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Data-Driven Modeling of Operating Characteristics of Hydroelectric Generating Units

  • Energy Market (R Sioshansi and A Mousavian, Section Editors)
  • Published:
Current Sustainable/Renewable Energy Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Hydroelectric generation is a potential flexible electricity source that can ease the transition to a decarbonized energy economy. As such, using scarce hydroelectric generating resources efficiently is important. We examine approaches to represent the operating characteristics of hydroelectric resources.

Recent Findings

Many hydroelectric-plant owners use water tables or generic unit characteristics for operational planning. Such practice may be inefficient, as it does not account for unit-specific operating-characteristic changes or time-related impacts, e.g., plant degradation. We demonstrate a data-driven approach to modeling plant operations that is unit-specific and depends solely on observable and controllable variables.

Summary

Numerical results using historical data for four hydroelectric units illustrate the proposed methodology.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Burrow A, Newman AM, Bazilian M. Reservoir design and operation for the food-energy-water Nexus. Current Sustainable/Renewable Energy Reports 2019;6:71–89.

    Article  Google Scholar 

  2. Avraam C, Zhang Y, Sankaranarayanan S, Zaitchik B, Moynihan E, Juturu P, Neff R, Siddiqui S. Optimization-based systems modeling for the food-energy-water nexus. Current Sustainable/Renewable Energy Reports 2021;8:4–16.

    Article  Google Scholar 

  3. García-Morales MB, Dubus L. Forecasting precipitation for hydroelectric power management: how to exploit GCM’s seasonal ensemble forecasts. Int J Climatol 2007;27:1691–1705.

    Article  Google Scholar 

  4. Boucher M-A, Ramos MH. Ensemble streamflow forecasts for hydropower systems. Handbook of hydrometeorological ensemble forecasting. In: Duan Q, Pappenberger F, Wood A, Cloke HL, and Schaake JC, editors. Berlin: Springer; 2019. p. 1289–1306.

  5. Singh VK, Singal SK. Operation of hydro power plants-a review. Renew Sustain Energy Rev 2017;69:610–619.

    Article  Google Scholar 

  6. Gfrerer H. Optimization of hydro energy storage plant problems by variational methods. Zeitschrift für Oper Res 1984;28:B87–B101.

    MathSciNet  MATH  Google Scholar 

  7. Bauer W, Gfrerer H, Wacker H. Optimization strategies for hydro energy storage plants. Zeitschrift für Oper Res 1984;28:B103–B131.

    MathSciNet  MATH  Google Scholar 

  8. Catalão J, Mariano SJPS, Mendes VMF, Ferreira LAFM. Scheduling of head-sensitive cascaded hydro systems: a nonlinear approach. IEEE Trans Power Syst 2009;24:337–346.

    Article  Google Scholar 

  9. Pousinho HMI, Contreras J, Catalão JPS. Short-term optimal scheduling of a price-maker hydro producer in a pool-based day-ahead market. IET Generation, Transmission & Distribution 2012;6:1243–1251.

    Article  Google Scholar 

  10. Philpott AB, Craddock M, Waterer H. Hydro-electric unit commitment subject to uncertain demand. Eur J Oper Res 2000;125:410–424.

    Article  Google Scholar 

  11. Pérez-Díaz JI, Chazarra M, García-González J, Cavazzini G, Stoppato A. Trends and challenges in the operation of pumped-storage hydropower plants. Renew Sustain Energy Rev 2015;44: 767–784.

    Article  Google Scholar 

  12. Séguin S., Fleten S-E, Côté P., Pichler A, Audet C. Stochastic short-term hydropower planning with inflow scenario trees. Eur J Oper Res 2017;259:1156–1168.

    Article  MathSciNet  Google Scholar 

  13. Marchand A, Gendreau M, Blais M, Guidi J. Optimized operating rules for short-term hydropower planning in a stochastic environment. Comput Manag Sci 2019;16:501–519.

