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Smart and Sustainable Energy Systems: Optimization, Modeling, and Management for Global Energy Challenges

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 10 September 2026 | Viewed by 5877

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Special Issue Editors

Dr. Ahmed Shaban

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Guest Editor

1. Department of Mechanical and Industrial Engineering, College of Engineering, Sultan Qaboos University, Muscat 123, Oman
2. Department of Mechanical Engineering, Faculty of Engineering, Fayoum University, Fayoum 63514, Egypt
Interests: industrial engineering; operations research; energy systems; smart energy management; renewable energy; quality control; supply chains; simulation; modeling and optimization
Special Issues, Collections and Topics in MDPI journals

Dr. Nasr Al-Hinai

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Guest Editor

Department of Mechanical and Industrial Engineering, College of Engineering, Sultan Qaboos University, Muscat 123, Oman
Interests: industrial engineering; operations research; production scheduling; energy optimization

Dr. Mahmood Al-Kindi

E-Mail Website
Guest Editor

Department of Mechanical and Industrial Engineering, College of Engineering, Sultan Qaboos University, Muscat 123, Oman
Interests: industrial engineering; engineering management; data analytics; risk analysis; lean Six Sigma; inventory control

Dr. Hakan Gultekin

E-Mail Website
Guest Editor

Department of Mechanical and Industrial Engineering, College of Engineering, Sultan Qaboos University, Muscat 123, Oman
Interests: operations research; production planning and scheduling; energy systems; optimization algorithms

Special Issue Information

Dear Colleagues,

Achieving sustainable, efficient, and resilient energy systems is one of the most critical challenges of our time. Energy systems today are increasingly complex, integrating diverse technologies, data streams, and decision layers—from strategic design and planning to operation, maintenance, and decommissioning. Enhancing energy efficiency and promoting energy conservation across all phases of the energy system lifecycle is essential for meeting global energy demands while minimizing environmental impacts.

This Special Issue aims to bring together state-of-the-art research addressing the modeling, optimization, analysis, and management of energy systems. We encourage submissions that span the full energy lifecycle—including system design, planning, operation, maintenance, and sustainability—with particular emphasis on energy efficiency, reliability, and conservation strategies. We invite interdisciplinary research that combines advanced analytical techniques, engineering practices, and decision-making tools to improve energy system performance and sustainability.

Scope and Topics of Interest

We welcome contributions from a wide range of disciplines and methodological perspectives. Topics of interest include, but are not limited to, the following:

Energy System Design and Planning

Optimization models for the sustainable and cost-effective design of energy systems;
Long-term planning of renewable and hybrid energy systems;
Energy infrastructure development under uncertainty;
Integration of energy efficiency and conservation goals in system design;
Lifecycle and techno-economic analyses of energy technologies.

Operation and Control of Energy Systems

Optimization and control strategies for energy-efficient operations;
Smart grid operation, microgrid control, and distributed energy management;
Energy demand forecasting and load management;
Demand response and dynamic pricing for conservation;
Real-time scheduling of energy generation and consumption.

Maintenance, Reliability, and Asset Management

Reliability engineering and predictive maintenance for energy assets;
Condition monitoring and failure analyses using advanced data analytics;
Asset health management and lifecycle extension strategies;
Risk-based maintenance and cost-performance trade-offs;
Resilience and fault-tolerant operations in energy networks.

Energy Analytics and Decision Support

AI and machine learning for energy forecasting, optimization, and diagnostics;
Big data applications in smart energy systems;
Predictive and prescriptive analytics for performance enhancement;
Multi-Criteria Decision Making (MCDM) for energy investment and policy decisions;
Data-driven decision support for improving energy conservation outcomes.

Sustainability, Efficiency, and Conservation

Metrics and models for energy efficiency evaluation;
Policy modeling and incentives for energy conservation;
Integration of energy systems with circular economy strategies;
Sustainable energy transitions and decarbonization pathways;
Environmental impact and lifecycle assessments of energy solutions.

Engineering and Project Management in Energy Systems

Planning and management of energy infrastructure projects;
Energy systems quality management and performance improvement;
Lean Six Sigma and continuous improvement methods in energy operations;
Total productive maintenance (TPM) for energy-intensive industries;
Engineering economics and project risk management;
Project integration and stakeholder management.

