Sustainable Energy and Artificial Intelligence

Abstract The deployment of AI-enabled battery digital twins for battery management systems (BMS) and DC fast-charging control is often approached as a purely technical task focused on improving prediction accuracy and charging speed. This study argues that such deployments are inherently socio-technical, shaped by boundary judgments about system purpose, beneficiaries, measures of success, decision authority, resources, expertise, and legitimacy. Using Critical Systems Heuristics (CSH), we conducted semi-structured interviews with 22 experts and stakeholders spanning BMS/battery engineering, fast-charging operations, system integration, and safety/regulatory perspectives. Interview transcripts were analyzed using template analysis aligned with the four CSH dimensions (motivation, control, expertise, and legitimacy) to contrast “what is” versus “what ought to be” boundary judgments. Results show that current practice is predominantly throughput-driven, with battery longevity, thermal safety, and grid impacts treated as secondary constraints; decision authority is fragmented across vendors, OEM-side BMS logic, operators, and utilities; and resource allocation underinvests in calibration, data governance, cybersecurity, and continuous validation. The boundary critique was translated into a structured set of CSH-informed design requirements, emphasizing health-aware charging control, explicit safety envelopes and fail-safe modes, calibrated sensing and data-quality gates, drift detection and continuous validation, auditable decision logging, and multi-stakeholder governance and representation mechanisms. The study contributes a boundary-aware pathway for designing and governing trustworthy digital-twin-enabled fast charging beyond algorithmic performance.

Abstract This paper presents a dual-mode artificial intelligence-based control approach for robust regulation of DC-DC two-switch forward converters under wide input voltage fluctuations and dynamic load conditions. Two neural network-driven strategies are explored: (1) an adaptive PI controller whose gains are optimized offline via a genetic algorithm and predicted using a feedforward neural network, and (2) an online neural network controller that directly computes the duty cycle in real time using online learning. Extensive simulation studies compare these neural approaches against classical PI and adaptive fuzzy controllers using various quantitative performance metrics, including steady-state error, overshoot, peak time, settling time, and integral error indices (IAE, ISE, ITAE). Simulation results under nominal conditions and multiple disturbance scenarios—including sudden load changes and input voltage drops—demonstrate that both AI-based approaches outperform conventional PI and fuzzy controllers. While the offline-trained adaptive PI controller excels in nominal conditions with minimal steady-state error and overshoot, the online neural network controller offers superior dynamic adaptability and robustness in real-time scenarios. Comparative performance metrics and radar-based multi-criteria evaluations confirm the suitability of these intelligent control strategies for real-time power electronics applications requiring high precision and resilience

Abstract The accelerating integration of artificial intelligence (AI) into sustainable energy policy presents both transformative opportunities and complex challenges for achieving equity and innovation in the global energy transition. While AI-driven frameworks promise enhanced efficiency, predictive analytics, and optimized resource allocation across renewable energy systems, prevailing policy approaches often overlook the underlying boundary judgments, stakeholder inclusivity, and socio-technical complexities inherent in these transitions. This study applies Critical Systems Heuristics (CSH) to systematically examine the assumptions, values, and power dynamics embedded within AI-assisted policy making for sustainable energy. Employing a qualitative research design that combines a systematic literature review with expert interviews from policy, technology, and community sectors, the analysis utilizes the twelve CSH boundary questions to contrast current “is” framings with normative “ought” expectations for just and innovative energy governance. Findings reveal that dominant AI-enabled policies frequently prioritize technical optimization and economic growth, with limited mechanisms for participatory decision-making, transparency, or recognition of marginalized communities. In contrast, stakeholders advocate for policy frameworks that explicitly address distributive and procedural justice, foster adaptive learning, ensure algorithmic transparency, and embed equity as a core design principle. The study proposes a CSH-informed conceptual framework to bridge these gaps highlighting pathways for inclusive stakeholder engagement, ethical AI deployment, and systemic innovation in sustainable energy governance. The results underscore the necessity of integrating critical systems thinking with digital innovation to advance socially legitimate and resilient energy transitions.

Abstract Frequency stability is the fundamental problem of keeping the PS's frequency within a reasonable range. The PS's stability would be threatened by frequency variation brought on by load disruptions and uncertainty pertaining to PS characteristics. The PS's LFC system is crucial for controlling and maintaining frequency stability. This work uses the adaptive Fuzzy-FOPID (FFOPID) controller in the LFC structure to address uncertainties and disturbances while improving frequency stability in a two-area PS (TAPS). Additionally, RL has been used to enhance the adaptive FFOPID controller's performance. The adaptive FFOPID controller's performance against the uncertainties and disruptions of the TAPS has been enhanced by RL, which has also increased the control system's environmental adaptability. In a number of scenarios, the suggested control (FFOPID-RL) method has been compared to FFOPID, PDSMC, and SMC control methods. The findings indicate that the control method is better at lowering frequency deviations, settling times, and damping frequency oscillations associated with the TAPS

