Stochastic method for predicting the output of electrical energy received from a solar panel

• Autorzy: Manusov Vadim Zinovievich , Kirgizov Alifbek Kirgizovich , Safaraliev Murodbek Kholnazarovich , Zicmane Inga , Beryozkina Svetlana , Sultonov Sherkhon

Identyfikatory

DOI

15199/48.2024.02.23

Warianty tytułu

PL

Stochastyczna metoda przewidywania wydajności energii elektrycznej odbieranej z panelu słonecznego

• Języki publikacji: EN

Abstrakty

EN

One of the most important and promising types of non-traditional (renewable) energy sources is solar energy. Solar energy has two important advantages: first, its quantity is huge, and it is almost inexhaustible, and second, its use does not hurt the environment. However, the practical use of solar energy is difficult due to the low surface density of solar radiation. In addition, there are problems associated with the solar radiation fluctuations varying during daytime and period of the year. This work aimed to develop a method that allows predicting the output power of the solar panel based on a stochastic model. The proposed stochastic approach considers the following factors: fluctuations (random deviations) of solar radiation at different time intervals, the variable nature of the consumers’ load, and the temperature mode of solar photovoltaic panels leading to assess their efficiency. The study presents a theoretical model with the future practical implementation.

PL

Jednym z najważniejszych i najbardziej obiecujących rodzajów nietradycyjnych (odnawialnych) źródeł energii jest energia słoneczna. Energia słoneczna ma dwie ważne zalety: po pierwsze jej ilość jest ogromna i jest prawie niewyczerpalna, a po drugie jej wykorzystanie nie szkodzi środowisku. Jednak praktyczne wykorzystanie energii słonecznej jest trudne ze względu na małą gęstość powierzchniową promieniowania słonecznego. Ponadto występują problemy związane ze zmiennymi wahaniami promieniowania słonecznego w ciągu dnia i pory roku. Praca ta miała na celu opracowanie metody pozwalającej przewidywać moc wyjściową panelu fotowoltaicznego na podstawie modelu stochastycznego. Proponowane podejście stochastyczne uwzględnia następujące czynniki: fluktuacje (losowe odchylenia) promieniowania słonecznego w różnych przedziałach czasowych, zmienny charakter obciążenia odbiorców oraz tryb temperaturowy paneli fotowoltaicznych prowadzący do oceny ich wydajności. W opracowaniu przedstawiono model teoretyczny wraz z przyszłą praktyczną implementacją.

Słowa kluczowe

EN

photovoltaic panel; renewable energy; stochastic method; solar energy; solar radiation

PL

panele fotowoltaiczne; energia odnawialna; metoda stochastyczna; energia słoneczna; promieniowanie słoneczne

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2024
• Tom: R. 100, nr 2
• Strony: 118--122
• Opis fizyczny: Bibliogr. 33 poz., rys.

Twórcy

autor

Manusov Vadim Zinovievich

• Novosibirsk State Technical University, Prospekt K. Marksa, 20, Novosibirsk, 630073, Russian Federation

autor

Kirgizov Alifbek Kirgizovich

• Department of Electric stations, academicians Rajabov's avenue 10, 734042, Dushanbe, Republic of Tajikistan

• https://orcid.org/0000-0001-6603-1393

autor

Safaraliev Murodbek Kholnazarovich

• Department of Automated Electrical Systems, Ural Federal University, 19, Mira Street, Yekaterinburg, 620002, Russian Federation

• https://orcid.org/0000-0003-3433-9742

autor

Zicmane Inga

• Faculty of Electrical and Environmental Engineering, Riga Technical University, LV 1048 Riga, Latvia

• https://orcid.org/0000-0002-3378-0731

autor

Beryozkina Svetlana

• College of Engineering and Technology, American University of the Middle East, Egalia 54200, Kuwait

• https://orcid.org/0000-0003-1593-129X

autor

Sultonov Sherkhon

• Department of Electric stations, academicians Rajabov's avenue 10, 734042, Dushanbe, Republic of Tajikistan

