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Energies
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Advances in Power Converters and Microgrids
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Advances in Power Converters and Microgrids

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 10 October 2025 | Viewed by 2979

Special Issue Editor

Dr. Vikram Roy Chowdhury

E-Mail Website
Guest Editor
Power Systems Engineering Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Interests: grid support; modular multilevel converter; control architecture; energy function; grid-forming converter; solid state transformer; microgrid
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the immense advancements in the field of renewable energy sources, a fully centralized grid is no longer just a dream. In today’s applications, scenarios such as these are more discrete and decentralized in terms of both power production and operation. The power converters used for such environments need to follow a wide range of rules in terms of IEEE codes, current ripples, operational limits, etc. Therefore, it is first necessary to understand the challenges in this scenario, before trying to solve them with new converter topologies, new granular-level control modifications, or with bigger system-level impacts, and then mitigating them.

This Special Issue aims to present new advances in converter topologies, their operation, and their major impact in the field of microgrids.

Topics of interest for publication include, but are not limited to, the following:

  • New converter topologies applied for DC/AC systems for renewable applications;
  • Applications based on solid-state transformers for renewable energy systems;
  • Advanced linear/nonlinear control architecture for renewable energy resources;
  • Impacts of using grid-forming converters in a microgrid and their operation;
  • Different topologies of grid-forming converters and their operation;
  • Application of grid-forming technologies to existing systems and their impacts.

Dr. Vikram Roy Chowdhury
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

  • DC/AC systems
  • converters
  • microgrid
  • grid-forming technologies

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Published Papers (3 papers)

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Research

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20 pages, 7543 KB  
Article
Numerically Enhanced Interfacings for Average-Value Models of Voltage-Source Converters in Nodal-Based EMT Simulators
by Seyyedmilad Ebrahimi and Juri Jatskevich
Energies 2025, 18(16), 4400; https://doi.org/10.3390/en18164400 - 18 Aug 2025
Viewed by 365
Abstract
Efficient simulations of converter-dominated power systems and microgrids significantly rely on average-value models (AVMs) of the converters. The conventional AVMs of voltage-source converters (VSCs) typically require a time-step delay for interfacing with the external circuits in non-iterative nodal-based electromagnetic transient (EMT) programs. This [...] Read more.
Efficient simulations of converter-dominated power systems and microgrids significantly rely on average-value models (AVMs) of the converters. The conventional AVMs of voltage-source converters (VSCs) typically require a time-step delay for interfacing with the external circuits in non-iterative nodal-based electromagnetic transient (EMT) programs. This time-step relaxation may lead to numerical inaccuracy and/or instability for simulations with large time-step sizes. This paper presents several alternative formulations and interfacing techniques for AVMs of VSCs, which eliminate undesirable time-step delays and result in robust and reliable interfaces that allow simulations at large time steps without significant compromise in numerical accuracy. This is achieved by formulating the VSCs as conductance matrices (and history terms), which are computed simultaneously with the solution of the external network. The advantages of the proposed techniques over the conventional methods are demonstrated in simulations of a VSC-dominated power system using the EMT program PSCAD. Full article
(This article belongs to the Special Issue Advances in Power Converters and Microgrids)
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Review

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25 pages, 2541 KB  
Review
Grid-Forming Converters for Renewable Generation: A Comprehensive Review
by Muhammad Waqas Qaisar and Jingyang Fang
Energies 2025, 18(17), 4565; https://doi.org/10.3390/en18174565 - 28 Aug 2025
Viewed by 286
Abstract
Grid-forming converters (GFMCs) play an increasingly vital role in integrating renewable energy sources into modern power systems. This article reviews GFMCs, emphasizing their importance in enabling reliable, stable, and resilient operation as power systems evolve toward low-inertia, inverter-dominated configurations. Various GFMC topologies are [...] Read more.
Grid-forming converters (GFMCs) play an increasingly vital role in integrating renewable energy sources into modern power systems. This article reviews GFMCs, emphasizing their importance in enabling reliable, stable, and resilient operation as power systems evolve toward low-inertia, inverter-dominated configurations. Various GFMC topologies are examined based on their suitability for grid-forming functions and performance across different voltage levels. Small-signal modeling approaches are presented to provide deeper insights into system dynamics and converter–grid interactions. The article reviews primary control strategies, including droop control, virtual synchronous machines, and virtual oscillator control, and discusses their impact on synchronization, stability, and power sharing. Finally, the article outlines GFMC applications and challenges, highlighting their impact on system stability. Full article
(This article belongs to the Special Issue Advances in Power Converters and Microgrids)
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33 pages, 6831 KB  
Review
Machine Learning and Artificial Intelligence Techniques in Smart Grids Stability Analysis: A Review
by Arman Fathollahi
Energies 2025, 18(13), 3431; https://doi.org/10.3390/en18133431 - 30 Jun 2025
Viewed by 2004
Abstract
The incorporation of renewable energy sources in power grids has necessitated innovative solutions for effective energy management. Smart grids have emerged as transformative systems which integrate consumer, generator and dual-role entities to deliver secure, sustainable and economical electricity supplies. This review explores the [...] Read more.
The incorporation of renewable energy sources in power grids has necessitated innovative solutions for effective energy management. Smart grids have emerged as transformative systems which integrate consumer, generator and dual-role entities to deliver secure, sustainable and economical electricity supplies. This review explores the important role of artificial intelligence and machine learning approaches in managing the developing stability characteristics of smart grids. This work starts with a discussion of the smart grid’s dynamic structures and subsequently transitions into an overview of machine learning approaches that explore various algorithms and their applications to enhance smart grid operations. A comprehensive analysis of frameworks illustrates how machine learning and artificial intelligence solve issues related to distributed energy supplies, load management and contingency planning. This review includes general pseudocode and schematic architectures of artificial intelligence and machine learning methods which are categorized into supervised, semi-supervised, unsupervised and reinforcement learning. It includes support vector machines, decision trees, artificial neural networks, extreme learning machines and probabilistic graphical models, as well as reinforcement strategies like dynamic programming, Monte Carlo methods, temporal difference learning and Deep Q-networks, etc. Examination extends to stability, voltage and frequency regulation along with fault detection methods that highlight their applications in increasing smart grid operational boundaries. The review underlines the various arrays of machine learning algorithms that emphasize the integration of reinforcement learning as a pivotal enhancement in intelligent decision-making within smart grid environments. As a resource this review offers insights for researchers, practitioners and policymakers by providing a roadmap for leveraging intelligent technologies in smart grid control and stability analysis. Full article
(This article belongs to the Special Issue Advances in Power Converters and Microgrids)
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