Research on modeling method for digitizing distribution substation areas based on big data from smart meters

• Autorzy: Bai Hao , Fu Yu , Yang Weichen , Li Yue , Cai Yongxiang , Xu Min

Identyfikatory

DOI

10.24425/aee.2024.150891

• Języki publikacji: EN

Abstrakty

EN

With the wide application of smart meters, real-time data collection in power grid operation can be carried out in real time, which provides big data support for the digital modeling of distribution station areas. At the same time, the digital transformation of the distribution area can be promoted through the digital modeling of the distribution area. In this situation, how to use the big data of smart meters to achieve the digital modeling of the distribution area needs to be further studied. Firstly, this paper briefly introduces the connotation of digitalization in the distribution station area. At the same time, aiming at the digitalization of the distribution station area, it analyzes the digitalization of station features, user features, new energy features, network parameters and operation features, and obtains the digitalization model. Finally, on the basis of the above, select an application scenario to introduce the application of digitalization modeling. The research results can provide a reference for the combination of big data application of smart meters and digitalization of the distribution area, and help the digital transformation of the distribution area.

Słowa kluczowe

EN

digital model; low voltage distribution network; numerous measurement data; smart meters

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2024
• Tom: Vol. 73, nr 3
• Strony: 703--720
• Opis fizyczny: Bibliogr. 27 poz., tab., wykr., wz.

Twórcy

autor

Bai Hao

• Electric Power Research Institute, China South Power Grid, Ltd. Guangzhou, China

• https://orcid.org/0009-0000-6285-9273

autor

Fu Yu

• Guizhou Power Grid Co. Ltd. Guiyang, China

autor

Yang Weichen

• Electric Power Research Institute, China South Power Grid, Ltd. Guangzhou, China

autor

Li Yue

• Guizhou Power Grid Co. Ltd. Guiyang, China

autor

Cai Yongxiang

• Guizhou Power Grid Co. Ltd. Guiyang, China

autor

Xu Min

• Electric Power Research Institute, China South Power Grid, Ltd. Guangzhou, China

