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Applications of Fuzzy Logic in Renewable Energy Systems
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Applications of Fuzzy Logic in Renewable Energy Systems

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 9273

Special Issue Editor

Prof. Dr. Marco Mussetta

E-Mail Website
Guest Editor
Department of Energy, Politecnico di Milano, 20133 Milano, MI, Italy
Interests: computational intelligence; optimization; machine learning; fuzzy logic; wireless sensor networks; renewable energy; photovoltaics; wind power; forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The main aim of this Special Issue is to provide a forum for researchers covering the whole range of fuzzy systems applications to renewable power generation and use in smart energy grids.

Renewable energy sources have significant impacts on power quality, electrical grid stability, and reliability: Indeed, major challenges are involved in the modeling, control, and general operation of these systems. Smart Grid technology employs information, communication, and automation technology to deploy a power grid integrated with smart power generation, transmission, distribution, and the integration of renewable energy sources. In particular, Smart Grids integrated with smart meters, electric vehicle charging stations, and home/building energy management system are the key enabling factor toward the Micro Grid, Smart Building, and Smart City concepts.

Moreover, since wind and solar PV power resources are intermittent, accurate predictions and modeling of wind speed and solar insolation are necessary. In addition, wind and solar photovoltaic generation require operating the systems near their maximum power output point. As a result, effective use of computational intelligence techniques, such as fuzzy systems for the controlling and modeling of renewable power generation in a Smart Grid, turns out to be critical for successful operations of the system.

This Special Issue would like to encourage original contributions regarding recent developments and ideas on applications of fuzzy logic in renewable energy systems and smart grids. Potential topics include but are not limited to: Fuzzy modeling of renewable power generation systems, fuzzy control of renewable power generation systems, prediction of renewable energy using fuzzy and neuro-fuzzy systems, fuzzy distribution systems automation, fuzzy control of distributed virtual power plants, fuzzy Logic application for Smart Grid and Smart Cities, fuzzy logic application for Micro Grid and energy management systems, fuzzy logic application for demand–response and Smart Buildings, fuzzy power quality, protection and reliability analysis of power system, novel applications in electric energy markets, and hybrid systems of computational intelligence techniques in Smart Grid and renewable power generation systems.

Prof. Dr. Marco Mussetta
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

  • fuzzy system
  • renewable power generation
  • smart grids
  • power grid integrated

Published Papers (3 papers)

