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Annual Temporal Scenario Probabilistic Prediction of Runoff and Associated Source & Load of Hydropower Station
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Qiang CHEN1, Ke ZHANG2, 3, Xi-gang SHU4, Ying ZHU4, Jia LEI5, Dan LI2
Water Resources and Power | 2023, 41(3) : 70 - 74
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Water Resources and Power | 2023, 41(3): 70-74
• HYDROLOGICAL FORECAST AND OPTIMAL SCHEDULING •
Annual Temporal Scenario Probabilistic Prediction of Runoff and Associated Source & Load of Hydropower Station
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Qiang CHEN1, Ke ZHANG2, 3, Xi-gang SHU4, Ying ZHU4, Jia LEI5, Dan LI2
Affiliations
  • 1.Centralized Control Center of Chongqing Branch, China Datang Group Co., Ltd., Chongqing 400020, China
  • 2.College of Electrical and New Energy, China Three Gorges University, Yichang 443002, China
  • 3.Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
  • 4.Chongqing Datang International Pengshui Hydropower Development Co., Ltd., Chongqing 409600, China
  • 5.Chongqing Datang International Wulong Hydropower Development Co., Ltd., Chongqing 408500, China
Published: 2023-03-25 doi: 10.20040/j.cnki.1000-7709.2023.20220960
Outline
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To fully consider the temporal volatility and randomness of runoff and associated source & load in the optimal dispatch of hydropower and reduce spillage water, a probabilistic prediction method of annual scenarios for hydropower runoff and associated source & load was proposed to simulate the typical annual temporal scenarios of average daily runoff and associated source & load and their probability of occurrence. Several typical ten-day scenarios were generated by clustering with a self-organization mapping net (SOM). Then a ten-day scenario simulation model was built based on a Markov-chain probability matrix, a multi-scenario conditional probability matrix, and the similarity principle— "the closer historical year, the larger weight." It ensures that the simulated scenarios accurately fit the statistical characteristics of actual data (randomness, seasonality, and conditional correlation) for intra-year and reflect the trend evolution year-to-year. Combined with the fluctuation checks, annual temporal scenarios were simulated by the Monte Carlo method. Finally, the k-means scenario reduction was used to obtain typical annual temporal scenarios and their probability of occurrence. The results of an actual hydropower example show that the proposed method has the advantages of high accuracy, strong adaptability, and comprehensive prediction information.

runoff prediction  /  power prediction  /  stochastic simulation  /  self-organization mapping network  /  conditional probability  /  Markov chain
Qiang CHEN, Ke ZHANG, Xi-gang SHU, Ying ZHU, Jia LEI, Dan LI. Annual Temporal Scenario Probabilistic Prediction of Runoff and Associated Source & Load of Hydropower Station[J]. Water Resources and Power, 2023 , 41 (3) : 70 -74 . DOI: 10.20040/j.cnki.1000-7709.2023.20220960
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Year 2023 volume 41 Issue 3
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20220960
  • Receive Date：2022-05-01
  • Online Date：2026-01-28
  • Published：2023-03-25
Article Data
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History
  • Received：2022-05-01
  • Revised：2022-05-27
Funding
Affiliations
    1.Centralized Control Center of Chongqing Branch, China Datang Group Co., Ltd., Chongqing 400020, China
    2.College of Electrical and New Energy, China Three Gorges University, Yichang 443002, China
    3.Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
    4.Chongqing Datang International Pengshui Hydropower Development Co., Ltd., Chongqing 409600, China
    5.Chongqing Datang International Wulong Hydropower Development Co., Ltd., Chongqing 408500, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20220960
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