The Influence of Methods for Constructing Solar Insolation and Wind Velocity Time Series on the Power System Operation Mode Prediction Accuracy
DOI:
https://doi.org/10.24160/1993-6982-2021-4-44-52Keywords:
solar insolation, cloudiness coefficient, wind velocity, renewable energy sourcesAbstract
Two different approaches to obtaining the time series of solar insolation and wind velocity data for engineering analyzes of power systems are considered. A stochastic model for obtaining solar insolation and wind velocity at daily sampling intervals based on monthly average parameters is described. A comparative analysis of calculating various combinations of complex power systems based on the use of photovoltaic converters (PVC), windmills, and fuel generators by applying different approaches to obtaining stochastic solar insolation and wind velocity data in the VizProRES software package is carried out. Based on the simulated data, a probabilistic assessment of systems with different equipment compositions is given. A comparison of the average costs of kWh obtained proceeding from long-term climatic data and generated based on the monthly averaged parameters has shown that the difference in the results for the “PVC + fuel generator” and “windmill + fuel generator” systems was less than one percent, and for the PVC + windmill + fuel generator was slightly more than 3%, which shows the possibility of using each approach for design analysis of power systems consisting of one or more probabilistic sources.
References
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7. Berisha X., Zeqiri A., Meha D. Solar Radiation — the Estimation of the Optimum Tilt Angles for South-facing Surfaces in Pristina // Preprints. 2017. V. 1. Pp. 1—13.
8. David M., Ramahatana F., Trombe P.J., Lauret P. Probabilistic Forecasting of the Solar Irradiance with Recursive ARMA and GARCH Models // Solar Energy. 2016. V. 133. Pp. 55—72.
9. Boland J. Time Series Modelling of Solar Radiation // Modeling Solar Radiation at the Earth’s Surface. Berlin: Springer, 2008.
10. Box G.E., Jenkins G.M., Reinsel G.C., Ljung G.M. Time Series Analysis: Forecasting and Control. N.-Y.: John Wiley & Sons, 2015.
11. Bendt P., Collares-Pereira M., Rabl A. The Frequency Distribution of Daily Insolation Values // Solar Energy. 1981. V. 27(1). Pp. 1—5.
12. Frid S.E., Lisitskaya N.V., Popel O.S. Cumulative Clearness Index Frequency Distributions on the Territory of the Russian Federation // Doklady Physics. 2018. V. 63. Pp. 89—92.
13. Brett A.C., Tuller S.E. The Autocorrelation of Hourly Wind Speed Observations // J. Appl. Meteorology. 1991. V. 30. Pp. 823—833.
14. Carapellucci R., Giordano L. A Methodology for the Synthetic Generation of Hourly Wind Speed Time Series Based on Some Known Aggregate Input Data // Appl. Energy. 2013. V. 101. Pp. 541—550.
15. Alexandridis A., Zapranis A. Weather Derivatives: Modeling and Pricing Weather-related Risk. N.-Y.: Springer, 2013. Pp. 299—326.
16. Aririguzo J.C., Ekwe B.E. Weibull Dstribution Analysis of Wind Energy Prospect for Umudike, Nigeria for Power Generation // Robotics and Computer-Integrated Manufacturing. 2019. V. 55. Pt. B. Pp. 160—163.
17. Serban A., Paraschiv L.S., Paraschiv S. Assessment of Wind Energy Potential Based on Weibull and Rayleigh Distribution Models // Energy Rep. 2020. V. 6. Suppl. 6. Pp. 250—267.
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19. Свидетельство о Государственной регистрации программы для ЭВМ № 2019618978. Программа расчета комплексной энергетической системы на основе возобновляемых источников энергии «VizProRES 2019» / Денисов К.С., Велькин В.И., Тырсин А.Н. 2019.
20. Денисов К.С., Велькин В.И., Тырсин А.Н. Решение задачи комплексного энергоснабжения автономного потребителя с целью уменьшения экономических затрат // Вестник ЮУрГУ. Серия «Энергетика». 2019. Т. 19. № 3. С. 84—92.
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---
Для цитирования: Денисов К.С., Хайретдинов И.Р., Велькин В.И., Тырсин А.Н. Анализ влияния способов построения временных рядов солнечной инсоляции и скорости ветра на точность прогноза режима энергетических систем // Вестник МЭИ. 2021. № 4. С. 44—52. DOI: 10.24160/1993-6982-2021-4-44-52.
---
Работа выполнена при поддержке: РФФИ (проект № 20-41-660008)
#
1. Graham V.A., Hollands K.G.T., Unny T.E. A Time Series Model for Kt with Application to Global Synthetic Weather Generation. Solar Energy. 1988;40;2:83—92.