    Article  MathSciNet  Google Scholar 

  14. • Taktak R, D’Ambrosio C. An overview on mathematical programming approaches for the deterministic unit commitment problem in hydro valleys. Energy Systems 2017;8:57–79. Considers various assumptions that are used for simplifications of unit-commitment models with hydroelectric generators, including linearizations of penstock loss.

    Article  Google Scholar 

  15. Piekutowski MR, Litwinowicz T, Frowd RJ. Optimal short-term scheduling for a large-scale cascaded hydro system. IEEE Trans Power Syst 1994;9:805–811.

    Article  Google Scholar 

  16. Blom E, Söder L, Risberg D. Performance of multi-scenario equivalent hydropower models. Electr Power Syst Res 2020;187:106486.

    Article  Google Scholar 

  17. Shawwash ZK, Siu TK, Russell SOD. The B.C. hydro short term hydro scheduling optimization model. IEEE Trans on Power Syst 2000;15:1125–1131.

    Article  Google Scholar 

  18. Rajšl I, Ilak P, Delimar M, Krajcar S. Dispatch method for independently owned hydropower plants in the same river flow. Energies 2012;5:3674–3690.

    Article  Google Scholar 

  19. Ikura Y, Gross G. Efficient large-scale hydro system scheduling with forced spill conditions. IEEE Trans Power Appar Syst 1984;PAS-103:3502–3520.

    Article  Google Scholar 

  20. Diniz AL, Esteves PPI, Sagastizábal CA. A mathematical model for the efficiency curves of hydroelectric units. 2007 IEEE Power Engineering Society General Meeting, Tampa, Florida; 2007. Institute of Electrical and Electronics Engineers.

  21. •• Hidalgo IG, Fontane DG, Lopes JEG, Andrade JGP, de Angelis AF. Efficiency curves for hydroelectric generating units. J Water Resour Plan Manag 2014;140:86–91. Uses historical measured power, head level, and water flow to fit efficiency curves for hydroelectric generating units to determine performance characteristics.

    Article  Google Scholar 

  22. Kong J, Skjelbred HI, Abgottspon H. Short-term hydro scheduling of a variable speed pumped storage hydropower plant considering head loss in a shared penstock. IOP Conference Series: Earth and Environmental Science 2019;240:082002.

    Article  Google Scholar 

  23. Breton M, Hachem S, Hammadia A. Accounting for losses in the optimization of production of hydroplants. IEEE Trans Energy Convers 2004;19:346–351.

    Article  Google Scholar 

  24. Streeter VL, Wylie EB. Fluid mechanics, seventh edition. New York: The McGraw-Hill Companies, Inc.; 1979.

    MATH  Google Scholar 

  25. Ruud PA. 2000. An introduction to classical econometric theory: Oxford University Press, New York.

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Acknowledgements

The authors thank the editors, reviewer, and Armin Sorooshian (University of Arizona) for helpful comments and discussions and staff of American Electric Power Company, Inc. for providing historical hydroelectric-unit operational data. Any opinions and conclusions that are expressed in this paper are solely those of the authors.

Funding

This work was supported by National Science Foundation grant 1808169.

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Authors and Affiliations

  1. Department of Integrated Systems Engineering, The Ohio State University, Baker Systems Engineering Building, 1971 Neil Avenue, Columbus, OH, 43210, USA

    Rachel Hunter-Rinderle & Ramteen Sioshansi

  2. Department of Electrical and Computer Engineering, The Ohio State University, Dreese Laboratories, 2015 Neil Avenue, Columbus, OH, 43210, USA

    Ramteen Sioshansi

Authors
  1. Rachel Hunter-Rinderle
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  2. Ramteen Sioshansi
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Correspondence to Ramteen Sioshansi.

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Hunter-Rinderle, R., Sioshansi, R. Data-Driven Modeling of Operating Characteristics of Hydroelectric Generating Units. Curr Sustainable Renewable Energy Rep 8, 199–206 (2021). https://doi.org/10.1007/s40518-021-00197-1

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  • DOI: https://doi.org/10.1007/s40518-021-00197-1

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