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Synthetic scenario generation of monthly streamflows conditioned to the El Niño–Southern Oscillation: application to operation planning of hydrothermal systems

  • Felipe TreistmanEmail author
  • Maria Elvira Piñeiro Maceira
  • Débora Dias Jardim Penna
  • Jorge Machado Damázio
  • Otto Corrêa Rotunno Filho
Original Paper
  • 13 Downloads

Abstract

The Brazilian Interconnected Power System is hydro dominated and characterized by large reservoirs presenting multi-year regulation capability, arranged in complex cascades over several river basins. In this way, the expansion and operation planning should take into account the uncertainties about the future inflows to hydroplants reservoirs. Currently, a stochastic model for synthetic scenarios generation of monthly streamflow, based on Periodic Auto-Regressive formulation, is used to address the uncertainty. This is the official model used in the Brazilian energy operation planning by the Ministry of Mines and Energy, the National Operator of Electrical System, the Chamber of Electric Energy Commercialization and the Energy Planning Company. Recently, a great scientific effort has been made to include relevant climatic information in stochastic streamflow models. Among several important climatic phenomena in the Brazilian hydrological cycles, El Niño–Southern Oscillation has been pointed as one of the most important. Although the stochastic models that include exogenous variables or that use wavelets present good results, they have limitations for long-term horizon projections or are not suitable for applications that use stochastic dual dynamic programming, which is the case of the Brazilian electrical system. This work proposes an improvement to the current scenario generation model, in order to consider the climate information, but still being suitable to be applied in SDDP algorithms. To achieve this goal, a Markov-Switching Periodic Auto-Regressive model is presented. It is demonstrated that the methodology is able to generate synthetic scenarios which better resembles the observed streamflow, mainly during periods when the streamflow are below-average.

Keywords

Synthetic streamflow scenario generation Multivariate spatial data El Niño–Southern Oscillation Energy operation planning 

Notes

Acknowledgements

The authors would like to express their gratitude towards the Brazilian Electrical Energy Research Center (CEPEL) and the Federal University of Rio de Janeiro (UFRJ) for the financial and technical support, and all the institutions that kindly provided the data used in this study. The authors are thankful and recognize that this study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES)- Finance Code 001. The authors are also grateful to the Brazilian Ministry of Mines and Energy (MME) and the Ministry of Science, Technology, Innovation and Communication (MCTIC), through the National Council for Scientific and Technological Development (CNPq) and the Financier of Studies and Projects (FINEP), and to the Fundação Carlos Chagas de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) for the means and support of the development of this research work.

