Editorial for the Special Issue: Sustainable and Intelligent Energy Systems and Processes—Recent Advances and Challenges

The global energy transition is characterized by the simultaneous challenges of decarbonization, digitalization, and decentralization. These challenges can be analyzed in terms of energy consumption and production across several sectors, including transportation, green energy trading, industry, power transmission, microgrids, and the integration of electricity and gas systems. In the transportation sector, research focuses on various aspects such as China and its 30 provinces [1], the use of biofuels for a sustainable future [2], blended fuels to reduce the carbon footprint of ships [3], and the suitability of different energy sources for automotive applications [4], as further evaluated in Contribution 4. Reducing the carbon footprint can also be achieved through the use of diverse energy sources and the trading of energy with green certificates [5]. Significant contributions to energy optimization and carbon footprint reduction have been made in the industrial sector, particularly with a focus on electric motors. Research on motors highlights that adopting appropriate control strategies [6] and compensating for stray load and iron losses [7] can reduce energy consumption and extend equipment lifespan, thereby positively impacting the environment. Ensuring reliable power transmission is another critical issue. Defect detection in transmission infrastructure can be addressed using unmanned aerial vehicles (UAVs) supported by deep learning techniques [8], as well as through the Cross-Scale Spatial Attention Detector (CSSAdet) for identifying defects in mechanical joints [9]. In the context of microgrids, the focus lies on maintaining frequency stability, where various approaches are proposed to handle power disturbances [10]. Additional studies address the scheduling problem in systems that integrate electrical and gas networks with wind farms [11], and explore the electricity–hydrogen–gas nexus, emphasizing both environmental and economic benefits [12], with extended operational strategies and scheduling models. These trends demand sustainable solutions that integrate renewable resources, increase efficiency, and ensure resilient and intelligent energy infrastructures.

This Special Issue “Sustainable and Intelligent Energy Systems and Processes: Recent Advances and Challenges” in Processes brings together thirteen contributions (twelve articles and one correction) that reflect cutting-edge developments in sustainable energy technologies, advanced control methods, and intelligent monitoring.

A group of studies focuses on intelligent monitoring and forecasting for power systems. An improved deep learning framework for defect diagnosis in transmission lines based on images captured by unmanned aerial vehicles (UAVs) enhances reliability by addressing imbalanced datasets and improving feature extraction, as reported in Contribution 1. The proposed method uses a generative adversarial network to enhance sample diversity and an improved GoogLeNet with focal loss to effectively recognize different types of transmission line defects. Similarly, a hybrid approach combining the Sparrow Search Algorithm with bi-directional long- and short-term memory networks, proposed in Contribution 2, significantly improves the accuracy of short-term electricity load forecasting. Accurate short-term power load forecasting is essential for maintaining the balance between power supply and demand. Tests on real power load data from Wuxi, China, show that the proposed model achieves a relative error of only 2%, outperforming other compared methods.

Extending intelligent sensing to the environmental domain, an affordable and portable monitoring system, investigates correlations among air pollutants to enable cost-efficient urban air pollution measurement. Monitoring air quality is essential for environmental safety, and Contribution 10 explores correlations between various pollutants to enable cost-effective monitoring in remote areas. A custom PCB-based portable system was developed to measure pollutants, and the results showed a moderate correlation between particulate and CO concentrations, suggesting that data from cheaper sensors can be used to estimate values typically measured by more expensive ones.

The integration of renewable energy with hydrogen technologies is another key theme. An operation strategy for wind-PV-based hydrogen production systems, explicitly modeling electrolyzer cold and hot start-up behavior, improves operational efficiency and highlights the role of hydrogen storage, as discussed in Contribution 3. This paper develops an optimized operational strategy by modeling the electrolyzer’s three-state and nonlinear power–current characteristics. Simulation results show that the proposed strategy increases system efficiency, with a 0.32% rise in daily operational income for every 10% increase in hydrogen storage capacity. Complementarily, a novel economic dispatch model for integrated energy systems, proposed in Contribution 5, accounts for renewable uncertainty, carbon trading, and electrolyzer thermal energy utilization, demonstrating cost and emission reductions. An IGDT-based economic scheduling strategy is proposed that models a coupled electricity–heat–hydrogen–gas system, incorporating electrolyzer thermal energy utilization and carbon trading mechanisms. Results show that the proposed method reduces carbon emissions by 2597.68 kg and operating costs by 44.65%, demonstrating effective cost control and flexibility for different risk preferences

