Математическое моделирование солнечно-дизельных комплексов
Ключевые слова:
математическое моделирование, солнечно-дизельный комплекс, солнечный фотоэлектрический модуль, , система накопления энергии, дизель-генераторная установка, распределенная энергетика
Аннотация
Проанализированы вопросы математического моделирования оборудования солнечно-дизельного комплекса (СДК) в контексте последующей разработки автоматизированной системы управления автономными гибридными энергокомплексами. Приведены математические модели солнечных фотоэлектрических модулей, аккумуляторных батарей, дизельных генераторных установок, а также упомянуты основные факторы, влияющие на работу данных элементов. Апробация итоговой математической модели СДК осуществлена на примере действующего энергетического комплекса, построенного в городе Верхоянск Республики Саха (Якутия), для трех условий работы. В последнее время наблюдается диверсификация энергетического комплекса через развитие возобновляемых источников энергии.
Литература
1. Объекты генерации в изолированных и труднодоступных территориях в России: аналитический доклад. М.: Аналитический центр при Правительстве Российской Федерации, 2020.
2. АСУ ТП для систем накопления электроэнергии — ключевой элемент энергетической инфраструктуры // Электроэнергия. Передача и распределение. 2023. № 4(79). С. 32—33.
3. Meyer E.L., van Dyk E.E. Assessing the Reliability and Degradation of Photovoltaic Module Performance Parameters // IEEE Trans. Reliab. 2004. V. 53(1). Pp. 83—92.
4. Dubey S., Sarvaiya J.N., Seshadri B. Temperature Dependent Photovoltaic (PV) Efficiency and Its Effect on PV Production in the World — a Review // Energy Proc. 2013. V. 33(3). Pp. 311—321.
5. Кирпичникова И.М., Махсумов И.Б. Построение энергетических характеристик солнечных модулей с учетом условий окружающей среды // Вестник ПНИПУ. Серия «Электротехника, информационные технологии, системы управления». 2020. № 34. С. 56—74.
6. Bright J.M. e. a. A Synthetic, Spatially Decorrelating Solar Irradiance Generator and Application to a LV Grid Model with High PV Penetration // Solar Energy. 2017. V. 147. Pp. 83—98.
7. Moretón R. e. a. From Broadband Horizontal to Effective In-plane Irradiation: a Review of Modelling and Derived Uncertainty for PV Yield Prediction // Renewable and Sustainable Energy Rev. 2017. V. 78. Pp. 886—903.
8. Abiola-Ogedengbe A., Hangan H., Siddiqui K. Experimental Investigation of Wind Effects on a Standalone Photovoltaic (PV) Module // Renewable Energy. 2015. V. 78. Pp. 657—665.
9. Brecl K., Topič M. Self-shading Losses of Fixed Free-standing PV Arrays // Renewable Energy. 2011. V. 36(11). Pp. 3211—3216.
10. Ndiaye A. e. a. Degradations of Silicon Photovoltaic Modules: a Literature Review // Solar Energy. 2013. V. 96. Pp. 140—151.
11. Xiong R. e. a. Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles // IEEE Access. 2018. V. 6. Pp. 1832—1843.
12. Chiasson J., Vairamohan B. Estimating the State of Charge of a Battery // IEEE Trans. Control Syst. Technol. 2005. V. 13(3). Pp. 465—470.
13. Anbuky H., Pascoe P.E. VRLA Battery State-of-charge Estimation in Telecommunication Power Systems // IEEE Trans. Industrial Electronics. 2000. V. 47. Pp. 565—573.
14. Sato S., Kawamura A. A New Estimation Method of State of Charge Using Terminal Voltage and Internal Resistance for Lead Acid Battery // Proc. Power Convers. Conf. Osaka, 2002. V. 2. Pp. 565—570.
15. Rodrigues S., Munichandraiah N., Shukla A.K. A Review of State-of-charge Indication of Batteries by Means of A.C. Impedance Measurements // J. Power Sources. 2000. V. 87. Pp. 12—20.
16. Huet F. A review of Impedance Measurements for Determination of the State-of-charge or State-of-health of Secondary Batteries // J. Power Sources. 1998. V. 70. Pp. 56—69.
17. How D.N.T. e. a. State of Charge Estimation for Lithium-ion Batteries Using Model-based and Data-driven Methods: a Review // IEEE Access. 2019. V. 7. Pp. 136116—136136.