Supply Chain and Logistics for Energy Systems

Renewable energy supply chain design and optimization;
Hydrogen and battery storage supply chain modeling;
Sustainable procurement and reverse logistics in energy sectors;
Resilience of energy distribution and transmission networks;
Logistics for decentralized and distributed energy systems;
Circular economy in energy systems.

Advanced Modeling and Optimization Techniques

Multi-objective and multi-criteria optimization for complex energy problems;
Robust, stochastic, and dynamic programming under uncertainty;
Simulation-based optimization for real-world energy systems;
AI-integrated optimization and digital twin modeling;
Metaheuristics, surrogate modeling, and hybrid solution techniques.

Dr. Ahmed Shaban
Dr. Nasr Al-Hinai
Dr. Mahmood Al-Kindi
Dr. Hakan Gultekin
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 and sustainable energy systems
energy system modeling and optimization
energy efficiency and conservation
lifecycle analysis of energy systems
resilient energy infrastructure
decision support for energy management
AI and data analytics in energy
energy system planning and operation
maintenance and reliability engineering
multi-criteria decision making (MCDM) in energy

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Published Papers (6 papers)

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Research

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15 pages, 5665 KB

Open AccessArticle

Energy Stability Strategy for Photovoltaic DC Energy Systems Using Supercapacitor-Based Ride-Through Control and Required Capacity Sizing

by Young Je Won, Sung-Yong Son and Jin Geun Shon

Energies 2026, 19(11), 2676; https://doi.org/10.3390/en19112676 - 2 Jun 2026

Abstract

Standalone photovoltaic DC energy systems must maintain bus voltage stability without grid support; however, abrupt load variations can cause a DC-bus voltage drop, reducing system reliability and disturbing connected equipment. Although battery-based energy storage is effective for long-duration power balancing, its response to instantaneous disturbances can be limited. This study proposes an energy stability strategy using supercapacitor-based ride-through control and required capacity sizing for fast DC-bus voltage support. The proposed controller continuously monitors the DC-bus voltage and, when a voltage drop is detected, immediately triggers supercapacitor discharge to compensate for the power deficit until the bus recovers. In addition, a design formulation is derived to estimate the required compensation energy, ride-through time, and minimum capacitance based on the expected power deficit, allowable DC-bus voltage drop, and initial supercapacitor voltage. Simulation results under step changes in load resistance show that the supercapacitor sized by the proposed method maintains the DC-bus voltage close to its reference value within the specified limit. Hardware experiments further validate the ride-through operation and show good agreement between the predicted and measured compensation times. Full article

(This article belongs to the Special Issue Smart and Sustainable Energy Systems: Optimization, Modeling, and Management for Global Energy Challenges)

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15 pages, 921 KB

Open AccessArticle

AIS-Based Seasonal Transformer Scheduling Using Real SCADA Load Data for Irrigation-Intensive Rural Grids

by Leyla Akbulut, Hasan Sh. Majdi, Fatma Özdemir, Atılgan Atılgan, Joanna Kocięcka and Daniel Liberacki

Energies 2026, 19(11), 2509; https://doi.org/10.3390/en19112509 - 22 May 2026

Viewed by 217

Abstract

Efficient electricity distribution in rural areas is strongly affected by seasonal agricultural energy demand, particularly in irrigation-intensive regions where electricity consumption increases substantially during summer periods. Conventional transformer operation strategies in such rural grids often fail to adapt to seasonal load variability, leading to unnecessary idle operation, increased technical losses, and reduced infrastructure efficiency. Existing approaches generally rely on static assumptions or simulated data, limiting their ability to represent real irrigation-driven seasonal load asymmetry. To address this issue, this study proposes a data-driven multi-objective seasonal transformer scheduling framework using a bio-inspired Artificial Immune System (AIS) algorithm. The model was developed using two years of empirical hourly SCADA load data and transformer operation records obtained from a real 380/154 kV TEİAŞ transmission substation in Central Anatolia, Türkiye. Hourly SCADA measurements were used for seasonal load characterization and objective-function evaluation, while transformer scheduling decisions were defined at the seasonal operational level. The proposed AIS-based scheduling strategy reduced annual technical energy losses by approximately 5.4 GWh, decreased operational costs by 10.81 million TL (≈360,000 USD), and lowered carbon emissions by about 2270 metric tons of CO₂ compared with conventional static transformer operation. The study presents a proof-of-concept framework integrating empirical SCADA measurements with AIS-assisted seasonal transformer scheduling for practical utility-scale operational planning in irrigation-dominated rural electricity networks. Full article

