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Challenges in the Management of Hydroelectric Generation in Power System Operations

  • Regional Renewable Energy (M Negrete-Pincetic, Section Editor)
  • Published:
Current Sustainable/Renewable Energy Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

The management of hydroelectric generation in the context of power system operations has been a difficult and important problem since the inception of power systems more than a century ago; however, various current developments are leading to important new associated challenges and opportunities: massive integration of variable renewable energy and other disruptive technologies, climate change effects on the availability of hydro inflows, and also new efficient techniques for optimization under uncertainty.

Recent Findings

Multistage stochastic optimization and stochastic dual dynamic programming are currently the dominant techniques for hydroelectric generation scheduling problems; however, there are many recent extensions and improvements on such techniques, and alternative approaches are being developed with significant potential for future concrete applications from power system operators and policy makers.

Summary

In this context, this paper presents a literature review on hydroelectric generation scheduling models, and a discussion on the critical challenges, open research questions, and future lines of research associated to this problem.

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References

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

  1. Shapiro A, Dentcheva D, Ruszczyński A. Lectures on stochastic programming. Lectures on Stochastic Programming. Society for Industrial and Applied Mathematics; 2009. https://doi.org/10.1137/1.9780898718751.

  2. •• Pereira MVF, Pinto LMVG. Multi-stage stochastic optimization applied to energy planning. Math Program. 1991;52(1–3):359–75. https://doi.org/10.1007/BF01582895.

    Article  MathSciNet  MATH  Google Scholar 

  3. Benders JF. Partitioning procedures for solving mixed-variables programming problems. Numer Math. 1962;4(1):238–52. https://doi.org/10.1007/BF01386316.

    Article  MathSciNet  MATH  Google Scholar 

  4. Pereira MVF. Optimal stochastic operations scheduling of large hydroelectric systems. Int J Electr Power Energy Syst. 1989;11(3):161–9. https://doi.org/10.1016/0142-0615(89)90025-2.

    Article  Google Scholar 

  5. Maceira MEP, Damázio JM. USE of the PAR(p) model in the stochastic dual dynamic programming optimization scheme used in the operation planning of the Brazilian hydropower system. Probability Eng Inform Sci. 2006;20(1):143–56. https://doi.org/10.1017/S0269964806060098.

    Article  MathSciNet  MATH  Google Scholar 

  6. Røtting TA, Gjelsvik A. Stochastic dual dynamic programming for seasonal scheduling in the norwegian power system. IEEE Trans Power Syst. 1992;7(1):273–9. https://doi.org/10.1109/59.141714.

    Article  Google Scholar 

  7. Mo B, Gjelsvik A, Grundt A. Integrated risk management of hydro power scheduling and contract management. IEEE Trans Power Syst. 2001;16(2):216–21. https://doi.org/10.1109/59.918289.

    Article  Google Scholar 

  8. Homem-De-Mello T, De Matos VL, Finardi EC. Sampling strategies and stopping criteria for stochastic dual dynamic programming: a case study in long-term hydrothermal scheduling. Energy Syst. 2011;2(1):1–31. https://doi.org/10.1007/s12667-011-0024-y.

    Article  Google Scholar 

  9. Bezerra B, Veiga A, Barroso LA, Pereira M. Assessment of parameter uncertainty in autoregressive streamflow models for stochastic long-term hydrothermal scheduling. In: IEEE Power and Energy Society General Meeting 2012. https://doi.org/10.1109/PESGM.2012.6345322.

  10. Philpott AB, De Matos VL. Dynamic sampling algorithms for multi-stage stochastic programs with risk aversion. Eur J Oper Res. 2012;218(2):470–83. https://doi.org/10.1016/j.ejor.2011.10.056.

    Article  MathSciNet  MATH  Google Scholar 

  11. Philpott A, De Matos V, Finardi E. On solving multistage stochastic programs with coherent risk measures. Oper Res. 2013;61(4):957–70. https://doi.org/10.1287/opre.2013.1175.

