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Energies
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Energy Systems Digitalization: Challenges and Opportunities for a Sustainable Future
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Energy Systems Digitalization: Challenges and Opportunities for a Sustainable Future

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

Deadline for manuscript submissions: 15 July 2026 | Viewed by 2254

Special Issue Editors

Prof. Dr. Antonio Moreno-Munoz

E-Mail Website
Guest Editor
Department of Electronics and Computer Engineering, University of Cordoba, 14071 Cordoba, Spain
Interests: internet of energy; smart grids; power quality; electronic instrumentation; usability of complex systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Joaquín Garrido-Zafra

E-Mail Website
Guest Editor
Department of Electronics and Computer Engineering, University of Cordoba, 14071 Cordoba, Spain
Interests: demand-side management; power system harmonics

Special Issue Information

Dear Colleagues,

The evolution of the smart grid concept from the end of the 20th century to the current digitalization of the electricity system reflects a significant change in its scope and sophistication. While the initial focus was on grid automation to improve efficiency and reliability, the emphasis was on remote monitoring and control of the electricity infrastructure. However, communication was mainly one-way, from utilities to consumers.

The current approach involves a comprehensive transformation of the electricity system, with digitalization at all levels, from generation to consumption. The goal is a bidirectional, flexible, and adaptable grid capable of massive integration of renewable energy and intelligent demand management. The consumer becomes an active player (the prosumer) with the ability to generate, store, and manage their own energy.

This Special Issue aims to address the current landscape by seeking original innovations in the application of technologies such as the following:

  • Internet of Things (IoT): for pervasive device and sensing connectivity.
  • Artificial Intelligence (AI) and Data Analytics: for optimized operation and maintenance of the grid.
  • Blockchain: for managing secure energy transactions and creating decentralized markets.
  • Machine-to-machine (M2M) communications: for seamless interaction between devices.
  • Cyber-physical system (CPS) architectures: for integrated control of the physical and digital network.
  • Cloud Computing: for scalable data storage and processing.

Prof. Dr. Antonio Moreno-Munoz
Prof. Dr. Joaquín Garrido-Zafra
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-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

  • smart grid
  • digitalization
  • power quality
  • distributed energy resources
  • internet of things
  • artificial intelligence

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Related Special Issue

Published Papers (2 papers)

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Research

21 pages, 448 KB  
Article
Data-Driven Evaluation of the Economic Viability of a Residential Battery Storage System Using Grid Import and Export Measurements
by Tim August Gebhard, Joaquín Garrido-Zafra and Antonio Moreno-Muñoz
Energies 2026, 19(4), 1072; https://doi.org/10.3390/en19041072 - 19 Feb 2026
Viewed by 421
Abstract
Battery-electric residential storage systems can increase the self-consumption of photovoltaic (PV) generation; however, economical sizing typically requires a high-resolution time series of PV production and household load behind the meter. In practice, such data are often unavailable. This work therefore presents a simulation [...] Read more.
Battery-electric residential storage systems can increase the self-consumption of photovoltaic (PV) generation; however, economical sizing typically requires a high-resolution time series of PV production and household load behind the meter. In practice, such data are often unavailable. This work therefore presents a simulation model for determining the economically optimal residential storage capacity based exclusively on smart-meter data at the point of common coupling (PCC), i.e., hourly import and export time series. Economic performance is assessed using net present value (NPV) over a multi-year evaluation horizon. In addition, technical constraints (SoC limits, power limits, charging/discharging efficiencies) as well as capacity degradation are considered via a semi-empirical aging model. For validation, a reproducible reference scenario is constructed using PVGIS generation data and the standard load profile H23, enabling a direct comparison between the conventional approach (consumption/generation) and the PCC approach (import/export). The results show that the capacity optimum can be reproduced consistently using PCC data, even under smart-meter-like integer kWh quantization. At the same time, large parts of the investigated parameter space indicate that, under the assumed scenarios, foregoing a storage system is often not economically sensible. Sensitivity analyses further highlight the strong impact of load shifting, in particular due to the charging time of electric vehicles. A case study using real PCC measurement data, together with a two-week-window analysis, demonstrates practical applicability and robustness under limited measurement durations. Full article
(This article belongs to the Special Issue Energy Systems Digitalization: Challenges and Opportunities for a Sustainable Future)
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19 pages, 3358 KB  
Article
Iterative Genetic Algorithm to Improve Optimization of a Residential Virtual Power Plant
by Anas Abdullah Alvi, Luis Martínez-Caballero, Enrique Romero-Cadaval, Eva González-Romera and Mariusz Malinowski
Energies 2025, 18(20), 5377; https://doi.org/10.3390/en18205377 - 13 Oct 2025
Cited by 2 | Viewed by 1081
Abstract
With the increasing penetration of renewable energy such as solar and wind power into the grid as well as the addition of modern types of versatile loads such as electric vehicles, the grid system is more prone to system failure and instability. One [...] Read more.
With the increasing penetration of renewable energy such as solar and wind power into the grid as well as the addition of modern types of versatile loads such as electric vehicles, the grid system is more prone to system failure and instability. One of the possible solutions to mitigate these conditions and increase the system efficiency is the integration of virtual power plants into the system. Virtual power plants can aggregate distributed energy resources such as renewable energy systems, electric vehicles, flexible loads, and energy storage, thus allowing for better coordination and optimization of these resources. This paper proposes a genetic algorithm-based optimization to coordinate the different elements of the energy management system of a virtual power plant, such as the energy storage system and charging/discharging of electric vehicles. It also deals with the random behavior of the genetic algorithm and its failure to meet certain constraints in the final solution. A novel method is proposed to mitigate these problems that combines a genetic algorithm in the first stage, followed by a gradient-based method in the second stage, consequently reducing the overall electricity bill by 50.2% and the simulation time by almost 95%. The performance is evaluated considering the reference set-points of operation from the obtained solution of the energy storage and electric vehicles by performing tests using a detailed model where power electronics converters and their local controllers are also taken into account. Full article
(This article belongs to the Special Issue Energy Systems Digitalization: Challenges and Opportunities for a Sustainable Future)
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