IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v250y2022ics0360544222006879.html
   My bibliography  Save this article

A new three-triangle based method to linearly concave hydropower output in long-term reservoir operation

Author

Listed:
  • Zheng, Hao
  • Feng, Suzhen
  • Chen, Cheng
  • Wang, Jinwen

Abstract

The nonlinearity and non-convexity of the hydropower output function (HOF) make it very challenging to search for the optimal solution to the hydropower scheduling problem, which however can be more easily solved with consistency by mathematical programming if the HOF can be properly linearized with high accuracy. This paper presents a new three-triangle based method to linearly concave the HOF without introducing any integer variables, and mathematically proves its equivalence in fitting accuracy to the all-triangle based method in which any active plane needs to be compared with all the other active planes to ensure it is active within its triangular grid, making the number of constraints be increased dramatically. The two methods are applied in approximating the HOFs of 4 hydropower reservoirs located in the Lancang River. The case studies show that the present linearization method can achieve a root-mean-square error (RMSE) at 2.05% on average of the installed power capacity, the same as the all-grid concaving method, but takes only less than 0.097s in solving the problem, 80 times faster than the all-grid concaving method. The present method should be very promising in solving the real-world hydropower scheduling problems, especially when the linearization needs to be done frequently during the solution procedure.

Suggested Citation

  • Zheng, Hao & Feng, Suzhen & Chen, Cheng & Wang, Jinwen, 2022. "A new three-triangle based method to linearly concave hydropower output in long-term reservoir operation," Energy, Elsevier, vol. 250(C).
  • Handle: RePEc:eee:energy:v:250:y:2022:i:c:s0360544222006879
    DOI: 10.1016/j.energy.2022.123784
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222006879
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2022.123784?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nils Löhndorf & David Wozabal & Stefan Minner, 2013. "Optimizing Trading Decisions for Hydro Storage Systems Using Approximate Dual Dynamic Programming," Operations Research, INFORMS, vol. 61(4), pages 810-823, August.
    2. Jian, Jinbao & Pan, Shanshan & Yang, Linfeng, 2019. "Solution for short-term hydrothermal scheduling with a logarithmic size mixed-integer linear programming formulation," Energy, Elsevier, vol. 171(C), pages 770-784.
    3. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    4. Fredo, Guilherme Luiz Minetto & Finardi, Erlon Cristian & de Matos, Vitor Luiz, 2019. "Assessing solution quality and computational performance in the long-term generation scheduling problem considering different hydro production function approaches," Renewable Energy, Elsevier, vol. 131(C), pages 45-54.
    5. Dias, Bruno Henriques & Tomim, Marcelo Aroca & Marcato, André Luís Marques & Ramos, Tales Pulinho & Brandi, Rafael Bruno S. & Junior, Ivo Chaves da Silva & Filho, João Alberto Passos, 2013. "Parallel computing applied to the stochastic dynamic programming for long term operation planning of hydrothermal power systems," European Journal of Operational Research, Elsevier, vol. 229(1), pages 212-222.
    6. D. Kumar & Falguni Baliarsingh, 2003. "Folded Dynamic Programming for Optimal Operation of Multireservoir System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 17(5), pages 337-353, October.
    7. Ximing Cai & Daene C. McKinney & Leon S. Lasdon & David W. Watkins, 2001. "Solving Large Nonconvex Water Resources Management Models Using Generalized Benders Decomposition," Operations Research, INFORMS, vol. 49(2), pages 235-245, April.
    8. Rovatti, Riccardo & D’Ambrosio, Claudia & Lodi, Andrea & Martello, Silvano, 2014. "Optimistic MILP modeling of non-linear optimization problems," European Journal of Operational Research, Elsevier, vol. 239(1), pages 32-45.
    9. Feng, Zhong-kai & Niu, Wen-jing & Wang, Sen & Cheng, Chun-tian & Jiang, Zhi-qiang & Qin, Hui & Liu, Yi, 2018. "Developing a successive linear programming model for head-sensitive hydropower system operation considering power shortage aspect," Energy, Elsevier, vol. 155(C), pages 252-261.
    10. Chuanxiong Kang & Cheng Chen & Jinwen Wang, 2018. "An Efficient Linearization Method for Long-Term Operation of Cascaded Hydropower Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3391-3404, August.
    11. Chuanxiong Kang & Min Guo & Jinwen Wang, 2017. "Short-Term Hydrothermal Scheduling Using a Two-Stage Linear Programming with Special Ordered Sets Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(11), pages 3329-3341, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David Lucas dos Santos Abreu & Erlon Cristian Finardi, 2022. "Continuous Piecewise Linear Approximation of Plant-Based Hydro Production Function for Generation Scheduling Problems," Energies, MDPI, vol. 15(5), pages 1-23, February.
    2. Cheng, Xianliang & Feng, Suzhen & Zheng, Hao & Wang, Jinwen & Liu, Shuangquan, 2022. "A hierarchical model in short-term hydro scheduling with unit commitment and head-dependency," Energy, Elsevier, vol. 251(C).
    3. Liao, Shengli & Liu, Zhanwei & Liu, Benxi & Cheng, Chuntian & Wu, Xinyu & Zhao, Zhipeng, 2021. "Daily peak shaving operation of cascade hydropower stations with sensitive hydraulic connections considering water delay time," Renewable Energy, Elsevier, vol. 169(C), pages 970-981.
    4. repec:prg:jnlcfu:v:2022:y:2022:i:1:id:572 is not listed on IDEAS
    5. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    6. Benedikt Finnah, 2022. "Optimal bidding functions for renewable energies in sequential electricity markets," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 1-27, March.
    7. Chuanxiong Kang & Cheng Chen & Jinwen Wang, 2018. "An Efficient Linearization Method for Long-Term Operation of Cascaded Hydropower Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3391-3404, August.
    8. Frank, Johannes, 2023. "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics 03/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    9. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    10. Paulo Júlio & Pedro M. Esperança, 2012. "Evaluating the forecast quality of GDP components: An application to G7," GEE Papers 0047, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Apr 2012.
    11. Cameron Roach & Rob Hyndman & Souhaib Ben Taieb, 2021. "Non‐linear mixed‐effects models for time series forecasting of smart meter demand," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1118-1130, September.
    12. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    13. Sakthivel, V.P. & Thirumal, K. & Sathya, P.D., 2022. "Short term scheduling of hydrothermal power systems with photovoltaic and pumped storage plants using quasi-oppositional turbulent water flow optimization," Renewable Energy, Elsevier, vol. 191(C), pages 459-492.
    14. Nikitopoulos, Christina Sklibosios & Thomas, Alice Carole & Wang, Jianxin, 2023. "The economic impact of daily volatility persistence on energy markets," Journal of Commodity Markets, Elsevier, vol. 30(C).
    15. I. Yu. Zolotova & V. V. Dvorkin, 2017. "Short-term forecasting of prices for the Russian wholesale electricity market based on neural networks," Studies on Russian Economic Development, Springer, vol. 28(6), pages 608-615, November.
    16. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
    17. Blaskowitz, Oliver & Herwartz, Helmut, 2011. "On economic evaluation of directional forecasts," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1058-1065, October.
    18. Thiyanga S. Talagala & Feng Li & Yanfei Kang, 2019. "Feature-based Forecast-Model Performance Prediction," Monash Econometrics and Business Statistics Working Papers 21/19, Monash University, Department of Econometrics and Business Statistics.
    19. Martin Guth, 2022. "Predicting Default Probabilities for Stress Tests: A Comparison of Models," Papers 2202.03110, arXiv.org.
    20. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    21. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:250:y:2022:i:c:s0360544222006879. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.