Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.2
3.0
Journals
Energies
Special Issues
Advances in Renewable Energy Power Forecasting and Integration: 2nd Edition
energies-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

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: 20 June 2025 | Viewed by 533

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

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 301
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop