Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Applying Johansen VECM cointegration approach to propose a forecast model of photovoltaic power output plant in Reunion Island

1 LE2P—Energy-Lab, University of Reunion Island, 97744 Saint-Denis, France
2 LCOMS, University of Lorraine, 57070 Metz, France

Topical Section: Solar Energy

Since 2007 Reunion Island, a French overseas region located in the Indian Ocean, aims to achieve energy self-sufficiency by 2030. The French government has made this insular zone an experimental territory for renewable energy resources (RES) by implementing great powers photovoltaic (PV) plants. However, the performance of PV conversion is highly climate dependent, and there have been many research contributions to show that the two main factors that influence PV cell efficiency are solar radiation and cell temperature. Moreover, considering the high variability of environmental factors on PV plants, the high penetration of PV in electric systems may threaten the stability and reliability of the electrical power grid. In this study, a linear relation analysis of time series data collected over one year is performed in order to investigate the dependent variable of PV power output from explanatory variables such as solar irradiance, cell temperature, wind speed and humidity. The originality of this paper is to apply cointegration methods, usual tools of econometrics, to PV systems. More precisely, this research work lies in the use a robust statistical method to model a vector cointegrating relationship linking the PV power output and the four environmental parameters mentioned above, to make accurate forecasts in a tropical area. The Johansen vector error correction model (VECM) cointegration approach is used to determine the most appropriate PV power output forecasting when the desired model is concerned with N explanatory variables and for N > 2. This long run equilibrium relationship has been tested over many years of data and the outcome is more than reliable when comparing the model to measured data.
  Figure/Table
  Supplementary
  Article Metrics

References

1. Selosse S, Garabedian S, Ricci O, et al. (2018) The renewable energy revolution of reunion island. Renewable Sustainable Energy Rev 89: 99-105.    

2. Omubo-Pepple VB, Israel-Cookey C, Alaminokuma GI (2009) Effects of temperature, solar flux and relative humidity on the efficient conversion of solar energy to electricity. Eur J Sci Res 35: 173-180.

3. Laronde R, Charki A, Bigaud D (2010) Reliability of photovoltaic modules based on climatic measurement data. International Metrology Conference CAFMET, 1-6.

4. Wan C, Zhao J, Song Y, et al. (2015) Photovoltaic and solar power forecasting for smart grid energy management. CSEE J Power Energy Syst 1: 38-46.    

5. Antonanzas J, Osorio N, Escobar R, et al. (2016) Review of photovoltaic power forecasting. Sol Energy 136: 78-111.    

6. Sobri S, Koohi-Kamali S, Abd Rahim N (2018) Solar photovoltaic generation forecasting methods: A review. Energy Convers Manage 156: 459-497.    

7. Al-Sabounchi AM (1998) Effect of ambient temperature on the demanded energy of solar sells at different inclinations. Renewable Energy 14: 149-155.    

8. Chandra S, Agrawal S, Chauhan DS (2018) Effect of ambient temperature and wind speed on performance ratio of polycrystalline solar photovoltaic module: An experimental analysis. Int Energy J 18: 171-180.

9. Amajama J, Ogbulezie JC, Akonjom NA, et al. (2016) Impact of wind on the output of photovoltaic panel and solar illuminance/intensity. Int J Eng Res Gen Sci, 4.

10. Kaldellis JK, Kapsali M, Kavadias KA (2014) Temperature and wind speed impact on the efficiency of PV installations. Experience obtained from outdoor measurements in Greece. Renewable Energy 66: 612-624.

11. Ketjoy N, Konyu M (2014) Study of dust effect on photovoltaic module for photovoltaic power plant. Energy Procedia 52: 431-437.    

12. Barbieri F, Rajakaruna S, Ghosh A (2017) Very short-term photovoltaic power forecasting with cloud modeling: A review. Renewable Sustainable Energy Rev 75: 242-263.    

13. Li Y, Sub Y, Shu L (2014) An ARMAX model for forecasting the power output of a grid connected photovoltaic system. Renewable Energy 66: 78-89.    

14. Raza MQ, Nadarajah M, Ekanayake C (2016) On recent advances in PV output power forecast. Sol Energy 136: 125-144.    

15. Zamo M, Mestre O, Arbogast P, et al. (2014) A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part I: Deterministic forecast of hourly production. Sol Energy 105: 792-803.    

16. Zamo M, Mestre O, Arbogast P, et al. (2014) A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production. Part II: Probabilistic forecast of daily production. Sol Energy 105: 804-816.

17. AlSkaif T, Dev S, Visser L, et al. (2020) A systematic analysis of meteorological variables for PV output power estimation. Renewable Energy 153: 12-22.    

18. Gujarati DN (2004) Basic of econometric. The McGraw-Hill Econometrics, Fourth Edition, Fourth Companies.

19. Enders W (1995) Applied economic time series. Wiley Series in Probability and Statistics.

20. Bacher P, Madsen H, Nielsen H (2009) Online short-term solar power forecasting. Sol Energy 83: 1772-1783.    

21. Li Y, Shu Y (2014) An ARMAX model for forecasting the power output of a grid connected photovoltaic system. Renewable Energy 66: 78-89.    

22. Chu Y, Urguhart B, Gohari S, et al. (2015) Short-term reforecasting of power output from a 48MWe solar PV plant. Sol Energy 112: 68-77.    

23. Bessa R, Trindade A, Silva C, et al. (2015) Probabilistic solar power forecasting in smart grids using distributed information. Int J Electr Power Energy Syst 72: 16-23.    

24. Pedro H, Coimbra C (2012) Assessment of forecasting techniques for solar power production with no exogenous inputs. Sol Energy 86: 2017-2028.    

25. Bouzerdoum M, Mellit A, Massi Pavan A (2013) A hybrid model (SARIMA-SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant. Sol Energy 98: 226-235.    

26. Jing H, Korolkiewicz M, Agrawal M, et al. (2013) Forecasting solar radiation on an hourly time scale using Coupled Auto-Regressive and Dynamical System (CARDS) model. Sol Energy 87: 136-149.    

27. Zamo M, Mestre O, Arbogast P, et al. (2014) A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part I: Deterministic forecast of hourly production. Sol Energy 105: 792-803.    

28. Kostylev V, Pavlovski A (2011) Solar power forecasting performance towards industry standards. 1st International Workshop on the Integration of Solar Power into Power Systems, Denmark.

29. Das UK, Soon Tey K, Seyedmahmoudian M, et al. (2018) Forecasting of photovoltaic power generation and model optimization: A review. Renewable Sustainable Energy Rev 81: 912-928.    

30. Diagne M, David M, Lauret P, et al. (2013) Review of solar irradiance forecasting methods and a proposition for small-scale insular grids. Renewable Sustainable Energy Rev 27: 65-76.    

31. Ramenah H, Casin P, Ba M, et al. (2018) Accurate determination of parameters relationship for photovoltaic power output by augmented dickey fuller and engle granger method. AIMS Energy 6: 19-48.    

32. Marcinkiewicz E (2014) Some aspects of application of VECM analysis for modeling causal relationships between spot and futures prices. Optimum Stud Ekon 71: 114-125.

33. Andrei D, Andrei L (2015) Vector error correction model in explaining the association of some macroeconomic variables in Romania. Procedia Econ finance 22: 568-576.    

34. Katircioglu ST (2009) Revisiting the tourism-led-growth hypothesis for Turkey using the bounds test and Johansen approach for cointegration. Tourism Manage 30: 17-20.    

35. Skoplaki E, Palyvos JA (2009) Operating temperature of photovoltaic modules: A survey of pertinent correlations. Renewable Energy 34: 23-29.    

36. Dubey S, Sarvaiya JN, Seshadri B (2013) Temperature dependent photovoltaic (PV) efficiency and its effect on PV production in the world-A Review. Energy Procedia 33: 311-321.    

37. Luo Y, Zhang L, Liu Z, et al. (2017) Performance analysis of a self-adaptive building integrated photovoltaic thermoelectric wall system in hot summer and cold winter zone of China. Energy 140: 584-600.    

38. Amajama J, Oku DE (2016) Wind versus UHF Radio signal. Int J Sci Eng Technol Res 5: 583-585.

39. Qasem H, Betts TR, Müllejans H, et al. (2014) Application dust-induced shading on photovoltaic modules. Photovolt Res 22: 218-226.    

40. Jalil A, Rao NH (2019) Chapter 8-Time series analysis (Stationarity, Cointegration, and Causality). Özcan B, Öztürk I, Éds. Environmental Kuznets Curve (EKC), Academic Press, 85-99.

41. Granger CWJ, Weiss AA (1983) Time series analysis of error-correction. Karlin S, Amemiya T, Goodman LA, Éds. Studies in Econometrics, Time Series, and Multivariate Statistics, Academic Press, 255-278.

42. Mills TC (2019) Chapter 14-Error correction, spurious regressions, and cointegration. Mills TC, Ed. Applied Time Series Analysis, Academic Press, 233-253.

43. Davinson R, MacKinnon JG (2009) Econometric Theory and Methods, Oxford University Press.

44. Hassani H, Yeganegi MR (2019) Sum of squared ACF and the Ljung-Box statistics. Phys A: Stat Mech Appl 520: 81-86.    

45. Ljung GM, Box GEP (1978) On a measure of lack of fit in time series models. Biometrika 65: 297-303.    

46. Hoffman JIE (2015) Chapter 6-Normal distribution. Hoffman JIE, Ed. Biostatistics for Medical and Biomedical Practitioners, Academic Press, 101-119.

47. Johansen S (1988) Statistical analysis of cointegration vectors. J Econ Dyn Control 12: 231-254.    

48. Johansen S (1991) Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models. Econometrica 59: 1551-1580.    

49. Johansen S (1995) Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. New York: Oxford University Press.

50. Fu T, Tang X, Cai Z, et al. (2020) Correlation research of phase angle variation and coating performance by means of Pearson's correlation coefficient. Prog Org Coat 139: 105459.    

© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved