Feasibility assessment of a solar plant for backup power supply of oil and gas production facilities

The study aims to assess the efficiency of using solar power plants in backup power supply systems of oil and gas production facilities located in remote and inaccessible areas. The object of research is located along the section of the constructed connecting gas pipeline of the “Power of Siberia” main gas pipeline in the Kazachinsko-Lensky district of the Irkutsk Oblast, Russian Federation. The work used a comprehensive assessment of the ability to supply electrical power to main gas pipeline facilities and load structuring of the crane assembly by reliability categories of electrical receivers. The solar potential in a given area was assessed using the method of calculating insolation, taking into account geolocation and weather conditions with a comprehensive consideration of the solar power plant operating conditions. The analysis of consumer requirements for power quality resulted in the load profile structuring of the crane gas pipeline assembly, which was to determine the installed capacities according to the categories of electrical receivers. A special group of consumers, as well as Category I, II, and III, were demonstrated to have the installed capacity of 5.3, 0.01–50, 25–320, and 0.3–58.4 kW, respectively. A total of 99.2% of highway consumers can use stand-alone power supplies, both main and backup. However, 55% of such consumers can use a stand-alone source as a single one. A single-crystal photovoltaic solar power plant is available for maximum performance in limited solar radiation environments. From March to September, solar panel power generation was established comparable or superior to crane assembly electricity consumption, while in November-January, consumption exceeded production. Thus, solar insolation in the considered area enables the efficient use of photovoltaic panels in combination with energy storage in backup and main power systems of main gas pipeline facilities in remote infrastructural conditions and limited access to centralized networks.

1. Sibgatullin A., Tolmachev V. Justification of the parameters of RES based energy complexes for trunk gas pipeline consumers. In: Proceedings of 2018 International Ural Conference on Green Energy. 4–6 October 2018, Chelyabinsk. Chelyabinsk: Institute of Electrical and Electronics Engineers Inc.; 2018, р. 114-121. https://doi.org/10.1109/URALCON.2018.8544285. EDN: OQCGPA.

2. Nurbosynov D.N., Tabachnikova T.V., Bashyrov R.F., Batanin А.V. Simulation model of the electrical complex of auxiliary equipment of an oil and gas production enterprise. In:International Scientific Electric Power Conference 2019: Materials Science and Engineering: IOP Conference Series. 2019;643:012096. https://doi.org/10.1088/1757-899X/643/1/012096.

3. Lombardi P., Sokolnikova T., Suslov K., Voropai N., Styczynsky Z.A. Isolated power system in Russia: a chance for renewable energies? Renewable Energy. 2016;90:532-541. https://doi.org/10.1016/j.renene.2016.01.016. EDN: VNHNDR.

4. Hassan Q., Algburi S., Sameen A.Z., Salman H.M., Jaszczur M. A review of hybrid renewable energy systems: solar and wind-powered solutions: challenges, opportunities, and policy implications. Results in Engineering. 2023;20:101621. https://doi.org/10.1016/j.rineng.2023.101621. EDN: BJHAUU.

5. Bezrukikh P.P. World energy development trends in the 21st century. Bulletin of Moscow Power Engineering Institute. 2022;3:43-52. (In Russ.). https://doi.org/10.24160/1993-6982-2022-3-43-52. EDN: RSVNKW.

6. Bulatov Yu.N., Kryukov A.V., Korotkova K.E. Digital twin of the distributed generation plant. In: 2nd International Scientific and Practical Conference on Actual Problems of the Energy Complex: Mining, Production, Transmission, Processing and Environmental Protection: Materials Science and Engineering: IOP Conference Series. 2020;976:012024. https://doi.org/10.1088/1757-899X/976/1/012024. EDN: NOQBXE.

7. Ustinov D.A., Konovalov Yu.V., Plotnikov I.G. Certification of electrical loads of oil and gas producing enterprises. St. Petersburg State Polytechnical University Journal. 2012;1:81-84. (In Russ.). EDN: PCALQP.