(This article belongs to the Special Issue Smart and Sustainable Energy Systems: Optimization, Modeling, and Management for Global Energy Challenges)

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27 pages, 617 KB

Open AccessArticle

Energy Substitution Effect and Supply Chain Transformation in China’s New Energy Vehicle Industry: Evidence from DEA-Malmquist and Tobit Model Analysis

by Wei Cheng, Lvjiang Yin, Tianjun Zhang, Tianxin Wu and Qian Sheng

Energies 2026, 19(1), 208; https://doi.org/10.3390/en19010208 - 30 Dec 2025

Viewed by 662

Abstract

The global shift towards sustainable energy and stringent climate policies has underscored the need for decarbonizing energy systems, electrifying transportation, and transforming supply chains. In this context, China’s new energy vehicle (NEV) industry, as the largest global producer and consumer of automobiles, is pivotal in advancing energy substitution and achieving carbon reduction goals. This study investigates the energy efficiency and supply chain transformation within China’s NEV sector, leveraging panel data from 12 representative provinces over the period 2017–2023. Employing a robust analytical framework that integrates the DEA-BCC model, Malmquist index, and Tobit regression, the study provides a dynamic and regionally differentiated assessment of NEV industry efficiency. The results reveal significant improvements in total factor energy efficiency, predominantly driven by technological progress. R&D intensity, infrastructure development, and environmental regulation are identified as key enablers of efficiency, while excessive government intervention tends to hinder performance. The findings offer valuable empirical insights and policy recommendations for optimizing China’s NEV industry in the context of energy system transformation and sustainable industrial development. Full article

(This article belongs to the Special Issue Smart and Sustainable Energy Systems: Optimization, Modeling, and Management for Global Energy Challenges)

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22 pages, 3880 KB

Open AccessArticle

Techno-Economic Assessment of Offshore Wind Energy-to-Electricity and Hydrogen Production Systems in Egypt and Oman: Insights for the MENA Region

by Suzan Abdelhady, Nasr Al-Hinai, Mahmood Al Kindi, Hakan Gultekin and Ahmed Shaban

Energies 2026, 19(1), 129; https://doi.org/10.3390/en19010129 - 26 Dec 2025

Viewed by 1174

Abstract

This paper presents a comprehensive techno-economic and environmental assessment of offshore wind-powered green hydrogen production systems in Egypt and Oman, two strategically located countries within the MENA region with substantial renewable energy potential. A 120 MW offshore wind farm configuration, employing Vestas 8 MW turbines, was simulated for each country and coupled with an electrolyzer system to evaluate electricity generation, hydrogen yield, system efficiency, and cost-effectiveness. The analysis shows that both Egypt and Oman achieve high annual capacity factors (51% and 49.7%, respectively), resulting in annual green hydrogen production of 11.5 million kg and 11.2 million kg. Despite Egypt’s more stable year-round wind profile and slightly lower Levelized Cost of Hydrogen (LCOH: $3.09/kg vs. $3.17/kg), Oman’s seasonal monsoon (Khareef) offers exceptional summer productivity, with peak capacity factors exceeding 74%. A dual-framework environmental assessment was conducted to quantify CO₂ emissions mitigation. In the first scenario, based on grid substitution, the systems could avoid up to 240,000 and 256,000 tonnes of CO₂ annually in Egypt and Oman, respectively. The second scenario evaluates emissions avoided by replacing conventional gray hydrogen, yielding reductions of 126,500 tCO₂/year and 123,200 tCO₂/year, respectively. These results highlight the flexibility of offshore hydrogen systems in addressing both electricity-sector and hydrogen-market decarbonization goals. Additionally, sensitivity analysis shows that increasing turbine hub height yields only marginal wind speed and cost improvements, suggesting limited economic justification under current site conditions. Overall, the study positions Egypt as a stable, year-round hydrogen producer and Oman as a high-output seasonal exporter, supporting a complementary regional strategy for green hydrogen leadership. Full article