2. Aguiar R.J., Collares-Pereira M. TAG: a Time Dependent Autoregressive Gaussian Model for Generating Synthetic Hourly Radiation. Solar Energy. 1992;49(3):167—174.
3. Duomarco J., Tierno J. Synthesis of Time Series for the Clearness Index Using a Reordering Algorithm. Nuovo Cimento. 1991;14(6):623—629.
4. Duffie J.A. Solar Engineering of Thermal Processes. N.-Y.: John Wiley & Sons, 2013.
5. Hassan Q., Jaszczur M., Przenzak E. Mathematical Model for the Power Generation from Arbitrarily Oriented Photovoltaic Panel. Web of Conf. 2017;14:01028.
6. Al-Aboosi F.Y. Models and Hierarchical Methodologies for Evaluating Solar Energy Availability under Different Sky Conditions Toward Enhancing Concentrating Solar Collectors use: Texas as a Case Study. Intern. J. Energy and Environmental Eng. 2020;11:177—205.
7. Berisha X., Zeqiri A., Meha D. Solar Radiation — the Estimation of the Optimum Tilt Angles for South-facing Surfaces in Pristina. Preprints. 2017;1:1—13.
8. David M., Ramahatana F., Trombe P.J., Lauret P. Probabilistic Forecasting of the Solar Irradiance with Recursive ARMA and GARCH Models. Solar Energy. 2016;133:55—72.
9. Boland J. Time Series Modelling of Solar Radiation. Modeling Solar Radiation at the Earth’s Surface. Berlin: Springer, 2008.
10. Box G.E., Jenkins G.M., Reinsel G.C., Ljung G.M. Time Series Analysis: Forecasting and Control. N.-Y.: John Wiley & Sons, 2015.
11. Bendt P., Collares-Pereira M., Rabl A. The Frequency Distribution of Daily Insolation Values. Solar Energy. 1981;27(1):1—5.
12. Frid S.E., Lisitskaya N.V., Popel O.S. Cumulative Clearness Index Frequency Distributions on the Territory of the Russian Federation. Doklady Physics. 2018;63:89—92.
13. Brett A.C., Tuller S.E. The Autocorrelation of Hourly Wind Speed Observations. J. Appl. Meteorology. 1991;30:823—833.
14. Carapellucci R., Giordano L. A Methodology for the Synthetic Generation of Hourly Wind Speed Time Series Based on Some Known Aggregate Input Data. Appl. Energy. 2013;101:541—550.
15. Alexandridis A., Zapranis A. Weather Derivatives: Modeling and Pricing Weather-related Risk. N.-Y.: Springer, 2013:299—326.
16. Aririguzo J.C., Ekwe B.E. Weibull Dstribution Analysis of Wind Energy Prospect for Umudike, Nigeria for Power Generation. Robotics and Computer-Integrated Manufacturing. 2019;55;B:160—163.
17. Serban A., Paraschiv L.S., Paraschiv S. Assessment of Wind Energy Potential Based on Weibull and Rayleigh Distribution Models. Energy Rep. 2020;6;6:250—267.
18. Sadullayev N.N. e. a. Statistical Analysis of Wind Energy Potential in Uzbekistan’s Bukhara Region Using Weibull Distribution. Appl. Solar Energy. 2019;55:126—132.
19. Svidetel'stvo o Gosudarstvennoy Registratsii Programmy dlya EVM № 2019618978. Programma Rascheta Kompleksnoy Energeticheskoy Sistemy na Osnove Vozobnovlyaemykh Istochnikov Energii «VizProRES 2019». Denisov K.S., Vel'kin V.I., Tyrsin A.N. 2019. (in Russian).
20. Denisov K.S., Vel'kin V.I., Tyrsin A.N. Reshenie Zadachi Kompleksnogo Energosnabzheniya Avtonomnogo Potrebitelya s Tsel'yu Umen'sheniya Ekonomicheskikh Zatrat. Vestnik YUUrGU. Seriya «Energetika». 2019;19;3:84—92. (in Russian).
21. NASA. Prognozirovanie Mirovykh Energeticheskikh Resursov [Elektron. Resurs] www.power.larc.nasa.gov/data-access-viewer/ (Data Obrashcheniya 18.11.2020).
---
For citation: Denisov K.S., Khairetdinov I.R., Velkin V.I., Tyrsin A.N. The Influence of Methods for Constructing Solar Insolation and Wind Velocity Time Series on the Power System Operation Mode Prediction Accuracy. Bulletin of MPEI. 2021;4:44—52. (in Russian). DOI: 10.24160/1993-6982-2021-4-44-52.
The work is executed at support: RFBR (Project No. 20-41-660008)
2. Aguiar R.J., Collares-Pereira M. TAG: a Time Dependent Autoregressive Gaussian Model for Generating Synthetic Hourly Radiation // Solar Energy. 1992. V. 49(3). Pp. 167—174.
