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Advances in Renewable Energy Power Forecasting and Integration: 2nd Edition
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Advances in Renewable Energy Power Forecasting and Integration: 2nd Edition

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: 24 November 2025 | Viewed by 1535

Special Issue Editor

Dr. Hamed Aly

E-Mail Website
Guest Editor
Electrical and Computer Engineering Department, Dalhousie University, Halifax, NS B3H 4R2, Canada
Interests: renewable energy sources; smart grid; applications of artificial intelligence in power systems; microgrids; power systems operation and control; energy management and optimization; distributed generation; power system dynamics and stability analysis; power electronics; vehicle to grid; power quality issues
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Renewable energy and smart grids have been paid more attention due to climate change and limited fuel resources. Renewable energy integration is crucial because of its fluctuation, nonlinearity, intermittency, and stochastic characteristics. The integration of more renewables into the main grid is expected to increase in the future, and this will lead to some critical issues related to power system stability and quality due to false data injection and inaccurate forecasting models, which will affect the performance of the utility grid. Forecasting and management are the main topics that are important for dealing with renewable energy issues and minimizing the cost of the generated power and CO2 emissions. This Special Issue is focused on developing the most recent and cutting-edge technology related to forecasting, management, and decision-making for renewable energy integration into the utility grid toward green energy for the future.

The aims of this Special Issue are to:

  1. Facilitate the integration of renewable energy by applying hybrid forecasting techniques for renewables.
  2. Improve renewable energy integration using advanced control techniques based on machine learning forecasting models.
  3. Improve the power system quality based on adaptive power electronics and filters.
  4. Improve the energy storage systems by applying different management techniques and decision-making to reduce the storage system's size, charging, and discharging.

Topics of interest for publication include, but are not limited to:

  1. Application of artificial intelligence in the engineering field.
  2. Forecasting techniques.
  3. Management and decision-making for renewable energy integration.
  4. Smart and microgrids.
  5. Power system operation and control.
  6. Power quality issues.
  7. Energy management and optimization.
  8. Distributed generation.
  9. Power system dynamics and stability analysis.

Dr. Hamed Aly
Guest Editor

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

  • renewable integration
  • forecasting
  • optimization
  • management
  • advanced control
  • application of artificial intelligence and deep learning in power systems
  • distributed generation

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Related Special Issue

Published Papers (3 papers)