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Research

19 pages, 2891 KiB  
Article
Multi-Criteria Fuzzy Evaluation of the Planned Offshore Wind Farm Investments in Poland
by Paweł Ziemba
Energies 2021, 14(4), 978; https://0-doi-org.brum.beds.ac.uk/10.3390/en14040978 - 12 Feb 2021
Cited by 26 | Viewed by 2145
Abstract
In recent years, the dynamic development of renewable energy has been visible all over the world, including Poland. Wind energy is one of the most used renewable energy sources. In Poland, by 2030, it is planned to commission at least six offshore wind [...] Read more.
In recent years, the dynamic development of renewable energy has been visible all over the world, including Poland. Wind energy is one of the most used renewable energy sources. In Poland, by 2030, it is planned to commission at least six offshore wind farms with a total capacity of 3.8 GW. It is estimated that these investments will increase Poland’s GDP by approximately PLN 60 billion and increase tax revenues by PLN 15 billion. Therefore, they could be a strong stimulus for the development of the Polish economy and may be of great importance in recovering from the crisis caused by the economic constraints related to the COVID-19 pandemic. The aim of the article is a multi-criteria evaluation of the investments planned in Poland in offshore wind farms and identification of potentially the most economically effective investments. To account for the uncertainty in this decision problem, a modified fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was used and a comprehensive sensitivity analysis was performed. As a result of the research, a ranking of the considered projects was constructed and the most preferred investments were identified. Moreover, it has been shown that all the investments considered are justified and recommended. Full article
(This article belongs to the Special Issue Applications of Fuzzy Logic in Renewable Energy Systems)
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26 pages, 6468 KiB  
Article
Energy Modeling of a Refiner in Thermo-Mechanical Pulping Process Using ANFIS Method
by Behnam Talebjedi, Ali Khosravi, Timo Laukkanen, Henrik Holmberg, Esa Vakkilainen and Sanna Syri
Energies 2020, 13(19), 5113; https://0-doi-org.brum.beds.ac.uk/10.3390/en13195113 - 01 Oct 2020
Cited by 11 | Viewed by 2449
Abstract
In the pulping industry, thermo-mechanical pulping (TMP) as a subdivision of the refiner-based mechanical pulping is one of the most energy-intensive processes where the core of the process is attributed to the refining process. In this study, to simulate the refining unit of [...] Read more.
In the pulping industry, thermo-mechanical pulping (TMP) as a subdivision of the refiner-based mechanical pulping is one of the most energy-intensive processes where the core of the process is attributed to the refining process. In this study, to simulate the refining unit of the TMP process under different operational states, the idea of machine learning algorithms is employed. Complicated processes and prediction problems could be simulated and solved by utilizing artificial intelligence methods inspired by the pattern of brain learning. In this research, six evolutionary optimization algorithms are employed to be joined with the adaptive neuro-fuzzy inference system (ANFIS) to increase the refining simulation accuracy. The applied optimization algorithms are particle swarm optimization algorithm (PSO), differential evolution (DE), biogeography-based optimization algorithm (BBO), genetic algorithm (GA), ant colony (ACO), and teaching learning-based optimization algorithm (TLBO). The simulation predictor variables are site ambient temperature, refining dilution water, refining plate gap, and chip transfer screw speed, while the model outputs are refining motor load and generated steam. Findings confirm the superiority of the PSO algorithm concerning model performance comparing to the other evolutionary algorithms for optimizing ANFIS method parameters, which are utilized for simulating a refiner unit in the TMP process. Full article
(This article belongs to the Special Issue Applications of Fuzzy Logic in Renewable Energy Systems)
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27 pages, 2928 KiB  
Article
Chattering-Free Single-Phase Robustness Sliding Mode Controller for Mismatched Uncertain Interconnected Systems with Unknown Time-Varying Delays
by Cong-Trang Nguyen, Thanh Long Duong, Minh Quan Duong and Duc Tung Le
Energies 2020, 13(1), 282; https://0-doi-org.brum.beds.ac.uk/10.3390/en13010282 - 06 Jan 2020
Cited by 4 | Viewed by 3067
Abstract
Variable structure control with sliding mode can provide good control performance and excellent robustness. Unfortunately, the chattering phenomenon investigated due to discontinuous switching gain restricting their applications. In this paper, a chattering free improved variable structure control (IVSC) for a class of mismatched [...] Read more.
Variable structure control with sliding mode can provide good control performance and excellent robustness. Unfortunately, the chattering phenomenon investigated due to discontinuous switching gain restricting their applications. In this paper, a chattering free improved variable structure control (IVSC) for a class of mismatched uncertain interconnected systems with an unknown time-varying delay is proposed. A sliding function is first established to eliminate the reaching phase in traditional variable structure control (TVSC). Next, a new reduced-order sliding mode estimator (ROSME) without time-varying delay is constructed to estimate all unmeasurable state variables of plants. Then, based on the Moore-Penrose inverse approach, a decentralized single-phase robustness sliding mode controller (DSPRSMC) is synthesized, which is independent of time delays. A DSPRSMC solves a complex interconnection problem with an unknown time-varying delay term and drives the system’s trajectories onto a switching surface from the initial time instance. Particularly, by applying the well-known Barbalat’s lemma, the chattering phenomenon in control input is alleviated. Moreover, a sufficient condition is established by using an appropriate Lyapunov theory and linear matrix inequality (LMI) method such that a sliding mode dynamics is asymptotically stable from the beginning time. Finally, a developed method is validated by numerical example with computer simulations. Full article
(This article belongs to the Special Issue Applications of Fuzzy Logic in Renewable Energy Systems)
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