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When replacing equipment at its end of life, industries often have the option to select higher efficiency technologies. The choice to invest in an improved piece of equipment rather than a simple in-kind replacement is driven by many factors, including costs, performance, and familiarity with new technology options. Engineering models that account for equipment stock turnover, such as ENERGY 2020, typically assume that this decision is primarily driven by the difference in marginal costs: higher up front capital costs must be, at minimum, balanced by lower lifetime energy costs for an upgrade to be pursued. Stock turnover analysis requires detailed data inputs regarding the costs and performance of new equipment. For simplicity, a common approach is to develop assumed correlations that reflect the trade-off between marginal capital cost and efficiency and compare these to energy prices to select an efficiency level for new equipment. In this study, we present a novel method to develop such trade-off curves based on a technology-explicit approach rather than a qualitative correlation assumption. We generate trade-off curves for two common types of industrial devices: electric machine drive and natural gas steam generation for the case study of the Canadian pulp and paper sector. The curves demonstrate that the efficiency of new devices can be expected to vary significantly based on energy prices. At current energy prices, we find that newly purchased machine drive and steam generation devices would have an optimal efficiency level of 91% and 75%, respectively. [ABSTRACT FROM AUTHOR]

State-of-the-art manufacturing of tissue paper by Through Air Drying provides excellent product performance, although at a high production cost and energy use. In this work, a laboratory scale vacuum suction box was used to mimic the initial dewatering and the Through Air Drying molding, together with a pilot-scale trial. The purpose was to investigate both how the laboratory scale corresponds to pilot scale testing and investigate how fabric design, basis weight, beating, and fibers affect dewatering and sheet caliper. This study reevaluates dewatering mechanisms during molding, challenging the previous hypothesis of pure air displacement dewatering. Results show a parallel mechanism of compression dewatering and air displacement. The influence of rush transfer is examined, impacting the sheets' visual appearance, thickness, and solids content. Correlations between molding box solids content and headbox freeness emphasize significance of fibers and beating levels. Pilot results confirm the link between former solids and molding box solids. Pilot trials validate the laboratory results, facilitating comprehensive simulation of full-scale manufacturing. This research reveals dewatering mechanisms, highlights operational parameters, and enables effective Through Air Drying process design and refinement. [ABSTRACT FROM AUTHOR]

The article presents the discussion on shortage of paper in Europe causing mill shutdowns and curtailments. Topics include socio-economic factors showing inflation, transportation challenges and labor shortage, posing a significant threat to the print and paper industries; and shortage of transportation trucks and drivers taking action on the reliability of deliveries.

This paper investigates the connection between climate change and energy security in Europe and provides empirical evidence that these issues are the two faces of the same coin. Using a panel of 39 European countries during the period 1980–2020, the empirical analysis presented in this paper indicates that increasing the share of nuclear, renewables, and other nonhydrocarbon energy and improving energy efficiency could lead to a significant reduction in carbon emissions and improve energy security throughout Europe. Accordingly, policies and reforms aimed at shifting away from hydrocarbons and increasing energy efficiency in distribution and consumption are key to mitigating climate change, reducing energy dependence, and minimizing exposure to energy price volatility. [ABSTRACT FROM AUTHOR]

A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy. [ABSTRACT FROM AUTHOR]

The increasing environmental consideration and the growth of instability of the energy market call for methods that can process the organic fraction of municipal solid waste (OFMSW) into fuel instead of disposal. Due to the pyrolysis and gasification that are efficient procedures to achieve valuable products, gasification of OFMSW with different particle sizes and compositions was carried out in the presence of Ni and Ni-Ce-loaded Y-zeolite in a multizone tubular kiln reactor. During the experiments, 500°C was applied in the first reactor zone, while the 2nd zone was at an elevated temperature of 600°C or 900°C in the presence of steam. The combined pyrolysis and gasification experiments were also carried out without a catalyst with the same operating conditions. The feedstock was collected from the organic fraction of a Hungarian mechanical-biological treatment (MBT) plant and was separated into four different fractions based on particle size: <1 cm dry biomass and fine particles, 1-2 cm paper-rich, 2-6 cm paper and plastic-rich feedstocks, and <6 cm mixture of these fractions. During the experimental work, product yields; gaseous product composition; the ratio of H2/CO, CO2/CO, and CH4/CO; and lower heating value were determined in the function of feedstock composition, the applied temperature, and catalysts. It was found that the hydrogen content and H2/CO ratio of gaseous products were increased due to catalyst application and temperature elevation in the 2nd reactor zone. The addition of Ce to the Ni/Y-zeolite catalyst was advantageous in the case of hydrogen formation at a lower temperature (600°C). The hydrogen and methane content of products obtained from the catalytic pyrolysis-gasification of paper and plastic-rich OFMSWs on elevated temperatures were higher, which increased the lower heating value of those products. Based on the elemental analysis of the obtained solid residues, it was found that paper/plastic-rich feedstock released hydrogen and carbon with a higher extent. 1-2 cm feedstock-related solid residues had the highest H/C ratio which caused a 12.5-12.8 MJ/kg gross heating value. As a result, combined pyrolysis and gasification appear to be an efficient method to attain valuable outputs from OFMSW not only in gas but also in solid products. [ABSTRACT FROM AUTHOR]

