MODELLING OF INTRADAY PHOTOVOLTAIC POWER PRODUCTION

Article Sidebar

PDF
Published: Jul 1, 2021
DOI: https://doi.org/10.22452/mjs.vol40no2.8%20%20%20
Keywords:
Intraday PV production, Sunrise, Sunset, NIG Distribution, AR process

Main Article Content

Noor ‘Adilah Ibrahim
Islamic Science University of Malaysia

Abstract

Photovoltaic (PV) productions should occur within a time interval of sunlight. Time mismatches are detected between sunrise and first production hour as well as sunset and last production hour in a transmission system operator, Amprion, Germany. Hence, in this paper, we investigate this effect using an additive function of two seasonalities and a stochastic process. Both seasonalities are based on the mimicked locations, corrected by a weighing scale, depending on the first and last production hours' coordinates. The result shows that the proposed deterministic model could capture the effect of sunrise and sunset. Also, the dynamics of random components are sufficiently explained by an autoregressive process of order two. Finally, the Normal Inverse Gaussian distribution is shown as the best distribution in explaining noise behaviour, particularly heavy tails in the production's residuals, compared to the Gaussian distribution.

Downloads

Download data is not yet available.

Article Details

How to Cite
Ibrahim, N. ’Adilah. (2021). MODELLING OF INTRADAY PHOTOVOLTAIC POWER PRODUCTION. Malaysian Journal of Science, 40(2), 105–124. https://doi.org/10.22452/mjs.vol40no2.8
Issue
Vol 40 No 2 (2021): June
Section
Original Articles

Transfer of Copyrights

  •  In the event of publication of the manuscript entitled [INSERT MANUSCRIPT TITLE AND REF NO.] in the Malaysian Journal of Science, I hereby transfer copyrights of the manuscript title, abstract and contents to the Malaysian Journal of Science and the Faculty of Science, University of Malaya (as the publisher) for the full legal term of copyright and any renewals thereof throughout the world in any format, and any media for communication.

 

Conditions of Publication

  •  I hereby state that this manuscript to be published is an original work, unpublished in any form prior and I have obtained the necessary permission for the reproduction (or am the owner) of any images, illustrations, tables, charts, figures, maps, photographs and other visual materials of whom the copyrights is owned by a third party.
  • This manuscript contains no statements that are contradictory to the relevant local and international laws or that infringes on the rights of others.
  • I agree to indemnify the Malaysian Journal of Science and the Faculty of Science, University of Malaya (as the publisher) in the event of any claims that arise in regards to the above conditions and assume full liability on the published manuscript.

 

Reviewer’s Responsibilities

  • Reviewers must treat the manuscripts received for reviewing process as confidential. It must not be shown or discussed with others without the authorization from the editor of MJS.
  • Reviewers assigned must not have conflicts of interest with respect to the original work, the authors of the article or the research funding.
  • Reviewers should judge or evaluate the manuscripts objective as possible. The feedback from the reviewers should be express clearly with supporting arguments.
  • If the assigned reviewer considers themselves not able to complete the review of the manuscript, they must communicate with the editor, so that the manuscript could be sent to another suitable reviewer.

 

Copyright: Rights of the Author(s)

  • Effective 2007, it will become the policy of the Malaysian Journal of Science (published by the Faculty of Science, University of Malaya) to obtain copyrights of all manuscripts published. This is to facilitate:
  • Protection against copyright infringement of the manuscript through copyright breaches or piracy.
  • Timely handling of reproduction requests from authorized third parties that are addressed directly to the Faculty of Science, University of Malaya.
  • As the author, you may publish the fore-mentioned manuscript, whole or any part thereof, provided acknowledgement regarding copyright notice and reference to first publication in the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers) are given. You may produce copies of your manuscript, whole or any part thereof, for teaching purposes or to be provided, on individual basis, to fellow researchers.
  • You may include the fore-mentioned manuscript, whole or any part thereof, electronically on a secure network at your affiliated institution, provided acknowledgement regarding copyright notice and reference to first publication in the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers) are given.
  • You may include the fore-mentioned manuscript, whole or any part thereof, on the World Wide Web, provided acknowledgement regarding copyright notice and reference to first publication in the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers) are given.
  • In the event that your manuscript, whole or any part thereof, has been requested to be reproduced, for any purpose or in any form approved by the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers), you will be informed. It is requested that any changes to your contact details (especially e-mail addresses) are made known.

 

 Copyright: Role and responsibility of the Author(s)

  • In the event of the manuscript to be published in the Malaysian Journal of Science contains materials copyrighted to others prior, it is the responsibility of current author(s) to obtain written permission from the copyright owner or owners.
  • This written permission should be submitted with the proof-copy of the manuscript to be published in the Malaysian Journal of Science  

References

Abuella, M., Chowdhury, B. (2016). Solar power forecasting using support vector regression. Proceedings of the American Society for Engineering Management International Annual Conference.