18. Rosewater D.M. e. a. Battery Energy Storage Models for Optimal Control // Ibid. Pp. 178357—178391.
19. Sindhuja S., Vasanth K. Modified Coulomb Counting Method of SOC Estimation for Uninterruptible Power Supply System’s Battery Management System // Proc. 2015 Intern. Conf. Control, Instrumentation, Communication and Computational Technologies. 2015. Pp. 197—203.
20. Linda O., William E. J., Huff M. Intelligent Neural Network Implementation for SOCI Development of Li/CFx Batteries // Proc. II Intern. Symp. Resilient Control Systems. 2009. Pp. 57—62.
21. Malkhandi S. Fuzzy Logic-based Learning System and Estimation of State-of-charge of Lead-acid Battery // Eng. Appl. Artif. Intell. 2006. V. 19(5). Pp. 479—485.
22. Xu L., Wang J.P., Chen Q.S. Kalman Filtering State of Charge Estimation for Battery Management System Based on a Stochastic Fuzzy Neural Network Battery Model // Energy Conversion and Management. 2012. V. 53. Pp. 33—39.
23. Wang J. e. a. Combined State of Charge Estimator for Electric Vehicle Battery Pack // Control Eng. Practice. 2007. V. 15. Pp. 1569—1576.
24. Лаврик А.Ю. Повышение энергоэффективности автономных электротехнических комплексов путем обоснования состава и режимов работы с учётом предиктивных алгоритмов управления нагрузкой: дис. … канд. техн. наук. СПб.: Санкт-Петербургский горный университет, 2021.
25. Kim J., Cho B.H. State-of-charge Estimation and State-of-health Prediction of a Li-ion Degraded Battery Based on an EKF Combined with a Per-unit System // IEEE Trans. Vehicular Technol. V. 60. Pp. 4249—4260.
26. Ismail M.S., Moghavvemi M., Mahlia T.M.I. Techno-economic Analysis of an Optimized Photovoltaic and Diesel Generator Hybrid Power System for Remote Houses in a Tropical Climate // Energy Conversion and Management. 2013. V. 69. Pp. 163—173.
27. Приказ № 323 от 30 декабря 2008 г. «Об утверждении порядка определения нормативов удельного расхода топлива при производстве электрической и тепловой энергии» (с изменениями на 30 ноября 2015 г.).
28. Правила технической эксплуатации дизельных электростанций. М.: Министерство топлива и энергетики Российской Федерации, 1993.
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Для цитирования: Андреева К.А., Васильева А.А., Васьков А.Г. Шуркалов П.С. Математическое моделирование солнечно-дизельных комплексов // Вестник МЭИ. 2024. № 2. С. 76—84. DOI: 10.24160/1993-6982-2024-2-76-84
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Работа выполнена в рамках проекта «Системы управления солнечно-дизельными комплексами» при поддержке Министерства науки и высшего образования Российской Федерации (грант № FSWF-2022-0006)
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1. Ob'ekty Generatsii v Izolirovannykh i Trudnodostupnykh Territoriyakh v Rossii: Analiticheskiy Doklad. M.: Analiticheskiy Tsentr pri Pravitel'stve Rossiyskoy Federatsii, 2020. (in Russian).
2. ASU TP dlya Sistem Nakopleniya Elektroenergii — Klyuchevoy Element Energeticheskoy Infrastruktury. Elektroenergiya. Peredacha I Raspredelenie. 2023;4(79):32—33. (in Russian).
3. Meyer E.L., van Dyk E.E. Assessing the Reliability and Degradation of Photovoltaic Module Performance Parameters. IEEE Trans. Reliab. 2004;53(1):83—92.
4. Dubey S., Sarvaiya J.N., Seshadri B. Temperature Dependent Photovoltaic (PV) Efficiency and Its Effect on PV Production in the World — a Review. Energy Proc. 2013;33(3):311—321.
5. Kirpichnikova I.M., Makhsumov I.B. Postroenie Energeticheskikh Kharakteristik Solnechnykh Moduley s Uchetom Usloviy Okruzhayushchey Sredy. Vestnik PNIPU. Seriya «Elektrotekhnika, Informatsionnye Tekhnologii, Sistemy Upravleniya». 2020;34:56—74. (in Russian).
6. Bright J.M. e. a. A Synthetic, Spatially Decorrelating Solar Irradiance Generator and Application to a LV Grid Model with High PV Penetration. Solar Energy. 2017;147:83—98.
7. Moretón R. e. a. From Broadband Horizontal to Effective In-plane Irradiation: a Review of Modelling and Derived Uncertainty for PV Yield Prediction. Renewable and Sustainable Energy Rev. 2017;78:886—903.