Up to now, the mainstream adoption of renewable energy has brought about substantial transformations in the electricity and energy sector. This shift has garnered considerable attention within the scientific community. Supercapacitors, known for their exceptional performance metrics like good charge/discharge capability, strong power density, as well as extended cycle longevity, have gained widespread traction across various sectors, including transportation and aviation. Metal‐organic frameworks (MOFs) with unique traits including adaptable structure, highly customizable synthetic methods, and high specific surface area, have emerged as strong candidates for electrode materials. For enhancing the performance, MOFs are commonly compounded with other conducting materials to increase capacitance. This paper provides a detailed analysis of various common preparation strategies and characteristics of MOFs. It summarizes the recent application of MOFs and their derivatives as supercapacitor electrodes alongside other carbon materials, metal compounds, and conductive polymers. Additionally, the challenges encountered by MOFs in the realm of supercapacitor applications are thoroughly discussed. Compared to previous reviews, the content of this paper is more comprehensive, offering readers a deeper understanding of the diverse applications of MOFs. Furthermore, it provides valuable suggestions and guidance for future progress and development in the field of MOFs. [ABSTRACT FROM AUTHOR]

In advancing the transition of the energy sector toward heightened sustainability and environmental friendliness, biopolymers have emerged as key elements in the construction of triboelectric nanogenerators (TENGs) due to their renewable sources and excellent biodegradability. The development of these TENG devices is of significant importance to the next generation of renewable and sustainable energy technologies based on carbon-neutral materials. This paper introduces the working principles, material sources, and wide-ranging applications of biopolymer-based triboelectric nanogenerators (BP-TENGs). It focuses on the various categories of biopolymers, ranging from natural sources to microbial and chemical synthesis, showcasing their significant potential in enhancing TENG performance and expanding their application scope, while emphasizing their notable advantages in biocompatibility and environmental sustainability. To gain deeper insights into future trends, we discuss the practical applications of BP-TENG in different fields, categorizing them into energy harvesting, healthcare, and environmental monitoring. Finally, the paper reveals the shortcomings, challenges, and possible solutions of BP-TENG, aiming to promote the advancement and application of biopolymer-based TENG technology. We hope this review will inspire the further development of BP-TENG towards more efficient energy conversion and broader applications. [ABSTRACT FROM AUTHOR]

This study explored the association between renewable energy uptake and digitalization in the sector by reviewing relevant literature (published 2010–2022), with the aim of identifying the existing utilization of digital technologies within the sector, challenges to adoption, and future prospects. Different search engines (SCOPUS, Web of Science, and Google Scholar) were used to locate relevant papers and documents. The results revealed the high significance of digital technologies in supporting the renewable energy sector, with high costs and security risks representing the key challenges. Most papers reviewed had a positive outlook, but recommended further research and development for effective energy transition and resilient infrastructure. The current drivers of the integration of digital technologies to support the diffusion of renewable energy sources appear to extend beyond energy demand and involve many aspects of sustainability and sustainable development. Compared with previous reviews, this work has unique scope and novelty since it considers the bigger picture of the coupling between digitalization and the renewable energy sector, with a greater focus on critical areas in these two interconnected bodies that need to be addressed. The relatively small sample of relevant papers (69 from 836 hits) located in the literature review confirms the need for more research covering the subject in greater depth. [ABSTRACT FROM AUTHOR]

