sustainability-logo

Journal Browser

Journal Browser

Sustainable Energy Transition: Advanced Control and Optimization Techniques for Power Grid Resilience and Efficiency

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 269

Special Issue Editor


E-Mail Website
Guest Editor
Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Bellavista 7, Santiago 8420524, Chile
Interests: multilevel inverters; renewable energy systems; energy storage; model predictive control; robust control methods; power electronics; electric vehicles; frequency regulation; fractional order control; fuzzy logic control; and model-free control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I am inviting submissions to a Special Issue of Sustainability entitled “Sustainable Energy Transition: Advanced Control and Optimization Techniques for Power Grid Resilience and Efficiency”.

Recently, the integration of different renewable energy systems (photovoltaic, wind, geothermal, wave, etc.) and energy storage devices (batteries, supercapacitors, superconducting magnetic energy storage, flywheels, etc.) has sharply increased to satisfy the energy transition goals. However, advanced integration, control, and design techniques are mandatory to integrate the added energy systems that have different characteristics and performance. For instance, power electronics converters are essential for grid integration, maximizing extracted energy, achieving grid resilience, increasing system efficiency, etc.

Accordingly, developing advanced control methods and design optimization techniques for future modern energy systems is essential. The control and optimization methods should push forward the energy transition goals with full reliance on renewable energy generation systems. Furthermore, enhancing the maximum power point tracking, overall system efficiency, power system reliability, and resilient operation of grid systems represent highly desirable aims. The aim of the present Special Issue is to attract original, high-quality papers and review articles proposing advanced control and optimization techniques for resilient energy transition, renewable energy systems, and energy storage devices. Major topics of interest include, but are not limited to, the following:

  • Resilient energy transition systems;
  • Power system reliability;
  • Advanced control methods (fractional order control, fuzzy logic control, model predictive control, sliding mode control, etc.);
  • Artificial intelligent control methods (machine learning, neural networks, deep learning, etc.);
  • Advanced design optimization methods;
  • New and modified optimization methods;
  • Renewable energy systems (photovoltaic, wind, wave, geothermal, etc.);
  • Energy storage devices and their control methods;
  • Electric vehicles and the vehicle-to-grid principle;
  • Power electronic converters and control methods;
  • Model-free control methods;
  • Fault-tolerant control strategies;
  • Robust control systems.

The Special Issue will present cutting-edge research results in these emerging fields as a basis for the resilient energy transition and reliable integration of renewable energy systems and energy storage devices into electrical grid systems.

Dr. Mokhtar 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. Sustainability 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

  • resilient grid
  • control systems
  • optimization methods
  • photovoltaic systems
  • energy storage
  • power electronics
  • robust control
  • wind generation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

23 pages, 5430 KiB  
Article
Pre-Solve Methodologies for Short-Run Identification of Critical Sectors in the ACSR Overhead Lines While Using Dynamic Line Rating Models for Resource Sustainability
by Hugo Algarvio
Sustainability 2025, 17(8), 3758; https://doi.org/10.3390/su17083758 - 21 Apr 2025
Abstract
Most transmission system operators (TSOs) use seasonally static models considering extreme weather conditions, serving as a reference for computing the transmission capacity of power lines. The use of dynamic line rating (DLR) models can avoid the construction of new lines, market splitting, false [...] Read more.
Most transmission system operators (TSOs) use seasonally static models considering extreme weather conditions, serving as a reference for computing the transmission capacity of power lines. The use of dynamic line rating (DLR) models can avoid the construction of new lines, market splitting, false congestions and the degradation of lines in a cost-effective way. The operation of power systems is planned based on market results, which consider transactions hours ahead of real-time operation using forecasts with errors. The same is true for the DLR. So, during real-time operation TSOs should rapidly compute the DLR of overhead lines to avoid considering an ampacity above their lines’ design, reflecting the real-time weather conditions. Considering that the DLR of the lines can affect the power flow of an entire region, the use of the complete indirect DLR methodology has a high computation burden for all sectors and lines in a region. So, this article presents and tests three pre-solve methodologies able to rapidly identify the critical sector of each line. These methodologies solve the problem of the high computation burden of the CIGRÉ thermodynamic model of overhead lines. They have been tested by using real data of the transmission grid and the weather conditions for two different regions in Portugal, leading to errors in the computation of the DLR lower than 1% in relation to the complete CIGRÉ model, identifying the critical sector in significantly less time. Full article
Show Figures

Figure 1

Back to TopTop