A LSTM-based approximate dynamic programming method for hydropower reservoir operation optimization

•ADP is proposed to avoid the redundant computation in DP’s recursive equation.•LSTM-based response surface model reduces intensive power output calculations.•ADP uses less time to provide satisfying results for hydropower reservoir operation. Dynamic programming (DP) is a classical method developed...

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Bibliographic Details
Published in:Journal of hydrology (Amsterdam) Vol. 625; p. 130018
Main Authors: Feng, Zhong-kai, Luo, Tao, Niu, Wen-jing, Yang, Tao, Wang, Wen-chuan
Format: Journal Article
Language:English
Published: Elsevier B.V 01.10.2023
Subjects:
Artificial intelligence
China
Curse of dimensionality
Dynamic programming
equations
hydrology
Long short-term memory
neural networks
power generation
Reservoir operation
Response surface
response surface methodology
water power
China
Long short-term memory
Response surface
Curse of dimensionality
Dynamic programming
Reservoir operation
Artificial intelligence
ISSN:0022-1694, 1879-2707
Online Access:Get full text
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Summary:•ADP is proposed to avoid the redundant computation in DP’s recursive equation.•LSTM-based response surface model reduces intensive power output calculations.•ADP uses less time to provide satisfying results for hydropower reservoir operation. Dynamic programming (DP) is a classical method developed to address the multi-stage hydropower reservoir operation problem, but still suffers from the serious dimensionality problem where the computational burden increases exponentially with the number of state variables. To improve the DP performance, this paper proposes a LSTM-based approximate dynamic programming (ADP) method for complex hydropower reservoir operation optimization. In ADP, the long short-term memory (LSTM) is treated as the response surface model to reduce redundant computations of power outputs in DP’s recursive equation, making obvious improvements in the execution efficiency. To fully assess its feasibility, the ADP method is used to find the scheduling schemes of a real-world reservoir system in China. Simulation results show that compared with the standard DP method, ADP effectively reduces the execution time while guarantee the solution quality in different cases. In the 1000-state and wet-year scenario, the ADP method achieves approximately 86.7% and 85.8% reductions in DP’s computation time for Longyangxia and Laxiwa reservoir with the goal of maximizing power generation. Thus, the LSTM-based response surface model is an effective tool to improve the DP performance in the hydropower reservoir operation problem.
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ISSN:0022-1694
1879-2707
DOI:10.1016/j.jhydrol.2023.130018