Application of the cybernetic approach to price-dependent demand response for underground mining enterprise electricity consumption
- 1 — Ph.D., Dr.Sci. Associate Professor Perm National Research Polytechnic University
- 2 — Ph.D., Dr.Sci. Head of Department George Agricola Graduate School of Technical Sciences ▪ Orcid
- 3 — Ph.D. Head of the Scientific and Educational Laboratory National Research University Higher School of Economics, branch in Perm ▪ Orcid
Abstract
The article considers a cybernetic model for the price-dependent demand response (DR) consumed by an underground mining enterprise (UGME), in particular, the main fan unit (MFU). A scheme of the model for managing the energy consumption of a MFU in the DR mode and the implementation of the cybernetic approach to the DR based on the IoT platform are proposed. The main functional requirements and the algorithm of the platform operation are described, the interaction of the platform with the UGME digital model simulator, on which the processes associated with the implementation of the technological process of ventilation and electricity demand response will be simulated in advance, is shown. The results of modeling the reduction in the load on the MFU of a mining enterprise for the day ahead are given. The presented solution makes it possible to determine in advance the necessary power consumption for the operation of the main power supply unit, manage its operation in an energy-saving mode and take into account the predicted changes in the planned one (e.g., when men hoisting along an air shaft) and unscheduled (e.g., when changing outdoor air parameters) modes. The results of the study can be used to reduce the cost of UGME without compromising the safety of technological processes, both through the implementation of energy-saving technical, technological or other measures, and with the participation of enterprises in the DR market. The proposed model ensures a guaranteed receipt of financial compensation for the UGME due to a reasonable change in the power consumption profile of the MFU during the hours of high demand for electricity, set by the system operator of the Unified Energy System.
Keywords:
demand response
cybernetic approach
system architecture
Internet of Things platform
underground mining enterprise
short-term load forecasting
digital twin
10.31897/PMI.2022.33
References
- Николаев А.В. Способ проветривания уклонных блоков нефтешахт, повышающий энергоэффективность подземной добычи нефти // Нефтяное хозяйство. 2016. № 11. C. 133-136.
- Vilhena Costa de L., Silva da J.M. Cost-saving electrical energy consumption in underground ventilation by the use of ventilation on demand // Mining Technology. 2020. Vol. 129. Iss. 1. P. 1-8. DOI: 10.1080/25726668.2019.1651581
- Wallace K., Prosser B., Stinnette J.D. The practice of mine ventilation engineering // International Journal of Mining Science and Technology. 2015. Vol. 25. Iss. 2. P. 165-169. DOI: 10.1016/j.ijmst.2015.02.001
- Каменских А.А. Разработка методов контроля и снижения поверхностных утечек воздуха на рудниках: Автореф. дис. ... канд. техн. наук. Пермь: Горный институт Уральского отделения РАН, 2011. 20 с.
- Николаев А.В., Алыменко Н.И., Садыков Р.И. Расчет величины поверхностных утечек воздуха на калийных рудниках // Вестник Пермского национального исследовательского политехнического университета. Геология. Нефтегазовое и горное дело. 2012. № 5. С. 115-121.
- Головатый И.И., Круглов Ю.В., Левин Л.Ю. Шахтная вентиляторная установка с системой автоматического управления для рециркуляционного проветривания калийных рудников // Горный журнал. 2010. № 8. С. 78-80.
- Круглов Ю.В., Левин Л.Ю., Зайцев А.В. Моделирование переходных процессов в вентиляционных сетях подземных рудников // Физико-технические проблемы разработки полезных ископаемых. 2011. № 5. С. 101-109.
- Зайцев А.В. Научные основы расчета и управления тепловым режимом подземных рудников: Автореф. дис. … д-ра техн. наук. Пермь: Пермский национальный исследовательский политехнический университет, 2019. 44 с.