References

  1. Aceituno P (1988) On the functioning of the Southern Oscillation in the South American Sector. Part I: surface climate. Month Weather Rev 116(3):505–524.  https://doi.org/10.1175/1520-0493(1988)116<0505:OTFOTS>2.0.CO;2 CrossRefGoogle Scholar
  2. Aliat B, Hamdi F (2018) On Markov-switching periodic ARMA models. Commun Stat Theory Methods 47(2):344–364.  https://doi.org/10.1080/03610926.2017.1303734 CrossRefGoogle Scholar
  3. Ambrizzi T, de Souza EB, Pulwarty RS (2004) The hadley and walker regional circulations and associated ENSO impacts on South American Seasonal Rainfall. In: Diaz H, Bradley R (eds) The Hadley circulation: present, past and future, Springer, Dordrecht, pp 203–235,  https://doi.org/10.1007/978-1-4020-2944-8_8 Google Scholar
  4. Anderson R, Rose B, Oliver L (2015) Use of IRI ensembles to characterize ENSO uncertainty in water supply forecasting for the lower colorado river in Texas. In: Watershed Management 2015, American Society of Civil Engineers, Reston, VA, pp 79–90,  https://doi.org/10.1061/9780784479322.008
  5. Barnston AG, Chelliah M, Goldenberg SB (1997) Documentation of a highly ENSO-related sst region in the equatorial pacific: research note. Atmosphere-Ocean 35(3):367–383.  https://doi.org/10.1080/07055900.1997.9649597 CrossRefGoogle Scholar
  6. Barnston AG, Tippett MK, L’Heureux ML, Li S, DeWitt DG (2012) Skill of real-time seasonal ENSO model predictions during 2002–11: is our capability increasing? Bull Am Meteorol Soc 93(5):631–651.  https://doi.org/10.1175/BAMS-D-11-00111.1 CrossRefGoogle Scholar
  7. Bertsekas DP, Bertsekas DP, Bertsekas DP, Bertsekas DP (1995) Dynamic programming and optimal control, vol 1. Athena scientific, BelmontGoogle Scholar
  8. Box GE, Cox DR (1964) An analysis of transformations. J R Stat Soc B, pp 211–252Google Scholar
  9. Box GE, Jenkins GM (1970) Time series analysis: forecasting and control. Holden-Day,Google Scholar
  10. Bracken C, Rajagopalan B, Zagona E (2014) A hiddenMarkovmodel combined with climate indices formultidecadal streamflow simulation. Water Resour Res 50:1–11.  https://doi.org/10.1002/2014WR015567 CrossRefGoogle Scholar
  11. Camilloni I, Barros V (2000) The Parana River Response to El Nino 1982–83 and 1997–1998 events. J Hydrometeorol 1:412–430.  https://doi.org/10.1175/1525-7541(2000)001 CrossRefGoogle Scholar
  12. Charbeneau RJ (1978) Comparison of the two- and three-parameter log normal distributions used in streamflow synthesis. Water Resour Res 14(1):149–150.  https://doi.org/10.1029/WR014i001p00149,CrossRefGoogle Scholar
  13. Chau K (2017) Use of meta-heuristic techniques in rainfall-runoff modelling. Water 9(3):186.  https://doi.org/10.3390/w9030186 CrossRefGoogle Scholar
  14. Chen WY (1982) Assessment of southern oscillation sea-level pressure indices. Month Weather Rev 110(7):800–807.  https://doi.org/10.1175/1520-0493(1982)110<0800:AOSOSL>2.0.CO;2 CrossRefGoogle Scholar
  15. de Almeida Pereira GA, Veiga Á (2019) Periodic copula autoregressive model designed to multivariate streamflow time series modelling. Water Resour Manag 33(10):3417–3431.  https://doi.org/10.1007/s11269-019-02308-6 CrossRefGoogle Scholar
  16. Dettinger M, Cayan D, McCabe G, Marengo J (2000) Multiscale streamflow variability associated with el niño/southern oscillation. In: Diaz H, Markgraf V (eds) El Niño and the southern oscillation–multiscale variability and global and regional impacts. Cambridge University Press, Cambridge, pp 113–146Google Scholar
