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Optimal Schedule of Hydropower and New Energy Power Systems

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Special Issue Editors

Dr. Zhipeng Zhao

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Guest Editor

School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China
Interests: hydropower and new energy power system optimization scheduling; power market bidding; power grid scheduling operation; artificial intelligence

Dr. Xiaoyu Jin

E-Mail Website
Guest Editor

School of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China
Interests: hydropower optimal operation; hydro-wind-solar operation; optimization approach; power market bidding

Special Issue Information

Dear Colleagues,

The increasing integration of hydropower with new energy sources, such as wind and solar power, presents both opportunities and challenges for modern power systems. As a flexible and dispatchable energy source, hydropower plays a crucial role in balancing the variability of renewables, providing fast response capabilities, frequency regulation, and peak load management. However, the operation of hydropower is constrained by water availability, environmental regulations, and multi-purpose reservoir management, making its coordination with other renewable sources a complex optimization problem. The fluctuating and intermittent nature of wind and solar power, coupled with these hydropower constraints and the need for reliable and efficient electricity supply, calls for advanced scheduling strategies that optimize power generation, storage, and distribution.

This Special Issue focuses on cutting-edge research in the optimal scheduling of hydropower and new energy power systems. Topics of interest include, but are not limited to, the following:

Hydropower and renewable energy coordination: Strategies for jointly optimizing hydropower and variable renewable energy sources.
Stochastic and robust optimization: Approaches to handle the uncertainties in renewable energy generation and water inflows.
Multi-objective scheduling: Trade-offs between economic, environmental, and reliability objectives in scheduling decisions.
Energy storage integration: The role of batteries, pumped storage, and other energy storage technologies in enhancing system flexibility.
AI and data-driven methods: Applications of machine learning, deep learning, and reinforcement learning in power scheduling.
Market mechanisms and policy implications: The impact of electricity market structures and regulatory policies on optimal scheduling.
Resilience and risk management: Methods for ensuring grid stability under extreme weather conditions and other uncertainties.

Dr. Zhipeng Zhao
Dr. Xiaoyu Jin
Guest Editors

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Keywords

hydropower scheduling
pumped storage
power system optimal operation
renewable energy integration
energy storage management
stochastic and robust optimization
multi-objective decision making
electricity market operations
grid flexibility and stability
sustainable energy planning
the impacts of water pricing policies and water resource management policies on carbon emissions
renewable energy development
integrated utilization of hydropower-related water resources
decarbonization strategies
grid stability and reliability
automatic generation control

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Research

18 pages, 1027 KiB

Open AccessArticle

Hybrid Multi-Branch Attention–CNN–BiLSTM Forecast Model for Reservoir Capacities of Pumped Storage Hydropower Plant

by Yu Gong, Hao Wu, Junhuang Zhou, Yongjun Zhang and Langwen Zhang

Energies 2025, 18(12), 3057; https://doi.org/10.3390/en18123057 (registering DOI) - 10 Jun 2025

Abstract

Pumped storage hydropower plants are important resources for scheduling urban energy storage, which realize the conversion of electric energy through upper and lower reservoir capacities. Dynamic forecasting of reservoir capacities is crucial for scheduling pumped storage and maximizing the economic benefits of pumped storage hydropower plants. In this work, a hybrid forecast network is proposed for both the upper and lower reservoir capacities of a pumped storage hydropower plant. A bidirectional long- and short-term memory network (BiLSTM) is designed as the baseline for the prediction model. A convolutional neural network (CNN) and Squeeze-and-Excitation (SE) attention mechanism are designed to extract local features from raw time series data to capture short-term dependencies. In order to better distinguish the effects of different data types on the reservoir capacity, the correlation between data and reservoir capacity is analyzed using the Spearman coefficient, and a multi-branch forecast model is established based on the correlation. A fusion module is designed to weight and fuse the branch prediction results to obtain the final reservoir capacities forecast model, namely, Multi-Branch Attention–CNN–BiLSTM. The experimental results show that the proposed model exhibits better forecast accuracy in forecasting the reservoir capacity compared with existing methods. Compared with BiLSTM, the

M A P E

of the forecast values of the reservoir capacities of the upper and lower reservoirs decreased by

1.93 %

and

2.2484 %

, the

R M S E

decreased by

16.9887 m^{3}

and

14.2903 m^{3}

, and the

R^{2}

increased by

0.1278

and

0.1276

, respectively. Full article

(This article belongs to the Special Issue Optimal Schedule of Hydropower and New Energy Power Systems)

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