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Electricity
Special Issues
Innovations in Smart Grid Technologies and Sustainable Energy Solutions
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Innovations in Smart Grid Technologies and Sustainable Energy Solutions

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

Deadline for manuscript submissions: 20 November 2025 | Viewed by 852

Special Issue Editor

Prof. Dr. Farhad Shahnia

E-Mail Website
Guest Editor
Discipline of Engineering and Energy, Murdoch University, Murdoch 6150, Australia
Interests: electric distribution systems power; microgrids; smart-grid-distributed energy resources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, "Innovations in Smart Grid Technologies and Sustainable Energy Solutions", will explore cutting-edge advancements and transformative technologies shaping the future of smart grids and sustainable energy systems. As the global energy landscape transitions toward decarbonization and digitalization, this Special Issue will highlight innovative approaches, methodologies, and tools to enhance the grid resilience, efficiency, and integration of renewable energy sources. Topics of interest include, but are not limited to, advanced grid management systems, energy storage solutions, demand response strategies, IoT and AI applications in energy systems, microgrid development, and policy frameworks for sustainable energy transitions. By bringing together research from academia, industry, and policymakers, this Special Issue will foster interdisciplinary collaboration and provide a comprehensive platform for sharing insights that address the challenges and opportunities in achieving a sustainable and smart energy future.

Prof. Dr. Farhad Shahnia
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. Electricity is an international peer-reviewed open access quarterly 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 1000 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 technologies
  • sustainable energy solutions
  • renewable energy integration
  • energy storage systems
  • grid resilience
  • artificial intelligence in energy
  • microgrid development
  • demand response strategies
  • decarbonization
  • IoT in energy management

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Published Papers (1 paper)

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Research

33 pages, 7507 KiB  
Article
A Neural Network-Based Model Predictive Control for a Grid-Connected Photovoltaic–Battery System with Vehicle-to-Grid and Grid-to-Vehicle Operations
by Ossama Dankar, Mohamad Tarnini, Abdallah El Ghaly, Nazih Moubayed and Khaled Chahine
Electricity 2025, 6(2), 32; https://doi.org/10.3390/electricity6020032 - 6 Jun 2025
Viewed by 588
Abstract
The growing integration of photovoltaic (PV) energy systems and electric vehicles (EVs) introduces new challenges in managing energy flow within smart grid environments. The intermittent nature of solar energy and the variable charging demands of EVs complicate reliable and efficient power management. Existing [...] Read more.
The growing integration of photovoltaic (PV) energy systems and electric vehicles (EVs) introduces new challenges in managing energy flow within smart grid environments. The intermittent nature of solar energy and the variable charging demands of EVs complicate reliable and efficient power management. Existing strategies for grid-connected PV–battery systems often fail to effectively handle bidirectional power flow between EVs and the grid, particularly in scenarios requiring seamless transitions between vehicle-to-grid (V2G) and grid-to-vehicle (G2V) operations. This paper presents a novel neural network-based model predictive control (NN-MPC) approach for optimizing energy management in a grid-connected PV–battery–EV system. The proposed method combines neural networks for forecasting PV generation, EV load demand, and grid conditions with a model predictive control framework that optimizes real-time power flow under various constraints. This integration enables intelligent, adaptive, and dynamic decision making across multiple objectives, including maximizing renewable energy usage, minimizing grid dependency, reducing transient responses, and extending battery life. Unlike conventional methods that treat V2G and G2V separately, the NN-MPC framework supports seamless mode switching based on real-time system status and user requirements. Simulation results demonstrate a 12.9% improvement in V2G power delivery, an 8% increase in renewable energy utilization, and a 50% reduction in total harmonic distortion (THD) compared to PI control. The results highlight the practical effectiveness and robustness of NN-MPC, making it an effective solution for future smart grids that require bidirectional energy management between distributed energy resources and electric vehicles. Full article
(This article belongs to the Special Issue Innovations in Smart Grid Technologies and Sustainable Energy Solutions)
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