electrified dashboards is increasing every month, from 45 in January 2016 to 285 in November 2017, an increase of 13% in the same year. This shows that intelligent control is more and more widely used in electrical automation instrument panels. Therefore, the research of a more intelligent control system of electrical automation instruments is a problem that has attracted much attention today.

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Simulation of the Process of Implementation of an Intelligent Electric Power Metering System

Cited by 8 publications

References 0 publications

Hierarchical Optimization Model Based on V2G Technology

Hierarchical Optimization Model Based on V2G Technology

Identification method of abnormal data of electric energy measurement based on BP neural network algorithm

Intelligent control of electric automation instrumentation based on blockchain

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