3. Duomarco J., Tierno J. Synthesis of Time Series for the Clearness Index Using a Reordering Algorithm // Nuovo Cimento. 1991. V. 14(6). Pp. 623—629.
4. Duffie J.A. Solar Engineering of Thermal Processes. N.-Y.: John Wiley & Sons, 2013.
5. Hassan Q., Jaszczur M., Przenzak E. Mathematical Model for the Power Generation from Arbitrarily Oriented Photovoltaic Panel // Web of Conf. 2017. V. 14. P. 01028.
6. Al-Aboosi F.Y. Models and Hierarchical Methodologies for Evaluating Solar Energy Availability under Different Sky Conditions Toward Enhancing Concentrating Solar Collectors use: Texas as a Case Study // Intern. J. Energy and Environmental Eng. 2020. V. 11. Pp. 177—205.
7. Berisha X., Zeqiri A., Meha D. Solar Radiation — the Estimation of the Optimum Tilt Angles for South-facing Surfaces in Pristina // Preprints. 2017. V. 1. Pp. 1—13.
8. David M., Ramahatana F., Trombe P.J., Lauret P. Probabilistic Forecasting of the Solar Irradiance with Recursive ARMA and GARCH Models // Solar Energy. 2016. V. 133. Pp. 55—72.
9. Boland J. Time Series Modelling of Solar Radiation // Modeling Solar Radiation at the Earth’s Surface. Berlin: Springer, 2008.
10. Box G.E., Jenkins G.M., Reinsel G.C., Ljung G.M. Time Series Analysis: Forecasting and Control. N.-Y.: John Wiley & Sons, 2015.
11. Bendt P., Collares-Pereira M., Rabl A. The Frequency Distribution of Daily Insolation Values // Solar Energy. 1981. V. 27(1). Pp. 1—5.
12. Frid S.E., Lisitskaya N.V., Popel O.S. Cumulative Clearness Index Frequency Distributions on the Territory of the Russian Federation // Doklady Physics. 2018. V. 63. Pp. 89—92.
13. Brett A.C., Tuller S.E. The Autocorrelation of Hourly Wind Speed Observations // J. Appl. Meteorology. 1991. V. 30. Pp. 823—833.
14. Carapellucci R., Giordano L. A Methodology for the Synthetic Generation of Hourly Wind Speed Time Series Based on Some Known Aggregate Input Data // Appl. Energy. 2013. V. 101. Pp. 541—550.
15. Alexandridis A., Zapranis A. Weather Derivatives: Modeling and Pricing Weather-related Risk. N.-Y.: Springer, 2013. Pp. 299—326.
16. Aririguzo J.C., Ekwe B.E. Weibull Dstribution Analysis of Wind Energy Prospect for Umudike, Nigeria for Power Generation // Robotics and Computer-Integrated Manufacturing. 2019. V. 55. Pt. B. Pp. 160—163.
17. Serban A., Paraschiv L.S., Paraschiv S. Assessment of Wind Energy Potential Based on Weibull and Rayleigh Distribution Models // Energy Rep. 2020. V. 6. Suppl. 6. Pp. 250—267.
18. Sadullayev N.N. e. a. Statistical Analysis of Wind Energy Potential in Uzbekistan’s Bukhara Region Using Weibull Distribution // Appl. Solar Energy. 2019. V. 55. Pp. 126—132.
19. Свидетельство о Государственной регистрации программы для ЭВМ № 2019618978. Программа расчета комплексной энергетической системы на основе возобновляемых источников энергии «VizProRES 2019» / Денисов К.С., Велькин В.И., Тырсин А.Н. 2019.
20. Денисов К.С., Велькин В.И., Тырсин А.Н. Решение задачи комплексного энергоснабжения автономного потребителя с целью уменьшения экономических затрат // Вестник ЮУрГУ. Серия «Энергетика». 2019. Т. 19. № 3. С. 84—92.
21. NASA. Прогнозирование мировых энергетических ресурсов [Электрон. ресурс] www.power.larc.nasa.gov/data-access-viewer/ (дата обращения 18.11.2020).
---
Для цитирования: Денисов К.С., Хайретдинов И.Р., Велькин В.И., Тырсин А.Н. Анализ влияния способов построения временных рядов солнечной инсоляции и скорости ветра на точность прогноза режима энергетических систем // Вестник МЭИ. 2021. № 4. С. 44—52. DOI: 10.24160/1993-6982-2021-4-44-52.
---
Работа выполнена при поддержке: РФФИ (проект № 20-41-660008)
#
1. Graham V.A., Hollands K.G.T., Unny T.E. A Time Series Model for Kt with Application to Global Synthetic Weather Generation. Solar Energy. 1988;40;2:83—92.
