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Solar radiation synthetic series for power purchase agreements

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Abstract

The present paper proposes a methodology based on the implementation and assessment of autoregressive (AR) solar radiation models for generating synthetic series and providing guidance on bidding strategies for power purchase agreements. The work considered conventional and periodic AR models with different lag orders, assessing the models against real solar radiation measurements. The synthetic series generation process developed 1000 1-year monthly solar radiation scenarios that were later employed for simulating electric energy production and power purchase agreement models. This application allowed one to evaluate the risk associated with the energy supply security, supporting bidding strategies in energy auctions. A real study case is also illustrated in detail, referring to a spot in the Brazilian best irradiance area.

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References

  • Anderson P, Vecchia A (1993) Asymptotic results for periodic autoregressive moving average processes. J Time Ser Anal 1–18

  • ANEEL (2016) Bidding notice n° 04/2016: annex II (in Portuguese). ANEEL, Brasilia

    Google Scholar 

  • Ballini R (2007) Models of time series for prediction of average monthly flows (in Portuguese). UNICAMP, Campinas

    Google Scholar 

  • Box G, Jenkins G (1976) Time series analysis, forecasting and control. Holden-Day, San Francisco

    Google Scholar 

  • Demirtas M, Yesilbudak M, Sagiroglu S, Colak I (2012) Prediction of solar radiation using meteorological data. International Conference on Renewable Energy Research and Applications (ICRERA). IEEE, Nagasaki, pp 1–4

  • Gairaa K, Chellali F, Benkaciali S, Messlem Y, Abdallah K (2015) Daily global solar radiation forecasting over a desert area using nar neural networks comparison with conventional methods. International Conference on Renewable Energy Research and Applications (ICRERA). IEEE, Palermo, pp 567–571

  • Grigonytė E, Butkevičiūtė E (2016) Short-term wind speed forecasting using ARIMA model. Energetika 62(1–2)

  • Huang J, Korolkiewicz M, Agrawal M, Boland J (2013) Forecasting solar radiation on an hourly time scale using a coupled autoregressive and dynamical system (CARDS) model. Solar Energy 136–149

  • Huang C, Wang L, Lai L (2018) Data-driven short-term solar irradiance forecasting based on information of neighboring sites. IEEE Trans Ind Electron 1

  • Ibrahim A, Ramadan M, ElSebaii A, ElBroullosey S (2011) Estimation of solar irradiance on inclined surfaces facing south in Tanta, Egypt. International Journal of Renewable Energy Research (IJRER), pp 18–25

  • Jones R, Brelsford W (1967) Time series with periodic structure. Biometrika 54:403–408

    Article  CAS  Google Scholar 

  • Kelman J (1980) A stochastic model for daily streamflow. J Hydrol 47:235–249

    Article  Google Scholar 

  • Kumar N, Sharma S, Sinha U, Nayak Y (2016) Prediction of solar energy based on intelligent ANN modeling. International Journal of Renewable Energy Research (IJRER), pp 183–188

  • Lourenco L, Gemignani M, Gouvea M, Salles M, Kagan N (2017) Time series modelling for solar irradiance estimation in northeast Brazil. International Conference on Renewable Energy Research and Applications (ICRERA). IEEE, San Diego, pp 401–405

  • Machado R (2016) Generation of hydro-eolic scenarios for medium-term energy operation planning using a periodic autoregressive model (in Portuguese). Master Dissertation, Federal University of Santa Catarina, Florianópolis

  • Marrugo N, Amaya D, Ramos O (2017) Behavior prediction algorithm of solar radiation and temperature in Cajicá, Colombia. International Journal of Renewable Energy Research (IJRER), pp 629–635

  • Omar M, Dolara A, Magistrati G, Mussetta M, Ogliari E, Viola F (2016) Day-ahead forecasting for photovoltaic power using artificial neural networks ensembles. International Conference on Renewable Energy Research and Applications (ICRERA). IEEE, Birmingham, pp 1152–1157

  • Pereira M, Oliveira G, Costa C, Kelman J (1984, Março) Stochastic streamflow models for hydroelectric systems. Water Resour Res 20:379–390

    Article  Google Scholar 

  • Reis R (2013) Periodic autoregressive models for prediction and generation of series of monthly average flows (in Portuguese). Doctoral dissertation, University of Sao Paulo, São Paulo

  • Vecchia A (1985) Maximum likelihood estimation for periodic autoregressive moving average models. Technometrics 27:375–384

    Article  Google Scholar 

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Authors and Affiliations

  1. Polytechnic School, Centro de Estudos em Regulação e Qualidade de Energia (ENERQ), University of São Paulo, Av. Prof. Luciano Gualberto, Travessa 3, no. 158, São Paulo, SP, Brazil

    Matheus Gemignani, Guido J. Rostegui, Nelson Kagan & Carlos F. M. Almeida

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  1. Matheus Gemignani
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  2. Guido J. Rostegui
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  3. Nelson Kagan
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  4. Carlos F. M. Almeida
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Correspondence to Matheus Gemignani.

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Responsible editor: Marcus Schulz

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Gemignani, M., Rostegui, G.J., Kagan, N. et al. Solar radiation synthetic series for power purchase agreements. Environ Sci Pollut Res 28, 12334–12350 (2021). https://doi.org/10.1007/s11356-018-3194-5

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  • DOI: https://doi.org/10.1007/s11356-018-3194-5

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