This paper examines the persistence of energy prices in international markets and its implications for market participants and policymakers. Using the ARFIMA model and log-periodogram regression technique, we estimate the long memory parameter in energy price indices. Our findings confirm the persistence in energy prices, indicating that past price movements have a lasting impact on future prices. For market participants, understanding the persistence of energy prices is crucial for developing effective trading strategies and risk management measures. The long-lasting effects of past price movements suggest the need for preparedness in the face of extended periods of price volatility. Policymakers can also benefit from this knowledge by implementing measures to stabilize energy markets and mitigate the impacts of price shocks. Furthermore, our study highlights the importance of incorporating the persistence of shocks in energy price forecasting models. By accounting for the longlasting effects of past price movements, these models can improve the accuracy of price forecasts and aid in decision-making for market participants. While our study provides valuable insights into the persistence of energy prices, further research is needed to investigate the root causes of persistence and its implications in a rapidly changing global economic environment. Additionally, exploring the individual factors that influence energy price indices can enhance our understanding of their dynamics and improve prediction accuracy. Overall, our findings contribute to the existing literature by confirming the presence of persistence in energy prices and providing valuable insights for market participants and policymakers. Further research in this area can offer more pragmatic advice and enhance decision-making in the energy markets. [ABSTRACT FROM AUTHOR]

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts aimed at better understanding the past, present, and future of climate changes in a multi-model context. A total of 21 model intercomparison projects (MIPs) were endorsed in its sixth phase (CMIP6), which included 190 different experiments that were used to simulate 40 000 years and produced around 40 PB of data in total. This paper presents the main findings obtained from the CPMIP (the Computational Performance Model Intercomparison Project), a collection of a common set of metrics, specifically designed for assessing climate model performance. These metrics were exclusively collected from the production runs of experiments used in CMIP6 and primarily from institutions within the IS-ENES3 consortium. The document presents the full set of CPMIP metrics per institution and experiment, including a detailed analysis and discussion of each of the measurements. During the analysis, we found a positive correlation between the core hours needed, the complexity of the models, and the resolution used. Likewise, we show that between 5 %–15 % of the execution cost is spent in the coupling between independent components, and it only gets worse by increasing the number of resources. From the data, it is clear that queue times have a great impact on the actual speed achieved and have a huge variability across different institutions, ranging from none to up to 78 % execution overhead. Furthermore, our evaluation shows that the estimated carbon footprint of running such big simulations within the IS-ENES3 consortium is 1692 t of CO 2 equivalent. As a result of the collection, we contribute to the creation of a comprehensive database for future community reference, establishing a benchmark for evaluation and facilitating the multi-model, multi-platform comparisons crucial for understanding climate modelling performance. Given the diverse range of applications, configurations, and hardware utilised, further work is required for the standardisation and formulation of general rules. The paper concludes with recommendations for future exercises aimed at addressing the encountered challenges which will facilitate more collections of a similar nature. [ABSTRACT FROM AUTHOR]

Effective forecasting of energy consumption structure is vital for China to reach its "dual carbon" objective. However, little attention has been paid to existing studies on the holistic nature and internal properties of energy consumption structure. Therefore, this paper incorporates the theory of compositional data into the study of energy consumption structure, which not only takes into account the specificity of the internal features of the structure, but also digs deeper into the relative information. Meanwhile, based on the minimization theory of squares of the Aitchison distance in the compositional data, a combined model based on the three single models, namely the metabolism grey model (MGM), back-propagation neural network (BPNN) model, and autoregressive integrated moving average (ARIMA) model, is structured in this paper. The forecast results of the energy consumption structure in 2023–2040 indicate that the future energy consumption structure of China will evolve towards a more diversified pattern, but the proportion of natural gas and non-fossil energy has yet to meet the policy goals set by the government. This paper not only suggests that compositional data from joint prediction models have a high applicability value in the energy sector, but also has some theoretical significance for adapting and improving the energy consumption structure in China. [ABSTRACT FROM AUTHOR]

Planning maintenance of medium voltage overhead lines is one of the most important issues in the studies on power distribution network, which will prevent and reduce unwanted interruption. In this paper, long‐term maintenance of medium voltage networks was planned by multi‐objective function, including an extended mixed‐integer linear model to optimize costs, energy not supplied (ENS), and average interruption duration index (SAIDI). In addition, the uncertainty about the annual growth rate of the load, the increase in the cost of goods and services and the increase in the selling price of energy as well as various constraints are all included in the desired objective function, which is one of the main innovations of this paper compared to other published studies. To apply the uncertainties, information gap decision theory (IGDT) has been used, and to solve the objective functions, LP‐Metric method has been used. The proposed method was implemented on the standard 11‐bus RBTS network by MATLAB and GAMS. The results showed that three different long‐term maintenance plans proposed here lead to the optimization of the annual maintenance costs of network, reduction in ENS and interruption, and increase in the reliability of the network based on the uncertainty of each feeder. [ABSTRACT FROM AUTHOR]

This paper provides a legal analysis of the direct line in the legislation of the European Union. The direct line, serving as a legal instrument to implement off-grid solutions, plays a crucial role in amplifying competition within the energy market. Compared to other instruments introduced by the First Liberalization Package in 1992, the direct line is not a subject commonly brought about by legal literature. The core part of legal analyses is divided into two parts, which focus on different approaches of direct line, i.e., European Union and selected Member States (Estonia, Latvia, Lithuania, and Poland). The motives of the Union in establishing the direct line instrument was to support opening the energy market to increase energy generating units. During the transposition of the direct line into national laws, analysed countries have directed the usage of this mechanism to promote renewable energy sources. The presented case studies create a rare pattern of how the Member States form their own interpretations and hence use the Union law in practice, which differs from the direction established in the Union forum. The research focused on legal dogmatic examining of regulations at both European and national levels, with a particular emphasis on the definition part while figuring a crucial role in shaping the direct line concept. The paper also presents other convoluted legal schemes, such as the permit issuance process for direct line construction. The conclusions assess the modifications in the European Union regime, but also their impact on electricity prices and green investments. [ABSTRACT FROM AUTHOR]

This paper presents a systematic literature review on the application of digital twins in the energy sector. Initially, we generated an overview through a survey of prior reviews, independent of market vertical, then followed by a more detailed review concentrating on the power production and distribution domains, as per the NIST (National Institute of Standards and Technology) smart grid standard. We implemented a rigorous method, which included seven stages, beginning with the collection of 2238 articles. We observed that the energy sector range was too broad and filtered by generation and distribution during the practical screening, resulting in 275 for further screening. This amount was then condensed to 81 papers that matched the quality screening criteria for synthesis and examination. In summary, digital twin architectures and frameworks include five components: the physical entity, bidirectional communication, the virtual entity (with modeling and simulation), data management, and services. Our study contributed by determining that distribution management is the most pertinent application of digital twins in the distribution domain and fault diagnosis in the generation domain. Furthermore, we found that digital twins involve multiple stakeholders whose role is rarely discussed in studies, and we identified a similar absence of emphasis for security. Research on security often presents the digital twin as an additional layer of protection, yet rarely investigates the security of the digital twin by itself. The potential limitations of our study to answer some of the technical research questions may be because of the criteria for the selection of papers. However, as the emphasis of this study is on the energy sector, it enabled domain-specific findings for generation and distribution. [ABSTRACT FROM AUTHOR]

This paper presents a detailed review of the existing literature on peer-to-peer (P2P) energy trading considering market architectures, trading strategies, and enabling technologies. P2P energy trading enables individual users in the electricity network to act as sellers or buyers and trade energy among each other. To facilitate the discussion on different aspects of P2P energy trading, this paper focuses on P2P market mechanisms, relevant bidding strategies, and auction models. In addition, to solve the energy management problems associated with P2P energy trading, this paper investigates widely used solution methods such as game-theoretic models, mathematical optimisation, as well as more recent machine learning techniques and evaluates them in a critical manner. The outcomes of this investigation along with the identification of the challenges and limitations will allow researchers to find suitable P2P energy trading mechanisms based on different market contexts. Moreover, the discussions on potential future research directions are expected to improve the effectiveness of P2P energy trading technologies. [ABSTRACT FROM AUTHOR]