Sustainable fuels and alternative energy pathways are also examined. Contribution 4 provides a comparative analysis of methanol and ethanol blending in gasoline and evaluates fossil, biomass, and CO₂ utilization routes, revealing long-term economic and environmental trade-offs. This study develops a refinery modeling framework combining machine learning, optimization, and carbon tracking to assess economic and environmental outcomes of blending fossil-, biomass-, and CO₂-derived alcohols. Results show that while coal-based alcohol is currently most economical, CCUM (CO₂-to-methanol) blending becomes the most sustainable and profitable option by 2050, achieving a 62.4% reduction in carbon footprint and strong long-term economic performance. In parallel, exergy and energy analyses of a poultry litter co-combustion process with a shell-and-tube heat exchanger (STHEs), reported in Contribution 11, highlight opportunities for efficient waste-to-energy conversion. This study applies both First and Second Law thermodynamic analyses to evaluate the STHE’s performance and identify exergy losses. Results show energy efficiencies of 75–92% but much lower exergy efficiencies (12–25%), emphasizing significant irreversibility, while optimal operating conditions were identified to minimize exergy loss and improve overall system efficiency.

Hybrid systems that simultaneously generate power and cooling form another important strand. Contribution 6 presents a cogeneration cycle based on ammonia–water mixtures, supported by energy, exergy, and economic analyses. This study analyzes the energy, exergy, and economic performance of a new ammonia–water cogeneration cycle that simultaneously produces power and cooling. The system integrates absorption cooling, internal rectification, and key components such as a turbine, compressor, and reheater to enhance efficiency. Results show an energy utilization factor of 0.58, an exergy efficiency of 0.26, production of 26.28 kW of power and 366.8 kW of cooling, with the condenser as the most costly component and the generator showing the highest exergy destruction. In Contribution 7, a solar-driven organic Rankine cycle integrated with absorption cooling is developed, demonstrating the potential of renewable-driven multi-functional energy systems. This study evaluates the technical feasibility of such a system in Mexico. Using a parabolic trough collector and storage system, the system’s performance was modeled with SAM software (version 2022.11.21) for various working fluids. Results show the highest energy utilization factor of 0.68 and exergy efficiency of 0.524 using benzene, with optimal condensing and cooling temperatures of 80 °C/20 °C and cooling temperatures of 0 °C and −10 °C, respectively.

Advances in intelligent control and electrification are also strongly represented. A model predictive current control method for induction motors, proposed in Contribution 12, incorporating iron core losses and saturation, reduces switching frequency while maintaining high performance. This paper investigates model predictive current control (MPCC) for induction motor (IM) drives using five IM models of varying complexity. Performance metrics such as stator current distortion, switching frequency, and rotor flux errors are evaluated via MATLAB R2022b Simulink simulations to identify the model offering the best balance of accuracy and practicality. Additionally, a control effort penalization (CEP) strategy is proposed to limit simultaneous switching and reduce inverter losses while maintaining performance comparable to a field-oriented controlled IM drive. In the area of battery systems, a novel method for state-of-charge (SoC) estimation and cell type identification based on transfer function analysis is proposed in Contribution 8, with a subsequent correction clarifying details of the error metric. The proposed approach compares the battery’s voltage response to a current pulse with a database of reference responses to determine SoC via integral squared error (ISE) and quadratic interpolation. Testing on multiple cell types shows the algorithm accurately estimates SoC and identifies cell type, with the actual SoC verified using Coulomb counting.

Finally, the role of intelligence in critical infrastructures is explored in Contribution 9, presenting a comprehensive review of intelligent electrical systems for nuclear power plants, which outlines the architecture, challenges, and future pathways of smart maintenance and operation. The paper reviews current AI and big data applications, outlines the architecture of smart electrical systems, and discusses the intelligentization of medium- and low-voltage equipment, addressing challenges such as information silos and manual inspection inefficiencies. Solutions, trends, and phased development principles are proposed, emphasizing a gradual evolution from automation to digitization and intelligentization, with short- and long-term goals for improved monitoring, inspection, and maintenance of nuclear power plant electrical systems.

Collectively, these contributions address several pressing gaps: the need for accurate modeling of renewable energy under uncertainty, integration of hydrogen and storage technologies, deployment of intelligent sensing and predictive control, and holistic evaluation of sustainability through energy, exergy, economic, and environmental perspectives.

Future research should extend these directions by developing digital twins and AI-enabled tools for resilient, cyber-secure energy management; advancing multi-energy integration with hydrogen and synthetic fuels; applying comprehensive techno-economic and lifecycle assessments to guide sustainable process design; and exploring cross-sectoral coupling to accelerate decarbonization.

This Special Issue demonstrates how the combination of sustainable technologies and intelligent methods can shape the future of energy systems. We thank all the authors for their valuable contributions and the reviewers for their essential role in ensuring the quality of this collection.