8. Abiola-Ogedengbe A., Hangan H., Siddiqui K. Experimental Investigation of Wind Effects on a Standalone Photovoltaic (PV) Module. Renewable Energy. 2015;78:657—665.
9. Brecl K., Topič M. Self-shading Losses of Fixed Free-standing PV Arrays. Renewable Energy. 2011;36(11):3211—3216.
10. Ndiaye A. e. a. Degradations of Silicon Photovoltaic Modules: a Literature Review. Solar Energy. 2013;96:140—151.
11. Xiong R. e. a. Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles. IEEE Access. 2018;6:1832—1843.
12. Chiasson J., Vairamohan B. Estimating the State of Charge of a Battery. IEEE Trans. Control Syst. Technol. 2005;13(3):465—470.
13. Anbuky H., Pascoe P.E. VRLA Battery State-of-charge Estimation in Telecommunication Power Systems. IEEE Trans. Industrial Electronics. 2000;47:565—573.
14. Sato S., Kawamura A. A New Estimation Method of State of Charge Using Terminal Voltage and Internal Resistance for Lead Acid Battery. Proc. Power Convers. Conf. Osaka, 2002;2:565—570.
15. Rodrigues S., Munichandraiah N., Shukla A.K. A Review of State-of-charge Indication of Batteries by Means of A.C. Impedance Measurements. J. Power Sources. 2000;87:12—20.
16. Huet F. A review of Impedance Measurements for Determination of the State-of-charge or State-of-health of Secondary Batteries. J. Power Sources. 1998;70:56—69.
17. How D.N.T. e. a. State of Charge Estimation for Lithium-ion Batteries Using Model-based and Data-driven Methods: a Review. IEEE Access. 2019;7:136116—136136.
18. Rosewater D.M. e. a. Battery Energy Storage Models for Optimal Control. Ibid:178357—178391.
19. Sindhuja S., Vasanth K. Modified Coulomb Counting Method of SOC Estimation for Uninterruptible Power Supply System’s Battery Management System. Proc. 2015 Intern. Conf. Control, Instrumentation, Communication and Computational Technologies. 2015:197—203.
20. Linda O., William E. J., Huff M. Intelligent Neural Network Implementation for SOCI Development of Li/CFx Batteries. Proc. II Intern. Symp. Resilient Control Systems. 2009:57—62.
21. Malkhandi S. Fuzzy Logic-based Learning System and Estimation of State-of-charge of Lead-acid Battery. Eng. Appl. Artif. Intell. 2006;19(5):479—485.
22. Xu L., Wang J.P., Chen Q.S. Kalman Filtering State of Charge Estimation for Battery Management System Based on a Stochastic Fuzzy Neural Network Battery Model. Energy Conversion and Management. 2012;53:33—39.
23. Wang J. e. a. Combined State of Charge Estimator for Electric Vehicle Battery Pack. Control Eng. Practice. 2007;15:1569—1576.
24. Lavrik A.Yu. Povyshenie Energoeffektivnosti Avtonomnykh Elektrotekhnicheskikh Kompleksov Putem Obosnovaniya Sostava i Rezhimov Raboty s Uchetom Prediktivnykh Algoritmov Upravleniya Nagruzkoy: Dis. … Kand. Tekhn. Nauk. SPb.: Sankt-Peterburgskiy Gornyy Universitet, 2021. (in Russian).
25. Kim J., Cho B.H. State-of-charge Estimation and State-of-health Prediction of a Li-ion Degraded Battery Based on an EKF Combined with a Per-unit System. IEEE Trans. Vehicular Technol;60:4249—4260.
26. Ismail M.S., Moghavvemi M., Mahlia T.M.I. Techno-economic Analysis of an Optimized Photovoltaic and Diesel Generator Hybrid Power System for Remote Houses in a Tropical Climate. Energy Conversion and Management. 2013;69:163—173.
27. Prikaz № 323 ot 30 dekabrya 2008 g. «Ob Utverzhdenii Poryadka Opredeleniya Normativov Udel'nogo Raskhoda Topliva pri Proizvodstve Elektricheskoy i Teplovoy Energii» (s Izmeneniyami na 30 Noyabrya 2015 g.). (in Russian).
