Digital Twins for Smart Grids: From Experimental Validation to Real-World Deployment

A special issue of Electricity (ISSN 2673-4826).

Deadline for manuscript submissions: 31 December 2026 | Viewed by 430

Editors


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Guest Editor
Grupo de Investigación en Alta Tensión-GRALTA, Escuela de Ingeniería Eléctrica y Electrónica, Universidad del Valle, Cali 760015, Colombia
Interests: digital twins; smart grids; microgrids; real-time simulation; digitalization; power system

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Guest Editor
School of Electrical and Electronic Engineering, Universidad del Valle, Cali 760015, Colombia
Interests: digital twins; smart grids; microgrids; distributed energy resources; hydrothermal power generation systems

Special Issue Information

Dear Colleague,

The rapid evolution of smart grids demands advanced tools capable of bridging the gap between theoretical development and real-world implementation. Digital twin (DT) technology has emerged as a transformative approach, enabling the creation of high-fidelity virtual replicas of physical power systems that can interact in real time with their physical counterparts. This Special Issue focuses on the role of digital twins in supporting the experimental validation, optimization, and deployment of innovative smart grid technologies. We invite contributions that address areas related to the design, development, and application of DT frameworks for power systems, including, but not limited to, distributed energy resources, microgrids, electric mobility, demand response, and grid resilience. Particular emphasis is placed on laboratory-scale implementations, hardware-in-the-loop testing, and cyber–physical platforms that facilitate accurate validation prior to field deployment. Additionally, topics such as data integration, real-time monitoring, predictive analytics, and interoperability are of strong interest. This Issue aims to highlight how digital twins can accelerate innovation cycles, reduce uncertainty, and enhance decision-making processes in modern power systems. By connecting experimental environments with real-world operations, DT-based approaches pave the way toward more reliable, efficient, and sustainable smart grids.

Dr. Eduardo Gómez-Luna
Dr. Juan David Mina-Casaran
Guest Editors

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Keywords

  • digital twins
  • smart grids
  • real-time simulation
  • experimental validation
  • digitalization
  • electrical networks

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Published Papers (1 paper)

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Research

55 pages, 904 KB  
Article
Operational-Cost-Oriented Day-Ahead BESS Scheduling in Active Distribution Networks: An AC-Feasible Framework with Post-Dispatch Battery-Aging Assessment
by Kevin Alexander Leyton-Valencia, Luis Fernando Grisales-Noreña and Fiderman Machuca-Martínez
Electricity 2026, 7(3), 83; https://doi.org/10.3390/electricity7030083 - 12 Aug 2026
Viewed by 201
Abstract
This paper presents an integrated day-ahead battery energy storage system (BESS) scheduling framework for radial active distribution networks with photovoltaic generation. Its main contribution is a reproducible evaluation chain combining non-ideal state-of-charge (SoC) feasibility correction, sequential AC power-flow verification, consistent metaheuristic benchmarking, and [...] Read more.
This paper presents an integrated day-ahead battery energy storage system (BESS) scheduling framework for radial active distribution networks with photovoltaic generation. Its main contribution is a reproducible evaluation chain combining non-ideal state-of-charge (SoC) feasibility correction, sequential AC power-flow verification, consistent metaheuristic benchmarking, and post-dispatch battery-aging analysis. The operating-cost objective coordinates hourly BESS active-power exchanges while enforcing storage and AC-network constraints. A parallel Coyote Optimization Algorithm (COA) is compared with parallel GWO, GA, PSO, and MVO implementations under a common formulation, correction procedure, evaluator, and computational environment. Validation uses modified 33-, 69-, and 136-bus radial feeders: 100 independent runs for the deterministic 33-bus benchmark, 100 independently optimized Monte Carlo scenarios for the 69-bus assessment, and seven representative daily profiles for the 136-bus weekly case. COA achieved an average cost reduction of 1.0084%, with the lowest dispersion of σ=0.0070%, in the 33-bus system; a mean scenario-wise reduction of 1.8337% in the 69-bus system; and a weekly reduction of 0.4402% in the 136-bus system. It obtained the lowest operating costs among the evaluated calibrated configurations, and all pairwise comparisons remained significant after Holm’s step-down adjustment applied separately within each system, although COA required greater computational effort than PSO. The reported schedules satisfied the imposed BESS and AC-network limits. Battery aging was evaluated only after scheduling and was not included in the optimization objective. The resulting cost-oriented schedules produced equivalent full-cycle values near 0.8 day−1 and projected 80% SoH lifetimes of approximately 6–8 years. These results provide an AC-feasible basis for comparing economic performance and post-dispatch battery-health implications under the evaluated conditions. Full article
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