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Optimal Schedule of Hydropower and New Energy Power Systems
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Optimal Schedule of Hydropower and New Energy Power Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: 29 August 2025 | Viewed by 1256

Special Issue Editors

Dr. Zhipeng Zhao

E-Mail Website
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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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Published Papers (3 papers)

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Research

21 pages, 4238 KiB  
Article
Fault Prediction of Hydropower Station Based on CNN-LSTM-GAN with Biased Data
by Bei Liu, Xiao Wang, Zhaoxin Zhang, Zhenjie Zhao, Xiaoming Wang and Ting Liu
Energies 2025, 18(14), 3772; https://doi.org/10.3390/en18143772 - 16 Jul 2025
Viewed by 228
Abstract
Fault prediction of hydropower station is crucial for the stable operation of generator set equipment, but the traditional method struggles to deal with data with an imbalanced distribution and untrustworthiness. This paper proposes a fault detection method based on a convolutional neural network [...] Read more.
Fault prediction of hydropower station is crucial for the stable operation of generator set equipment, but the traditional method struggles to deal with data with an imbalanced distribution and untrustworthiness. This paper proposes a fault detection method based on a convolutional neural network (CNNs) and long short-term memory network (LSTM) with a generative adversarial network (GAN). Firstly, a reliability mechanism based on principal component analysis (PCA) is designed to solve the problem of data bias caused by multiple monitoring devices. Then, the CNN-LSTM network is used to predict time series data, and the GAN is used to expand fault data samples to solve the problem of an unbalanced data distribution. Meanwhile, a multi-scale feature extraction network with time–frequency information is designed to improve the accuracy of fault detection. Finally, a dynamic multi-task training algorithm is proposed to ensure the convergence and training efficiency of the deep models. Experimental results show that compared with RNN, GRU, SVM, and threshold detection algorithms, the proposed fault prediction method improves the accuracy performance by 5.5%, 4.8%, 7.8%, and 9.3%, with at least a 160% improvement in the fault recall rate. Full article
(This article belongs to the Special Issue Optimal Schedule of Hydropower and New Energy Power Systems)
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17 pages, 2066 KiB  
Article
A Mid-Term Scheduling Method for Cascade Hydropower Stations to Safeguard Against Continuous Extreme New Energy Fluctuations
by Huaying Su, Yupeng Li, Yan Zhang, Yujian Wang, Gang Li and Chuntian Cheng
Energies 2025, 18(14), 3745; https://doi.org/10.3390/en18143745 - 15 Jul 2025
Viewed by 175
Abstract
Continuous multi-day extremely low or high new energy outputs have posed significant challenges in relation to power supply and new energy accommodations. Conventional reservoir hydropower, with the advantage of controllability and the storage ability of reservoirs, can represent a reliable and low-carbon flexibility [...] Read more.
Continuous multi-day extremely low or high new energy outputs have posed significant challenges in relation to power supply and new energy accommodations. Conventional reservoir hydropower, with the advantage of controllability and the storage ability of reservoirs, can represent a reliable and low-carbon flexibility resource to safeguard against continuous extreme new energy fluctuations. This paper proposes a mid-term scheduling method for reservoir hydropower to enhance our ability to regulate continuous extreme new energy fluctuations. First, a data-driven scenario generation method is proposed to characterize the continuous extreme new energy output by combining kernel density estimation, Monte Carlo sampling, and the synchronized backward reduction method. Second, a two-stage stochastic hydropower–new energy complementary optimization scheduling model is constructed with the reservoir water level as the decision variable, ensuring that reservoirs have a sufficient water buffering capacity to free up transmission channels for continuous extremely high new energy outputs and sufficient water energy storage to compensate for continuous extremely low new energy outputs. Third, the mathematical model is transformed into a tractable mixed-integer linear programming (MILP) problem by using piecewise linear and triangular interpolation techniques on the solution, reducing the solution complexity. Finally, a case study of a hydropower–PV station in a river basin is conducted to demonstrate that the proposed model can effectively enhance hydropower’s regulation ability, to mitigate continuous extreme PV outputs, thereby improving power supply reliability in this hybrid renewable energy system. Full article
(This article belongs to the Special Issue Optimal Schedule of Hydropower and New Energy Power Systems)
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18 pages, 1027 KiB  
Article
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 - 10 Jun 2025
Viewed by 445
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 [...] Read more.
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 MAPE of the forecast values of the reservoir capacities of the upper and lower reservoirs decreased by 1.93% and 2.2484%, the RMSE decreased by 16.9887m3 and 14.2903m3, and the R2 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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