28. Pravila Tekhnicheskoy Ekspluatatsii Dizel'nykh Elektrostantsiy. M.: Ministerstvo Topliva i Energetiki Rossiyskoy Federatsii, 1993. (in Russian)
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For citation: Andreeva K.A., Vasil’eva A.A., Vas’kov A.G., Shurkalov P.S. Mathematical Modeling of Solar-diesel Systems. Bulletin of MPEI. 2024;2:76—84. (in Russian). DOI: 10.24160/1993-6982-2024-2-76-84
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The work is executed within the Framework of the Project «Solar-diesel Complex Management Systems» with the Support of the Ministry of Science and Higher Education of the Russian Federation (Grant No. FSWF-2022-0006)
2. АСУ ТП для систем накопления электроэнергии — ключевой элемент энергетической инфраструктуры // Электроэнергия. Передача и распределение. 2023. № 4(79). С. 32—33.
3. Meyer E.L., van Dyk E.E. Assessing the Reliability and Degradation of Photovoltaic Module Performance Parameters // IEEE Trans. Reliab. 2004. V. 53(1). Pp. 83—92.
4. Dubey S., Sarvaiya J.N., Seshadri B. Temperature Dependent Photovoltaic (PV) Efficiency and Its Effect on PV Production in the World — a Review // Energy Proc. 2013. V. 33(3). Pp. 311—321.
5. Кирпичникова И.М., Махсумов И.Б. Построение энергетических характеристик солнечных модулей с учетом условий окружающей среды // Вестник ПНИПУ. Серия «Электротехника, информационные технологии, системы управления». 2020. № 34. С. 56—74.
6. Bright J.M. e. a. A Synthetic, Spatially Decorrelating Solar Irradiance Generator and Application to a LV Grid Model with High PV Penetration // Solar Energy. 2017. V. 147. Pp. 83—98.
7. Moretón R. e. a. From Broadband Horizontal to Effective In-plane Irradiation: a Review of Modelling and Derived Uncertainty for PV Yield Prediction // Renewable and Sustainable Energy Rev. 2017. V. 78. Pp. 886—903.
8. Abiola-Ogedengbe A., Hangan H., Siddiqui K. Experimental Investigation of Wind Effects on a Standalone Photovoltaic (PV) Module // Renewable Energy. 2015. V. 78. Pp. 657—665.
9. Brecl K., Topič M. Self-shading Losses of Fixed Free-standing PV Arrays // Renewable Energy. 2011. V. 36(11). Pp. 3211—3216.
10. Ndiaye A. e. a. Degradations of Silicon Photovoltaic Modules: a Literature Review // Solar Energy. 2013. V. 96. Pp. 140—151.
11. Xiong R. e. a. Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles // IEEE Access. 2018. V. 6. Pp. 1832—1843.
12. Chiasson J., Vairamohan B. Estimating the State of Charge of a Battery // IEEE Trans. Control Syst. Technol. 2005. V. 13(3). Pp. 465—470.
13. Anbuky H., Pascoe P.E. VRLA Battery State-of-charge Estimation in Telecommunication Power Systems // IEEE Trans. Industrial Electronics. 2000. V. 47. Pp. 565—573.
14. Sato S., Kawamura A. A New Estimation Method of State of Charge Using Terminal Voltage and Internal Resistance for Lead Acid Battery // Proc. Power Convers. Conf. Osaka, 2002. V. 2. Pp. 565—570.
15. Rodrigues S., Munichandraiah N., Shukla A.K. A Review of State-of-charge Indication of Batteries by Means of A.C. Impedance Measurements // J. Power Sources. 2000. V. 87. Pp. 12—20.
16. Huet F. A review of Impedance Measurements for Determination of the State-of-charge or State-of-health of Secondary Batteries // J. Power Sources. 1998. V. 70. Pp. 56—69.
17. How D.N.T. e. a. State of Charge Estimation for Lithium-ion Batteries Using Model-based and Data-driven Methods: a Review // IEEE Access. 2019. V. 7. Pp. 136116—136136.
18. Rosewater D.M. e. a. Battery Energy Storage Models for Optimal Control // Ibid. Pp. 178357—178391.
19. Sindhuja S., Vasanth K. Modified Coulomb Counting Method of SOC Estimation for Uninterruptible Power Supply System’s Battery Management System // Proc. 2015 Intern. Conf. Control, Instrumentation, Communication and Computational Technologies. 2015. Pp. 197—203.
20. Linda O., William E. J., Huff M. Intelligent Neural Network Implementation for SOCI Development of Li/CFx Batteries // Proc. II Intern. Symp. Resilient Control Systems. 2009. Pp. 57—62.