    Article  MathSciNet  MATH  Google Scholar 

  12. •• Homem-De-Mello T, Pagnoncelli BK. Risk aversion in multistage stochastic programming: a modeling and algorithmic perspective. Eur J Oper Res. 2016;249(1):188–99. https://doi.org/10.1016/j.ejor.2015.05.048.

    Article  MathSciNet  MATH  Google Scholar 

  13. Shapiro A, Tekaya W, Soares MP, Da Costa JP. Worst-case-expectation approach to optimization under uncertainty. Oper Res. 2013;61(6):1435–49. https://doi.org/10.1287/opre.2013.1229.

    Article  MathSciNet  MATH  Google Scholar 

  14. Rudloff B, Street A, Valladão DM. Time consistency and risk averse dynamic decision models: definition, interpretation and practical consequences. Eur J Oper Res. 2014;234(3):743–50. https://doi.org/10.1016/j.ejor.2013.11.037.

    Article  MathSciNet  MATH  Google Scholar 

  15. Brigatto A, Street A, Valladão DM. Assessing the cost of time-inconsistent operation policies in hydrothermal power systems. IEEE Trans Power Syst. 2016;32(6):4541–50. https://doi.org/10.1109/TPWRS.2017.2672204.

    Article  Google Scholar 

  16. Shapiro A. Analysis of stochastic dual dynamic programming method. Eur J Oper Res. 2011;209:63–72. https://doi.org/10.1016/j.ejor.2010.08.007.

    Article  MathSciNet  MATH  Google Scholar 

  17. Shapiro A, Tekaya W, Da Costa JP, Soares MP. Risk neutral and risk averse stochastic dual dynamic programming method. Eur J Oper Res. 2013;224(2):375–91. https://doi.org/10.1016/j.ejor.2012.08.022.

    Article  MathSciNet  MATH  Google Scholar 

  18. Brandi RBDS, Marcato ALM, Dias BH, Ramos TP, Junior ICDS. A convergence criterion for stochastic dual dynamic programming: application to the long-term operation planning problem. IEEE Trans Power Syst. 2018;33(4):3678–90. https://doi.org/10.1109/TPWRS.2017.2787462.

    Article  Google Scholar 

  19. •• Ding L, Ahmed S, Shapiro A. A Python package for multi-stage stochastic programming. Optimization Online. 2019:1–42.

  20. Löhndorf N, Shapiro A. Modeling time-dependent randomness in stochastic dual dynamic programming. Eur J Oper Res. 2019;273(2):650–61. https://doi.org/10.1016/j.ejor.2018.08.001.

    Article  MathSciNet  MATH  Google Scholar 

  21. •• Zou J, Ahmed S, Sun XA. Stochastic dual dynamic integer programming. Math Program. 2019;175(1–2):461–502. https://doi.org/10.1007/s10107-018-1249-5.

    Article  MathSciNet  MATH  Google Scholar 

  22. Hjelmeland MN, Zou J, Helseth A, Ahmed S. Nonconvex medium-term hydropower scheduling by stochastic dual dynamic integer programming. IEEE Transact Sustain Energy. 2019;10(1):481–90. https://doi.org/10.1109/TSTE.2018.2805164.

    Article  Google Scholar 

  23. •• Dowson O, Kapelevich L. SDDP.jl: a Julia package for stochastic dual dynamic programming. Optimization Online. 2017.

  24. •• Asamov T, Salas DF, Powell WB. SDDP vs. ADP: the effect of dimensionality in multistage stochastic optimization for grid level energy storage. 2016.

  25. Löhndorf N, Minner S. Optimal day-ahead trading and storage of renewable energies—an approximate dynamic programming approach. Energy Syst. 2010;1(1):61–77. https://doi.org/10.1007/s12667-009-0007-4.

    Article  Google Scholar 

  26. Löhndorf N, Wozabal D, Minner S. Optimizing trading decisions for hydro storage systems using approximate dual dynamic programming. Oper Res. 2013;61(4):810–23. https://doi.org/10.1287/opre.2013.1182.

