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Enhancing Sustainability Through Power System Flexibility, Smart Grids, and Energy Digitalization

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 7921

Special Issue Editors

Dr. Mikaeel Ahmadi

E-Mail Website
Guest Editor
Graduate School of Engineering and Science, University of the Ryukyus, Nishihara, Okinawa 903-0213, Japan
Interests: sustainable energy; power system flexibility; renewable power generation; power system optimization; power distribution system; e-mobility; energy efficiency; smart grid
Special Issues, Collections and Topics in MDPI journals
Dr. Atsushi Yona

E-Mail Website
Guest Editor
Graduate School of Engineering and Science, University of the Ryukyus, Nishihara, Okinawa 903-0213, Japan
Interests: renewable energy technologies; energy conversion; smart grid; power generation; energy modeling; distributed generation; energy conservation; energy power systems analysis

Special Issue Information

Dear Colleagues,

I am delighted to announce the launch of a Special Issue of Sustainability focused on "Enhancing Sustainability through Power System Flexibility, Smart Grids, and Energy Digitalization".

In today's rapidly evolving energy landscape, the integration of flexibility, smart grid technologies, and energy digitalization plays a pivotal role in achieving sustainability objectives.

This Special Issue seeks to explore innovative research and insights into the intersection of power system flexibility, smart grid solutions, and energy digitalization, with a primary focus on promoting sustainability across various sectors. We invite original research papers, reviews, and case studies that address topics including, but not limited to, the following:

  • Novel approaches to enhancing power system flexibility for variable renewable energy integration (VRE).
  • Integration of smart grid technologies for enhanced grid resilience and reliability.
  • Applications of artificial intelligence, machine learning, and data analytics in optimizing energy systems.
  • Demand response strategies and their role in shaping future energy landscapes.
  • Blockchain and distributed ledger technologies for peer-to-peer energy trading and decentralized energy management.
  • Cybersecurity challenges and solutions in the context of smart grids and digitalized energy systems.
  • Policy and regulatory frameworks driving the adoption of smart grid and digitalization initiatives.
  • Economic and environmental implications of transitioning towards flexible and digitally enabled energy systems.
  • Case studies highlighting successful implementations of smart grid and energy digitalization projects worldwide.
  • Resilience assessment of power systems with an emphasis on flexibility and sustainability
  • Innovative technologies and solutions for enhancing power system flexibility in urban energy systems.
  • Human factors and behavioral aspects in the adoption of power system flexibility solutions.
  • Market mechanisms and regulatory aspects influencing power system flexibility adoption in smart grids.

We encourage submissions that contribute to a deeper understanding of how power system flexibility, smart grid technologies, and energy digitalization can collectively contribute to sustainable energy transitions and address pressing environmental challenges. Manuscripts should align with relevant Sustainable Development Goals (SDGs) and offer practical insights for policymakers, industry stakeholders, and researchers alike.

Submissions should adhere to the submission guidelines of Sustainability and will undergo a rigorous peer review process to ensure the highest quality of published work. We look forward to receiving your contributions and fostering insightful discussions on this topic of critical importance.

Please feel free to contact me if you have any questions or require further information regarding this Special Issue.

Dr. Mikaeel Ahmadi
Dr. Atsushi Yona
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 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. Sustainability 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 2400 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
  • power system flexibility
  • sustainability
  • energy digitalization
  • SDGs
  • energy management
  • electrical vehicle
  • renewable energy
  • sustainable transportation

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Published Papers (5 papers)