Almeida, M. P., Perpiñãn, O., Narvarte, L. (2015). PV power forecast using a nonparametric PV model. Solar Energy, 115, pp. 354-368.

Antonanzas, J., Osorio, N., Escobar, R., Urraca, R., Martinez-de-Pison, F. J., Antonanzas-Torres, F. (2016). Review of photovoltaic power forecasting. Solar Energy, 136, pp. 78-111.

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

Barndorff-Nielsen, O. E. (1998). Processes of normal inverse Gaussian type. Finance & Stochastics, 2(1), 41-68.

Benmouiza, K., Cheknane, Ali. (2016). Small-scale solar radiation forecasting using ARMA and nonlinear autoregressive neural network models. Theoretical and Applied Climatology, 124, 945-958.

Benth, F. E., Ŝaltytė Benth, J. (2004). The normal inverse Gaussian distribution and spot price modelling in energy markets. International Journal of Theoretical and Applied Finance, 7(2), 177-192.

Benth, F. E., Ŝaltytė Benth, J. (2005). Stochastic modelling of temperature variations with a view towards weather derivatives. Applied Mathematical Finance, 12(1), 53-85.

Benth, F. E., Henriksen, P. N. (2011). Pricing of basket options using univariate normal inverse Gaussian approximates. Journal of Forecasting, 30, 355-376.

Benth, F. E., Ŝaltytė Benth, J. (2012). Modelling and Pricing in Financial Markets for Weather Derivatives. World Scientific, Singapore.

Benth, F. E., Che Taib, C. M. I. (2013). On the speed towards the mean for continuous time autoregressive moving average processes with applications to energy markets. Energy Economics, 40, 259-268.

Benth, F. E., Ibrahim, N. A. (2017). Stochastic modelling of photovoltaic power generation and electricity prices. Journal of Energy Markets, 10 (3), 1-33.

Bølviken, E., Benth, F. E. (2000). Quantification of risk in Norwegian stocks via the normal inverse Gaussian distribution. R. Norberg et al. (eds) Proceedings of the 10th AFIR Colloquium in Tromsø. AFIR, 87-98.

Brockwell, P. J., Davis, R. A. (1991). Time Series: Theory and Methods, 2nd ed. Springer, New York.

Cervone, G., Clemente-Harding, L., Alessandrini, S., Monache, L. D. (2017). Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble. Renewable Energy, 108, pp. 274-286.

Chattopadhyay, F. K. (2017). Optimization of spatial balancing and storage needs for large-scale power system integration of fluctuating solar energy (Doctoral Dissertation, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany). Retrieved from http://oops.unioldenburg.de/3217/1/chaopt17.pdf.

Chow, C. W., Belongie, S., Kleissl, J. (2015). Cloud motion and stability estimation for intrahour solar forecasting. Solar Energy, 115, pp. 645-655.

Das, U. K., Tey, K. S., Seyedmahmoudian, M., Mekhilef, S., Idris, M. Y. I., van Dementer, W., Horan, B., Stojcevski, A. (2018). Forecasting of photovoltaic power generation and model optimization. Renewable and Sustainable Energy Reviews, 81, pp. 912-928.

David, M., Ramahatana, F., Trombe, P. J., Lauret, P. (2016). Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models. Solar Energy 133, 55-72.

Do, M-T., Soubdhan, T., Robyns, B. (2016). A study on the minimum duration of training data to provide a high accuracy forecast for PV generation between two different climatic zones. Renewable Energy, 85, pp. 959-964.

Dolara, A., Grimaccia, F., Leva, S., Mussetta, M., Ogliari, E. (2015a). A physical hybrid artificial neural network for short term forecasting of PV plant power output. Energies, 8, pp. 1138 - 1153. (doi:10.3390/en8021138).

Dolara, A., Leva, S., Manzolini, G. (2015b). Comparison of different physical models for PV power output prediction. Solar Energy, 119, pp. 83 - 99.

Duffie, J.A., Beckman, W.A. (2013). Solar Engineering of Thermal Processes, 4th edn. Wiley (http://doi.org/b993).

Felice, M. D., Petitta, M., Ruti, P. M. (2015). Short-term predictability of photovoltaic production in Italy. Renewable Energy, 80, pp. 197 - 204.

Gandoman, F. H., Raeisi, F., Ahmadi, A. (2016). A literature review on estimating of PV-array hourly power under cloudy weather conditions. Renewable and Sustainable Energy Reviews, 63, pp. 579-592.