    Article  MathSciNet  MATH  Google Scholar 

  27. • Salas DF, Powell WB. Benchmarking a scalable approximate dynamic programming algorithm for stochastic control of grid-level energy storage. INFORMS J Comput. 2018;30(1):106–23. https://doi.org/10.1287/ijoc.2017.0768.

    Article  MathSciNet  Google Scholar 

  28. •• Gauvin C, Delage E, Gendreau M. Decision rule approximations for the risk averse reservoir management problem. Eur J Oper Res. 2017;261(1):317–36. https://doi.org/10.1016/j.ejor.2017.01.044.

    Article  MathSciNet  MATH  Google Scholar 

  29. • Gauvin C, Delage E, Gendreau M. A successive linear programming algorithm with non-linear time series for the reservoir management problem. Comput Manag Sci. 2018;15(1):55–86. https://doi.org/10.1007/s10287-017-0295-4.

    Article  MathSciNet  MATH  Google Scholar 

  30. •• Bodur M, Luedtke JR. Two-stage linear decision rules for multi-stage stochastic programming. Math Program. 2018. https://doi.org/10.1007/s10107-018-1339-4.

  31. Delage E, Ye Y. Distributionally robust optimization under moment uncertainty with application to data-driven problems. Oper Res. 2010;58(3):595–612. https://doi.org/10.1287/opre.1090.0741.

    Article  MathSciNet  MATH  Google Scholar 

  32. Ben-Tal A, Den Hertog D, De Waegenaere A, Melenberg B, Rennen G. Robust solutions of optimization problems affected by uncertain probabilities. Manag Sci. 2013;59(2):341–57. https://doi.org/10.1287/mnsc.1120.1641.

    Article  Google Scholar 

  33. Mohajerin Esfahani P, Kuhn D. Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations. Math Program. 2018;171(1–2):115–66. https://doi.org/10.1007/s10107-017-1172-1.

    Article  MathSciNet  MATH  Google Scholar 

  34. •• Huang J, Zhou K, Guan Y. A study of distributionally robust multistage stochastic optimization. 2017.

  35. Philpott A, De Matos V, Kapelevich L. Distributionally robust SDDP. Comput Manag Sci. 2018;15:431–54. https://doi.org/10.1007/s10287-018-0314-0.

    Article  MathSciNet  MATH  Google Scholar 

  36. Zhang Z, Zhang Q, Singh VP. Univariate streamflow forecasting using commonly used data-driven models: literature review and case study. Hydrol Sci J. 2018;63(7):1091–111. https://doi.org/10.1080/02626667.2018.1469756.

    Article  Google Scholar 

  37. Dashti H, Conejo AJ, Jiang R, Wang J. Weekly two-stage robust generation scheduling for hydrothermal power systems. IEEE Trans Power Syst. 2016;31(6):4554–64. https://doi.org/10.1109/TPWRS.2015.2510628.

    Article  Google Scholar 

  38. Bezerra B, Veiga Á, Barroso LA, Pereira M. Stochastic long-term hydrothermal scheduling with parameter uncertainty in autoregressive streamflow models. IEEE Trans Power Syst. 2017:1. https://doi.org/10.1109/TPWRS.2016.2572722.

  39. Castro Souza R, Luí A, Marcato M, Dias BH, Luiz F, Oliveira C. Optimal operation of hydrothermal systems with hydrological scenario generation through bootstrap and periodic autoregressive models. Eur J Oper Res. 2012;222:606–15. https://doi.org/10.1016/j.ejor.2012.05.020.

    Article  Google Scholar 

  40. •• Lohmann T, Hering AS, Rebennack S. Spatio-temporal hydro forecasting of multireservoir inflows for hydro-thermal scheduling. Eur J Oper Res. 2016;255(1):243–58. https://doi.org/10.1016/j.ejor.2016.05.011.

    Article  MathSciNet  MATH  Google Scholar 

  41. Yurekli K, Kurunc A, Ozturk F. Application of linear stochastic models to monthly flow data of Kelkit stream. Ecol Model. 2005;183(1):67–75. https://doi.org/10.1016/j.ecolmodel.2004.08.001.