  17. Diniz A, Santos T, Cabral R, Santos L, Maceira ME, Costa F (2018) Short/mid-term hydrothermal dispatch and spot pricing for large-scale systems - the case of Brazil. In: 20th power systems computation conference–PSCC, Dublin, IrelandGoogle Scholar
  18. Erkyihun ST, Rajagopalan B, Zagona E, Lall U, Nowak K (2016) Wavelet-based time series bootstrap model formultidecadal streamflow simulation using climate indicators. Water Resour Res 52(1):600–612.  https://doi.org/10.1002/2015WR018249 CrossRefGoogle Scholar
  19. Erkyihun ST, Zagona E, Rajagopalan B (2017) Wavelet and hidden markov-based stochastic simulation methods comparison on colorado river streamflow. J Hydrol Eng 22(9):04017033.  https://doi.org/10.1061/(ASCE)HE.1943-5584.0001538 CrossRefGoogle Scholar
  20. Gelati E, Madsen H, Rosbjerg D (2010) Markov-switching model for nonstationary runoff conditioned on El Niño information. Water Resour Res 46(2):W02517.  https://doi.org/10.1029/2009WR007736
  21. Gelati E, Madsen H, Rosbjerg D (2011) Stochastic reservoir optimization using El Niño information: case study of Daule Peripa. Ecuador. Hydrol Res 42(5):413.  https://doi.org/10.2166/nh.2011.009 CrossRefGoogle Scholar
  22. Gelati E, Madsen H, Rosbjerg D (2014) Reservoir operation using El Niño forecasts-case study of Daule Peripa and Baba. Ecuador. Hydrol Sci J 59(8):1559–1581.  https://doi.org/10.1080/02626667.2013.831978 CrossRefGoogle Scholar
  23. Grimm AM (2003) The El Niño impact on the summer monsoon in Brazil: regional processes versus remote influences. J Clim 16(2):263–280.  https://doi.org/10.1175/1520-0442(2003)016<0263:TENIOT>2.0.CO;2 CrossRefGoogle Scholar
  24. Grimm AM (2004) How do La Niña events disturb the summer monsoon system in Brazil? Clim Dyn 22(2–3):123–138.  https://doi.org/10.1007/s00382-003-0368-7
  25. Grimm AM, Tedeschi RG (2009) ENSO and extreme rainfall events in South America. J Clim 22(7):1589–1609.  https://doi.org/10.1175/2008JCLI2429.1 CrossRefGoogle Scholar
  26. Hamilton JD (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57(2):357.  https://doi.org/10.2307/1912559 CrossRefGoogle Scholar
  27. Hipel KW, McLeod AI (1994) Time series modelling of water resources and environmental systems, vol 45. Elsevier, AmsterdamCrossRefGoogle Scholar
  28. Huang B, Thorne PW, Banzon VF, Boyer T, Chepurin G, Lawrimore JH, Menne MJ, Smith TM, Vose RS, Zhang HM (2017) Extended reconstructed sea surface temperature version 5 (ERSSTv5): upgrades, validations, and intercomparisons. J Clim 5:JCLI–D–16–0836.1.  https://doi.org/10.1175/JCLI-D-16-0836.1 CrossRefGoogle Scholar
  29. Jardim D, Maceira M, Falcao D (2001) Stochastic streamflow model for hydroelectric systems using clustering techniques. In: Power tech proceedings, 2001 IEEE Porto, IEEE, vol 3,  https://doi.org/10.1109/PTC.2001.964916
  30. Jensen JD, Bolkesjø TF, Sønju-Moltzau B (2016) Joint use of hydrological modeling and large-scale stochastic optimization techniques applied to the nordic power system. Energy Procedia 87:19–27.  https://doi.org/10.1016/j.egypro.2015.12.353 CrossRefGoogle Scholar
  31. Kelman J, de M Vieira A, Rodriguez-Amaya JE, (2000) El Niño influence on streamflow forecasting. Stoch Environ Res Risk Assess 14(2):123–138.  https://doi.org/10.1007/PL00009776 CrossRefGoogle Scholar
  32. Kisi O, Choubin B, Deo RC, Yaseen ZM (2019) Incorporating synoptic-scale climate signals for streamflow modelling over the mediterranean region using machine learning models. Hydrol Sci J 64(10):1240–1252.  https://doi.org/10.1080/02626667.2019.1632460 CrossRefGoogle Scholar
  33. Kousky VE, Higgins RW (2007) An alert classification system for monitoring and assessing the ENSO Cycle. Weather Forecast 22(2):353–371.  https://doi.org/10.1175/WAF987.1 CrossRefGoogle Scholar
