Sustainable Energy Future Research Articles

A novel incremental ensemble learning for real-time explainable forecasting of electricity price

The development of a stable, safe, secure and sustainable energy future is a challenge for all countries these days. In terms of electricity price, its volatile nature makes its prediction a complex task. A precise real-time forecast of the electricity price can have significant consequences for the economy and risks faced. This work presents a new ensemble learning algorithm for making real-time predictions of electricity price in Spain. It combines long and short-term behavior patterns following an online incremental learning approach, keeping the model always up to date. The detection of novelties and unexpected behaviors in the time series streams allows the algorithm to provide more accurate predictions than the reference machine learning algorithms with which it is compared. In addition, the proposed algorithm predicts in real-time and the predictions obtained are interpretable, thus contributing to the Explainable Artificial Intelligence.

The Role of Data Analytics in Enhancing Public Administration in Europe

Sustainable development (SD) represents a growth approach that tries to maintain a social, economic and environmental balance. SD aims to meet the needs of the present without compromising the needs of future generations. For a sustainable development, a key element is represented by renewable energy (RE). The use of RE on an increasingly large scale brings with it a series of advantages and positively influences aspects such as resource conservation, air and water quality improvement, climate change, greenhouse gas emissions, etc. Realization of RE through different ways such as wind energy, solar power, hydropower, etc. and by combining them, European citizens can take an important step in realizing a more sustainable and renewable energy future. The article presents a data mining analysis on the use of RE in EU countries, taking into account the share of industry use such as heating and cooling, transport and electricity. The results of the analysis aim to identify countries that show a similar behavior from the point of view of the use of RE. Based on them, policies and strategies at the EU level can be founded. Keywords: renewable energy, sustainable development, European Union, data mining

An electrocatalytic iodine oxidations-based configuration for hydrogen and I2/I3− co-productions driven by the Zn-air/iodine battery

Electrochemical water splitting and energy storage are key for a sustainable energy future, despite the challenges related to undesirable overpotentials and high voltage requirements. Herein, we introduce a synergistic approach between a low overpotential hydrogen evolution reaction (HER) and a low voltage zinc-air/iodine battery (ZAIB) by coupling with iodide oxidation half reactions. By developing a Pt/Co3O4 electrocatalyst in two steps and with under 2% Pt loading, we achieve an unprecedented low full cell potential for hydrogen generation at 0.574 V, exhibiting an ultra-high reduction of energy consumption of 64.7%. The Pt/Co3O4 electrode also enables ZAIB to record a power density of 154 mW cm−2 at an ultra-low charging potential of 1.68 V. Mechanistic studies and DFT calculations of the novel electrode confirm an electron rich Pt-Co interface and favorable Pt-I interactions, facilitating both HER and IOR reactions. Our design provides critical technology for potential large-scale renewable energy projects.

Particle Swarm Optimization for multi-chiller system: Capacity configuration and load distribution

This study applies Particle Swarm Optimization (PSO) to enhance the energy efficiency of a multi-chiller system in a large office building, with a focus on optimizing capacity configuration and load distribution. Given the rising demand for sustainable energy solutions in buildings, where HVAC systems, particularly chillers, account for a significant portion of energy consumption, this research aims to reduce energy use and improve system performance. EnergyPlus simulations, based on Baltimore weather data and a reference large office model, were conducted to build a baseline dataset. This dataset was then used in Python to calculate energy consumption and optimize load distribution and capacity configuration. PSO was applied in three stages: optimizing capacity configuration using five built-in EnergyPlus algorithms, optimizing load distribution, and simultaneously optimizing both. The results showed that optimizing capacity configuration alone improved performance, even using traditional load distribution methods. Load distribution optimization outperformed other algorithms and converged at a 0.7 Part Load Ratio (PLR) for staging. The integrated PSO application achieved an 11.3 % reduction in energy consumption, a 21.5 % improvement in Coefficient of Performance (COP), and a 12.8 % increase in the Seasonal Energy Efficiency Ratio (SEER) by precise operation of 15 different combinations throughout the entire load range. These results demonstrate the potential of PSO to significantly enhance the efficiency of multi-chiller systems, providing a novel approach to optimizing both capacity configuration and load distribution. This research contributes to a robust framework for improving energy performance in building systems and offers valuable insights for future sustainable energy solutions.