• https://orcid.org/0000-0003-2322-5272

Bibliografia

• [1] Qazi, A., Faya z, H, W adi, A., Raj, R.G., Rahim, N.A., Khan , W .A. The Artificial Neural Network for Solar Radiation Prediction and Designing Solar Systems. A Systematic Literature Review, Journal of Cleaner Production, (2015), 1-12.
• [2] Sa fara lie v M. Kh., Odinaev I. N., Ah yoev J . S., Rasu lzod a Kh. N., Otashbeko v R. A. Energy Potential Estimation of the Region’s Solar Radiation Using a Solar Tracker, Applied Solar Energy 56 (2020)., 270-275.
• [3] Malino wsk i M., Leo n J.I., Abu -Rub H. Photovoltaic Energy Systems. In Power Electronics in Renewable Energy Systems and Smart Grid: Technology and Applications, Wiley, (2019), 347-389.
• [4] Pad iyar K.R., Ku lkarn i A.M.Solar Power Generation and Energy Storage in Dynamics and Control of Electric Transmission and Microgrids, Wiley, (2019), 391-414.
• [5] Singh G.K. Solar power generation by PV (photovoltaic) technology: A review, Energy 53 (2013), 1-13.
• [6] Ka tir aei F., Agu ero J .R. Solar PV integration challenges, IEEE Power and Energy Magazine 9 (2011), 62-71.
• [7] Denh olm P., Marg olis R.M. Evaluating the limits of solar photovoltaics (PV) in traditional electric power systems, Energy policy 35 (2007)., 2852-2861.
• [8] Kirgizo v A.K., Su ltono v Sh .M. Mathematical Simulation of Energy Transformation Processes in an Energy Center, Applied Solar Energy, 54 (2018), 314-317.
• [9] Mukhammadiev M.M., Mamadiyaro v E.K., Urishe v B.U., et a l. Technological model of micro power facilities based on renewable energy sources with the energy storage, Applied Solar Energy, 48 (2012), 152-156.
• [10] Ad ino yi M.J., Said S.A. Effect of dust accumulation on the power outputs of solar photovoltaic modules, Renewable energy, 60 (2013), 633-636.
• [11] K. Berz ina K., Zic mane I., Kas peruk s K. Assessment of the Use of PV Panels with Energy Accumulation Option for Riga City Office Building, International Journal of Photoenergy, (2019), 1- 11.
• [12] Ve remiichuk, Y., Ya r moliuk, O., Pus to vyi, A., Mahn itk o, A., Zic mane , I., L o mane , T. Features of Electricity Distribution Using Energy Storage in Solar Photovoltaic Structure, Latvian Journal of Physics and Technical Sciences, 57 (2020), no.5, 18-29.
• [13] Dong J., Olama M.M., Kuru ganti T., Me lin A.M., Djouad i S.M., Zhan g Y., Xue Y. Novel stochastic methods to predict short-term solar radiation and photovoltaic power, Renewable Energy, 145 (2020), 333-346.
• [14] Pua h B.K., Cho ng L.W ., W on g Y.W ., Bega m K.M., Khan N., Juman M.A., Ra jkumar R.K. A regression unsupervised incremental learning algorithm for solar irradiance prediction, Renewable Energy 164 (2021), 908-925.
• [15] Gu lin M., Pa vlo vić T., Vašak M. Photovoltaic panel and array static models for power production prediction: Integration of manufacturers’ and on-line data, Renewable Energy 97 (2016), 399-413.
• [16] Tou ati F., Chowdhur y N.A., Benhmed K., A. J.R. San Pedro Gonzales A. J.R, Al-Hitmi M.A., M. Ben amma r M., A. Gastli A., Be n-Bra him L . Long-term performance analysis and power prediction of PV technology in the State of Qatar, Renewable Energy 113 (2017), 952-965.
• [17] International standard IEC 61215:2021 RLV – Terrestrial photovoltaic (PV) modules - Design qualification and type approval - Part 1: Test requirements, 96 p. Online:
• [18] Sha rma V., Chan del S.S. Performance and degradation analysis for long term reliability of solar photovoltaic systems: a review, Renewable and Sustainable Energy Reviews 27 (2013), 753-767.