  34. Lima CHR, Lall U (2010) Climate informed monthly streamflow forecasts for the Brazilian hydropower network using a periodic ridge regression model. J Hydrol 380(3–4):438–449.  https://doi.org/10.1016/j.jhydrol.2009.11.016 CrossRefGoogle Scholar
  35. Maçaira PM, Oliveira FLC, Ferreira PGC, de Almeida FVN, Souza RC (2017) Introducing a causal PAR(p) model to evaluete the influence of climate variables in reservoir inflows: a Brazilian Case. Pesquisa Oper 37(1):107–128.  https://doi.org/10.1590/0101-7438.2017.037.01.0107 CrossRefGoogle Scholar
  36. Maceira M, Bezerra C (1997) Stochastic streamflow model for hydroelectric systems. In: 5th conference on probabilistic methods applied to power systems–PMAPS, Vancouver, CanadaGoogle Scholar
  37. Maceira M, Terry L, FSCosta, JMDamázio, ACGMelo (2002) Chain of optimization models for setting the energy dispatch and spot price in the Brazilian system. Power system computation conference 2:24–28, http://www.pscc-central.org/uploads/tx_ethpublications/s43p01.pdf
  38. Maceira ME, Penna D, Diniz A, Vasconcellos C, Pinto R, Cruz C, Melo A (2018) Twenty years of application of stochastic dual dynamic programming in official and agent studies in Brazil–main features and improvements on the Newave Model. In: 20th power systems computation conference–PSCC, Dublin, IrelandGoogle Scholar
  39. Maceira MEP, Damázio JM (2006) Use of PAR(p) model in the stochastic dual dynamic programming optimization scheme used in the operation plannin of the Brazilian hydropower system. Prob Eng Inf Sci 20(01):143–156.  https://doi.org/10.1017/S0269964806060098 CrossRefGoogle Scholar
  40. Ja Marengo (1995) Variations and change in south American streamflow. Clim Change 31(1):99–117.  https://doi.org/10.1007/BF01092983 CrossRefGoogle Scholar
  41. Marengo JA, Tomasella J, Uvo CR (1998) Trends in streamflow and rainfall in tropical South America: Amazonia, eastern Brazil, and northwestern Peru. J Geophys Res 103(D2):1775–1783.  https://doi.org/10.1029/97JD02551 CrossRefGoogle Scholar
  42. Martins L, Azevedo A, Soares S (2014) Nonlinear medium-term hydro-thermal scheduling with transmission constraints. IEEE Trans Power Syst 29(4):1623–1633CrossRefGoogle Scholar
  43. Nowak KC, Rajagopalan B, Zagona E (2011) Wavelet Auto-Regressive Method (WARM) for multi-site streamflow simulation of data with non-stationary spectra. J Hydrol 410(1–2):1–12.  https://doi.org/10.1016/j.jhydrol.2011.08.051 CrossRefGoogle Scholar
  44. ONS (2018) Pen (2018) Planejamento da Operação Energética 2018/2022. Operador Nacional do Sistema Elétrico, Rio de Janeiro, p 2018Google Scholar
  45. Pereira G, Veiga A (2018) Par (p)-vine copula based model for stochastic streamflow scenario generation. Stoch Env Res Risk Assess 32(3):833–842CrossRefGoogle Scholar
  46. Pereira MV, Pinto LM (1991) Multi-stage stochastic optimization applied to energy planning. Math Program 52(1–3):359–375CrossRefGoogle Scholar
  47. Pezzi LP, Cavalcanti IFA (2001) The relative importance of ENSO and tropical Atlantic sea surface temperature anomalies for seasonal precipitation over South America: a numerical study. Clim Dyn 17:205–212.  https://doi.org/10.1007/s003820000104 CrossRefGoogle Scholar
  48. Pina J, Tilmant A, Anctil F (2017) Horizontal approach to assess the impact of climate change on water resources systems. J Water Resour Plan Manag 143(4):1–11.  https://doi.org/10.1061/(ASCE)WR.1943-5452.0000737 CrossRefGoogle Scholar
  49. Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1992) Numerical recipes in fortranGoogle Scholar
