Improving the efficiency of electrical energy distribution with decentralized electric heating control based on nash equilibrium
DOI:
https://doi.org/10.15587/1729-4061.2020.213492Keywords:
decentralized control system, limited resource distribution, collective behavior of automatic machine, electric heatingAbstract
The widespread use of electricity determines the development of new methods for effective control of electrical energy consumers in the face of changing constraints. A model of a decentralized control system for a group of electric room heaters based on the collective behavior of automatic machines interacting with a random environment with a limited resource distribution is studied.
The considered problem differs from the known ones in that the distribution participants are limited in the use of the resource by the “all or nothing” condition. This means that each electric heater at the current time can use a fixed amount of energy resources or refuse it, and the third is not provided. The decision to connect the heaters to the electrical network is made when performing the Nash equilibrium. The Nash equilibrium condition in this work means that the unused power of the electrical network is lower than the power of any heater not connected to the electrical network.
The self-organization procedure of a group of electric heaters is studied. A model of a control system for electric heaters has been developed with the task of distributing a limited resource of electrical energy based on Nash equilibrium, using the principles of decentralized control, information technologies for the development and implementation of control actions by a group of heaters. The experiments carried out have confirmed the effectiveness of a decentralized electric heating control system and allow us to recommend it for practical use. It is shown that the proposed approach opens the way to the construction of cost-effective intelligent electric heating systemsReferences
- Zhuravlevich, S. (2020). V Belarusi nachinayut massovo stroit' elektricheskie mnogoetazhki. Obyasnyaem, kakie u nih minusy. Available at: https://realty.tut.by/news/building/669062.html
- Il'chenko, V. (2017). Kak zhivut goroda Ukrainy, kotorye otkazalis' ot tsentral'nogo otopleniya. Available at: https://energo.delo.ua/energo-government-policy/goroda-kotorye-sdalis-i-otkazalis-ot-centralnogo-otoplenija-331568/
- Fajda, L. F., Sobolev, S. A., Fajda, E. L. (2004). Pat. No. 2259022 C1 RF. Method for controlling group of electric-heating devices. No. 2004107224/09; declareted: 10.03.2004; published: 20.08.2005, Bul. No. 23.
- Zhang, X., Shi, W., Li, X., Yan, B., Malkawi, A., Li, N. (2016). Decentralized temperature control via HVAC systems in energy efficient buildings: An approximate solution procedure. 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). doi: https://doi.org/10.1109/globalsip.2016.7905980
- Villar, J. R., de la Cal, E., Sedano, J. (2009). A fuzzy logic based efficient energy saving approach for domestic heating systems. Integrated Computer-Aided Engineering, 16 (2), 151–163. doi: https://doi.org/10.3233/ica-2009-0302
- Tkachov, V., Gruhler, G., Zaslavski, A., Bublikov, A., Protsenko, S. (2018). Development of the algorithm for the automated synchronization of energy consumption by electric heaters under condition of limited energy resource. Eastern-European Journal of Enterprise Technologies, 2 (8 (92)), 50–61. doi: https://doi.org/10.15587/1729-4061.2018.126949
- Karavas, C.-S., Kyriakarakos, G., Arvanitis, K. G., Papadakis, G. (2015). A multi-agent decentralized energy management system based on distributed intelligence for the design and control of autonomous polygeneration microgrids. Energy Conversion and Management, 103, 166–179. doi: https://doi.org/10.1016/j.enconman.2015.06.021
- Shi, W., Li, N., Chu, C.-C., Gadh, R. (2017). Real-Time Energy Management in Microgrids. IEEE Transactions on Smart Grid, 8 (1), 228–238. doi: https://doi.org/10.1109/tsg.2015.2462294
- Sosnin, K., Tkachev, V., Shkola, N., Martynenko, A. (2016). Model of decision support system based on fuzzy sets for grain drying control. 20th International Drying Symposium. Gifu.
- Morkun, V., Savytskyi, O., Ruban, S. (2015). The use of heat pumps technology in automated distributed system for utilization of low-temperature energy of mine water and ventilation air. Metallurgical and Mining Industry, 6, 118–121.
- Golinko, I., Galytska, I. (2020). Mathematical Model of Heat Exchange for Non-stationary Mode of Water Heater. Advances in Computer Science for Engineering and Education II. ICCSEEA 2019. AISC, 938, 58–67. doi: https://doi.org/10.1007/978-3-030-16621-2_6
- Herasina, O. V., Husiev, O. Y., Korniienko, V. I. (2019). Neuro-fuzzy forecasting of non-linear processes of blast furnace production. Radio Electronics, Computer Science, Control, 1, 89–97. doi: https://doi.org/10.15588/1607-3274-2019-1-9
- Wernstedt, F., Davidsson, P., Johansson, C. (2007). Demand side management in district heating systems. Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems - AAMAS ’07. doi: https://doi.org/10.1145/1329125.1329454
- Kupin, A. I., Muzyka, I. O., Kuznetsov, D. I. (2017). Structure of decision support system of information system intelligent climate control residential. Radio Electronics, Computer Science, Control, 1, 171–177. Available at: http://nbuv.gov.ua/UJRN/riu_2017_1_21
- Tsetlin, M. L. (1969). Issledovaniya po teorii avtomatov i modelirovaniyu biologicheskih sistem. Moscow: Nauka, 316.
- Varshavskiy, V. I. (1973). Kollektivnoe povedenie avtomatov. Moscow: Nauka, 405.
- Burkov, V. N. (1977). Osnovy matematicheskoy teorii aktivnyh sistem. Moscow: Nauka, 256.
- Zaslavsky, A. M., Tkachov, V. V., Protsenko, S. M., Bublikov, A. V., Suleimenov, B., Orshubekov, N., Gromaszek, K. (2017). Self-organizing intelligent network of smart electrical heating devices as an alternative to traditional ways of heating. Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2017. doi: https://doi.org/10.1117/12.2281225
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Oleksander Zaslavsky, Victor Tkachov, Kostiantyn Sosnin
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.
