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Continuous Piecewise Linear Approximation of Plant-Based Hydro Production Function for Generation Scheduling Problems

Author

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  • David Lucas dos Santos Abreu

    (Electric Energy Systems Planning Laboratory (LabPlan), Federal University of Santa Catarina, Florianópolis 88040-900, Brazil)

  • Erlon Cristian Finardi

    (Electric Energy Systems Planning Laboratory (LabPlan), Federal University of Santa Catarina, Florianópolis 88040-900, Brazil
    Institute of Systems and Computer Engineering, Research and Development of Brazil (INESC P&D Brasil), Santos 11055-300, Brazil)

Abstract

An essential challenge in generation scheduling (GS) problems of hydrothermal power systems is the inclusion of adequate modeling of the hydroelectric production function (HPF). The HPF is a nonlinear and nonconvex function that depends on the head and turbined outflow. Although the hydropower plants have multiple generating units (GUs), due to a series of complexities, the most attractive modeling practice is to represent one HPF per plant, i.e., a single function is built for representing the plant generation instead of the generation of each GU. Furthermore, due to the computation time constraints and representation of nonlinearities, the HPF must be given by a piecewise linear (PWL) model. This paper presented some continuous PWL models to include the HPF per plant in GS problems of hydrothermal systems. Depending on the type of application, the framework allows a choice between the concave PWL for HPF modeled with one or two variables and the nonconvex (more accurate) PWL for HPF dependent only on the turbined outflow. Basically, in both PWL models, offline, mixed-integer linear (or quadratic) programming techniques are used with an optimized pre-selection of the original HPF dataset obtained through the Ramer-Douglas-Peucker algorithm. As a highlight, the framework allows the control of the number of hyperplanes and, consequently, the number of variables and constraints of the PWL model. To this end, we offer two possibilities: (i) minimizing the error for a fixed number of hyperplanes, or (ii) minimizing the number of hyperplanes for a given error. We assessed the performance of the proposed framework using data from two large hydropower plants of the Brazilian system. The first has 3568 MW distributed in 50 Bulb-type GUs and operates as a run-of-river hydro plant. In turn, the second, which can vary the reservoir volume by up to 1000 hm 3 , possesses 1140 MW distributed in three Francis-type units. The results showed a variation from 0.040% to 1.583% in terms of mean absolute error and 0.306% to 6.356% regarding the maximum absolute error even with few approximations.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1699-:d:757768
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    References listed on IDEAS

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    1. Toriello, Alejandro & Vielma, Juan Pablo, 2012. "Fitting piecewise linear continuous functions," European Journal of Operational Research, Elsevier, vol. 219(1), pages 86-95.
    2. Steffen Rebennack & Vitaliy Krasko, 2020. "Piecewise Linear Function Fitting via Mixed-Integer Linear Programming," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 507-530, April.
    3. Li, Chaoshun & Wang, Wenxiao & Chen, Deshu, 2019. "Multi-objective complementary scheduling of hydro-thermal-RE power system via a multi-objective hybrid grey wolf optimizer," Energy, Elsevier, vol. 171(C), pages 241-255.
    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. 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.
    6. Nazari-Heris, M. & Mohammadi-Ivatloo, B. & B. Gharehpetian, G., 2017. "Short-term scheduling of hydro-based power plants considering application of heuristic algorithms: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 116-129.
    7. Lingxun Kong & Christos T. Maravelias, 2020. "On the Derivation of Continuous Piecewise Linear Approximating Functions," INFORMS Journal on Computing, INFORMS, vol. 32(3), pages 531-546, July.
    8. Gupta, Akshita & Kumar, Arun & Khatod, Dheeraj Kumar, 2019. "Optimized scheduling of hydropower with increase in solar and wind installations," Energy, Elsevier, vol. 183(C), pages 716-732.
    9. Martin N. Hjelmeland & Arild Helseth & Magnus Korpås, 2019. "Medium-Term Hydropower Scheduling with Variable Head under Inflow, Energy and Reserve Capacity Price Uncertainty," Energies, MDPI, vol. 12(1), pages 1-15, January.
    10. Ali Thaeer Hammid & Omar I. Awad & Mohd Herwan Sulaiman & Saraswathy Shamini Gunasekaran & Salama A. Mostafa & Nallapaneni Manoj Kumar & Bashar Ahmad Khalaf & Yasir Amer Al-Jawhar & Raed Abdulkareem A, 2020. "A Review of Optimization Algorithms in Solving Hydro Generation Scheduling Problems," Energies, MDPI, vol. 13(11), pages 1-21, June.
    11. 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.
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