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Research

21 pages, 452 KiB  
Article
Heat-Loss Based Method for Real-Time Monitoring Method for Hydroelectric Power Plant Efficiency
by Lorenzo Battisti, Lorenzo Tieghi and Soheil Fattahi
Energies 2025, 18(10), 2586; https://doi.org/10.3390/en18102586 - 16 May 2025
Viewed by 351
Abstract
In energy transition scenarios, hydropower remains the largest source of renewable electricity generation. However, with respect to other means of renewable energy exploitation, like wind turbines or photovoltaics, very few technological advancements are to be expected, due to the technological maturity of hydropower [...] Read more.
In energy transition scenarios, hydropower remains the largest source of renewable electricity generation. However, with respect to other means of renewable energy exploitation, like wind turbines or photovoltaics, very few technological advancements are to be expected, due to the technological maturity of hydropower turbines. Therefore, an increase in power production of hydropower plants can only be possible thanks to an optimization of the operation and maintenance policies, leading to improved performance, reducing energy losses and downtimes. This work proposes a practical approach to the continuous monitoring of the operational conditions of hydropower plants through the non-invasive measurement of the electrical efficiency of the generator group. To achieve this, a heat-loss based method is introduced, which enables the measurement of both the electrical generator losses and the electrical input power, along with their associated uncertainties. This method is applicable for plants of any size, does not require a production shutdown, and, since it is applied to the electrical generator, can be used with different turbine types, including Kaplan, Francis, and Pelton. It also relies on relatively simple instruments such as thermo-cameras, thermo-resistances, thermo-couples, and flow meters to measure key variables, including cooling water inlet and outlet temperatures, electrical machine external and frame temperatures, undisturbed ambient temperature, electrical power absorbed, and cooling water flow rate. The proposed methodology has been tested and validated through the application to a laboratory test rig. In all test conditions, the heat loss-based method showed a smaller relative error than the standard efficiency measurement methods. Full article
(This article belongs to the Special Issue Advances in Renewable Energy Power Forecasting and Integration: 2nd Edition)
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16 pages, 4730 KiB  
Article
DTCformer: A Temporal Convolution-Enhanced Autoformer with DILATE Loss for Photovoltaic Power Forecasting
by Quanhui Qiu, Dejun Ning, Qiang Guo, Jiang Wei, Huichang Chen, Lihui Sui, Yi Liu, Zibing Du and Shipeng Liu
Energies 2025, 18(10), 2450; https://doi.org/10.3390/en18102450 - 10 May 2025
Viewed by 404
Abstract
Photovoltaic power forecasting plays a crucial role in the integration of renewable energy into the power grid. However, existing methods suffer from issues such as cumulative multi-step prediction errors and the limitations of traditional evaluation metrics (e.g., MSE, MAE). To address these challenges, [...] Read more.
Photovoltaic power forecasting plays a crucial role in the integration of renewable energy into the power grid. However, existing methods suffer from issues such as cumulative multi-step prediction errors and the limitations of traditional evaluation metrics (e.g., MSE, MAE). To address these challenges, this study introduces DTCformer, a generative forecasting model based on Autoformer. The proposed model integrates a Temporal Convolution Feedforward Network module and a Variable Selection Embedding module, effectively capturing inter-variable dependencies and temporal periodicity. Furthermore, it incorporates the DILATE loss function, which significantly enhances both forecasting accuracy and robustness. Experimental results on publicly available datasets demonstrate that DTCformer surpasses mainstream models, improving overall performance metrics (DILATE values) by 5.0–42.3% in 24 h, 48 h, and 72 h forecasting tasks. Full article
(This article belongs to the Special Issue Advances in Renewable Energy Power Forecasting and Integration: 2nd Edition)
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26 pages, 809 KiB  
Article
Robust Optimal Sizing of a Stand-Alone Hybrid Renewable Energy System Using Machine Learning-Based Uncertainty Sets
by Ali Keyvandarian, Ahmed Saif and Ronald Pelot
Energies 2025, 18(5), 1130; https://doi.org/10.3390/en18051130 - 25 Feb 2025
Viewed by 424
Abstract
This study introduces an adaptive robust approach for optimally sizing hybrid renewable energy systems (HRESs) comprising solar panels, wind turbines, batteries, and a diesel generator. It integrates vector auto-regressive models (VAR) and neural networks (NN) into dynamic uncertainty sets (DUSs) to address temporal [...] Read more.
This study introduces an adaptive robust approach for optimally sizing hybrid renewable energy systems (HRESs) comprising solar panels, wind turbines, batteries, and a diesel generator. It integrates vector auto-regressive models (VAR) and neural networks (NN) into dynamic uncertainty sets (DUSs) to address temporal auto-correlations and cross-correlations among uncertain parameters like energy demand and solar and wind energy supply. These DUSs are compared to static and independent dynamic uncertainty sets based on time series (TS) from the literature. An exact iterative algorithm is developed to solve the problem effectively. A case study of a northern Ontario community evaluates the proposed framework and the solution method using real test data. Simulation reveals a 10.7% increase in capital cost on average but a 36.2% decrease in operational cost, resulting in a 16.4% total cost reduction and an 8.1% improvement in system reliability compared to the nominal model employing point estimates. Furthermore, the proposed VAR- and NN-based DUSs significantly outperform classical static and TS-based dynamic sets, underscoring the necessity of considering cross-correlations in uncertainty quantification. Full article
(This article belongs to the Special Issue Advances in Renewable Energy Power Forecasting and Integration: 2nd Edition)
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