ANALYSIS AND EVALUATION OF THE EFFECTIVENESS OF INNOVATIVE METHODS IN ENERGY

The global energy landscape is undergoing a transformative shift as the demand for energy continues to grow due to industrialization, urbanization, and population expansion. Traditional energy sources, particularly fossil fuels, have long dominated the energy sector, contributing to significant environmental challenges such as climate change, air pollution, and resource depletion. As a result, there is an urgent need to develop and implement innovative energy technologies that can meet growing energy demands while minimizing environmental impact. This paper presents a comprehensive analysis of contemporary innovations in the energy sector, with a focus on the integration of renewable energy sources, optimization of energy conversion processes, and the deployment of advanced energy management systems. These innovations are assessed in terms of their potential to enhance energy efficiency, sustainability, and reliability. By synthesizing the latest research findings and practical implementations, this study aims to identify key trends, challenges, and opportunities in the ongoing transition towards a more sustainable energy future. Keywords: renewable energy sources, energy efficiency, integration, energy consumption.

Parameter identification of PV solar cells and modules using bio dynamics grasshopper optimization algorithm

AbstractThe escalating global population and energy demands underscore the critical role of renewable energy sources, particularly solar power, in mitigating environmental degradation caused by traditional fossil fuels. This paper emphasizes the advantages of solar energy, especially photovoltaic (PV) systems, which have become pivotal in hybrid energy systems. However, accurate modelling and identification of PV cell parameters pose challenges, prompting the adoption of meta‐heuristic optimization algorithms. This work explores the limitations of existing algorithms and introduces a novel approach, the bio‐dynamics grasshopper optimization algorithm (BDGOA). The BDGOA addresses deficiencies in both exploration and exploitation phases, exhibiting exceptional convergence speed and efficiency. The algorithm's simplicity, achieved through the implementation of an elimination phase and controlled search space, enhances its performance without intricate calculations. The study evaluates the BDGOA by applying it to identify unknown parameters of five solar modules. The algorithm's effectiveness is demonstrated through the extraction of parameters for RTC France, PWP201, SM55, KC200GT, and SW255 models, validated against experimental data under diverse conditions. The paper concludes with insights into the impact of radiation and temperature on module parameters. The subsequent sections of the paper delve into the intricacies of the PV cell and module model, articulate the formulation of the proposed algorithm, present simulations, and analyse the obtained results. The BDGOA emerges as a promising solution, overcoming the limitations of existing algorithms and contributing significantly to the advancement of accurate and efficient PV cell parameter identification, thereby propelling progress towards a sustainable energy future.

OI url: https://doi.org/10.30574/wjarr.2024.23.3.2934

Artificial intelligence (AI) has emerged as a key enabler in optimizing renewable energy systems, significantly contributing to global efforts toward environmental sustainability. This review explores the application of AI technologies in enhancing the efficiency, reliability, and integration of renewable energy sources such as solar, wind, and hydropower. It focuses on how machine learning (ML), deep learning (DL), and other AI-driven algorithms improve energy forecasting, grid management, and storage optimization. Survey data and case studies demonstrate the potential of AI to minimize energy waste, reduce costs, and lower greenhouse gas emissions, reinforcing its role in transitioning to a sustainable energy future. The review concludes with a discussion of challenges and future research directions.

Wind power forecasting with metaheuristic-based feature selection and neural networks

Accurate forecasting of wind power generation is crucial for ensuring a stable and efficient energy supply, reducing the environmental impact of energy production, and promoting a cleaner and more sustainable energy supply. Inaccurate forecasts can lead to a mismatch between wind power generation and energy demand, resulting in wasted energy, increased emissions, and reduced grid stability. Therefore, improving the accuracy of wind power generation forecasting is essential for optimizing energy storage and grid management, reducing the reliance on fossil fuels, decreasing greenhouse gas emissions, and promoting a more sustainable energy future. This study proposes an innovative approach to enhance wind power generation forecasting accuracy by leveraging the strengths of metaheuristic algorithms for feature selection and integrating them with Neural Networks (NN). Specifically, five distinct algorithms - Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Teaching-Learning-Based Optimization (TLBO), and Evolutionary Mating Algorithm (EMA) - are integrated with NN model to identify optimal feature subsets from a comprehensive dataset of 18 diverse features. The results show that the GA consistently outperforms other algorithms in selecting the most influential features, leading to improved precision in wind power predictions. Notably, the GA achieves the best root mean square error (RMSE) of 37.1837 and the best mean absolute error (MAE) of 18.6313, outperforming the other algorithms and demonstrating the importance of feature selection in improving the accuracy of wind power forecasting. This innovative framework advances the field of renewable energy forecasting and provides valuable insights into optimizing feature sets for improved predictions across diverse domains.

