Optimization of a multiple reservoir system operation using a combination of genetic algorithm and discrete differential dynamic programming: a case study in Mae Klong system, Thailand

Due to limited water resources and the increasing demand for agricultural products, it is significantly important to operate surface water reservoirs optimally, especially those located in arid and semi-arid regions. This paper investigates uncertainty-based optimal operation of a multi-purpose water reservoir system by using four optimization models. The models include dynamic programming (DP), stochastic DP (SDP) with inflow classification (SDP/Class), SDP with inflow scenarios (SDP/Scenario), and sampling SDP (SSDP) with historical scenarios (SSDP/Hist). The performance of the models was tested in Zayandeh-Rud Reservoir system in Iran by evaluating how their release policies perform in a simulation phase. While the SDP approaches were better than the DP approach, the SSDP/Hist model outperformed the other SDP models. We also assessed the effect of ensemble streamflow predictions (ESPs) that were generated by artificial neural networks on the performance of SSDP/Hist. Application of the models to the Zayandeh-Rud case study demonstrated that SSDP in combination with ESPs and the K-means technique, which was used to cluster a large number of ESPs, could be a promising approach for real-time reservoir operation.

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Optimization of a multiple reservoir system using a simulated annealing--A case study in the Mae Klong system, Thailand

Paddy and Water Environment ◽

10.1007/s10333-005-0010-x ◽

2005 ◽

Vol 3 (3) ◽

pp. 137-147 ◽

Cited By ~ 20

Author(s):

Janejira Tospornsampan ◽

Ichiro Kita ◽

Masayuki Ishii ◽

Yoshinobu Kitamura

Keyword(s):

Simulated Annealing ◽

Reservoir System ◽

Multiple Reservoir

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An optimal algorithm for cascaded reservoir operation by combining the grey forecasting model with DDDP

Water Science & Technology Water Supply ◽

10.2166/ws.2017.070 ◽

2017 ◽

Vol 18 (1) ◽

pp. 142-150 ◽

Cited By ~ 1

Author(s):

Yong Peng ◽

Xiaoli Zhang ◽

Wei Xu ◽

Yajun Shi ◽

Zixin Zhang

Keyword(s):

Power Generation ◽

Dynamic Programming ◽

Reservoir Operation ◽

Optimal Algorithm ◽

Computing Time ◽

Forecasting Model ◽

Differential Dynamic Programming ◽

Reservoir System ◽

Grey Forecasting ◽

Grey Forecasting Model

Abstract The operation of cascaded reservoirs is a complex problem, and lots of algorithms have been developed for optimal cascaded reservoir operation. However, the existing algorithms usually have disadvantages such as the ‘curse of dimensionality’ and prematurity. This study proposes a grey discrete differential dynamic programming (GDDDP) algorithm for effectively optimizing the cascaded reservoir operation model, which is a combination of the grey forecasting model and discrete differential dynamic programming (DDDP). Additionally, a modification of the grey forecasting model is presented for better forecast accuracy. The proposed method is applied to optimize the Baishan-Fengman cascaded reservoir system in the northeast of China. The results show that GDDDP obtains more power generation than DDDP with less computing time in three cases, i.e., dry years, wet years and the whole series. Especially in the case of the whole series, the power generation of GDDDP is 2.13 MWH more than that of DDDP, while the computing time is decreased by 66,161 ms. Moreover, the power generation of GDDDP is comparable with that of dynamic programming but the computing time is much less. All these indicate GDDDP has high accuracy and efficiency, which implies that it is practicable for the operation of a cascaded reservoir system.

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