Integration of deep learning and improved multi-objective algorithm to optimize reservoir operation for balancing human and downstream ecological needs

•A multi-objective simulation-optimization (MOSO) framework optimizes reservoir operation.•Trade-offs among hydropower generation, ecological flow and ecological water temperature demands are achieved.•LSTM_1DCNN outperforms conventional LSTM in predicting dam discharge temperature.•ε-MOACOR provide...

Full description

Saved in:
Bibliographic Details
Published in:Water research (Oxford) Vol. 253; p. 121314
Main Authors: Qiu, Rujian, Wang, Dong, Singh, Vijay P., Wang, Yuankun, Wu, Jichun
Format: Journal Article
Language:English
Published: England Elsevier Ltd 01.04.2024
Subjects:
Algorithms
Animals
Deep Learning
domain
Ecological flow
Ecosystem
fish
Humans
Models, Theoretical
Multi-objective trade-offs
neural networks
power generation
Reservoir optimization operation
system optimization
Water
water power
Water temperature
Multi-objective trade-offs
Deep learning
Reservoir optimization operation
Water temperature
Ecological flow
ISSN:0043-1354, 1879-2448, 1879-2448
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•A multi-objective simulation-optimization (MOSO) framework optimizes reservoir operation.•Trade-offs among hydropower generation, ecological flow and ecological water temperature demands are achieved.•LSTM_1DCNN outperforms conventional LSTM in predicting dam discharge temperature.•ε-MOACOR provides a superior convergence behavior with better diverse solutions.•Large inflow discharge and uneven dam discharge volume improve ecological water temperature guarantee index. Dam (reservoir)-induced alterations of flow and water temperature regimes can threaten downstream fish habitats and native aquatic ecosystems. Alleviating the negative environmental impacts of dam-reservoir and balancing the multiple purposes of reservoir operation have attracted wide attention. While previous studies have incorporated ecological flow requirements in reservoir operation strategies, a comprehensive analysis of trade-offs among hydropower benefits, ecological flow, and ecological water temperature demands is lacking. Hence, this study develops a multi-objective ecological scheduling model, considering total power generation, ecological flow guarantee index, and ecological water temperature guarantee index simultaneously. The model is based on an integrated multi-objective simulation-optimization (MOSO) framework which is applied to Three Gorges Reservoir. To that end, first, a hybrid long short-term memory and one-dimensional convolutional neural network (LSTM_1DCNN) model is utilized to simulate the dam discharge temperature. Then, an improved epsilon multi-objective ant colony optimization for continuous domain algorithm (ε-MOACOR) is proposed to investigate the trade-offs among the competing objectives. Results show that LSTM _1DCNN outperforms other competing models in predicting dam discharge temperature. The conflicts among economic and ecological objectives are often prominent. The proposed ε-MOACOR has potential in resolving such conflicts and has high efficiency in solving multi-objective benchmark tests as well as reservoir optimization problem. More realistic and pragmatic Pareto-optimal solutions for typical dry, normal and wet years can be generated by the MOSO framework. The ecological water temperature guarantee index objective, which should be considered in reservoir operation, can be improved as inflow discharge increases or the temporal distribution of dam discharge volume becomes more uneven. [Display omitted]
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0043-1354
1879-2448
1879-2448
DOI:10.1016/j.watres.2024.121314