21. Malkhandi S. Fuzzy Logic-based Learning System and Estimation of State-of-charge of Lead-acid Battery // Eng. Appl. Artif. Intell. 2006. V. 19(5). Pp. 479—485.
22. Xu L., Wang J.P., Chen Q.S. Kalman Filtering State of Charge Estimation for Battery Management System Based on a Stochastic Fuzzy Neural Network Battery Model // Energy Conversion and Management. 2012. V. 53. Pp. 33—39.
23. Wang J. e. a. Combined State of Charge Estimator for Electric Vehicle Battery Pack // Control Eng. Practice. 2007. V. 15. Pp. 1569—1576.
24. Лаврик А.Ю. Повышение энергоэффективности автономных электротехнических комплексов путем обоснования состава и режимов работы с учётом предиктивных алгоритмов управления нагрузкой: дис. … канд. техн. наук. СПб.: Санкт-Петербургский горный университет, 2021.
25. Kim J., Cho B.H. State-of-charge Estimation and State-of-health Prediction of a Li-ion Degraded Battery Based on an EKF Combined with a Per-unit System // IEEE Trans. Vehicular Technol. V. 60. Pp. 4249—4260.
26. Ismail M.S., Moghavvemi M., Mahlia T.M.I. Techno-economic Analysis of an Optimized Photovoltaic and Diesel Generator Hybrid Power System for Remote Houses in a Tropical Climate // Energy Conversion and Management. 2013. V. 69. Pp. 163—173.
27. Приказ № 323 от 30 декабря 2008 г. «Об утверждении порядка определения нормативов удельного расхода топлива при производстве электрической и тепловой энергии» (с изменениями на 30 ноября 2015 г.).
28. Правила технической эксплуатации дизельных электростанций. М.: Министерство топлива и энергетики Российской Федерации, 1993.
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Для цитирования: Андреева К.А., Васильева А.А., Васьков А.Г. Шуркалов П.С. Математическое моделирование солнечно-дизельных комплексов // Вестник МЭИ. 2024. № 2. С. 76—84. DOI: 10.24160/1993-6982-2024-2-76-84
---
Работа выполнена в рамках проекта «Системы управления солнечно-дизельными комплексами» при поддержке Министерства науки и высшего образования Российской Федерации (грант № FSWF-2022-0006)
#
1. Ob'ekty Generatsii v Izolirovannykh i Trudnodostupnykh Territoriyakh v Rossii: Analiticheskiy Doklad. M.: Analiticheskiy Tsentr pri Pravitel'stve Rossiyskoy Federatsii, 2020. (in Russian).
2. ASU TP dlya Sistem Nakopleniya Elektroenergii — Klyuchevoy Element Energeticheskoy Infrastruktury. Elektroenergiya. Peredacha I Raspredelenie. 2023;4(79):32—33. (in Russian).
3. Meyer E.L., van Dyk E.E. Assessing the Reliability and Degradation of Photovoltaic Module Performance Parameters. IEEE Trans. Reliab. 2004;53(1):83—92.
4. Dubey S., Sarvaiya J.N., Seshadri B. Temperature Dependent Photovoltaic (PV) Efficiency and Its Effect on PV Production in the World — a Review. Energy Proc. 2013;33(3):311—321.
5. Kirpichnikova I.M., Makhsumov I.B. Postroenie Energeticheskikh Kharakteristik Solnechnykh Moduley s Uchetom Usloviy Okruzhayushchey Sredy. Vestnik PNIPU. Seriya «Elektrotekhnika, Informatsionnye Tekhnologii, Sistemy Upravleniya». 2020;34:56—74. (in Russian).
6. Bright J.M. e. a. A Synthetic, Spatially Decorrelating Solar Irradiance Generator and Application to a LV Grid Model with High PV Penetration. Solar Energy. 2017;147:83—98.
7. Moretón R. e. a. From Broadband Horizontal to Effective In-plane Irradiation: a Review of Modelling and Derived Uncertainty for PV Yield Prediction. Renewable and Sustainable Energy Rev. 2017;78:886—903.
8. Abiola-Ogedengbe A., Hangan H., Siddiqui K. Experimental Investigation of Wind Effects on a Standalone Photovoltaic (PV) Module. Renewable Energy. 2015;78:657—665.
9. Brecl K., Topič M. Self-shading Losses of Fixed Free-standing PV Arrays. Renewable Energy. 2011;36(11):3211—3216.
10. Ndiaye A. e. a. Degradations of Silicon Photovoltaic Modules: a Literature Review. Solar Energy. 2013;96:140—151.
11. Xiong R. e. a. Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles. IEEE Access. 2018;6:1832—1843.
12. Chiasson J., Vairamohan B. Estimating the State of Charge of a Battery. IEEE Trans. Control Syst. Technol. 2005;13(3):465—470.