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Research

25 pages, 5958 KiB  
Article
Characterization of Energy Profile and Load Flexibility in Regional Water Utilities for Cost Reduction and Sustainable Development
by B. M. Ruhul Amin, Rakibuzzaman Shah, Suryani Lim, Tanveer Choudhury and Andrew Barton
Sustainability 2025, 17(8), 3364; https://doi.org/10.3390/su17083364 - 9 Apr 2025
Viewed by 779
Abstract
Water utilities use a significant amount of electrical energy due to the rising demand for wastewater treatment driven by environmental and economic reasons. The growing demand for energy, rising energy costs, and the drive toward achieving net-zero emissions require a sustainable energy future [...] Read more.
Water utilities use a significant amount of electrical energy due to the rising demand for wastewater treatment driven by environmental and economic reasons. The growing demand for energy, rising energy costs, and the drive toward achieving net-zero emissions require a sustainable energy future for the water industry. This can be achieved by integrating onsite renewable energy sources (RESs), energy storage, demand management, and participation in demand response (DR) programs. This paper analyzes the energy profile and load flexibility of water utilities using a data-driven approach to reduce energy costs by leveraging RESs for regional water utilities. It also assesses the potential for DR participation across different types of water utilities, considering peak-load shifting and battery storage installations. Given the increasing frequency of extreme weather events, such as bushfires, heatwaves, droughts, and prolonged cold and wet season floods, regional water industries in Australia serve as a relevant case study of sectors already impacted by these challenges. First, the data characteristics across the water and energy components of regional water industries are analyzed. Next, barriers and challenges in data acquisition and processing in water industries are identified and recommendations are made for improving data coordination (interoperability) to enable the use of a single platform for identifying DR opportunities. Finally, the energy profile and load flexibility of regional water industries are examined to evaluate onsite generation and battery storage options for participating in DR operations. Operational data from four regional sites across two regional Australian water utilities are used in this study. Full article
(This article belongs to the Special Issue Enhancing Sustainability Through Power System Flexibility, Smart Grids, and Energy Digitalization)
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33 pages, 997 KiB  
Article
MAS-DR: An ML-Based Aggregation and Segmentation Framework for Residential Consumption Users to Assist DR Programs
by Petros Tzallas, Alexios Papaioannou, Asimina Dimara, Napoleon Bezas, Ioannis Moschos, Christos-Nikolaos Anagnostopoulos, Stelios Krinidis, Dimosthenis Ioannidis and Dimitrios Tzovaras
Sustainability 2025, 17(4), 1551; https://doi.org/10.3390/su17041551 - 13 Feb 2025
Viewed by 1308
Abstract
The increasing complexity of energy grids, driven by rising demand and unpredictable residential consumption, highlights the need for efficient demand response (DR) strategies and data-driven services. This paper proposes a machine learning-based framework for DR that clusters users based on their consumption patterns [...] Read more.
The increasing complexity of energy grids, driven by rising demand and unpredictable residential consumption, highlights the need for efficient demand response (DR) strategies and data-driven services. This paper proposes a machine learning-based framework for DR that clusters users based on their consumption patterns and categorizes individual usage into distinct profiles using K-means, Hierarchical Agglomerative Clustering, Spectral Clustering, and DBSCAN. Key features such as statistical, temporal, and behavioral characteristics are extracted, and the novel Household Daily Load (HDL) approach is used to identify residential consumption groups. The framework also includes context analysis to detect daily variations and peak usage periods for individual users. High-impact users, identified by anomalies such as frequent consumption spikes or grid instability risks using IsolationForest and kNN, are flagged. Additionally, a classification service integrates new users into the segmented portfolio. Experiments on real-world datasets demonstrate the framework’s effectiveness in helping energy managers design tailored DR programs. Full article
(This article belongs to the Special Issue Enhancing Sustainability Through Power System Flexibility, Smart Grids, and Energy Digitalization)
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12 pages, 2713 KiB  
Article
A Method to Handle Unbalanced Systems in Branch Current-Based State Estimators
by Andrés Llombart, Luis Parada, Miguel Torres, Noemi Galán and Diego Martinez
Sustainability 2025, 17(3), 942; https://doi.org/10.3390/su17030942 - 24 Jan 2025
Viewed by 746
Abstract
The undergoing energy transition to a sustainable future requires, among other things, the application of demand side management (DSM) techniques to maintain grid stability and allow a smooth performance. To successfully implement DSM strategies, near real-time monitoring of the grid is required. This [...] Read more.