    Article  Google Scholar 

  42. Mondal MS, Wasimi SA. Generating and forecasting monthly flows of the Ganges river with PAR model. J Hydrol. 2006;323(1–4):41–56. https://doi.org/10.1016/j.jhydrol.2005.08.015.

    Article  Google Scholar 

  43. Lorca Á, Sun XA, Litvinov E, Zheng T. Multistage adaptive robust optimization for the unit commitment problem. Oper Res. 2016;64(1):32–51. https://doi.org/10.1287/opre.2015.1456.

    Article  MathSciNet  MATH  Google Scholar 

  44. Lorca Á, Sun XA. Adaptive robust optimization with dynamic uncertainty sets for multi-period economic dispatch under significant wind. IEEE Trans Power Syst. 2015;30(4):1702–13. https://doi.org/10.1109/TPWRS.2014.2357714.

    Article  Google Scholar 

  45. Bertsimas D, Litvinov E, Sun XA, Zhao J, Zheng T. Adaptive robust optimization for the security constrained unit commitment problem. IEEE Trans Power Syst. 2013;28(1):52–63. https://doi.org/10.1109/TPWRS.2012.2205021.

    Article  Google Scholar 

  46. Street A, Brigatto A, Valladão DM. Co-optimization of energy and ancillary services for hydrothermal operation planning under a general security criterion. IEEE Trans Power Syst. 2017;32(6):4914–23. https://doi.org/10.1109/TPWRS.2017.2672555.

    Article  Google Scholar 

  47. Maluenda B, Negrete-Pincetic M, Olivares DE, Lorca Á. Expansion planning under uncertainty for hydrothermal systems with variable resources. Int J Electr Power Energy Syst. 2018;103:644–51. https://doi.org/10.1016/j.ijepes.2018.06.008.

    Article  Google Scholar 

  48. Vicuna S, Maurer EP, Joyce B, Dracup JA, Purkey D. The sensitivity of California water resources to climate change scenarios. J Am Water Resour Assoc. 2007;43(2):482–98. https://doi.org/10.1111/j.1752-1688.2007.00038.x.

    Article  Google Scholar 

  49. Vicuna S, Leonardson R, Hanemann MW, Dale LL, Dracup JA. Climate change impacts on high elevation hydropower generation in California’s Sierra Nevada: a case study in the Upper American River. Clim Chang. 2008;87(1):123–37. https://doi.org/10.1007/s10584-007-9365-x.

    Article  Google Scholar 

  50. Garreaud RD, Boisier JP, Rondanelli R, Montecinos A, Sepúlveda HH, Veloso-Aguila D. The Central Chile Mega Drought (2010–2018): a climate dynamics perspective. Int J Climatol. 2019. https://doi.org/10.1002/joc.6219.

  51. Pereira-Bonvallet E, Püschel-Løvengreen S, Matus M, Moreno R. Optimizing hydrothermal scheduling with non-convex irrigation constraints: case on the Chilean electricity system. In: Energy Procedia. Elsevier Ltd; 2016. p. 132–40. https://doi.org/10.1016/j.egypro.2015.12.342.

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Funding

This research was partially supported by CONICYT/FONDECYT/11170423 and CONICYT-PFCHA/National Doctorate Program/2019-21190693.

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Authors and Affiliations

  1. OCM-Lab at the Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

    Álvaro Lorca, Marcel Favereau & Daniel Olivares

  2. Department of Industrial and Systems Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

    Álvaro Lorca & Marcel Favereau

  3. UC Energy Research Center, Pontificia Universidad Católica de Chile, Santiago, Chile

    Álvaro Lorca & Daniel Olivares

Authors
  1. Álvaro Lorca
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Correspondence to Álvaro Lorca.

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Lorca, Á., Favereau, M. & Olivares, D. Challenges in the Management of Hydroelectric Generation in Power System Operations. Curr Sustainable Renewable Energy Rep 7, 94–99 (2020). https://doi.org/10.1007/s40518-020-00152-6

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