- Gendler S.G., Kovshov S.V. Estimation and reduction of mining-induced damage of the environment and work area air in mining and processing of mineral stuff for the building industry // Eurasian mining. 2016. № 1. С. 45-49. DOI: 10.17580/em.2016.01.08
- Николаев А.В. Энергоэффективное кондиционирование шахтного воздуха в неглубоких рудниках // Горный журнал. 2017. № 3. С. 71-74. DOI: 10.17580/gzh.2017.03.13
- Кудж С.А., Цветков В.Я. Сетецентрическое управление и кибер-физические системы // Образовательные ресурсы и технологии. 2017. № 2(19). С. 86-92. DOI: 10.21777/2500-2112-2017-2-86-92
- Piette M., Sezgen O., Watson D. et al. Development and evaluation of fully automated demand response in large facilities // Lawrence Berkeley National Laboratory, 2004.
- Sezgen O., Goldman C.A., Krishnarao P. Option value of electricity demand response // Energy. 2007. Vol. 32. Iss. 2. P. 108-119. DOI: 10.1016/j.energy.2006.03.024
- Valero S., Ortiz M., Senabre C. et al. Methods for customer and demand response policies selection in new electricity markets // IET Generation, Transmission & Distribution. 2007. Vol. 1. Iss. 1. P. 104-110. DOI: 10.1049/iet-gtd:20060183
- Chasparis G.C., Pichler M., Spreitzhofer J., Esterl T. A cooperative demand-response framework for day-ahead optimization in battery pools // Energy Informatics. 2019. Vol. 2. P. 1-17. DOI: 10.1186/s42162-019-0087-x
- Soares L.J., Medeiros M.C. Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data // International Journal of Forecasting. 2008. Vol. 24. Iss. 4. Р. 630-644. DOI: 10.1016/j.ijforecast.2008.08.003
- Xu Y., Li N., Low S.H. Demand Response With Capacity Constrained Supply Function Bidding // IEEE Transactions on Power Systems. 2016. Vol. 31. № 2. P. 1377-1394. DOI: 10.1109/TPWRS.2015.2421932
- Boikov A., Payor V., Savelev R., Kolesnikov A. Synthetic Data Generation for Steel Defect Detection and Classification Using Deep Learning. Symmetry. 2021. Vol. 13. Iss. 7. № 1176. DOI: 10.3390/sym13071176
- Zhukovskiy Y.L., Kovalchuk M.S., Batueva D.E., Senchilo N.D. Development of an Algorithm for Regulating the Load Schedule of Educational Institutions Based on the Forecast of Electric Consumption within the Framework of Application of the Demand Response // Sustainability. 2021. Vol. 13 (24). № 13801. DOI: 10.3390/su132413801
- Shabalov M.Yu., Zhukovskiy Yu.L., Buldysko A.D. et al. The influence of technological changes in energy efficiency on the infrastructure deterioration in the energy sector // Energy Reports. 2021. Vol. 7. P. 2664-2680. DOI: 10.1016/j.egyr.2021.05.001
- Savard C., Iakovleva E., Ivanchenko D., Rassõlkin A. Accessible battery model with aging dependency // Energies. 2021. Vol. 14. Iss. 12. № 3493. DOI: 10.3390/en14123493
- Senchilo N.D., Ustinov D.A. Method for determining the optimal capacity of energy storage systems with a long-term forecast of power consumption // Energies. 2021. Vol. 14. Iss. 21. № 7098. DOI: 10.3390/en14217098
- Dicks F., Clausen E. Ventilation on Demand // Mining Report. 2017. Vol. 153. № 4. P. 334-341.
- Nikolaev A.V., Alymenko N.I., Kamenskikh A.A. et al. Factors defining value and direction of thermal pressure between the mine shafts and impact of the general mine natural draught on ventilation process of underground mining companies // IOP Conference. Series: Earth and Environmental Science. 2017. Vol. 87. № 052020. P. 561-566. DOI: 10.2991/aime-17.2017.91
- Rogers D.P., Tsirkunov V.V. Weather and Climate Resilience: Effective Preparedness through National Meteorological and Hydrological Services // World Bank Publications. Washington, DC: Directions in Development – Environment and Sustainable Development, 2013. 152 p.