Designing TaC Virtual Substrates for Vertical AlxGa1−xN Power Electronics Devices

Power electronics are critical for a sustainable energy future, playing a key role in electrification and integration of renewable energy sources into the grid. Advances in ultrawide band gap materials are needed to handle higher powers in smaller form factors while reducing electrical and thermal losses. High Al content AlxGa1−xN is theoretically capable of meeting these demands, but its impact in power electronics has been severely restricted by a lack of substrates that can satisfy conductivity, lattice matching, and/or thermal expansion requirements. We demonstrate that electrically conductive TaC can be used as a virtual substrate for AlxGa1−xN heteroepitaxy. Scaleably sputtered TaC grown on Al2O3, followed by high-temperature face-to-face annealing, produces a thin film TaC template with an effective hexagonal lattice constant matched to Al0.70Ga0.30N. Annealing of the TaC promotes recrystallization, significantly improving crystallinity and reducing crystalline defects from as-deposited columnar grains to a step-and-terrace surface morphology, enabling the subsequent growth of high-quality Al0.70Ga0.30N by molecular beam epitaxy. X-ray diffraction and scanning transmission electron microscopy confirm that the AlxGa1−xN layer is heteroepitaxially aligned, strain-free, and lattice-matched, transitioning abruptly from TaC to AlxGa1−xN without intermediate phases. These results demonstrate TaC virtual substrates as electrically conductive, lattice-matched, and thermally compatible templates for vertical AlxGa1−xN devices that can meet the growing power needs of a sustainable energy future. Published by the American Physical Society 2024

Ni/V-MgnHm nanoclusters: Recent advances toward improving the dehydrogenation thermodynamics for efficient hydrogen storage

Understanding the properties of solid-state materials at the nanoscale is a crucial scientific endeavor in the pursuit of a sustainable energy future. Since magnesium hydride is the most practical material for hydrogen storage, research is still ongoing to reduce the enthalpy for dehydrogenation. In this computational theory, we systematically investigated the enthalpy of dehydrogenation of MgnHm nanoclusters of different sizes (0.5–2.8 nm) through density functional theory (DFT) and ab-initio molecular dynamic (AIMD) simulations. Our results revealed that increasing the MgnHm nanocluster size can remarkably improve the formation energy compared to the MgH2-bulk stabilization; the formation energy shifted from −65.12 kJ/mol for (0.5 nm)-MgnHm nanocluster to −51.37 kJ/mol for (2.8 nm)-MgnHm nanocluster. As compared to bulk phase, the lower stability of the nanoclusters indicates that it is much easier to desorb hydrogen from the surface. According to the effects of compressive/tensile strains, (2.8 nm)-MgnHm nanocluster does not improve the dehydrogenation temperature in the desired direction. Unlike metal doping, 9.10%@Ni and 10.14%@V-doped (2.8 nm)-MgnHm nanocluster exhibit formation energies of −47.27 and −46.34 kJ/mol with calculated desorption temperatures of 362.89 and 355.75 K, respectively, which ensures the room temperature applicability of this hydrogen storage material.

Catalyzing green transformation: mitigating oil price impact on CO2 emissions in Saudi Arabia via renewable energy transition

AbstractExploring the relationship between international oil prices, income, and carbon dioxide (CO2) emissions in Saudi Arabia, this study examines if renewable energy consumption plays a lowering tool in international oil prices' impact on CO2 emissions, employing conventional econometric methods and the functional coefficient approach. The study reveals that the interaction between renewable energy consumption and international oil prices has a negative and statistically significant impact on CO2 emissions. This emphasizes the potential for Saudi Arabia to reduce carbon emissions by prioritizing renewable energy projects. In addition, a positive and statistically significant relationship between income and CO2 emissions is found, emphasizing the need to decouple economic growth from emissions growth. Furthermore, an interesting decoupling effect between oil price elasticity of CO2 emissions and per capita GDP is noted from the early 2000s–2015. This indicates that economic growth driven by rising oil prices can be managed to mitigate environmental impact, showcasing Saudi Arabia's commitment to sustainable development. Policy recommendations involve intensifying efforts to promote renewable energy implementation, lowering fossil fuel dependence in power generation, and incentivizing emissions reduction for a more sustainable energy future.

Long-term performance analysis of a large-scale photoVoltaic plant in extreme desert conditions

This study comprehensively evaluates the performance and operational challenges of a 9 MW grid-connected photovoltaic (PV) system in Timmimoun, southern Algeria, after eight years of operation. We identified a significant linear correlation (R2 = 0.73) between increased ambient temperatures and decreased PV module efficiency, underscoring the need for effective thermal management strategies. The investigation also examines potential solutions, such as cooling mechanisms, optimization of panel orientation and tilt angles, and development of temperature-resistant materials. Adhering to the International Energy Agency IEC 61724 standard, our analysis reveals a yield factor of 5.04 h/day, a yield ratio of 7.04 h/day, a performance ratio of 73 %, and a capacity factor of 21 % in 2023. Over its operational lifespan, the system has generated 133.43 GW h of energy, mitigating 1.01 105 tons of CO2 emissions. This long-term study provides critical insights into the performance and reliability of PV systems in hot desert climates, offering valuable guidance for future large-scale solar installations and contributing to the transition towards a sustainable energy future.

