The influence of considering the temporal correlation of the inflows in the optimal operation program calculated through stochastic dynamic programming

1993 ◽  
Vol 28 (1) ◽  
pp. 19-25 ◽  
Author(s):  
Jorge Fernando Rivera ◽  
Robert Wilber Ferrero
Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 625
Author(s):  
Xinyu Wu ◽  
Rui Guo ◽  
Xilong Cheng ◽  
Chuntian Cheng

Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time.


Water ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 220
Author(s):  
Hongxue Zhang ◽  
Lianpeng Zhang ◽  
Jianxia Chang ◽  
Yunyun Li ◽  
Ruihao Long ◽  
...  

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.


2020 ◽  
Author(s):  
Bin Liang ◽  
Matteo Giuliani ◽  
Liping Zhang ◽  
Senlin Chen ◽  
Andrea Castelletti

<p>Although being one of the most important approaches to design optimal water reservoir operating policies, the Stochastic Dynamic Programming is challenged by the three curses of dimensionality, modeling, and multiple objectives that make it unsuitable in most practical applications. Increased hydrological variability induced by climate change and human activities further challenges the control of hydraulic infrastructures calling for more flexible and efficient approaches to operation design. Tree-based fitted Q-iteration (FQI) is a value-based, offline and batch mode reinforcement learning method, which employs the principles of continuous approximation of value function through non parametric randomized ensemble of regression tree, i.e. Extremely Randomized Tree. So far FQI has been used for relatively simple systems, including one dam and several state variables, and looking at historical hydrology. In this work, we explore the potential for FQI to design reservoir network operation under varying hydro-climatological conditions. The approach is demonstrated on a real-world case study concerning the optimal operation of a network of three water reservoirs in the Qingjiang River basin, China. Preliminary results show that the computational efficiency and performance of the policies derived by FQI are all satisfactory compare to traditional Stochastic Dynamic Programming, and the advantages in terms of computational efficiency and policies performance become more relevant when evaluated considering uncertain hydro-climatological and socio-economic conditions that requires using more information for conditioning the control policy. </p>


Sign in / Sign up

Export Citation Format

Share Document