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Advanced Solutions for Enhancing Power Quality and Reliability of Electrical Systems

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

Deadline for manuscript submissions: 25 September 2026 | Viewed by 2158

Editors

Dr. Angela Russo

E-Mail Website
Guest Editor
Dipartimento Energia “Galileo Ferraris”, Politecnico di Torino, 10129 Torino, Italy
Interests: integration of distributed energy resources in electric distribution systems; distribution system and microgrid optimization; power quality; decision-making under uncertainty in power systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Carmen L.T. Borges

E-Mail Website
Guest Editor
Electrical Engineering Department, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-598, Brazil
Interests: analysis and optimization of power systems; renewable energy; reliability; stochastic simulation; distributed generation; high performance computing

Special Issue Information

Dear Colleagues,

In the current energy transition process, the electrification and the increasing presence of variable renewable energy, as well as updated operating paradigms, require more reliable and efficient electrical systems characterized by high levels of power quality and reliability. In this framework, tools and solutions for improving service quality in electrical networks are becoming more and more important and strategic.

This Special Issue aims to present and disseminate the most recent advances related to solutions for enhancing power quality and reliability.

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

  • Power quality disturbances;
  • Impact of variable renewable energy;
  • Electric vehicle charging stations and energy storage systems;
  • Role of microgrids; 
  • Monitoring and metrics;
  • Mitigation technologies and solutions;
  • New technologies and modeling.

Dr. Angela Russo
Prof. Dr. Carmen L.T. Borges
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 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-anonymized 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

  • electric power systems
  • power quality
  • reliability
  • new grid paradigms
  • variable renewable energy
  • grid solutions

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Published Papers (3 papers)

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Research

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33 pages, 23149 KB  
Article
Efficient Methods for Modeling Correlations Among Renewable Energy Sources in State-Space Sampling Monte Carlo Simulation
by Carmen L. T. Borges, Mateus O. Vaz, Andressa S. Santos, Gonçalo Fontenele, Roberta C. Souza, Claudio E. Carvalho and Angela Russo
Energies 2026, 19(13), 3163; https://doi.org/10.3390/en19133163 - 3 Jul 2026
Viewed by 375
Abstract
This paper compares two combined methods for modeling correlations among multiple random variables in the state-space sampling approach that is adopted in non-sequential Monte Carlo Simulation. The first method combines the Rank Score (Iman and Conover method) with Latin Hypercube sampling, while the [...] Read more.
This paper compares two combined methods for modeling correlations among multiple random variables in the state-space sampling approach that is adopted in non-sequential Monte Carlo Simulation. The first method combines the Rank Score (Iman and Conover method) with Latin Hypercube sampling, while the second method combines Kernel Density Estimation with Bayesian Networks to represent dependency between variables. The methods are compared in terms of goodness-of-fit and computational efficiency, using statistical metrics, spatial correlation matrices, and scatter plots. The multiple time series were aggregated into single ones (Total and Net Generation) in order to validate the accuracy of each model in representing multi-dimensional correlated variables. The methods were evaluated for 3 large-dimensional cases based on the actual Brazilian energy system, with 10, 19 and 28 time series of 5-, 3- and 2-year horizons, respectively. The results show that the statistical dependencies of the historical data were accurately captured by both models, with comparable goodness-of-fit, but a higher computational efficiency of the first. Full article
(This article belongs to the Special Issue Advanced Solutions for Enhancing Power Quality and Reliability of Electrical Systems)
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28 pages, 67271 KB  
Article
Characterizing the Spatiotemporal Complexity of Power Outages in the U.S. Power Grid: A Reliability Assessment Perspective
by Qun Yu, Zhiyi Zhou, Tongshuai Jin, Weimin Sun and Jiongcheng Yan
Energies 2026, 19(5), 1252; https://doi.org/10.3390/en19051252 - 2 Mar 2026
Viewed by 744
Abstract
With the intensification of climate change, deepening energy transition, and increasing social vulnerability, extreme power outage events pose escalating challenges to the governance capacity of modern power systems. Existing evaluation frameworks primarily focus on engineering reliability and economic loss estimation, lacking systematic quantification [...] Read more.
With the intensification of climate change, deepening energy transition, and increasing social vulnerability, extreme power outage events pose escalating challenges to the governance capacity of modern power systems. Existing evaluation frameworks primarily focus on engineering reliability and economic loss estimation, lacking systematic quantification of the governance complexity arising from multidimensional interacting pressures behind outage events. This creates a blind spot in both theoretical research and governance practice, hindering differentiated resilience decision-making. To address this gap, this study develops a four-dimensional evaluation framework of power outage governance complexity encompassing event attributes, external environment, internal system, and social impacts. Based on county-level outage data and multi-source auxiliary data in the United States from 2015 to 2024 and employing the XGBoost–SHAP interpretable machine learning approach, we construct the Power Outage Complexity Index (POCI) for all U.S. counties and systematically analyze its spatiotemporal evolution and core driving factors. The results show that outage governance complexity in the U.S. power grid exhibits a significant upward trend during 2015–2024, with an average annual growth rate of 1.84%. Spatially, significant positive autocorrelation is observed, and 146 high-complexity hotspot counties are identified, mainly clustered along the East and West Coasts, the Gulf Coast, and the Southwest. Driver analysis reveals that social impact and event attribute dimensions together account for nearly 90% of the variance in complexity, with cumulative outage exposure burden, outage frequency, and large-scale event ratio being the most critical drivers. Theoretically, this study extends power resilience research from an engineering-physical paradigm to a socio-technical governance paradigm and provides a reproducible methodological framework for assessing governance complexity in critical infrastructure systems. Practically, the POCI can serve as a governance diagnostic tool for the power industry and regulators, supporting resilience investment prioritization, emergency resource optimization, and differentiated governance strategy formulation. It also provides empirical evidence for safeguarding energy security in highly vulnerable communities and promoting energy resilience equity. Full article
(This article belongs to the Special Issue Advanced Solutions for Enhancing Power Quality and Reliability of Electrical Systems)
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Review