Bibliografia

• [1] Caixia W., Zhiyong S., Zhifeng L., Qinmiao L., Bowen H., Bibin Huang., Liping J., Key technologies and prospects of demand-side resource utilization in power system based on new energy, Automation of Electric power Systems, vol. 45, no. 16, pp. 37–48 (2021), DOI: 10.7500/AEPS20210323004.
• [2] Xiandong T., Jun L., Zhicheng X., Li Y., Guoqiang J., Baoguo S., Electricity supply and demand situation in the “14th Five-Year Plan” under the “Double carbon” goal, China Electric Power (in Chinese), vol. 54, no. 5, pp. 1–6 (2021), DOI: 10.11930/j.issn.1004-9649.202103047.
• [3] Jun L., Wanxing S., Riliang L., Peng W., Guangxian L., Jiaying L., Zhiming Z., Design and application of Distribution Internet of Things, High Voltage Technology (in Chinese), vol. 45, no. 6, pp. 1681−1688 (2019), DOI: 10.13336/j.1003-6520.hve.20190604001.
• [4] Kai Z., Haowen Key S., Technologies and practices of 10kV distribution iot smart station, Electrical Technology, vol. 21, no. 7, pp. 80–84 (2020), DOI: 10.3969/j.issn.1673-3800.2020.07.016.
• [5] Yongjun Z., Yingqi Y., Liwu L., Kangye Z., Qinhao L., Siliang L., Huangqing H., Technology Prospect of new low-voltage Distribution system driven by dual-carbon target, Automation of Electric Power Systems, vol. 46, no. 22, pp. 1–12 (2022), DOI: 10.7500/AEPS20210922006.
• [6] Lingchao G., Liming Y., Zhiwei Y., Fei Z., Research on Hybrid Index Method of Double-LayerB+Tree for Power Big Data Considering Knowledge Graph, Journal of Physics: Conference Series, vol. 1771, no. 1, 012004 (2021), DOI: 10.1088/1742-6596/1771/1/012004.
• [7] Xiaohua L., Juncheng G., Shaolin W., Hairong Z., Smart grid abnormal power consumption detection framework, Automation Technology and Application, vol. 39, no. 12, pp. 124−128 (2019), DOI: 10.3969/j.issn.1003-7241.2020.12.028.
• [8] Xiaodong X., Qianyun L., Tao L., Yu Z., Qiyu W., Station area recognition Based on smart meter data and fuzzy C-means algorithm, Journal of Nanjing Institute of Technology (Natural Science Edition), vol. 18, no. 4, pp. 1–7 (2019), DOI: 10.13960/j.issn.1672-2558.2020.04.001.
• [9] Yi S., Yehua M., Zekun L., Xudong Z., Fei L., Comprehensive clustering method of user load characteristics and adjustable potential for power big data, Proceedings of the CSEE (in Chinese), vol. 41, no. 18, pp. 6259–6271 (2021), DOI: 10.13334/j.0258-8013.pcsee.201928.
• [10] Zhiyong Y., Zekun X., Li Y., Quan X., Yuehuan L., Peiqiang L., Xi H., Review of smart grid big data research, Guangdong Electric Power, vol. 34, no. 1, pp. 1–12 (2021), DOI: 10.3969/j.issn.1007- 290X.2021.001.001.
• [11] Chen S., Yunni C., Mengshuo J., Ying C., Shaowei Huang., Concept, characteristics and application prospect of digital twin of power system, Proceedings of the CSEE (in Chinese), vol. 42, no. 2, pp. 487–498 (2022), DOI: 10.13334/j.0258-8013.pcsee.211594.
• [12] Tianjiao P., Sheng C., Qi Z., Xinying W., Dongxia Z., Framework design and application prospect of digital twin system of energy Internet, Proceedings of the CSEE, vol. 41, no. 6, pp. 2012−2028 (2021), DOI: 10.13334/j.0258-8013.pcsee.201757.
• [13] Chengshan W., Bo D., Hao Y., Jianzhong W., Jinpeng Y., Peng L., Digital twin technology and application of smart city integrated energy system, Proceedings of the CSEE, vol. 41, no. 5, pp. 1597–1607 (2021), DOI: 10.13334/j.0258-8013.pcsee.201804.
• [14] Zhelin L., Chengshan W., Peng L., Hao Y., Li Y., Estimation and application of distribution network state based on multi-source measurement data fusion, Proceedings of the CSEE (in Chinese), vol. 41, no. 8, pp. 2605–2615 (2021), DOI: 10.13334/j.0258-8013.pcsee.201416.
• [15] Dan L., Geng Y., Guohong Z., Guangzhi D., Rui M., Construction of evaluation index system of “access to electricity” based on demand side in the context of digital transformation, Power supply, vol. 40, no. 2, pp. 60–67 (2023), DOI: 10.19421/j.cnki.1006-6357.2023.02.009.
• [16] Liyun C., Hanqi Y., Digital transformation and upgrading management of distribution network operation and maintenance for power grid enterprises, Enterprise Management, iss. S1, pp. 158–159 (2022).
• [17] Qilu S., Application research of digital Intelligent operation and Maintenance Technology of distribution network, Internet of Things Technology, vol. 11, no. 11, pp. 93–95 (2021), DOI: 10.16667/j.issn.2095- 1302.2021.11.027.
• [18] Lei Q., Gang A., Jie Y., Jianping H., Wei D., Jie T., Bing L., Digital intelligent power distribution area construction research, Rural Electrification, vol. 3, pp. 27–31 (2023), DOI: 10.13882/j.cnki. ncdqh.2023.03.005.
• [19] Rui L., Mingyang L., Yanzhang G., Development strategy of power supply reliability of China Southern Power Grid under Digital Transformation, Power Supply and Electricity, vol. 38, no. 3, pp. 38–44 (2021), DOI: 10.19421/j.cnki.1006-6357.2021.03.006.
• [20] Hongquan L., Hao D., Cong W., Helong S., Yanchao C., Aichun G., Dingli L., Research and Application of Digital Technology Scheme in Distribution Station Area, Electrotechnical Engineering, vol. 22, pp. 143–146 (2021), DOI: 10.19768/j.cnki.dgjs.2021.22.048.
• [21] Vasiliev N.V., Kartashev D.A., Yu Krishtopa N., Analysis of operating modes of three-phase distribution networks using their digital models of 0.4kV, IOP Conference Series: Earth and Environmental Science, vol. 979, no. 1, 012110 (2022), DOI: 10.1088/1755-1315/979/1/012110.
• [22] Guang C., Shaobing C., Zekun L., Xiaoyi C., Research on big data analysis technology for smart meter quality in modern supply chain, Automation and Instrumentation, vol. 5, no. 16, pp. 180–183+188 (2021), DOI: 10.14016/j.cnki.1001-9227.2021.05.180.
• [23] Huaying L., Big data analysis and abnormal detection of smart electricity meters, Master Thesis, Changchun University of Technology (2020).
• [24] Lei P., Yan Y., Hao L., Longquan F., Abnormal power consumption detection based on smart meter big data, Computing Technology and Automation, vol. 39, no. 2, pp. 177–183 (2020), DOI: 10.16339/j.cnki.jsjsyzdh.202002035.
• [25] Limin J., Guixiong H., GB/T 31367-2015, Guidelines for Energy Efficiency Evaluation of medium and low voltage distribution Network, China Electricity Council (2015).
• [26] Jian D., Research on low-voltage distribution network planning in Chongshanchang Village, Anji County, Master Thesis, North China Electric Power University (Beijing) (2016).
• [27] Andruszkiewicz J., Lorenc J., Weychan A., Distributed generation as efficient measure to improve power generation adequacy, Archives of Electrical Engineering, vol. 68, no. 2, pp. 373–385 (2019), DOI: 10.24425/aee.2019.128274.