8. Karamov D.N., Suslov K.V. Structural optimization of autonomous photovoltaic systems with storage battery replacements. Energy Reports. 2021;7(1):349-358. https://doi.org/10.1016/j.egyr.2021.01.059. EDN: KDAVAN.

9. Zarei T., Abdolzadeh M., Yaghoub M. Comparing the impact of climate on dust accumulation and power generation of PV modules: a comprehensive review. Energy for Sustainable Development. 2022;66:238-270. https://doi.org/10.1016/j.esd.2021.12.005.

10. Kirpichnikova I.M., Sudhakar K., Makhsumov I.B., Martyanov A.S., Priya S.S. Thermal model of photo-voltaic module with heat protective film. Case Studies in Thermal Engineering. 2022;30:101744. https://doi.org/10.1016/j.csite.2021.101744. EDN: LUBYCI.

11. Markvart T., Castañer L. Practical handbook of photovoltaics: fundamentals and applications. Hoboken: John Wiley& Sons Inc.; 2012, 1244 p. https://doi.org/10.1016/B978-185617390-2/50007-6.

12. Obukhov S.G., Plotnikov I.A., Kryuchkova M. Simulation of electrical characteristics of a solar panel. In: 4th International Conference on Modern Technologies for Non-Destructive Testing: Materials Science and Engineering: IOP Conference Series. 2016;132:012017. https://doi.org/10.1088/1757-899X/132/1/012017. EDN: UWZRQM.

13. Okhotkin G.P. The method of calculating power of solar power. Vestnik Chuvashskogo universiteta. 2013;3:222- 230. (In Russ.). EDN: RUBSRL.

14. Konovalov Yu.V., Khaziev A.N. Insolation calculations of a photovoltaic power plant taking into account locationbased and weather parameters. iPolytech Journal. 2022;26(3):439-450. (In Russ.). https://doi.org/10.21285/1814-3520-2022-3-439-450. EDN: CQEYQC.

15. Mitrofanov S.V., Baykasenov D.K. Operation of a solar power plant with dual-axis solar tracker. iPolytech Journal. 2023;27(4):737-748. (In Russ.). https://doi.org/10.21285/1814-3520-2023-4-737-748. EDN: HNSEUI.

16. Aguila-Leon J., Vargas-Salgado C., Chiñas-Palacios C., Díaz-Bello D. Solar photovoltaic maximum power point tracking controller optimization using grey wolf optimizer: a performance comparison between bio-inspired and traditional algorithms. Expert Systems with Applications. 2023;211(5):118700. https://doi.org/10.1016/j.eswa.2022.118700.

17. Rylov A.V., Ilyushin P.V., Kulikov A.L., Suslov K.V. Testing photovoltaic power plants for participation in general primary frequency control under various topology and operating conditions. Energies. 2021;14(16):5179. https://doi.org/10.3390/en14165179.

18. Obukhov S.G., Plotnikov I.A., Klimova G.N. Parameter identification of photovoltaic converter models. iPolytech Journal. 2023;27(3):539-551. (In Russ.). https://doi.org/10.21285/1814-3520-2023-3-539-551. EDN: LNPHGL.

19. El-Dabah M.А., El-Sehiemy R.А., Hasanien H.М., Saad B. Photovoltaic model parameters identification using Northern Goshawk optimization algorithm. Energy. 2023;262(B):125522. https://doi.org/10.1016/j.energy.2022.125522. EDN: XWSIAR.

20. Liu S., Parihar K., Pathak M., Sidorov D.N. Neural network fusion optimization for photovoltaic power forecasting. iPolytech Journal. 2024;28(1):111-123. https://doi.org/10.21285/1814-3520-2024-1-111-123. EDN: PHOEXF.

21. Shakirov V.A., Artemyev A.Yu. Technique for considering the influence of cloudiness on the solar radiation flux according to the archives of meteorological stations. Systems. Methods. Technologies. 2014;4:79-83. (In Russ.). EDN: TFBEIR.

22. Markovics D., Mayer M.J. Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction. Renewable and Sustainable Energy Reviews. 2022;161:112364. https://doi.org/10.1016/j.rser.2022.112364. EDN: SDOHIV.