13. Anbuky H., Pascoe P.E. VRLA Battery State-of-charge Estimation in Telecommunication Power Systems. IEEE Trans. Industrial Electronics. 2000;47:565—573.
14. Sato S., Kawamura A. A New Estimation Method of State of Charge Using Terminal Voltage and Internal Resistance for Lead Acid Battery. Proc. Power Convers. Conf. Osaka, 2002;2:565—570.
15. Rodrigues S., Munichandraiah N., Shukla A.K. A Review of State-of-charge Indication of Batteries by Means of A.C. Impedance Measurements. J. Power Sources. 2000;87:12—20.
16. Huet F. A review of Impedance Measurements for Determination of the State-of-charge or State-of-health of Secondary Batteries. J. Power Sources. 1998;70:56—69.
17. How D.N.T. e. a. State of Charge Estimation for Lithium-ion Batteries Using Model-based and Data-driven Methods: a Review. IEEE Access. 2019;7:136116—136136.
18. Rosewater D.M. e. a. Battery Energy Storage Models for Optimal Control. Ibid:178357—178391.
19. Sindhuja S., Vasanth K. Modified Coulomb Counting Method of SOC Estimation for Uninterruptible Power Supply System’s Battery Management System. Proc. 2015 Intern. Conf. Control, Instrumentation, Communication and Computational Technologies. 2015:197—203.
20. Linda O., William E. J., Huff M. Intelligent Neural Network Implementation for SOCI Development of Li/CFx Batteries. Proc. II Intern. Symp. Resilient Control Systems. 2009:57—62.
21. Malkhandi S. Fuzzy Logic-based Learning System and Estimation of State-of-charge of Lead-acid Battery. Eng. Appl. Artif. Intell. 2006;19(5):479—485.
22. Xu L., Wang J.P., Chen Q.S. Kalman Filtering State of Charge Estimation for Battery Management System Based on a Stochastic Fuzzy Neural Network Battery Model. Energy Conversion and Management. 2012;53:33—39.
23. Wang J. e. a. Combined State of Charge Estimator for Electric Vehicle Battery Pack. Control Eng. Practice. 2007;15:1569—1576.
24. Lavrik A.Yu. Povyshenie Energoeffektivnosti Avtonomnykh Elektrotekhnicheskikh Kompleksov Putem Obosnovaniya Sostava i Rezhimov Raboty s Uchetom Prediktivnykh Algoritmov Upravleniya Nagruzkoy: Dis. … Kand. Tekhn. Nauk. SPb.: Sankt-Peterburgskiy Gornyy Universitet, 2021. (in Russian).
25. Kim J., Cho B.H. State-of-charge Estimation and State-of-health Prediction of a Li-ion Degraded Battery Based on an EKF Combined with a Per-unit System. IEEE Trans. Vehicular Technol;60:4249—4260.
26. Ismail M.S., Moghavvemi M., Mahlia T.M.I. Techno-economic Analysis of an Optimized Photovoltaic and Diesel Generator Hybrid Power System for Remote Houses in a Tropical Climate. Energy Conversion and Management. 2013;69:163—173.
27. Prikaz № 323 ot 30 dekabrya 2008 g. «Ob Utverzhdenii Poryadka Opredeleniya Normativov Udel'nogo Raskhoda Topliva pri Proizvodstve Elektricheskoy i Teplovoy Energii» (s Izmeneniyami na 30 Noyabrya 2015 g.). (in Russian).
28. Pravila Tekhnicheskoy Ekspluatatsii Dizel'nykh Elektrostantsiy. M.: Ministerstvo Topliva i Energetiki Rossiyskoy Federatsii, 1993. (in Russian)
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For citation: Andreeva K.A., Vasil’eva A.A., Vas’kov A.G., Shurkalov P.S. Mathematical Modeling of Solar-diesel Systems. Bulletin of MPEI. 2024;2:76—84. (in Russian). DOI: 10.24160/1993-6982-2024-2-76-84
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The work is executed within the Framework of the Project «Solar-diesel Complex Management Systems» with the Support of the Ministry of Science and Higher Education of the Russian Federation (Grant No. FSWF-2022-0006)
Опубликован
2023-12-21
Выпуск
Раздел
Энергетические системы и комплексы (технические науки) (2.4.5)
