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Advances in Renewable Energy Power Forecasting and Integration

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

Deadline for manuscript submissions: 24 July 2024 | Viewed by 1334

Special Issue 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 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 crucially important 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 important topics for dealing with renewable energy issues and minimizing the cost of the generated power as well as CO2 emissions. This Special Issue is focused on the development of the most recent and cutting-edge technology related to forecasting, management, and decision making for renewable energy integration into the utility grid towards 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 by 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 sizing and its 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 micro grids.
  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

Published Papers (1 paper)

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Research

28 pages, 5830 KiB  
Article
Automatic Generation Control of a Multi-Area Hybrid Renewable Energy System Using a Proposed Novel GA-Fuzzy Logic Self-Tuning PID Controller
by Gama Ali, Hamed Aly and Timothy Little
Energies 2024, 17(9), 2000; https://doi.org/10.3390/en17092000 - 23 Apr 2024
Cited by 1 | Viewed by 707
Abstract
Human activities overwhelm our environment with CO2 and other global warming issues. The current electricity landscape necessitates a superior, continuous power supply and addressing such environmental concerns. These issues can be resolved by incorporating renewable energy sources (RESs) into the utility grid. [...] Read more.
Human activities overwhelm our environment with CO2 and other global warming issues. The current electricity landscape necessitates a superior, continuous power supply and addressing such environmental concerns. These issues can be resolved by incorporating renewable energy sources (RESs) into the utility grid. Thus, this paper presents an optimized hybrid fuzzy logic self-tuning PID controller to control the automatic generation control (AGC) of various renewable sources. This controller regulates the frequency deviations of the power system and governs the change in the tie-line load of a multi-area hybrid energy system composed of wind, biomass, and photovoltaic energy sources. MATLAB Simulink software was applied to design and test the system. The PID controller has been tuned using four algorithms, namely, genetic algorithm (GA), pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO), and we compared the results with the proposed novel optimized PID controller (GA-fuzzy logic self-tuning technique) to validate it. The results show the superiority of the proposed hybrid GA-fuzzy logic self-tuning algorithm over the other algorithms in bringing the power system back to its regular operation. The paper also proposes an operation strategy to lower the utilization of biomass energy in the presence of other renewable energy sources. Full article
(This article belongs to the Special Issue Advances in Renewable Energy Power Forecasting and Integration)
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