Honsberg, C., Bowden, S. (2016). Photovoltaic education network. Web page. URL:www.pveducation.org.

IEA PVPS. (2017). Snapshot of Global Photovoltaic Markets (Report No. T1-31:2017). Retrieved from http://www.iea-pvps.org/_leadmin/dam/public/report/statistics/IEA-PVPS-A Snapshot of Global PV-1992-2016 1.pdf.

Larson, D. P., Nonnenmacher, L., Coimbra, C. F. M. (2016). Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest. Renewable Energy, 91, pp. 11-20.

Leva, S., Dolara, A., Grimaccia, F., Mussetta, M., Ogliari, E. (2017). Analysis and validation of 24 hours ahead of neural network forecasting of photovoltaic output power. Mathematics and Computers in Simulation, 131, pp. 88-100.

Li, Y., He, Y., Su, Y., Shu, L. (2016). Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines. Applied Energy, 180, pp. 392-401.

Lipperheide, M., Bosch, J. L., Kleissl, J. (2015). Embedded nowcasting method using cloud speed persistence for a photovoltaic power plant. Solar Energy, 112, pp. 232-238.

Mambrini, T., Dubois, A. M., Longeaud, C., Badosa, J., Hae_elin, M., Prieur, L., Radivoniuk, V. (2015). Photovoltaic yield: Correction method for the mismatch between the solar spectrum and the reference ASTMG AM1.5G spectrum. EPJ Photovoltaics, 6, 60701. Doi: 10.1051/epjpv/2014011.

Martinez-Anido, C. B., Botor, B., Florita, A. R., Draxl, C., Lu, S., Hamann, H. F., Hodge, B-M. (2016). The value of day-ahead solar power forecasting improvement. Solar Energy, 129, pp. 192 – 203.

Massida, L., Marrocu, M. (2017). Use of multilinear adaptive regression splines and numerical weather prediction to forecast the power output of a PV plant in Borkum, Germany. Solar Energy, 146, pp. 141-149.

Ogliari, E., Dolara, A., Manzolini, G., Leva, S. (2017). Physical and hybrid methods comparison for the day ahead PV output power forecast. Renewable Energy, 113, pp. 11 - 21.

Prakash, S., Gopinath, N. P., Suganthi, J. (2018). Wind and solar energy forecasting system using artificial neural networks. International Journal of Pure and Applied mathematics, 118(5), pp. 845-854.

Rana, M., Koprinska, I., Agelidis, V. G. (2016). Univariate and multivariate methods for very short-term solar photovoltaic power forecasting. Energy Conversion and Management, 121, pp. 380-390.

Raza, M. Q., Nadarajah, M., Ekanayake, C. (2016). On recent advances in PV output power forecast. Solar Energy, 136, pp. 125-144.

Sfetsos, A., Coonick, A. H. (1999). Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques. Solar Energy 68(2), 169-178.

Sun, H., Yan, D., Zhao, N., Zhou, J. (2015). Empirical investigation on modeling solar radiation series with ARMA-GARCH models. Energy Conversion and Management 92, 385-395.

van der Meer, D. W., Widén, J., Munkhammar, J. (2018). Review on probabilistic forecasting of photovoltaic power production and electricity consumption. Renewable and Sustainable Energy Reviews, 81, pp. 1484-1512.

Vaz, A. G. R., Elsinga, B., van Sark, W. G. J. H. M., Brito, M. C. (2016). An artificial neural network to assess the impact of neighbouring photovoltaic system in power forecasting in Utrecht, the Netherlands. Renewable Energy, 85, pp. 631-641.

Veraart, A. E. D and Zdanowicz, H. (2016). Modelling and predicting photovoltaic power generation in the EEX Market. Working paper, Social Science Research Network (http://doi.org/b964).

Wang, G., Su, Y., Shu, L. (2016). One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models. Renewable Energy, 96, pp. 469-478.

Wirth, H. (2018). Recent Facts about Photovoltaics in Germany. Press and Public Relation: Germany. Retrieved from https://www.ise.fraunhofer.de/content/dam/ise/en/documents/ publications /studies/recentfacts-about-photovoltaics-in-germany.pdf.

Wol_, B., Kühnert, J., Lorenz, E., Kramer, O., Heinemann, D. (2016). Comparing support vector regression for PV power forecasting to a physical model approach using measurement, numerical weather prediction, and cloud motion data. Solar Energy, 135, pp. 197 - 208.

Wu, Ji., Chan, C. K. (2011). Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN. Solar Energy 85, 808-817.

Zhu, H., Lian, W., Lu, L., Dai, S., Hu, Y. (2017). An improved forecasting method for photovoltaic power based on adaptive BP neural network with a scrolling time window. Energies, 10(1542). doi:10.3390/en10101542.