Abstract The pharmaceutical supply chain is a critical and energy-intensive network, particularly due to temperature-sensitive products requiring continuous refrigeration. Ensuring product quality and safety while minimizing energy consumption has become an essential challenge. Despite growing interest in digital technologies for pharmaceutical logistics, a structured synthesis of AI-driven energy optimization and IoT-enabled sustainability in cold chains remains lacking. This review systematically analyzes the application of the Internet of Things (IoT), Artificial Intelligence (AI), and complementary digital technologies in enhancing energy efficiency, operational performance, and sustainability of pharmaceutical cold chains. A total of 76 peer-reviewed studies—including empirical investigations, case studies, architecture proposals, optimization frameworks, and conceptual models—were examined to identify technological trends, methodological approaches, and practical outcomes. Findings highlight how IoT-enabled sensors, Radio Frequency Identification (RFID) systems, and cloud-based platforms facilitate real-time monitoring, intelligent inventory management, and predictive decision-making. Integration with AI supports demand forecasting, anomaly detection, and energy-aware cold-chain optimization, while blockchain ensures secure, traceable, and tamper-resistant recording of events. Results indicate that digital solutions can reduce energy consumption, minimize waste, improve regulatory compliance, and enhance operational resilience. Challenges such as cybersecurity, standardization gaps, high implementation costs, and organizational barriers remain critical considerations. By synthesizing technological innovations, operational strategies, and sustainability implications, this review provides a structured roadmap for AI- and IoT-enabled energy-efficient supply chain management, offering actionable guidance for researchers, policymakers, and industry practitioners seeking sustainable and smart pharmaceutical logistics solutions.

Abstract The transition to clean energy demands advanced thermal energy storage (TES) solutions, especially for high-temperature applications like concentrated solar power (CSP) and industrial processes. Molten salt nanofluids—formed by dispersing nanoparticles in molten salts—offer a promising pathway to enhance thermal properties while maintaining high thermal stability and cost-effectiveness. This review summarizes the recent progress in the development, properties, preparation, and potential applications of these materials. Key focus areas include enhancements in specific heat capacity and thermal conductivity, which are critical for efficient heat storage and transfer. Notably, experimental studies report up to 100% increases in specific heat—defying classical predictions—possibly due to interfacial nanolayers, ionic rearrangement, or secondary nanostructures. Thermal conductivity improvements vary depending on nanoparticle type, morphology, and dispersion quality. The review also covers common base salts (nitrates, carbonates, chlorides) and a wide range of nanoparticle additives. Preparation methods such as ultrasonication and in-situ synthesis are discussed, along with challenges related to nanoparticle agglomeration, sedimentation, and long-term stability. Viscosity, corrosion behavior, and thermal cycling stability are also examined, as they critically affect system efficiency, pumping power, and material compatibility. Molten salt nanofluids hold strong potential for CSP, geothermal energy, enhanced oil recovery, and next-generation nuclear systems. However, commercialization is hindered by uncertainties in scalability, lifecycle impacts, and regulatory readiness. The review highlights the need for standardized methodologies, cross-disciplinary collaboration, and integrated performance-sustainability assessments to advance these materials toward practical deployment.

Abstract The frequency stability is a crucial aspect of an islanded microgrid, especially considering the presence of RES with low inertia. These RES, such as wind turbines and photovoltaic systems, pose a potential threat to the frequency stability of the microgrid. To address this challenge, the concept of VIC has been introduced in islanded microgrids. This paper investigates the application of VIC not only to the ESS but also to the WT and PVS. The proposed method aims to enhance the frequency stability of the microgrid. The results of this study compare the performance of the proposed method, which includes VIC for PVS, WT, and ESS, with other scenarios. These scenarios include The VIC for PVS and ESS, VIC for ESS only, and a method without VIC. The simulation results, obtained using MATLAB software, demonstrate that the proposed method significantly improves the frequency stability of the microgrid under load disturbances and disturbances originating from RES. Moreover, the proposed method exhibits robustness in the face of uncertainties associated with microgrid parameters.

Abstract Creating a formal common language, beyond the ambiguities of natural languages, between different stakeholders, from analysts to test engineers, is one of the key goals of software modeling. Although notations are standard in software modeling languages such as UML, junior engineers’ interpretation of models varies. Model interpretation in the presence of experienced people increases the learning rate for junior engineers. One of the potentials of large language models is the ability to interpret images and models. This research aims to use large language models as an assistant to interpret UML models to increase junior engineers’ learning rate and understanding of the software models. We conducted an evaluation study to examine how helpful an LLM can be to help interpret the software models. Although large language models are still not very accurate in interpreting UML models, the experiment’s results showed that students’ learning rates increased by LLMs as model interpretation assistants. In other words, the large language model worked well as a teaching assistant. The detailed results of this exploratory study are reported in this paper.

Abstract Electric motors are vital in energy systems for converting energy efficiently. Their reliability ensures consistent performance, making them indispensable in renewable energy applications and industrial processes. Bearings are crucial for motor operation, yet they are vulnerable to faults that can impair performance and reduce lifespan. Conventional fault detection methods require high sampling rate vibration data and expensive sensors. This paper presents a cost-effective solution by introducing a hybrid model combining Multiscale Convolutional Neural Networks (MSCNN) with Long Short-Term Memory (LSTM) networks for bearing fault diagnosis using low sampling rate data. We developed dedicated IoT-based hardware and a web server for real-time monitoring. Our approach leverages MSCNN for spatial feature extraction and LSTM for temporal pattern recognition, achieving high diagnostic accuracy with lower resolution data. Experimental results show that our MSCNN-LSTM model provides diagnostic accuracy comparable to or surpassing that of high sampling rate methods, offering a robust and economical solution for bearing fault detection.

Abstract This paper presents an insulin dosing policy for individuals with Type 1 Diabetes (T1D), utilizing Linear Matrix Inequality (LMI) techniques in combination with the TakagiSugeno (TS) fuzzy approximator. The primary goal is to regulate blood glucose levels by employing robust control strategies for the nonlinear dynamics of the glucose-insulin system, which are described using the Bergman Minimal Model. The proposed approach systematically incorporates insulin pump constraints, such as maximum insulin delivery rates, to ensure practical applicability in real-world scenarios. Simulation results demonstrate that the proposed controller maintains blood glucose levels within a safe range for over 84% of the time, with average glucose levels reduced to as low as 95mg/dL under the least restrictive input constraints. Furthermore, the controller effectively mitigates meal-induced disturbances while minimizing hypoglycemia risks, demonstrating its robustness under varying parameter uncertainties. This research highlights the potential of the proposed method for use in closed-loop insulin delivery systems, offering a promising solution for personalized and adaptive diabetes management.

Abstract In recent years, due to the energy crisis, the attention of researchers and energy industry experts has been drawn to the use of renewable energy sources. Among renewable energy sources, hydroelectricity is of great importance due to its major advantages, such as its widespread distribution on the earth's surface. Also Studies have shown that hydrokinetic energy can be a suitable alternative to fossil fuels. Savonius turbines are one type of turbine that, when combined with hydrokinetic systems, will produce clean and accessible energy. The Savonius vertical wind turbine must be started non-automatically. Additionally in savonius wind turbine slow speed reduces productivity and increases costs. When this turbine To be used in a flowing water Its characteristics will change and need to be optimized. Accordingly, numerous studies have been conducted on the optimization of these turbines. Accordingly, this article will review and investigate published articles in In this field. It also describes how each of the effective parameters affects the performance of these systems.

Abstract This paper presents an approach for automated unsupervised video summarization, that means, nothing more than video is needed to train the model. The goal is to extract a sequence of frames from an input video and assign each frame a score between 0 and 1. By doing so, we can select a subset of the most informative and diverse shots to make a summarized video. We build upon the foundation of SUM-GAN, particularly SUM-GAN-SLA, which utilize Generative Adversarial Networks to compare and distinguish between the original video and its regenerated counterpart. A key contribution of our work lies in the novel biLSTM-based self-attention network that we introduce to handle the crucial scoring layer of our model. We adjusted several aspects of the model, particularly in the loss functions and learning steps, to enhance the training process and achieve superior performance compared to state-of-the-art unsupervised and even supervised methods. To ensure a fair comparison, we evaluate our proposed model using two widely used datasets: SumMe and TVSum. The experimental results highlight the effectiveness of our proposed approach in automated unsupervised video summarization, achieving a 1.2% improvement over the best-performing methods' average F-score on SumMe and TVSum datasets. Additionally, our method ranks second among state-of-the-art unsupervised methods on each dataset. Notably, the top-performing methods exhibited inconsistent results across datasets, underscoring the broader applicability of our approach to diverse types of videos. Furthermore, our method demonstrates competitive performance compared to supervised approaches, with the best supervised method surpassing our results by only 0.75%.

Abstract Frequency stability is the fundamental problem of keeping the PS's frequency within a reasonable range. The PS's stability would be threatened by frequency variation brought on by load disruptions and uncertainty pertaining to PS characteristics. The PS's LFC system is crucial for controlling and maintaining frequency stability. This work uses the adaptive Fuzzy-FOPID (FFOPID) controller in the LFC structure to address uncertainties and disturbances while improving frequency stability in a two-area PS (TAPS). Additionally, RL has been used to enhance the adaptive FFOPID controller's performance. The adaptive FFOPID controller's performance against the uncertainties and disruptions of the TAPS has been enhanced by RL, which has also increased the control system's environmental adaptability. In a number of scenarios, the suggested control (FFOPID-RL) method has been compared to FFOPID, PDSMC, and SMC control methods. The findings indicate that the control method is better at lowering frequency deviations, settling times, and damping frequency oscillations associated with the TAPS