Cylindrical Composite Structural Design for Underwater Compressed Air Energy Storage

Abstract The utilization of renewable energy sources is pivotal for future energy sustainability. However, the effective utilization of this energy in marine environments necessitates the implementation of energy storage systems to compensate for energy losses induced by intermittent power usage. Underwater compressed air energy storage(UWCAES) is a cost-effective and emission-free method for storing energy underwater. This technology has proven to be effective and viable, and it offers significant benefits in terms of energy efficiency and sustainability. In this paper, a cylindrical composite structure UWCAES tank is designed. At first, the materials and shapes of the different forms of air containers were evaluated, and the relationship between container diameter, length, and number of air containers was analyzed on the basis of the range of energy storage densities for different kinds of systems. Subsequently, the materials to be applied in the tanks were investigated and selected according to the actual working conditions of the system. Eventually, finite element models and prediction formula were developed and the influence of changes in the thicknesses of the steel reinforcement were discussed. The results demonstrated that the storage tank possesses adequate environmental resistance and load-bearing capacity, which can provide a reference for its practical engineering implementation.

Overview of Startups Developing Artificial Intelligence for the Energy Sector

The energy industry is experiencing a major change due to fast progress in artificial intelligence (AI). Startup companies in this revolution use AI technologies like Machine Learning (ML), predictive analytics, and optimization algorithms to improve energy efficiency, optimize grid management, and incorporate renewable energy sources. AI-powered solutions allow for a more accurate prediction of demand, immediate monitoring, and automated decision-making processes, significantly enhancing operational efficiency and sustainability. Through promoting a more effective energy system, these advancements play a vital role in the worldwide battle against climate change and carbon dioxide emissions. Adding to the progress of AI, quantum computing (QC) shows great potential despite being a nascent area. The collaboration of AI and QC is poised to transform the energy industry by offering unmatched computational capabilities. This blend of technologies can tackle intricate energy obstacles like enhancing power grids and enhancing battery storage, which traditional computers cannot currently handle. Combining QC with AI speeds up innovation, providing advanced solutions that improve the resilience and efficiency of energy networks. This paper discusses the latest advancements, possible effects, and upcoming paths of new companies leading in AI and QC innovations within the energy industry. Their joint responsibility is highlighted in advancing a sustainable and intelligent energy future, as well as tackling crucial environmental issues and lessening the impact of climate change.

Solid-state Batteries: Overcoming Challenges and Harnessing Opportunities for a Sustainable Energy Future

Herein we reviewed solid-state batteries (SSBs) as an emerging and promising alternative to conventional lithium-ion batteries, offering enhanced energy density, safety, and longevity. Solid-state batteries (SSBs) use a solid electrolyte instead of the liquid or gel electrolytes found in conventional lithium-ion batteries. SSBs can achieve energy densities of up to 500 Wh/kg, compared to 350 Wh/kg for conventional lithium-ion batteries. This paper explores the potential of SSBs to revolutionize grid stability and energy storage. The unique characteristics of SSBs, including their solid-state electrolytes (SSEs) and absence of liquid components, significantly mitigate safety concerns associated with traditional batteries. Moreover, SSBs exhibit higher faster charging rates, making them ideal for applications demanding high energy storage capacity and rapid response times. The paper further discusses the key challenges and advancements in SSB development, including material selection, manufacturing processes, and cost reduction. It also highlights the anode and cathode materials but also the interface challenges faced during the design and development of SSBs. By addressing the limitations of current energy storage solutions, SSBs offer a promising pathway toward a more resilient and sustainable energy future.

Green hydrogen revolution for a sustainable energy future

Green hydrogen revolution for a sustainable energy future

From Mechanism-Based Retaining Glycosidase Inhibitors to Activity-Based Glycosidase Profiling.

