An advanced method for improving the reliability of power losses probabilistic characteristics calculation to determine the optimal wind power capacity and placement tasks
| Parent link: | International Journal of Electrical Power & Energy Systems (JEPE).— .— Amsterdam: Elsevier Science Publishing Company Inc. Vol. 147.— 2023.— Article number 108846, 16 p. |
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| Beste egile batzuk: | , , , , |
| Gaia: | Due to the trend of increasing electricity consumption that has observed over the past decades, the sustainable development of electric power systems is a relevant task. Taking into account the desire of the leading countries for energy independence, low-carbon energy based on renewable energy sources is being actively penetrated nowadays. One of the main tasks for the renewable energy sources installation into existing electric power systems with no additional changes in network is to maximize power generation with the lowest power losses and to comply with the grid requirements. Considering the stochastic nature of renewable energy, various optimization algorithms are being developed and used for this task. In this regard, numerical methods of deterministic and probabilistic modeling of power systems with, e.g., wind turbine generators are used to determine the optimal placement and capacity. The main problem is that obtaining a reliable result by actual statistical and heuristic methods, based mostly on a group of Monte Carlo methods, does not have a full solution in the whole range of functional dependence, due to the complexity of modeling the input arguments of rare repeatability. As part of the solution to the optimal power flow problems, namely, not exceeding the specified probability density functions of the state parameters, it can lead to false conclusions. This paper proposes a methodology for calculating the probabilistic characteristics of steady-state parameters, increasing the reliability and speed of their calculation by taking into account the values of rare repeatability. The results of research is the refinement of the power flow values (compared with the Monte Carlo method) using the developed method based on selection of interval boundaries of input and output probabilistic data (SIBD method), and the definition of minimum possible power losses as part of the problem of determining the optimal wind power capacity and placement in power system for IEEE-14 and IEEE-57 schemes Текстовый файл AM_Agreement |
| Hizkuntza: | ingelesa |
| Argitaratua: |
2023
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| Gaiak: | |
| Sarrera elektronikoa: | https://doi.org/10.1016/j.ijepes.2022.108846 |
| Formatua: | Baliabide elektronikoa Liburu kapitulua |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=684734 |
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| 200 | 1 | |a An advanced method for improving the reliability of power losses probabilistic characteristics calculation to determine the optimal wind power capacity and placement tasks |f M. Andreev, Y. Bay, A. Kievets [et al.] | |
| 203 | |a Текст |b визуальный |c электронный | ||
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| 320 | |a References : 49 tit | ||
| 330 | |a Due to the trend of increasing electricity consumption that has observed over the past decades, the sustainable development of electric power systems is a relevant task. Taking into account the desire of the leading countries for energy independence, low-carbon energy based on renewable energy sources is being actively penetrated nowadays. One of the main tasks for the renewable energy sources installation into existing electric power systems with no additional changes in network is to maximize power generation with the lowest power losses and to comply with the grid requirements. Considering the stochastic nature of renewable energy, various optimization algorithms are being developed and used for this task. In this regard, numerical methods of deterministic and probabilistic modeling of power systems with, e.g., wind turbine generators are used to determine the optimal placement and capacity. The main problem is that obtaining a reliable result by actual statistical and heuristic methods, based mostly on a group of Monte Carlo methods, does not have a full solution in the whole range of functional dependence, due to the complexity of modeling the input arguments of rare repeatability. As part of the solution to the optimal power flow problems, namely, not exceeding the specified probability density functions of the state parameters, it can lead to false conclusions. This paper proposes a methodology for calculating the probabilistic characteristics of steady-state parameters, increasing the reliability and speed of their calculation by taking into account the values of rare repeatability. The results of research is the refinement of the power flow values (compared with the Monte Carlo method) using the developed method based on selection of interval boundaries of input and output probabilistic data (SIBD method), and the definition of minimum possible power losses as part of the problem of determining the optimal wind power capacity and placement in power system for IEEE-14 and IEEE-57 schemes | ||
| 336 | |a Текстовый файл | ||
| 371 | 0 | |a AM_Agreement | |
| 461 | 1 | |t International Journal of Electrical Power & Energy Systems (JEPE) |c Amsterdam |n Elsevier Science Publishing Company Inc. | |
| 463 | 1 | |t Vol. 147 |v Article number 108846, 16 p. |d 2023 | |
| 610 | 1 | |a Optimization algorithms | |
| 610 | 1 | |a Probabilistic characteristics | |
| 610 | 1 | |a Renewable energy sources | |
| 610 | 1 | |a Uncertainty modeling | |
| 610 | 1 | |a Wind speed | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a электронный ресурс | |
| 701 | 1 | |a Andreev |b M. V. |c specialist in the field of electric power engineering |c Associate Professor of Tomsk Polytechnic University, Candidate of technical sciences |f 1987- |g Mikhail Vladimirovich |9 18322 | |
| 701 | 1 | |a Bay |b Yu. D. |c Specialist in the field of electric power engineering |c Assistant of the Department of Tomsk Polytechnic University |f 1991- |g Yuly Dmitrievich |9 21200 | |
| 701 | 1 | |a Kievets |b A. V. |c power industry specialist |c Research Engineer of Tomsk Polytechnic University |f 1993- |g Anton Vladimirovich |9 21628 | |
| 701 | 1 | |a Rudnik |b V. E. |c Specialist in the field of electric power engineering |c Research Engineer of Tomsk Polytechnic University |f 1995- |g Vladimir Evgenevich |9 21532 | |
| 701 | 1 | |a Razzhivin |b I. A. |c Specialist in the field of electric power engineering |c Associate Professor of Tomsk Polytechnic University, Candidate of Technical Sciences |f 1989- |g Igor Andreevich |9 20549 | |
| 801 | 0 | |a RU |b 63413507 |c 20260206 | |
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