Incorporating Penalty Function to Reduce Spill in Stochastic Dynamic Programming Based Reservoir Operation of Hydropower Plants

2010 ◽  
Vol 5 (5) ◽  
pp. 531-538 ◽  
Author(s):  
Deependra Kumar Jha ◽  
Naoto Yorino ◽  
Yoshifumi Zoka ◽  
Yutaka Sasaki ◽  
Yuki Hayashi ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Penalty Function ◽  
Reservoir Operation ◽  
Stochastic Dynamic Programming ◽  
Stochastic Dynamic ◽  
Hydropower Plants

scholarly journals Reservoir operation using grey fuzzy stochastic dynamic programming

2002 ◽  
Vol 16 (12) ◽  
pp. 2395-2408 ◽  
Author(s):  
Fi-John Chang ◽  
Shyh-Chi Hui ◽  
Yen-Chang Chen
Keyword(s):  
Dynamic Programming ◽  
Reservoir Operation ◽  
Stochastic Dynamic Programming ◽  
Stochastic Dynamic

Stochastic dynamic programming models for reservoir operation optimization

1984 ◽  
Vol 20 (11) ◽  
pp. 1499-1505 ◽  
Author(s):  
Jery R. Stedinger ◽  
Bola F. Sule ◽  
Daniel P. Loucks
Keyword(s):  
Dynamic Programming ◽  
Reservoir Operation ◽  
Stochastic Dynamic Programming ◽  
Programming Models ◽  
Stochastic Dynamic ◽  
Operation Optimization

Fuzzy-State Stochastic Dynamic Programming for Reservoir Operation

2004 ◽  
Vol 130 (6) ◽  
pp. 460-470 ◽  
Author(s):  
Seyed Jamshid Mousavi ◽  
Mohammad Karamouz ◽  
Mohammad Bagher Menhadj
Keyword(s):  
Dynamic Programming ◽  
Reservoir Operation ◽  
Stochastic Dynamic Programming ◽  
Stochastic Dynamic ◽  
Fuzzy State

scholarly journals Study on the Optimal Operation of a Hydropower Plant Group Based on the Stochastic Dynamic Programming with Consideration for Runoff Uncertainty

Water ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 220
Author(s):  
Hongxue Zhang ◽  
Lianpeng Zhang ◽  
Jianxia Chang ◽  
Yunyun Li ◽  
Ruihao Long ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Water Resources ◽  
Stochastic Dynamic Programming ◽  
Optimal Operation ◽  
Hydropower Plant ◽  
Stochastic Dynamic ◽  
Operation Model ◽  
Plant Operation ◽  
Hydropower Plants ◽  
Transfer Probability

Hydropower plant operation reorganizes the temporal and spatial distribution of water resources to promote the comprehensive utilization of water resources in the basin. However, a lot of uncertainties were brought to light concerning cascade hydropower plant operation with the introduction of the stochastic process of incoming runoff. Therefore, it is of guiding significance for the practice operation to investigate the stochastic operation of cascade hydropower plants while considering runoff uncertainty. The runoff simulation model was constructed by taking the cascade hydropower plants in the lower reaches of the Lancang River as the research object, and combining their data with the copula joint function and Gibbs method, and a Markov chain was adopted to construct the transfer matrix of runoff between adjacent months. With consideration for the uncertainty of inflow runoff, the stochastic optimal operation model of cascade hydropower plants was constructed and solved by the SDP algorithm. The results showed that 71.12% of the simulated monthly inflow of 5000 groups in the Nuozhadu hydropower plant drop into the reasonable range. Due to the insufficiency of measured runoff, there were too many 0 values in the derived transfer probability, but after the simulated runoff series were introduced, the results significantly improved. Taking the transfer probability matrix of simulated runoff as the input of the stochastic optimal operation model of the cascade hydropower plants, the operation diagram containing the future-period incoming water information was obtained, which could directly provide a reference for the optimal operation of the Nuozhadu hydropower plant. In addition, taking the incoming runoff process in a normal year as the standard, the annual mean power generation based on stochastic dynamic programming was similar to that based on dynamic programming (respectively 305.97 × 108kW⋅h and 306.91 × 108kW⋅h), which proved that the operation diagram constructed in this study was reasonable.

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