The emerging energy transition is particularly described as a move towards a cleaner, lower-carbon system. In the context of the global shift towards sustainable energy sources, this paper reviews the potential and roadmap for hydrogen energy as a crucial component of the clean energy landscape. The primary objective is to present a comprehensive literature overview, illuminating key themes, trends, and research gaps in the scientific discourse concerning hydrogen production and energy policy. This review focuses particularly on specified geographic contexts, with an emphasis on understanding the unique energy policies related to renewable energy in Brazil, Austria, and Germany. Given their distinct social systems and developmental stages, this paper aims to delineate the nuanced approaches these countries adopt in their pursuit of renewable energy and the integration of hydrogen within their energy frameworks. Brazil exhibits vast renewable energy potential, particularly in wind and solar energy sectors, positioning itself for substantial growth in the coming years. Germany showcases a regulatory framework that promotes innovation and technological expansion, reflecting its highly developed social system and commitment to transitioning away from fossil fuels. Austria demonstrates dedication to decarbonization, particularly through the exploration of biomethane for residential heating and cooling. [ABSTRACT FROM AUTHOR]

Due to concerns with air pollution and climate change, governments and transport operators around the world have engaged in transforming their fossil-fueled vehicles into electric vehicles (EVs). It is essential to build a model for the electrifying process to minimize the operation costs. This paper presents a systematic analytical approach for the electrification of a fire ambulance service station. This approach begins with the selection of suitable EVs to replace the current service vehicles. Subsequently, an in-depth analysis is conducted to determine the practical utilization of EVs at the station. The model proposes two charging strategies: immediate charging upon an EVs' return and smart charging. Based on the chosen EVs and charging strategies, a comprehensive assessment of the load profiles for the planned EV charging station is performed. In accordance with the load profiles, a mathematical model to minimize the infrastructure and operation costs of the charging station is proposed. Various pricing schemes are compared to identify the most efficient pricing scheme for the charging station, and economic analyses of the EVs and traditional ambulance vehicles are proposed in this paper. The test results indicate that the progressive pricing scheme is well suited for immediate charging strategies, whereas smart charging should opt for the time-of-use pricing scheme. Selecting the appropriate pricing scheme has the potential to significantly reduce electric energy costs. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on the impacts of peer-to-peer (P2P) energy markets on distribution systems, specifically focusing on voltage, power loss, and congestion. While P2P energy markets create opportunities for direct trading between prosumers and consumers, ensuring compliance with distribution system constraints remains a challenge. This paper proposes an iterative method and graphical interpretation in order to assess complex interactions, addressing the persistent issue of network constraints. Additionally, this paper proposes a method to determine distribution locational marginal prices (DLMPs) for real-time traditional energy markets. This ensures effective coordination among sellers, buyers, and the distribution system operator. The proposed method aims to prevent negative impacts on distribution system operation via the determination of the allowable maximum trading power (MTP), ensuring empirical validity and practical implementation via operating conditions and forecast errors, thus distinguishing it from prior studies. This paper also establishes a model for P2P energy market interactions, utilizing linear estimations for efficient DLMP updates. The contributions of this paper enhance the understanding and operation of P2P energy markets, and is supported by simulation results validating the proposed method. [ABSTRACT FROM AUTHOR]

Effective energy futures price prediction is an important work in the energy market. However, the existing research on the application of "decomposition-prediction" framework still has shortcomings in noise processing and signal reconstruction. In view of this, this paper first uses PSO to optimize VMD to improve the effectiveness of single decomposition, and further uses SGMD to capture the remaining key information after extracting low-frequency modal components by using PSO-VMD technology. Further, combined with LSTM to predict each component, a new PSO-VMD-SGMD-LSTM hybrid model is innovatively constructed. The empirical research results based on the real energy market transaction price show that compared with the benchmark model, the hybrid model proposed in this paper has obvious forecasting advantages in different forecasting scenarios. [ABSTRACT FROM AUTHOR]

This paper reviews evidence on how the fast growth in renewable energy technologies can trigger social tipping dynamics that potentially accelerate a system-wide energy transition. It does so by reviewing a variety of literature across several disciplines addressing socio-technical dimensions of energy transitions. The tipping dynamics in wind and solar power create potential for cascading effects to energy demand sectors, including household energy demand. These most likely start with shift actions and adoption of household-scale batteries and heat pumps. Key enablers are strong regulations incentivising reductions in demand and setting minimum efficiency levels for buildings and appliances. While there is evidence of spillovers to more environmentally friendly behaviour, the extent of these and the key leverage points to bring them about present a knowledge gap. Moreover, these behavioural feedback loops require strong additional policy support to "make them stick". Understanding the economic and social tipping dynamics in a system can empower decision-makers, fostering realistic energy transition policies. This paper highlights energy communities as a promising niche for leveraging tipping dynamics. Ultimately, bridging the gap between these tipping dynamics and institutional reforms is crucial for unlocking the full potential of sustainable energy systems. [ABSTRACT FROM AUTHOR]