  50. Rasmusson EM, Carpenter TH (1982) Variations in tropical sea surface temperature and surface wind fields associated with the southern oscillation/El Niño. Month weather Rev110(5):354–384.  https://doi.org/10.1175/1520-0493(1982)110<0354:VITSST>2.0.CO;2 CrossRefGoogle Scholar
  51. Rasmusson EM, Wallace JM (1983) Meteorological aspects of the El Nino/Southern Oscillation. Science 222(4629):1195–1202.  https://doi.org/10.1126/science.222.4629.1195 CrossRefGoogle Scholar
  52. Salas JD, Delleur J, Yevjevich V, Lane W (1980) Applied modeling of hydrologic time series. Water Resources Publication, New YorkGoogle Scholar
  53. Serago JM (November 2017) Vogel RM (2018) Parsimonious nonstationary flood frequency analysis. Adv Water Resour 112:1–16.  https://doi.org/10.1016/j.advwatres.2017.11.026 CrossRefGoogle Scholar
  54. Sharma S, Srivastava P, Fang X, Kalin L (2015) Performance comparison of adoptive neuro fuzzy inference system (anfis) with loading simulation program c++ (lspc) model for streamflow simulation in el niño southern oscillation (enso)-affected watershed. Expert Syst Appl 42(4):2213–2223CrossRefGoogle Scholar
  55. Stedinger JR (1980) Fitting log normal distributions to hydrologic data. Water Resour Res 16(3):481–490CrossRefGoogle Scholar
  56. Terry L, Pereira M, Araripe Neto T, Silva L, Sales P (1986) Coordinating the energy generation of the brazilian national hydrothermal electrical generating system. Interfaces 16(1):16–38CrossRefGoogle Scholar
  57. Tilmant A, Kinzelbach W (2012) The cost of noncooperation in international river basins. Water Resour Res 48(1):1–12.  https://doi.org/10.1029/2011WR011034 CrossRefGoogle Scholar
  58. Timmermann A, Si An, Js Kug, Ff Jin, Cai W, Capotondi A, Cobb K, Lengaigne M, McPhaden MJ, Stuecker MF, Stein K, Wittenberg AT, Ks Yun, Bayr T, Hc Chen, Chikamoto Y, Dewitte B, Dommenget D, Grothe P, Guilyardi E, Yg Ham, Hayashi M, Ineson S, Kang D, Kim S, Kim W, Jy Lee, Li T, Jj Luo, McGregor S, Planton Y, Power S, Rashid H, Hl Ren, Santoso A, Takahashi K, Todd A, Wang G, Wang G, Xie R, Yang Wh, Yeh SW, Yoon J, Zeller E, Zhang X (2018) El Niño-Southern Oscillation complexity. Nature 559(7715):535–545.  https://doi.org/10.1038/s41586-018-0252-6 CrossRefGoogle Scholar
  59. Tippett MK, Barnston AG, Li S (2012) Performance of recent multimodel ENSO forecasts. J Appl Meteorol Climatol 51(3):637–654.  https://doi.org/10.1175/JAMC-D-11-093.1 CrossRefGoogle Scholar
  60. Uvo CB, Repelli CA, Zebiak SE, Kushnir Y (1998) The relationships between tropical Pacific and Atlantic SST and Northeast Brazil monthly precipitation. J Clim 11(4):551–562.  https://doi.org/10.1175/1520-0442(1998)011<0551:TRBTPA>2.0.CO;2 CrossRefGoogle Scholar
  61. Wilks DS (2011) Statistical methods in the atmospheric sciences, vol 100. Academic Press, New YorkGoogle Scholar
  62. Yaseen ZM, Sulaiman SO, Deo RC, Chau KW (2019) An enhanced extreme learning machine model for river flow forecasting: state-of-the-art, practical applications in water resource engineering area and future research direction. J Hydrol 569:387–408CrossRefGoogle Scholar

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© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Electrical Energy Research Center (Cepel)Rio de JaneiroBrazil
  2. 2.Civil Engineering Program, Alberto Luiz Coimbra Institute for Postgraduate Studies and Research in Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ)Rio de JaneiroBrazil
  3. 3.State University of Rio de Janeiro (UERJ)Rio de JaneiroBrazil

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