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Technologies
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Innovative Power System Technologies—Second Edition
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Innovative Power System Technologies—Second Edition

A Special Issue of Technologies (ISSN 2227-7080).

Deadline for manuscript submissions: 31 January 2027 | Viewed by 1225

Editors

Prof. Dr. Edimar José De Oliveira

E-Mail Website1 Website2
Guest Editor
Department of Electrical Energy, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil
Interests: electric power system measurement; observability; electric power transmission networks; optimal power flow; power system
Special Issues, Collections and Topics in MDPI journals
Dr. Janaína Gonçalves De Oliveira

E-Mail Website
Guest Editor
1. Department of Electrical Energy, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil
2. Department of Electrical Engineering, Uppsala University, Box 65, 751 03 Uppsala, Sweden
Interests: electrical engineering control systems; power electronics applied to energy storage and renewable energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global shift toward sustainable and resilient energy systems has accelerated the development and adoption of innovative power system technologies, which play a critical role in addressing the challenges associated with the integration of renewable energy sources—such as solar, wind, tidal, and wave energy—into modern power grids.

While these renewable sources promise to meet growing energy demand and support net-zero emission targets, they also introduce new complexities related to grid stability, supply reliability, and operational safety. Emerging solutions span multiple levels, from advanced control strategies at the energy conversion device level to cooperative control among distributed energy resources and the strategic use of energy storage and hybrid systems to mitigate variability in generation and Green Hydrogen (GH2) production.

This Special Issue focuses on the latest advancements in power system innovation, including the control and optimization of individual and aggregated renewable units, smart grid integration, novel hybrid and co-located power parks, and advanced energy storage systems. We aim to highlight both the theoretical developments and practical implementations that drive the transformation of energy systems worldwide.

Prof. Dr. Edimar José De Oliveira
Dr. Janaína Gonçalves De Oliveira
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. Technologies is an international peer-reviewed open access monthly 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 1800 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

  • innovative power system technologies
  • renewable energy integration
  • hybrid and co-located power parks
  • advanced control strategies
  • optimization and planning methods
  • grid stability and regulation
  • energy storage systems and their applications
  • GH2 production, transportation and application

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Published Papers (2 papers)

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37 pages, 1740 KB  
Article
Fully Native DPL-Based Conductor Sizing Optimization for Distribution Networks in DIgSILENT PowerFactory
by Víctor Mario Vélez-Marín, Oscar Danilo Montoya and Jesús C. Hernández
Technologies 2026, 14(8), 477; https://doi.org/10.3390/technologies14080477 - 2 Aug 2026
Viewed by 340
Abstract
This paper presents a fully native optimization framework, implemented within DIgSILENT PowerFactory, which is aimed at solving the optimal conductor sizing problem (OCSP) in electrical distribution systems under realistic operating conditions. Our methodology integrates a tabu search algorithm (TSA) directly with the three-phase [...] Read more.
This paper presents a fully native optimization framework, implemented within DIgSILENT PowerFactory, which is aimed at solving the optimal conductor sizing problem (OCSP) in electrical distribution systems under realistic operating conditions. Our methodology integrates a tabu search algorithm (TSA) directly with the three-phase power flow routines and database objects available in the DigSILENT programming language (DPL), thereby eliminating the need for external data exchange and synchronization between independent optimization and network simulation environments. Our framework considers balanced and unbalanced operating conditions while incorporating peak demand, multilevel demand, and hourly demand load profiles. The optimization process minimizes annual investment and operating costs while satisfying voltage regulation and conductor ampacity constraints. The methodology was validated using a 27-bus benchmark system and the IEEE 33- and 123-bus distribution systems under different operating scenarios. The numerical results indicate that chronological demand scenarios significantly influence conductor allocation decisions and annual operating costs. Compared to the conventional peak demand load profile, the multilevel and hourly load profiles produced lower annual costs by distributing conductor sizing decisions across multiple operating states instead of considering worst-case loading conditions. Additionally, the unbalanced scenarios increased the operating losses and modified the conductor selection patterns due to unequal phase loading and current asymmetries. The proposed TSA-DPL implementation maintained stable convergence behavior and low statistical dispersion under all the evaluated benchmark systems and operating conditions. Even for the IEEE 123-bus feeder under unbalanced hourly operating conditions, the standard deviation remained below 0.70% of the average annual cost, confirming the robustness and repeatability of the methodology. Although the detailed three-phase chronological simulations increased the computational requirements, the proposed implementation demonstrated computational applicability to the evaluated benchmark systems. Overall, the proposed TSA-DPL framework constitutes a robust native implementation for realistic conductor sizing studies in modern three-phase distribution systems. Full article
(This article belongs to the Special Issue Innovative Power System Technologies—Second Edition)
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18 pages, 936 KB  
Article
Multi-Stage Probabilistic Transmission Expansion Planning Under Generation Uncertainty and N-1 Security Using the Pack-Based Grey Wolf Optimizer
by Edimar José de Oliveira, Lucas Santiago Nepomuceno, Arthur Neves de Paula, Raphael Paulo Braga Poubel and Leonardo Willer de Oliveira
Technologies 2026, 14(6), 329; https://doi.org/10.3390/technologies14060329 - 28 May 2026
Viewed by 637
Abstract
Multi-Stage Transmission Network Expansion Planning (MS-TNEP) is critical for adapting power grids to long-term renewable integration. However, the simultaneous incorporation of N-1 security, active power losses, and uncertainties regarding the spatial and temporal growth of power generation capacity imposes prohibitive computational complexity. This [...] Read more.
Multi-Stage Transmission Network Expansion Planning (MS-TNEP) is critical for adapting power grids to long-term renewable integration. However, the simultaneous incorporation of N-1 security, active power losses, and uncertainties regarding the spatial and temporal growth of power generation capacity imposes prohibitive computational complexity. This paper proposes a probabilistic MS-TNEP model evaluated over a 20-year horizon. To overcome this computational intractability, a hybrid decomposition framework is employed. The investment subproblem determines the discrete decisions for network investment via a metaheuristic, while the probabilistic operation subproblem utilizes linear programming to assess the operational feasibility of these decisions under multiple spatial and temporal growth of power generation capacity scenarios, active power losses, and N-1 contingencies. Furthermore, a novel Pack-Based Grey Wolf Optimizer (PBGWO) is introduced. The approach is validated on the Garver and the Southern Brazilian equivalent systems under multiple scenarios for the growth of both wind and conventional power generation capacity. Comparative analysis against the Genetic Algorithm, the standard Grey Wolf Optimizer, and the Whale Optimization Algorithm reveals that PBGWO is a highly competitive approach for MS-TNEP problems, consistently identifying the most cost-effective expansion plan. Full article
(This article belongs to the Special Issue Innovative Power System Technologies—Second Edition)
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