Sustainable Energy and Artificial Intelligence

Abstract Amid escalating environmental concerns and the imperative for sustainable resource management, optimizing recycling processes has become a paramount challenge. This research proposes an advanced methodology for sorting recyclable waste by integrating a robotic arm with four degrees of freedom and a conveyor system, guided by a genetic algorithm (GA). The system processes waste represented as color-coded cubes with assigned values, categorizing them based on predefined metrics to maximize efficiency.
To address the constraints of the robotic arm's limited operational range and the dynamic nature of the conveyor belt, a genetic algorithm with variable-length chromosomes was employed. This approach optimizes the sorting process by prioritizing high-value items while adhering to stringent temporal and spatial constraints. The methodology was simulated and validated using RoboDK software, with Python utilized for algorithm implementation.
The findings demonstrate substantial improvements in sorting efficiency and cumulative value compared to traditional sequential methods. This study underscores the potential of integrating robotic systems with intelligent optimization algorithms to advance industrial recycling operations, enhancing automation efficiency and sustainable recycling practices at an industrial scale.

Abstract Climate change has significantly reshaped energy supply and demand by increasing the frequency and severity of extreme events, thereby intensifying challenges related to the sustainability and security of energy systems. In response, artificial intelligence has emerged as a critical enabling technology with the potential to improve forecasting accuracy, operational flexibility, and system resilience under non-stationary climatic conditions. Nevertheless, the future integration of AI into energy systems is subject to profound uncertainties arising from climatic dynamics, governance structures, and policy environments. This study investigates plausible futures of AI-enabled climate-responsive energy systems in Iran through a scenario-based approach. The research adopts an applied mixed-methods design. Initially, key drivers influencing the future development of climate-responsive energy systems were identified through a systematic literature review and expert interviews. Subsequently, Cross-Impact Analysis using the MICMAC method was employed to examine the influence–dependence relationships among these drivers. The analysis identified climate and energy security pressures and environmental and market-based policy stringency as the dominant exogenous drivers shaping system evolution. Based on the alternative states of these two critical uncertainties, four plausible future scenarios were constructed. The results indicate that under high climate pressure, strong and coherent governance combined with market-oriented environmental policies can convert stress into a driver of structural transformation and systematic AI deployment. Conversely, weak policy frameworks even when climate pressures are moderate lead to missed opportunities and the accumulation of long-term vulnerabilities. Overall, the study highlights that managing uncertainty through effective governance and strategic AI adoption is central to achieving resilient energy futures.

Abstract Over the past decade, the development of advanced heating, cooling, and thermal energy storage systems has increasingly focused on innovative solutions such as nanofluids, metal foams and phase change materials (PCMs). Through the coordinated use of complementary experimental and modeling approaches. This project systematically assessed the degree of heat transfer and overall thermal performance enhancement achievable in heat exchangers and thermal energy storage systems by integrating nanofluids, metal foams, and PCMs. This article provides an overview of the main aspects of this comprehensive research activity. Particular attention is devoted to the comparison between mesoscopic and macroscopic heat transfer modeling in metal foams and nanofluids, as well as to the experimental datasets acquired and analyzed throughout the research program. Overall, this multiscale review indicates that the combined use of metal foams, nanofluids, and phase change materials can effectively enhance heat transfer characteristics and thermal energy storage performance, while also highlighting the remaining modeling and practical challenges associated with their implementation in engineering systems.

Abstract Modern Multi Terminal HVDC (MT HVDC) grids are expanding rapidly with large scale renewable integration, yet disturbance detection still faces challenges in achieving the required speed, adaptability, and selectivity. Recent advances highlight a shift toward data driven approaches that learn disturbance signatures directly from measurements, reducing reliance on handcrafted features, threshold tuning, and complex signal processing pipelines.
This study proposes a hybrid AI based disturbance detection method that integrates a lightweight LSTM network with an XGBoost classifier for real time operation in MT HVDC systems. The method relies solely on voltage measurements sampled at 1 kHz, significantly reducing data rate requirements while preserving the temporal resolution necessary for fast decision making. Voltage waveforms are segmented into 1 ms windows, from which integrated voltage and rate of change features are extracted and encoded into compact temporal embeddings by the LSTM. These embeddings are subsequently classified by XGBoost to provide fast, interpretable, and probabilistic disturbance decisions.
A detailed MT‑HVDC test system was developed in PSCAD to generate a wide range of fault scenarios and operating conditions. Comprehensive benchmarking and multi‑objective hyperparameter optimization identified an efficient single‑layer LSTM with hidden size 8 and batch size 32 as the optimal balance between accuracy and computational efficiency. Results demonstrate strong generalization capability and robust performance under non‑ideal conditions.
Overall, the proposed method offers a scalable, data centric and deployment ready solution that enhances disturbance detection speed, improves fault clearance verification, and contributes to reducing Total Fault Clearance Time in next generation HVDC protection architectures.

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 Significant advances have been made in electrical energy distribution networks in recent years. Distributed Generation (DG) technology is rapidly advancing, particularly in response to the needs of sensitive loads in the network that demand high reliability. This paper explores using distributed generation sources to increase capacity credit (CC) in Electrical energy distribution. This article focused on studying wind sources. The issue of planning DG in the distribution network is represented as a non-linear optimization problem. The goal is to make wind power more reliable, reduce losses, and improve capacity credit. The problem model includes the network's and DG's technical and economic constraints. Two methods, Monte Carlo and k-means, have been used to model uncertainties in network load and wind power generation during the planning process. The cut-set is used to assess the network's reliability. The IEEE 33-bus distribution network was studied using the teaching learning-based optimization algorithm in two scenarios to improve response efficiency. The article found that DG can provide up to 33% of the network load in capacity credit.