2. Aguiar R.J., Collares-Pereira M. TAG: a Time Dependent Autoregressive Gaussian Model for Generating Synthetic Hourly Radiation. Solar Energy. 1992;49(3):167—174.
3. Duomarco J., Tierno J. Synthesis of Time Series for the Clearness Index Using a Reordering Algorithm. Nuovo Cimento. 1991;14(6):623—629.
4. Duffie J.A. Solar Engineering of Thermal Processes. N.-Y.: John Wiley & Sons, 2013.
5. Hassan Q., Jaszczur M., Przenzak E. Mathematical Model for the Power Generation from Arbitrarily Oriented Photovoltaic Panel. Web of Conf. 2017;14:01028.
6. Al-Aboosi F.Y. Models and Hierarchical Methodologies for Evaluating Solar Energy Availability under Different Sky Conditions Toward Enhancing Concentrating Solar Collectors use: Texas as a Case Study. Intern. J. Energy and Environmental Eng. 2020;11:177—205.
7. Berisha X., Zeqiri A., Meha D. Solar Radiation — the Estimation of the Optimum Tilt Angles for South-facing Surfaces in Pristina. Preprints. 2017;1:1—13.
8. David M., Ramahatana F., Trombe P.J., Lauret P. Probabilistic Forecasting of the Solar Irradiance with Recursive ARMA and GARCH Models. Solar Energy. 2016;133:55—72.
9. Boland J. Time Series Modelling of Solar Radiation. Modeling Solar Radiation at the Earth’s Surface. Berlin: Springer, 2008.
10. Box G.E., Jenkins G.M., Reinsel G.C., Ljung G.M. Time Series Analysis: Forecasting and Control. N.-Y.: John Wiley & Sons, 2015.
11. Bendt P., Collares-Pereira M., Rabl A. The Frequency Distribution of Daily Insolation Values. Solar Energy. 1981;27(1):1—5.
12. Frid S.E., Lisitskaya N.V., Popel O.S. Cumulative Clearness Index Frequency Distributions on the Territory of the Russian Federation. Doklady Physics. 2018;63:89—92.
13. Brett A.C., Tuller S.E. The Autocorrelation of Hourly Wind Speed Observations. J. Appl. Meteorology. 1991;30:823—833.
14. Carapellucci R., Giordano L. A Methodology for the Synthetic Generation of Hourly Wind Speed Time Series Based on Some Known Aggregate Input Data. Appl. Energy. 2013;101:541—550.
15. Alexandridis A., Zapranis A. Weather Derivatives: Modeling and Pricing Weather-related Risk. N.-Y.: Springer, 2013:299—326.
16. Aririguzo J.C., Ekwe B.E. Weibull Dstribution Analysis of Wind Energy Prospect for Umudike, Nigeria for Power Generation. Robotics and Computer-Integrated Manufacturing. 2019;55;B:160—163.
17. Serban A., Paraschiv L.S., Paraschiv S. Assessment of Wind Energy Potential Based on Weibull and Rayleigh Distribution Models. Energy Rep. 2020;6;6:250—267.
18. Sadullayev N.N. e. a. Statistical Analysis of Wind Energy Potential in Uzbekistan’s Bukhara Region Using Weibull Distribution. Appl. Solar Energy. 2019;55:126—132.
19. Svidetel'stvo o Gosudarstvennoy Registratsii Programmy dlya EVM № 2019618978. Programma Rascheta Kompleksnoy Energeticheskoy Sistemy na Osnove Vozobnovlyaemykh Istochnikov Energii «VizProRES 2019». Denisov K.S., Vel'kin V.I., Tyrsin A.N. 2019. (in Russian).
20. Denisov K.S., Vel'kin V.I., Tyrsin A.N. Reshenie Zadachi Kompleksnogo Energosnabzheniya Avtonomnogo Potrebitelya s Tsel'yu Umen'sheniya Ekonomicheskikh Zatrat. Vestnik YUUrGU. Seriya «Energetika». 2019;19;3:84—92. (in Russian).
21. NASA. Prognozirovanie Mirovykh Energeticheskikh Resursov [Elektron. Resurs] www.power.larc.nasa.gov/data-access-viewer/ (Data Obrashcheniya 18.11.2020).
---
For citation: Denisov K.S., Khairetdinov I.R., Velkin V.I., Tyrsin A.N. The Influence of Methods for Constructing Solar Insolation and Wind Velocity Time Series on the Power System Operation Mode Prediction Accuracy. Bulletin of MPEI. 2021;4:44—52. (in Russian). DOI: 10.24160/1993-6982-2021-4-44-52.
The work is executed at support: RFBR (Project No. 20-41-660008)
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Published
2021-01-22
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Renewable Energy Installations (05.14.08)
