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Анотація
Эволюция энергетических систем в сторону парадигмы интеллектуальных сетей производства и распределения электроэнергии во многом определяется развитием новых технологий и их приложений. В статье рассматривается подход, который использует достижения информационных технологий (Information Technology) и технологии данных (Data Technology) для управления производством, передачей и распределением энергии. Рассмотрена интеллектуальная инфраструктура, которая направлена на управление сетями электроснабжения, включающими в качестве потребителей энергии, холодильные системы. Снижение потребления энергии в холодильных системах базируется на применении облачного компьютинга, который предоставляет необходимые интерфейсы и набор услуг для взаимодействия с интеллектуальными счетчиками и автоматизации системы распределения. Для сжатия большого объема данных, которые генерируются в результате мониторинга показателей работы холодильного прибора, используется «вейвлет» – преобразование сигналов. Дана оценка снижения потребления энергии в холодильной системе за счет технологии облачного компьютинга.
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Як цитувати
Петренко, М. А., & АртеменкоC. В. (2018). Облачный компьютинг для снижения потребления энергии в холодильных системах. Refrigeration Engineering and Technology, 53(6). https://doi.org/10.15673/ret.v53i6.926
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АВТОМАТИКА, КОМП’ЮТЕРНІ ТА ТЕЛЕКОМУНІКАЦІЙНІ ТЕХНОЛОГІЇ
Посилання
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2. Qaisar E. Introduction to cloud computing for developers: Key concepts, the players and their offerings/ 2012. Proc. of IEEE TCF Pro IT, pp. 1–6.
3. Popeanga J. Cloud Computing and Smart Grids. 2012. Database Systems Journal, vol. 3, no. 3, pp. 57–66.
4. Lakhani A. The Definition of Cloud Computing. 2011. http://www.cloudcentrics.com
5. Mell P., and Grance T. The NIST Definition of Cloud Computing, US National Institute of Science and Technology Std., 2011. [Online]. Available: http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf
6. Guo Y., Pan M., and Fang Y. Optimal Power Management of Residential Customers in the Smart Grid, 2012. IEEE Trans. on Parallel and Distributed Systems, vol. 23, no. 9, pp. 1593 – 1606.
7. Markovic D.S., et al. Smart power grid and cloud computing. 2013. Renew. Sustain. Energy Rev. 24, pp. 566—577.
8. Nozaki Y., Tominaga T., Iwasaki N., and Takeuchi A. A technical approach to achieve smart grid advantages using energy management systems. 2011. Proc. of IEEE Intl. Conf. on WCSP, pp. 1–5.
9. Fang X., Misra S., and Yang D. Smart Grid. The New and Improved Power Grid: A Survey. 2012. IEEE Comm. Surveys & Tutorials, vol. 14, no. 944-980.
10. Hashmi M., Hanninen S., and Maki K. Survey of smart grid concepts, architectures, and technological demonstrations worldwide. 2011. in Proc. of IEEE PES ISGT, pp. 1–7.
11. Zhou J., Hu R. Q., and. Qian Y. Scalable Distributed Communication Architectures to Support Advanced Metering Infrastructure in Smart Grid. 2012. IEEE Trans. on Parallel and Distributed Systems, vol. 23, no. 9, pp. 1632 – 1642.
12. Metke A. and Ekl R. Smart Grid security technology. 2010. Proc. of IEEE Conf. on ISGT, pp. 1–7.
13. Luo F., Dong Z. Y., Chen Y., Xu Y., Meng K., and Wong K. P. Hybrid cloud computing platform: The next generation IT backbone for smart grid. 2012. Proc. of IEEE Conf. on PES General Meeting, pp. 1–7.
14. Metke A. and Ekl R. Smart Grid security technology. 2010. Proc. of IEEE Conf. on ISGT, pp. 1–7.
15. Smart Grids European Technology Platform. 2011. www.smartgrids.eu.
16. Goulden M., Redwell B., Rennick-Egglestone S., Rodden T., Spence A. 2014. Smart grids, smart users? The role of the user in demand side management Energy Research & Social Science 2, 21–29
17. Petrenko M. О., Trishin F. A., Mazur V. О. (2017) Energy Efficient Refrigeration Systems in a Smart Grid. Refrigeration Engineering and Technology, 53 (4), 42-49 (in Russian) DOI: http://dx.doi.org/10.15673/ret.v53i4.708
18. Prodan R. and Ostermann S. A Survey and Taxonomy of Infrastructure as a Service and Web Hosting Cloud Providers. 2009. Proc. of Intl. Conf. on Grid Computing. 2009. pp. 1–10.
19. Asnafieva N.M. Veivlet analiz: osnovy teorii i primery primeneniia. 1996. Uspekhi fizicheskikh nauk. 66, No.11, 1146 – 1170.
20. https://www.mathworks.com/products/wavelet.html
21. Benefits of demand response in electricity markets and recommendations for achieving them. Report to the United States Congress, Feb. 2006. https://eetd.lbl.gov/sites/all/files/publications/report-lbnl-1252d.pdf
22. Erol-Kantarci M. and Mouftah H. TOU-Aware Energy Management and Wireless Sensor Networks for Reducing Peak Load in Smart Grids. , 2010. Proc. of IEEE Conf. on VTC-Fall, pp. 1–5.
23. Nezamabadi P. and. Gharehpetian G. Electrical energy management of virtual power plants in distribution networks with renewable energy resources and energy storage systems. 2011. Proc. of Electrical Power Distribution Networks, Bandar Abbas. pp.1-5
24. Mazur V. Fuzzy thermoeconomic optimization of energy-transforming systems. 2007. Applied Energy 84, pp. 749–762.