Activity-based protein profiling (ABPP) is an effective technology for the identification and functional annotation of enzymes in complex biological samples. ABP designs are normally directed to an enzyme active site nucleophile, and within the field of Carbohydrate-Active Enzymes (CAZymes), ABPP has been most successful for those enzymes that feature such a residue: retaining glycosidases (GHs). Several mechanism-based covalent and irreversible retaining GH inhibitors have emerged over the past sixty years. ABP designs based on these inhibitor chemistries appeared since the turn of the millennium, and we contributed to the field by designing a suite of retaining GH ABPs modeled on the structure and mode of action of the natural product, cyclophellitol. These ABPs enable the study of both exo- and endo-acting retaining GHs in human health and disease, for instance in genetic metabolic disorders in which retaining GHs are deficient. They are also finding increasing use in the study of GHs in gut microbiota and environmental microorganisms, both in the context of drug (de)toxification in the gut and that of biomass polysaccharide processing for future sustainable energy and chemistries. This account comprises the authors' view on the history of mechanism-based retaining GH inhibitor design and discovery, on how these inhibitors served as blueprints for retaining GH ABP design, and on some current and future developments on how cyclophellitol-based ABPs may drive the discovery of retaining GHs and their inhibitors.

ENGINEERING CHALLENGES AND SOLUTIONS IN SMART GRID INTEGRATION WITH ELECTRIC VEHICLES

This paper explores the critical engineering challenges and innovative solutions for successfully integrating intelligent grids and electric vehicles (EVs), emphasizing the increasing need for a resilient and adaptive electrical grid as global EV adoption accelerates. It comprehensively examines the technological, infrastructural, and regulatory obstacles that must be addressed to ensure seamless integration, focusing on advanced energy management systems, grid stability amidst fluctuating demand, and incorporating renewable energy sources. The study delves into the infrastructural requirements, including the expansion of charging networks, upgrades to transmission and distribution systems, and the implementation of vehicle-to-grid (V2G) technologies, while also analyzing the necessary regulatory and policy frameworks, stressing the importance of clear standards, incentives, and public-private collaboration. The paper offers a forward-looking perspective on overcoming current challenges by reviewing recent advancements in innovative grid technology—such as high-capacity energy storage and artificial intelligence (AI) use for predictive maintenance and load balancing. It highlights the need for interdisciplinary collaboration among engineers, policymakers, and industry leaders to develop a cohesive strategy for future energy distribution while underscoring the role of AI in optimizing grid performance, predicting energy consumption patterns, and enhancing overall efficiency. Ultimately, the paper provides a comprehensive analysis of the current state of smart grid and EV integration, offering actionable insights for stakeholders and concluding with recommendations for future research and development priorities, with a strong emphasis on continued innovation and cooperation to achieve a sustainable and resilient energy future.

Towards Energy Equity: Understanding and Addressing Multifaceted Energy Inequality

Given the pressing necessity to mitigate the consequences of climate change, it is of the utmost importance to establish a carbon-neutral society. Nevertheless, sustainability is significantly impeded by energy inequality. It is the term used to describe the unequal distribution, access, and utilization of energy resources among demographic groups, which has been further exacerbated by the pandemic and geopolitical tensions. This research aims to conceptualize and quantify energy inequality in Lithuania and compare it with EU data in order to bolster the ambitious objective of a climate-neutral Europe by 2050. This article elucidates the intricacy of energy inequality by utilizing a Lithuanian population survey and a literature review, which are supplemented by an EU macroeconomic analysis. The findings underline the necessity of locally tailored solutions and underscore the significant disparities in energy access between Lithuanian regions and demographic groups. Targeted policy measures are necessary to overcome economic, technological, and socio-political obstacles that impede progress toward a climate-neutral society. The necessity of a multidimensional approach and global cooperation is underscored by a comparative analysis of EU statistics, which reveals variable progress in addressing energy inequality. Theis research is a pertinent contribution to the discourse on sustainability and social justice, and it offers policy-makers, practitioners, and stakeholders guidance for a more inclusive and sustainable energy future.

Enhancing Smart Grid Efficiency through Machine Learning-Based Renewable Energy Optimization

Managing renewable energy in smart grids poses a significant challenge due to the inherent uncertainty and variability of energy sources like solar and wind power. To address this issue, we propose a novel approach that leverages the strengths of both Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) algorithms. Our method utilizes ELM to model and predict renewable energy generation, enabling more accurate forecasting and planning. Meanwhile, PSO optimizes the parameters of the ELM algorithm, ensuring optimal performance and efficiency. We evaluated our approach using a dataset of solar energy production and compared its performance to existing optimization techniques. The results show that our ELM-PSO approach significantly improves the accuracy of renewable energy predictions and reduces energy costs in smart grids. The implications of our research are far-reaching, as our approach can be applied to various renewable energy systems, including wind turbines, solar panels, and hydroelectric power plants. By enhancing the efficiency and reliability of renewable energy utilization, we can create a more sustainable and resilient energy future.