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The sensitivity of the process of optimal decisions making in electrical networks with renewable energy sources

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Warianty tytułu
PL
Wrażliwość procesu optymalizacji decyzji w sieciach elektroenergetycznych z odnawialnymi źródłami energii
Języki publikacji
EN
Abstrakty
EN
Are showed the necessity of the functioning of control automatization systems of conditions of bulk power system with renewable energy sources (RES) by taking into account the sensitivity. It is advisable to carry out optimal control of the bulk power system by introducing control parameters into the optimality (insensitivity) area. The permissible deviation range of the control parameters from their optimal values is determined by the solution of the direct and reverse sensitivity of the task. The direct and indirect tasks of the sensitivity can and should be solved in relative units using similarity theory methods. Showed that in this case the limits of the permissible optimality range are determined analytically. Comparing the allowable values of the optimality of the individual control parameters, it is possible to rank them and set the appropriate order and intensity of their actions. This makes it possible to compensate for the disturbances in the bulk power system that are carried out due to the instability of RES generation, in the most rational way.
PL
Wskazane jest przeprowadzenie optymalnej kontroli systemu zasilania jałowego poprzez wprowadzenie parametrów kontrolnych w obszarze optymalności (niewrażliwości). Dopuszczalny zakres odchyleń parametrów kontrolnych od ich wartości optymalnych jest określony przez rozwiązanie bezpośredniej i odwrotnej czułości zadania. Zadanie czułości bezpośredniej i odwrotnej można i powinno być rozwiązywane w jednostkach względnych przy użyciu metod teorii podobieństwa. Wykazanoź, że w tym przypadku granice dopuszczalnego zakresu optymalności są ustalane analitycznie. Porównując dopuszczalne wartości optymalności poszczególnych parametrów kontrolnych, można je uszeregować i ustawić odpowiednią kolejność i intensywność ich działań. Pozwala to w najbardziej racjonalny sposób zrekompensować zakłócenia w systemie zasilania, które są spowodowane niestabilnością wytwarzania odnawialnych źródeł energii.
Rocznik
Strony
32--38
Opis fizyczny
Bibliogr. 28 poz., rys.
Twórcy
  • Department of Electrical Stations and Systems, Vinnytsia National Technical University, Khmelnytsky highway 95, 21021 Vinnytsya, Ukraine
  • Department of Electrical Stations and Systems, Department of Electrical Stations and Systems, Vinnytsia National Technical University, Khmelnytsky highway 95, 21021 Vinnytsya, Ukraine
  • Regional Innovational Center at the Faculty of Electrical Engineering University of West Bohemia, Plzen, Czech Republic
  • Department of Electric Stations and Systems, Vinnytsia National Technical University, Khmelnytsky highway 95, 21021, Vinnytsya, Ukraine
Bibliografia
  • [1] Buchholz B.M., Stychinski E.A., Smart Grids – fundamentals and technologies of the energy systems of the future. M .: IDMEI, 2017. – 461 p.
  • [2] European Smart Grids Technology Platform // European Commission. Directorate-General for Research Sustainable Energy System, EUR 22040,2006, 44 p.
  • [3] P. Lezhniuk, S. Kravchuk, O. Buslavets, Selfoptimization Modes of Electric Grids with Renewable Energy Sources Using the Principle of Least Action // 2019 IEEE 6th International Conference on Energy Smart Systems (ESS). 2019. DOI: 10.1109/ESS.2019.8764181
  • [4] K. Mahmud, A. K. Sahoo, E. Fernandez, P. Sanjeevikumarand J. B. Holm-Nielsen, Computational Tools for Modeling and Analysis of Power Generation and Transmission Systems of the Smar Grid, IEEE Systems Journal,2020, pp.1-12
  • [5] Os t rem K. , Wi t tenmar k B. , Computer Control Systems. Per.sangl, 1987,480 p.
  • [6] Korotkov V.F. , Automatic regulation in power systems. M .: ID MEI, 2013, 416 p.
  • [7] Fausto A.Canales, Jakub Jurasz, Alexandre Beluco, Alexander Kies, Assessing temporal complementarity between three variable energy sources by means of correlation and compromise programming, Preprint, 2019,pp.1-17
  • [Online]. Available: https://arxiv.org/abs/ 1905. 00117
  • [8] Y. Xu, Z. Yang, W. Gu, M. Liand Z. Deng, Robust Real-Time Distributed Optimal Control Based Energy Management in a SmartGrid, in IEEE Transactions on Smart Grid, vol. 8, no. 4, pp. 1568-1579, July 2017.