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52 pages, 11923 KB  
Review
Inertia Response and Frequency Stability in Renewable Energy-Dominated Power Systems: Review of Virtual Inertia Techniques
by Zahid Ullah, Michele De Santis and Luigi Rubino
Energies 2026, 19(13), 3063; https://doi.org/10.3390/en19133063 - 29 Jun 2026
Viewed by 480
Abstract
As global power systems transition toward increasing penetration of renewable energy sources (RESs), such as solar and wind, maintaining frequency stability in converter-dominated low-inertia grids has become a critical challenge. This review examines the role of inertia in power system dynamics, emphasising the [...] Read more.
As global power systems transition toward increasing penetration of renewable energy sources (RESs), such as solar and wind, maintaining frequency stability in converter-dominated low-inertia grids has become a critical challenge. This review examines the role of inertia in power system dynamics, emphasising the consequences of reduced mechanical inertia, the resulting increase in the rate of change of frequency (RoCoF), and the associated stability risks in grids with high inverter-based penetration. Inertial, primary, and secondary frequency response mechanisms are discussed alongside potential cascading failures, protection system triggering, and pathways toward fully renewable grids are assessed. Virtual inertia techniques, including synchronverters, swing-equation-based methods, virtual synchronous generators (VSGs), droop control, Virtual Oscillator Control (VOC), and matching control, are evaluated in terms of benefits, limitations, implementation complexity, and Technology Readiness Levels (TRLs). A key contribution is a multi-criteria evaluation framework that classifies these methods by control adaptability, scalability, and communication requirements, providing system operators with a structured basis for strategy selection. A comparative assessment of Phase-Locked Loop (PLL) synchronisation methods, including SRF-PLL, DDSRF-PLL, FLL-PLL, and Kalman filter-based approaches, is presented under weak-grid, unbalanced, and harmonic-distorted conditions. The integration of virtual inertia with energy storage technologies, such as batteries, supercapacitors, and flywheels, is also discussed, along with its role as an ancillary service within evolving electricity markets and grid codes. Collectively, this study provides a unified reference to advance intelligent, scalable, and deployment-ready frequency control in low-inertia renewable power systems, offering both theoretical insights and practical guidance for future high-RES grid architectures. Full article
(This article belongs to the Special Issue Advanced Solutions for Enhancing Power Quality and Reliability of Electrical Systems)
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