(This article belongs to the Special Issue Smart and Sustainable Energy Systems: Optimization, Modeling, and Management for Global Energy Challenges)

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Review

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50 pages, 3177 KB

Open AccessReview

Computational Entropy Modeling for Sustainable Energy Systems: A Review of Numerical Techniques, Optimization Methods, and Emerging Applications

by Łukasz Łach

Energies 2026, 19(3), 728; https://doi.org/10.3390/en19030728 - 29 Jan 2026

Viewed by 1130

Abstract

Thermodynamic entropy generation quantifies irreversibility in energy conversion processes, providing rigorous thermodynamic foundations for optimizing efficiency and sustainability in thermal and energy systems. This critical review synthesizes advances in computational entropy modeling across numerical methods, optimization strategies, and sustainable energy applications. Computational fluid dynamics, finite element methods, and lattice Boltzmann methods enable spatially resolved entropy analysis in convective, conjugate, and microscale systems, but exhibit varying maturity levels and accuracy–cost trade-offs. The minimization of entropy generation and the integration of artificial intelligence demonstrate quantifiable performance improvements in heat exchangers, renewable energy systems, and smart grids, with reported efficiency gains of 15 to 39% in specific applications under controlled conditions. While overall performance depends critically on system scale, operating regime, and baseline configuration, persistent limitations still constrain practical deployment. Systematic conflation between thermodynamic entropy (quantifying physical irreversibility) and information entropy (measuring statistical uncertainty) leads to inappropriate method selection; validation challenges arise from entropy’s status as a non-directly-measurable state function; high-order maximum entropy models achieve superior uncertainty quantification but require prohibitive computational resources; and standardized benchmarking protocols remain absent. Research fragmentation across thermodynamics, information theory, and machine learning communities limits integrated frameworks capable of addressing multi-scale, transient, multiphysics systems. This review provides structured, cross-method, application-aware synthesis identifying where computational entropy modeling achieves industrial readiness versus research-stage development, offering forward-looking insights on physics-informed machine learning, unified theoretical frameworks, and real-time entropy-aware control as critical directions for advancing sustainable energy system design. Full article

(This article belongs to the Special Issue Smart and Sustainable Energy Systems: Optimization, Modeling, and Management for Global Energy Challenges)

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33 pages, 5677 KB

Open AccessReview

Voltage Control for DC Microgrids: A Review and Comparative Evaluation of Deep Reinforcement Learning

by Sharafadeen Muhammad, Hussein Obeid, Abdelilah Hammou, Melika Hinaje and Hamid Gualous

Energies 2025, 18(21), 5706; https://doi.org/10.3390/en18215706 - 30 Oct 2025

Cited by 5 | Viewed by 1871

Abstract

Voltage stability in DC microgrids (DC MG) is crucial for ensuring reliable operation and component safety. This paper surveys voltage control techniques for DC MG, classifying them into model-based, model-free, and hybrid approaches. It analyzes their fundamental principles and evaluates their strengths and limitations. In addition to the survey, the study investigates the voltage control problem in a critical scenario involving a DC/DC buck converter with an input LC filter. Two model-free deep reinforcement learning (DRL) control strategies are proposed: twin-delayed deep deterministic policy gradient (TD3) and proximal policy optimization (PPO) agents. Bayesian optimization (BO) is employed to enhance the performance of the agents by tuning their critical hyperparameters. Simulation results demonstrate the effectiveness of the DRL-based approaches: compared to benchmark methods, BO-TD3 achieves the lowest error metrics, reducing root mean square error (RMSE) by up to 5.6%, and mean absolute percentage error (MAPE) by 7.8%. Lastly, the study outlines future research directions for DRL-based voltage control aimed at improving voltage stability in DC MG. Full article

(This article belongs to the Special Issue Smart and Sustainable Energy Systems: Optimization, Modeling, and Management for Global Energy Challenges)

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