  • [9] L. Rashidi, J. Moshtaghand V. M. Meiabady, Decentralized control for optimal voltage regulationin smart distribution grids, 2014 Smart Grid Conference (SGC), Tehran, 2014, pp. 1-7.
  • [10] Burman A.P., Rozanov Yu. K . , Shakaryan Yu.G., Managing electricity flows and improving the efficiency of electric power systems, ID MEI, 2012, 336 p.
  • [11] A. Donchev , Optimal control systems: Perturbations, approximations, and sensitivity analysis: Per. from English, World, 1987.,156 p.
  • [12] Efim Rozenwasser, Rafael Yusupov, Sensitivity of Automatic Control Systems, 2019.
  • [Online]. Available: https://books.google.cz/books?hl=uk&lr=&id=asm4DwAAQBAJ &oi=fnd&pg=PT17&dq=theory+of+sensitivity&ots=UzqvGkbaNk &sig=J-Q5M_Idvd7z_Td8-KehG8RtWnE&redir_esc=y#v=one page&q=theory%20of%20sensitivity&f=false
  • [13] B.T. Rearden, Perturbation Theory Eigenvalue Sensitivity Analysis with Monte Carlo Techniques, Nuclear Science and Engineering,146:3,367-382,DOI: 10.13182/NSE03-03
  • [14] Angela Dean, Daniel Voss, Danel Dragul j i c , Design and Analysis of Experiments, Second Edition, 2017. Springer Texts in Statistics. Pp. 840. DOI: 10.1007/978-3-319-52250-0
  • [15] Ananicheva S.S., Mezentsev P.E., Myzin A.L . , Electric power systems and networks: development models., Moscow, 2018,148 p.
  • [16] Venikov V.A, . Theory of similarity and modeling, Higher School, 1976, 479 p.
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  • [18] Petro D. Lezhniuk, Vyacheslav O. Komar, Dmytro S. Sobchuk, Vira V. Tepti a , Michalina Gr yniewi c z -Jawor s ka, Integral Index of Quality for Evaluation of Impact of Distributive Generation Sources on Electric Network Modes, Advances in Science and Technology. Research Journal, Volume 11, ?2, June 2017, pages 65–71. DOI: 10.12913/22998624/70760
  • [19] Petro Lezhniuk, Vyacheslav Komar , Natal ya Sobchuk, Olena Sikorska, Evaluation of Functioning Quality of Local Electrical Systems by the Criterion Method Based on Markov Processes, III Internftional Scientific and Practical Conference. Modeling, Control and Information Fechnologies. Rivne, Ukraine, pp. 169-172.
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  • [21] Shvedov G.V., Sipacheva O.V., Savchenko O.V. , Losses of electricity during its transportation through electric networks: calculation, analysis, rationing and reduction, ID MEI, 2013, 424 p.
  • [22] Lezhniuk P.D., Kulik V.V., Obolonsky D.I., Modeling and compensation of the influence of heterogeneity of electric networks on the efficiency of their modes, Electricity, 2007, No. 11, pp. 2-8 .
  • [23] P e t r o Lezhniuk, I r yn a Gunko, Sergi y Kravchuk. Selfoptimization Local Electric Systems Modes With Renewable Energy Sources, Przegląd Elektrotechniczny, (2019), nr 6, 27-31
  • [24] Efim Rozenwasser, Rafael Yusupov, Sensitivity of control systems.M .: Nauka, 1981, 464 p.
  • [25] Kostenko V. V., Kuznichenko V. M. , Lapshyn V. I . , Decision-Making: Criterion Metod of Multi-Level System Research, Naukovyi Visny kNHU, 2018, nr 5, pp. 130-135. DOI: 10.29202/nvnfu/2018-5/16
  • [26] Tikhonov A.N., Goncharsky A.V., Stepanov V.V., Yagola A.G. Numerical methods for solving ill-posed problems, M: Nauka, 1990, 232 p.
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-68fa4273-e29c-44ea-b22a-9ba0bac0a78f
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