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Operating Rules Derivation of Jinsha Reservoirs System with Parameter Calibrated Support Vector Regression

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  • Chang-ming Ji
  • Ting Zhou
  • Hai-tao Huang

Abstract

The reservoir optimal operation depends on not only specific characteristics of reservoirs and hydropower stations but also stochastic inflows. The key issue of actual hydropower operation is to make an approximate optimal decision triggered by limited inflow forecasts. To implement actual optimal operation of hydropower system with limited inflows forecast, this paper makes use of Support Vector Regression (SVR) to derive optimal operating rules. To improve the performance of SVR, parameters in SVR model are calibrated with grid search and cross validation techniques. The trained SVR model describes the complex nonlinear relationships between reservoir operation decisions and factors by considering both generalization and regression performance, which overcomes local optimization and over fitting deficits. Hybrid programming platform is further developed to implement system simulation. This SVR model along with simulation platform is applied to the largest hydropower base in China – Jinsha system. Three scenarios are developed for comparison: deterministic optimal operation, SVR based simulation with calibrated parameters, SVR based simulation with default parameters. Comprehensive evaluation indicates that, operating rules derived from SVR presents a reliable performance in system power generation and output processes with respect to ideal deterministic results, especially when the parameters are calibrated. Hybrid programming technique provides a feasible and compatible platform for future research. Copyright Springer Science+Business Media Dordrecht 2014

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  • Chang-ming Ji & Ting Zhou & Hai-tao Huang, 2014. "Operating Rules Derivation of Jinsha Reservoirs System with Parameter Calibrated Support Vector Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2435-2451, July.
    • Handle: RePEc:spr:waterr:v:28:y:2014:i:9:p:2435-2451
    DOI: 10.1007/s11269-014-0610-6
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    2. Zhong-kai Feng & Wen-jing Niu & Peng-fei Shi & Tao Yang, 2022. "Adaptive Neural-Based Fuzzy Inference System and Cooperation Search Algorithm for Simulating and Predicting Discharge Time Series Under Hydropower Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2795-2812, June.
    3. Xinyu Wu & Yuan Lei & Chuntian Cheng & Qilin Ying, 2023. "An Optimal Operation Method for Parallel Hydropower Systems Combining Reservoir Level Control and Power Distribution," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1729-1745, March.
    4. Jiang, Zhiqiang & Li, Anqiang & Ji, Changming & Qin, Hui & Yu, Shan & Li, Yuanzheng, 2016. "Research and application of key technologies in drawing energy storage operation chart by discriminant coefficient method," Energy, Elsevier, vol. 114(C), pages 774-786.
    5. Zhiqiang Jiang & Yaqi Qiao & Yuyun Chen & Changming Ji, 2018. "A New Reservoir Operation Chart Drawing Method Based on Dynamic Programming," Energies, MDPI, vol. 11(12), pages 1-17, November.
    6. Ak, Mumtaz & Kentel, Elcin & Savasaneril, Secil, 2017. "Operating policies for energy generation and revenue management in single-reservoir hydropower systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1253-1261.
    7. Xinyu Wu & Rui Guo & Xilong Cheng & Chuntian Cheng, 2021. "Combined Aggregated Sampling Stochastic Dynamic Programming and Simulation-Optimization to Derive Operation Rules for Large-Scale Hydropower System," Energies, MDPI, vol. 14(3), pages 1-15, January.

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