- Kychkin A., Nikolaev A. IoT-based Mine Ventilation Control System Architecture with Digital Twin // 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). 2020. № 9111995. 5 p. DOI: 10.1109/ICIEAM48468.2020.9111995
- Бедрицкий А.И., Коршунов А.А., Хандожко Л.А., Шаймарданов М.З. Гидрометеорологическая безопасность и устойчивое развитие России // Право и безопасность. 2007. № 1-2 (22-23). С. 7-13.
- Nikolaev A., Alymenko N., Kamenskih A., Nikolaev V. The results of air treatment process modeling at the location of the air curtain in the air suppliers and ventilation shafts // E3S Web of Conferences. 2017. Vol. 15. № 02004. 7 p. DOI: 10.1051/e3sconf/20171502004
- Andersen F.M., Jensen S.G., Larsen H.V. et al. Analyses of Demand Response in Denmark. Denmark. Roskilde: Riso National Laboratory Information Service Department, 2006. 100 p.
- Xu B., Zheng J., Wang Q. Analysis and Design of Real-Time Micro-Environment Parameter Monitoring System Based on Internet of Things // IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). 15-18 December, 2016. Chengdu, China. 2016. P. 368-371. DOI: 10.1109/iThings-GreenCom-CPSCom-SmartData.2016.87
- Gubbi J., Buyya R., Marusic S., Palaniswamia M. Internet of Things (IoT): A vision, architectural elements, and future directions // Future Generation Computer Systems. 2013. Vol. 29. Iss. 7. P. 1645-1660. DOI: 10.1016/j.future.2013.01.010
- Kychkin A., Deryabin A.S., Neganova E., Markvirer V. IoT-Based Energy Management Assistant Architecture Design // 2019 IEEE 21st Conference on Business Informatics (CBI). IEEE Computer Society. 2019. Vol. 1. P. 522-530. DOI: 10.1109/CBI.2019.00067
- Mijić D., Varga E. Unified IoT Platform Architecture Platforms as Major IoT Building Blocks // International Conference on Computing and Network Communications (CoCoNet). 2018. P. 6-13. DOI: 10.1109/CoCoNet.2018.8476881
- Lu Hou, Shaohang Zhao, Xiong Xiong et al. Internet of Things Cloud: Architecture and Implementation // IEEE Communications Magazine. 2016. Vol. 54. Iss.12. P. 32-39. DOI: 10.1109/MCOM.2016.1600398CM
- Kychkin A.V. Synthesizing a system for remote energy monitoring in manufacturing // Metallurgist. 2016. Vol. 59. № 9-10. P. 752-760. DOI: 10.1007/s11015-016-0170-5
- Lyakhomskiy A., Perfileva E., Kychkin A., Genrikh N. A software- hardware system of remote monitoring and analysis of the energy data // Russian Electrical Engineering. 2015. Vol. 86 (6). P. 314-319. DOI: 10.3103/S1068371215060103
- Faizrakhmanov R.A., Frank T., Kychkin A.V., Fedorov A.B. Sustainable energy consumption control using the MY-JEVIS energy management data system // Russian Electrical Engineering. 2011. Vol. 82 (11). P. 607-611. DOI: 10.3103/S1068371211110022
- Vöth S., Vasilyeva M. Potential of Modelica for the creation of digital twins // Advances in raw material industries for sustainable development goals. London: Taylor & Francis Group, 2020. P. 386-389. DOI: 10.1201/9781003164395
- Vöth S., Bogdanov V., Pomazov D. Modeling of Efficiencies on Basis of Power Flow Directions using Modelica on the Example of Hoisting Systems // Project: Safety and Availability of Cranes. 2020. 6 p.
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