The undergoing energy transition to a sustainable future requires, among other things, the application of demand side management (DSM) techniques to maintain grid stability and allow a smooth performance. To successfully implement DSM strategies, near real-time monitoring of the grid is required. This can be achieved through a distribution system state estimator (DSSE). Conventional approaches to state estimation (SE) typically rely on the assumption of a balanced reference bus, which is reasonable for transmission systems but may not be applicable to low-voltage distribution networks, even more with significant distributed generation (DG) penetration. To address this problem, a branch current-based low-voltage DSSE for unbalanced three-phase systems is developed. The algorithm incorporates a virtual bus to account for highly unbalanced systems, enabling it to obtain a more accurate estimation of the grid state. The proposed method is compared to the conventional balanced reference bus method through multiple simulations under different load conditions in the IEEE European low-voltage test feeder. Full article
(This article belongs to the Special Issue Enhancing Sustainability Through Power System Flexibility, Smart Grids, and Energy Digitalization)
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19 pages, 7585 KiB  
Article
Microgrid Optimization Strategy for Charging and Swapping Power Stations with New Energy Based on Multi-Agent Reinforcement Learning
by Hongbin Sun, Zhenyu Duan and Anyun Yang
Sustainability 2024, 16(23), 10663; https://doi.org/10.3390/su162310663 - 5 Dec 2024
Viewed by 1499
Abstract
Aiming at the coordinated control of charging and swapping loads in complex environments, this research proposes an optimization strategy for microgrids with new energy charging and swapping stations based on adaptive multi-agent reinforcement learning. First, a microgrid model including charging and swapping loads, [...] Read more.
Aiming at the coordinated control of charging and swapping loads in complex environments, this research proposes an optimization strategy for microgrids with new energy charging and swapping stations based on adaptive multi-agent reinforcement learning. First, a microgrid model including charging and swapping loads, photovoltaic power generation, and wind power generation was constructed, and the Markov decision process was used to characterize the stochastic characteristics of new energy power generation, including charging and swapping loads. The deep relationship between uncertainty factors and charging and swapping laws was explored, and an adaptive multi-agent deep reinforcement learning method was used to optimize the random action selection process, improve the convergence speed of the coordinated optimization model, and realize coordinated control of multiple charging and swapping loads. Finally, through the analysis of different scenarios, the effectiveness of the proposed adaptive multi-agent reinforcement learning model for coordinated control of charging and swapping loads was verified. The results show that the proposed method has a faster convergence speed and can effectively optimize the charging process of charging and swapping loads, reducing power fluctuations of the newly connected energy grid. Full article
(This article belongs to the Special Issue Enhancing Sustainability Through Power System Flexibility, Smart Grids, and Energy Digitalization)
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30 pages, 23030 KiB  
Article
Assessment of Wind Energy Potential and Optimal Site Selection for Wind Energy Plant Installations in Igdir/Turkey
by Gökhan Şahin, Ahmet Koç, Sülem Şenyiğit Doğan and Wilfried van Sark
Sustainability 2024, 16(20), 8775; https://doi.org/10.3390/su16208775 - 11 Oct 2024
Cited by 1 | Viewed by 2685
Abstract
Wind energy is an eco-friendly, renewable, domestic, and infinite resource. These factors render the construction of wind turbines appealing to nations, prompting numerous governments to implement incentives to augment their installed capacity of wind turbines. Alongside augmenting the installed capacity of wind turbines, [...] Read more.
Wind energy is an eco-friendly, renewable, domestic, and infinite resource. These factors render the construction of wind turbines appealing to nations, prompting numerous governments to implement incentives to augment their installed capacity of wind turbines. Alongside augmenting the installed capacity of wind turbines, identifying suitable locations for their installation is crucial for optimizing turbine performance. This study aims to evaluate potential sites for wind power plant installation via a GIS, a mapping technique. The Analytic Hierarchy Process (AHP) was employed to assess the locations, including both quantitative and qualitative aspects that significantly impact the wind farm suitability map. Utilizing the GIS methodology, all datasets were examined through height and raster transformations of land surface temperature, plant density index, air pressure, humidity, wind speed, air temperature, land cover, solar radiation, aspect, slope, and topographical characteristics, resulting in the creation of a wind farm map. The correlation between the five-year meteorological data and environmental parameters (wind direction, daily wind speed, daily maximum and minimum air temperatures, daily relative humidity, daily average air temperature, solar radiation duration, daily cloud cover, air humidity, and air pressure) influencing the wind power plant in Iğdır province, including Iğdır Airport, Karakoyunlu, Aralık, and Tuzluca districts, was analyzed. If wind energy towers are installed at 1 km intervals across an area of roughly 858,180 hectares in Igdir province, an estimated 858,180 GWh of wind energy can be generated. The GIS-derived wind power plant map indicates that the installation sites for wind power plants are located in regions susceptible to wind erosion. Full article
(This article belongs to the Special Issue Enhancing Sustainability Through Power System Flexibility, Smart Grids, and Energy Digitalization)
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