• [19] Ton ui, J .K., Tripana gnos topou los Y. Performance improvement of PV/T solar collectors with natural air flow operation, Solar Energy 82 (2008), 1-12.
• [20] Po we rs L ., Ne wmille r J ., To wns end T. Measuring and modeling the effect of snow on photovoltaic system performance. in 35th IEEE Photovoltaic Specialists Conference (PVSC), Honolulu, (2010), 000973-000978.
• [21] Manus o v V.Z., Kirg izo v A.K., Sulton o v S.M. Optimization of the Operating Mode of a Hybrid Power Complex Consisting of Renewable Energy Sources, in XIV International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE), IEEE, Novosibirsk, (2018), 286- 289.
• [22] Asa no va S.M., et. al. Optimization of the structure of autonomous distributed hybrid power complexes and energy balance management in them, International Journal of Hydrogen Energy 46.70 (2021), 34542-34549.
• [23] Asa no v M.S. e t a l., Algorithm for calculation and selection of micro hydropower plant taking into account hydrological parameters of small watercourses mountain rivers of Central Asia, Int. J. Hydrogen Energy, 46 (2021), № 75, 37109-37119.
• [24] Kim S.K., Jeon J.H., Cho C.H., Kim E.S., Ahn J .B. Modeling and simulation of a grid-connected PV generation system for electromagnetic transient analysis, Solar Energy 83 (2009), 664- 678.
• [25] Tyu k ho v I., Sc hakhr aman ya n M., Stre bko v D., Tikhono v A., Vignola F. Modelling of solar irradiance using satellite images and direct terrestrial measurements with PV modules, in Proc. of SPIE, the International Society for Optical Engineering. Optical Modeling and Measurements for Solar Energy Systems III, San Diego. 7410 (2009), 48-56
• [26] Ar nold M., Nege nborn R.R., Ande rsson G., Schutter B.D. Model-Based Predictive Control Applied to Multi-Carrier Energy Systems, in Proc. of the IEEE PES General Meeting, Calgary, (2009), 1-8
• [27] Lukutin B.V., Sur zhikova O.A., Shandaro va E.B. (2012). Renewable energy in decentralized power supply, Moscow: Energoatomizdat, 231 p. (In Russian)
• [28] Mas ih A., et al. Application of Dual Axis Solar Tracking System in Qurghonteppa, Tajikistan, in IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE), (2019), 250-254.
• [29] Kha sanzod a, N., Sa faralie v, M., Zic mane , I., Be r yozk ina, S., Rahimo v, J., Ah yoe v, J. Use of smart grid based wind resources in isolated power systems. Energy 253, (2022), 124188.
• [30] Kha sanzod a, N., Zic mane , I., Beryoz kina , S., Sa fara lie v, M., Su ltonov, S., Kirg izov, A. Regression model for predicting the speed of wind flows for energy needs based on fuzzy logic. Renewable Energy 191, (2022) ,723-731.
• [31] Manusov V.Z., Beryozkina S., Naza ro v M.H., M. Safaraliev, I. Zicmane, P.V. Matrenin, A.H. Ghu lo mzoda , Optimal management of energy consumption in an autonomous power system considering alternative energy sources. Mathematics 10 (3), (2022), 525
• [32] International Renewable Energy Agency (IRENA). Concentrating Solar Power. Renewable Energy Technologies: Cost analysis series. Volume 1, Power Sector, Issue 2/5, https://www.irena.org//media/Files/IRENA/Agency/Publication/2012/ RE_Technologies_Cost_Analysis-CSP.pdf. (accessed 27 September 2021).
• [33] W ang X., Ku rdg elas h vili L ., Byrn e J., Barne tt A. The value of module efficiency in lowering the levelized cost of energy of photovoltaic systems, Renewable and Sustainable Energy Reviews 15 (2011), 48-54.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).