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Energies
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Energy Systems: Optimization, Modeling, and Simulation
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Energy Systems: Optimization, Modeling, and Simulation

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "K: State-of-the-Art Energy Related Technologies".

Deadline for manuscript submissions: 5 May 2026 | Viewed by 791

Special Issue Editor

Dr. Fengjuan Wang

E-Mail Website
Guest Editor
Business School, Sichuan University, Chengdu 610064, China
Interests: hybrid energy systems optimization; power system resilience; renewable energy integration

Special Issue Information

Dear Colleagues,

The energy sector is undergoing a transformative shift as societies around the world strive for sustainable, reliable, resilient, and economically viable energy solutions. The need to integrate renewable energy sources, improve energy efficiency, and reduce greenhouse gas emissions has sparked extensive research into optimizing the generation, distribution, and consumption of energy. As grids become increasingly complex with the rise of decentralized systems, electric vehicles, and smart technologies, robust approaches in optimization, modelling, and simulation are essential. These tools allow researchers and practitioners to address multifaceted challenges, from ensuring grid stability and forecasting demand to designing resilient systems that can adapt to the variable nature of renewable resources.

This Special Issue welcomes contributions across a wide range of topics that advance the understanding and performance of energy systems through innovative optimization, modelling, and simulation methods. Areas of interest include, but are not limited to, the following:

  • Advanced optimization algorithms and application.
  • Simulation techniques for renewable integration.
  • Predictive models for energy demand and supply.
  • Machine learning for real-time grid management.
  • Optimization for balancing cost and environmental impacts.
  • Modelling of hybrid energy systems and microgrids.
  • Resilience against disruptions.
  • Improved energy storage solutions.
  • Innovative demand response strategies.

Dr. Fengjuan Wang
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 250 words) can be sent to the Editorial Office for assessment.

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

  • optimization
  • modelling
  • simulation
  • renewable Integration
  • hybrid energy systems
  • decentralized energy systems
  • energy storage
  • energy resilience

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Published Papers (1 paper)

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Research

19 pages, 1201 KB  
Article
Application of the Directed Cone Method for the Identification of Mathematical Models of Electromechanical Systems
by Bohdan Melnyk, Mykola Dyvak, Andriy Melnyk, Ewaryst Tkacz, Arkadiusz Banasik, Joanna Chwał and Radosław Dzik
Energies 2025, 18(22), 5949; https://doi.org/10.3390/en18225949 - 12 Nov 2025
Viewed by 303
Abstract
Electromechanical systems are inherently hybrid in nature, combining electrical and mechanical processes, and their increasing complexity requires the development of universal and computationally efficient mathematical models. In this study, we propose a macromodeling approach that represents the electromechanical system as a “black box,” [...] Read more.
Electromechanical systems are inherently hybrid in nature, combining electrical and mechanical processes, and their increasing complexity requires the development of universal and computationally efficient mathematical models. In this study, we propose a macromodeling approach that represents the electromechanical system as a “black box,” in which internal physical processes are disregarded and the system behavior is defined solely by the relationship between input and output signals. The identification of such macromodels is reduced to solving a nonlinear optimization problem. To address this challenge, the directed cone method is applied, which searches for the global minimum of the objective function through stochastic movement across the hyperplane defined by the optimization problem. Several algorithmic improvements of the directed cone method are investigated, including step-size adaptation, simultaneous adaptation of step size and hypercone opening angle, and a tunneling procedure. Their effectiveness is evaluated using the construction of a macromodel of a single-phase asynchronous motor as a case study. Performance was assessed according to computational complexity (measured as the number of objective function evaluations until convergence), relative modeling accuracy, and the dynamics of progression toward the global minimum. The experimental results show that the tunneling-based algorithm provides the highest modeling accuracy with the lowest computational cost, whereas the step-size-only adaptation was found to be the least effective. The proposed approach demonstrates the feasibility of constructing accurate macromodels of electromechanical systems that can be integrated into computer-aided modeling environments such as MATLAB/Simulink R2023b. Future work will focus on extending the approach to a broader class of electromechanical systems and developing hybrid algorithms to enhance robustness with respect to model nonlinearity. Full article
(This article belongs to the Special Issue Energy Systems: Optimization, Modeling, and Simulation)
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