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Advances of Modeling Methods in Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: closed (28 September 2023) | Viewed by 3351

Special Issue Editors


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Guest Editor
Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China
Interests: dynamics of rotating machinery; prognostics of energy and power systems

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Guest Editor
Data Sciences and Product Development, Johnson Controls, Milwaukee, WI 53209, USA
Interests: machine learning; smart energy systems; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Diverse energy and power systems have been playing a significantly critical role in the revolution of sustainable energy supply for the future, such as gas turbines, wind turbines, photovoltaic panels, building heating, ventilation and air-conditioning (HVAC) systems, etc., which have a great impact on energy resources and efficiencies. Due to the emerging artificial intelligence, traditional modeling techniques in these energy systems have met challenges in still leveraging physics model- and/or first principle-based approaches, and opportunities in incorporating data-driven and/or machine learning methods. Moreover, with the rapid development of hardware and computing techniques, new modeling approaches for energy systems have become more and more important for system design, integration, analysis, control, and management.

This Special Issue aims to present and disseminate the most recent advances related to modeling theory, approaches, and applications of energy systems.

Topic of interests for publication include but are not limited to:

  • New modeling theory and fundamentals for energy systems;
  • Modeling methods, design, and analysis for energy systems;
  • New physics- and/or first principle-based modeling approaches of energy systems;
  • Data-driven and/or machine learning approaches of energy systems;
  • Modeling techniques for smart energy systems;
  • Modeling approaches for different applications, such as prediction, fault detection and diagnosis, control, manufacturing, etc., in energy systems;
  • Identification of key challenges and opportunities for future research of modeling methods in energy systems.

Dr. Chao Liu
Dr. Zhanhong Jiang
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. 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

  • modeling, design, and analysis
  • machine learning
  • prediction, forecasting, and prognostics

Published Papers (2 papers)

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21 pages, 6435 KiB  
Article
Comparative Study of Global Sensitivity Analysis and Local Sensitivity Analysis in Power System Parameter Identification
by Chuan Qin, Yuqing Jin, Meng Tian, Ping Ju and Shun Zhou
Energies 2023, 16(16), 5915; https://0-doi-org.brum.beds.ac.uk/10.3390/en16165915 - 10 Aug 2023
Cited by 1 | Viewed by 1215
Abstract
In the process of parameter identification, sensitivity analysis is mainly used to determine key parameters with high sensitivity in the model. Sensitivity analysis methods include local sensitivity analysis (LSA) and global sensitivity analysis (GSA). The LSA method has been widely used for power [...] Read more.
In the process of parameter identification, sensitivity analysis is mainly used to determine key parameters with high sensitivity in the model. Sensitivity analysis methods include local sensitivity analysis (LSA) and global sensitivity analysis (GSA). The LSA method has been widely used for power system parameter identification for a long time, while the GSA has started to be used in recent years. However, there is no clear conclusion on the impact of different sensitivity analysis methods on parameter identification results. Therefore, this paper compares and studies the roles that LSA and GSA can play in different parameter identification methods, providing clear guidance for the selection of sensitivity analysis methods and parameter identification methods. The conclusion is as follows. If the identification strategy that only identifies key parameters with high sensitivity is adopted, we recommend still using the existing LSA method. If using a groupwise alternating identification strategy (GAIS) for high- and low-sensitivity parameters, either LSA or GSA can be used. To improve the identification accuracy, it is more important to improve the identification strategy than to change the sensitivity analysis method. When the accuracy of the non-key parameters with low sensitivity cannot be confirmed, using the GAIS is an effective method for ensuring identification accuracy. In addition, it should be noted that the high sensitivity of a parameter does not necessarily mean that the parameter is identifiable, which is revealed by the examples used in this paper. Full article
(This article belongs to the Special Issue Advances of Modeling Methods in Energy Systems)
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24 pages, 4310 KiB  
Article
Parameter Estimation of a Grid-Tied Inverter Using In Situ Pseudo-Random Perturbation Sources
by Ian Paul Gerber, Fredrick Mukundi Mwaniki and Hendrik Johannes Vermeulen
Energies 2023, 16(3), 1414; https://0-doi-org.brum.beds.ac.uk/10.3390/en16031414 - 31 Jan 2023
Cited by 1 | Viewed by 1569
Abstract
Inverters are playing an increasingly important role in the electrical utility grid due to the proliferation of renewable energy sources. Obtaining inverter models with accurate parameters is, therefore, essential for grid studies and design. In this paper, a methodology to estimate the output [...] Read more.
Inverters are playing an increasingly important role in the electrical utility grid due to the proliferation of renewable energy sources. Obtaining inverter models with accurate parameters is, therefore, essential for grid studies and design. In this paper, a methodology to estimate the output impedance and parameters of a residential grid-tied inverter is proposed. The methodology is first verified through simulation. A sensitivity analysis is conducted to determine the influence of the filter and controller parameters on the output impedance of the inverter. The simulated output impedance, voltage, and current are used in a parameter estimation methodology to obtain filter and controller parameters. It is shown that up to seven parameters can be estimated accurately. The proposed methodology is further investigated through a practical experiment. Two perturbation sources, the pseudo-random binary sequence perturbation and pseudo-random impulse sequence perturbation, are used, in turn, to perturb a residential grid-tied inverter that delivers up to 1.6 kW with the aim of obtaining its output impedance. The output impedances obtained through both pseudo-random sources are compared. It is shown that a pseudo-random binary sequence perturbation source applied in series between the grid and the inverter under test allows for the best estimation of the grid-tied inverter’s output impedance. A black-box modeling approach aimed at estimating an analytical transfer function of the output impedance from experimental data is also discussed. Full article
(This article belongs to the Special Issue Advances of Modeling Methods in Energy Systems)
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