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Medium-Term Hydro Generation Scheduling (MTHGS) with Chance Constrained Model (CCM) and Dynamic Control Model (DCM)

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Medium-Term Hydro Generation Scheduling (MTHGS) plays an important role in the operation of hydropower systems. In the first place, this paper presents a Chance Constrained Model for solving the optimal MTHGS problem. The model recognizes the impact of inflow uncertainty and the constraints involving hydrologic parameters subjected to uncertainty are described as probabilistic statements. It aims at providing a more practical technique compared to the traditional deterministic approaches used for MTHGS. The stochastic inflow is expressed as a simple discrete-time Markov chain and Stochastic Dynamic Programming is adopted to solve the model. Then in order to use the information of long-term inflow forecast to improve dispatching decisions, a Dynamic Control Model is developed. Short-term forecast results of the current period and long-term forecast results of the remaining period are treated as inputs of the model. Finally, the two methods are applied to MTHGS of Xiluodu hydro plant in China. The results are compared to those obtained from Deterministic Dynamic Programming with hindsight and advantages and disadvantages of the two methods are analyzed.

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This work is supported by the Key Program of the Major Research Plan of the National Natural Science Foundation of China (No. 91547208), the National Natural Science Foundation of China (No. 51579107), the National Key R&D Program of China (2016YFC0402205), the State Key Program of National Natural Science of China (No. 51239004) and the Training Program of the Major Research Plan of the National Natural Science Foundation of China (No. 91647114). Special thanks are given to the anonymous reviewers and editors for their constructive comments.

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Correspondence to Jianzhong Zhou or Mengfei Xie.

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Zhou, J., Xie, M., He, Z. et al. Medium-Term Hydro Generation Scheduling (MTHGS) with Chance Constrained Model (CCM) and Dynamic Control Model (DCM). Water Resour Manage 31, 3543–3555 (2017).

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  • Medium-term hydro generation scheduling
  • Chance constrained model
  • Stochastic inflow
  • Stochastic dynamic programming
  • Dynamic control model