Mathematical Modeling of Solar-diesel Systems
DOI:
https://doi.org/10.24160/1993-6982-2024-2-76-84Keywords:
mathematical modeling, solar-diesel system, solar photovoltaic module, energy storage system, diesel-generator set, distributed energy systemsAbstract
Matters concerned with mathematical modeling of the solar-diesel system (SDS) equipment are analyzed in the context of the subsequent development of an automated control system for such autonomous hybrid energy complexes. The mathematical models of solar photovoltaic modules, storage batteries, and diesel generator sets are given, and the main factors affecting the operation of these elements are mentioned. The final mathematical model of an SDS has been tested in application to the operating energy complex built in the city of Verkhoyansk of the Republic of Sakha (Yakutia) for 3 operating conditions.
References
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.
---
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)
---
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
---
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.
---
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)
---
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
---
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)
Downloads
Published
2023-12-21
Issue
Section
Energy Systems and Complexes (2.4.5)
