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Artificial Intelligence Applications in Smart Energy Systems
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Artificial Intelligence Applications in Smart Energy Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 10 March 2025 | Viewed by 2539

Special Issue Editors

Dr. Viorica Rozina Chifu

E-Mail Website
Guest Editor
Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 26-28 Baritiu Street, 400027 Cluj-Napoca, Romania
Interests: bio-inspired computing; machine learning; smart environments; ontologies and semantics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Costin Badica

E-Mail Website
Guest Editor
Department of Computers and Information Technology, Faculty of Automation, Computers and Electronics, University of Craiova, 107 Decebal Blvd, Craiova, Romania
Interests: artificial intelligence; multi-agent systems; software engineering; distributed systems; formal methods
Special Issues, Collections and Topics in MDPI journals
Dr. Azeddine Houari

E-Mail Website
Guest Editor
IREENA Laboratory, University of Nantes, 44602 Saint-Nazaire, France
Interests: renewable energy systems; microgrids; distributed generation; power electronics; power quality; system stability; control of power systems; energy management systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Our society is shifting from an energy system based on fossil fuels to a decentralized energy ecosystem incorporating renewable energy sources at the edge of the grid. In the energy transition towards a decentralized and sustainable energy system, the management of local energy systems, such as microgrids, virtual power plants, or energy communities, will play a significant role. The flexibility of various energy assets such as smart buildings, heat pumps, energy storage, EVs, power-to-hydrogen, electricity metering, and electromobility, when combined with citizen engagement strategies and socio-economic models, can create new opportunities to optimize the electricity grids in synergy with other energy carriers and to deliver cross-sectorial integrated services.

To capitalize on the emerging opportunities, novel AI-driven solutions are needed to consider the coordination of energy resources at the local level, enabled by digital technologies and power electronics. The purpose of this Special Issue is to present cutting-edge research and recent advancements that contribute to the progress of the topic under consideration, including the following:

  • Model and control in renewable-powered microgrids;
  • Local energy communities;
  • Physics informed Machine Learning models for energy forecasting;
  • Energy hubs and multi-carrier energy systems;
  • Results of local energy systems pilot cases;
  • Prosumers and smart buildings flexibility coordination;
  • Citizen engagement strategies and socio-economic models in smart energy communities;
  • P2P energy trading in local energy communities and markets;
  • Buildings integration in multi-energy systems;
  • Privacy and cyber security in smart local energy systems;
  • Decentralized nature-inspired optimization applications in microgrid energy management systems;
  • Advanced power electronic technologies for renewable energy systems;
  • Electric vehicles in local energy systems;
  • Agent-based modeling and simulation of local energy systems.

Dr. Viorica Rozina Chifu
Prof. Dr. Costin Badica
Dr. Azeddine Houari
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. Applied Sciences 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 2400 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

  • artificial intelligence
  • decentralized nature-inspired optimization applications
  • smart energy communities
  • machine learning
  • electrical vehicle
  • smart building

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Published Papers (2 papers)

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Research

26 pages, 3396 KiB  
Article
Carbon Quota Allocation Prediction for Power Grids Using PSO-Optimized Neural Networks
by Yixin Xu, Yanli Sun, Yina Teng, Shanglai Liu, Shiyu Ji, Zhen Zou and Yang Yu
Appl. Sci. 2024, 14(24), 11996; https://doi.org/10.3390/app142411996 (registering DOI) - 21 Dec 2024
Abstract
Formulating a scientifically sound and efficient approach to allocating carbon quota aligned with the carbon peaking goal is a fundamental theoretical and practical challenge within the context of climate-oriented trading in the power sector. Given the highly irrational allocation of carbon allowances in [...] Read more.
Formulating a scientifically sound and efficient approach to allocating carbon quota aligned with the carbon peaking goal is a fundamental theoretical and practical challenge within the context of climate-oriented trading in the power sector. Given the highly irrational allocation of carbon allowances in China’s power sector, as well as the expanding role of renewable energy, it is essential to rationalize the use of green energy in the development of carbon reduction in the power sector. This study addresses the risk of “carbon transfer” within the power industry and develops a predictive model for CO₂ emission based on multiple influential factors, thereby proposing a carbon quota distribution scheme adapted to green energy growth. The proposed model employs a hybrid of the gray forecasting model-particle swarm optimization-enhanced back-propagation neural network (GM-PSO-BPNN) for forecasting and allocating the total carbon quota. Assuming consistent total volume control through 2030, carbon quota is distributed to regional power grids in proportion to actual production allocation. Results indicate that the PSO algorithm mitigates local optimization constraints of the standard BP algorithm; the prediction error of carbon emissions by the combined model is significantly smaller than that of the single model, while its identification accuracy reaches 99.46%. With the total national carbon emissions remaining unchanged in 2030, in the end, the regional grids received the following quota values: 873.29 million tons in North China, 522.69 million tons in Northwest China, 194.15 million tons in Northeast China, 1283.16 million tons in East China, 1556.40 million tons in Central China, and 1085.37 million tons in the Southern Power Grid. The power sector can refer to this carbon allowance allocation standard to control carbon emissions in order to meet the industry’s emission reduction standards. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Smart Energy Systems)
15 pages, 5894 KiB  
Article
Optimal Control Strategy for Floating Offshore Wind Turbines Based on Grey Wolf Optimizer
by Seydali Ferahtia, Azeddine Houari, Mohamed Machmoum, Mourad Ait-Ahmed and Abdelhakim Saim
Appl. Sci. 2023, 13(20), 11595; https://doi.org/10.3390/app132011595 - 23 Oct 2023
Viewed by 1606
Abstract
Due to the present trend in the wind industry to operate in deep seas, floating offshore wind turbines (FOWTs) are an area of study that is expanding. FOWT platforms cause increased structural movement, which can reduce the turbine’s power production and increase structural [...] Read more.
Due to the present trend in the wind industry to operate in deep seas, floating offshore wind turbines (FOWTs) are an area of study that is expanding. FOWT platforms cause increased structural movement, which can reduce the turbine’s power production and increase structural stress. New FOWT control strategies are now required as a result. The gain-scheduled proportional-integral (GSPI) controller, one of the most used control strategies, modifies the pitch angle of the blades in the above-rated zone. However, this method necessitates considerable mathematical approximations to calculate the control advantages. This study offers an improved GSPI controller (OGSPI) that uses the grey wolf optimizer (GWO) optimization method to reduce platform motion while preserving rated power output. The GWO chooses the controller’s ideal settings. The optimization objective function incorporates decreasing the platform pitch movements, and the resulting value is used to update the solutions. The effectiveness of the GWO in locating the best solutions has been evaluated using new optimization methods. These algorithms include the COOT optimization algorithm, the sine cosine algorithm (SCA), the African vultures optimization algorithm (AVOA), the Harris hawks optimization (HHO), and the whale optimization algorithm (WOA). The final findings show that, compared to those caused by the conventional GSPI, the suggested OGSPI may successfully minimize platform motion by 50.48%. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Smart Energy Systems)
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