Offshore wind energy is considered as one of the most promising resources of clean and renewable energy to replace fossil fuels. Additionally, its cost is expected to be lower than onshore wind energy as the technology matures. Offshore wind turbines (OWTs) normally operate in harsh ocean environments, which could impact their structural integrity. Scour erosion around foundations of OWTs can substantially change the overall stiffness of these structures and shorten their lifetime. Currently, there are a limited number of studies on countermeasures and their engineering requirements for decreasing the scouring effect; this is due to their different hydraulic circumstances, such as their stability, reliability, and resistance capacity. To this end, advancements in scour protection measures in the offshore energy sector are evaluated in this paper through a thorough and critical review following the PRISMA systematic literature mapping approach. This includes 68 papers on scour protection and over 30 scour protection designs for various types of wind turbine foundations. Here, we aim to provide an overview of the latest scouring protection measures and their comprehensive assessment, as well as their prospects and future challenges. The findings of this study will provide key insights into scour protection measures for OWTs and will subsequently contribute to the future growth of the offshore renewable energy sector. [ABSTRACT FROM AUTHOR]

The global energy crisis, which began in 2021 due to the extraordinary economic recovery after the pandemic and intensified after Russia's invasion of Ukraine in February 2022, has changed the conditions of energy management, paying more attention to energy efficiency. Natural gas prices have reached record levels and, consequently, so have electricity prices in some markets. Oil prices have reached their highest level since 2008. Higher energy prices have contributed to sharply increased inflation. Households are again becoming interested in buying coal as a source of heat. High energy and gas prices have pushed many families into poverty and forced some factories to cut production or even close. They have also slowed economic growth to the point where some countries are heading for a serious recession. Paradoxically, the negative effects of the energy crisis may accelerate the introduction of cleaner, sustainable, renewable energy such as wind and solar energy. The energy crisis is comparable to the oil crisis of the 1970s, when it contributed to significant advances in energy efficiency. The current crisis has highlighted the importance of investments in renewable energy resources and initiated the process of integrating regional markets, developing energy efficiency and promoting renewable energies. The aim of this article is to comprehensively explore the complex relationship between energy awareness, consumption patterns, and energy efficiency, with a focus on both individual consumers and industries, during the global energy crisis. This paper is based on a literature review, overarching policy documents, energy reports, and other secondary documents. The primary research method was the systematic literature review method, based on which the impact of the global energy crisis on energy efficiency was evaluated. This study emphasizes the diverse influences on energy awareness, ranging from economic factors to consumer preferences and environmental consciousness. The findings of the paper underscore the significant responsibility of industries in contributing to energy-saving efforts and the active role of consumers in the energy market. The responsibility of industries in contributing to energy efficiency is highlighted, with a call for a comprehensive approach that integrates energy-saving criteria into product development and corporate social responsibility. [ABSTRACT FROM AUTHOR]

Before the COVID-19 pandemic, ventilation in buildings was not always given its due importance. The World Health Organization has highlighted the important role of air exchange with the outdoors in improving the air quality in buildings; buildings should, therefore, be equipped with mechanical ventilation or adequate air conditioning systems. This paper aims to investigate different retrofit solutions for air conditioning, evaluating them in terms of energy consumption and cost and the impact of increased outdoor air exchange rates on countering the propagation of COVID-19; the latter is the main novelty of the paper. As a case study, we take an existing office building located in Central Italy that was previously not equipped with a mechanical ventilation system (a system with primary air was introduced during the study). The energy analysis was conducted using dynamic simulation software after validation through energy bills; energy and economic analyses were conducted considering different external-air exchange rates. An optimal number of outdoor air changes was found to mitigate the risk of COVID-19 infection, a finding in line with the international literature. The increase in air changes with outdoor air leads to a rise in energy consumption and costs. These values were evaluated for different air conditioning systems and operational schedules. These drawbacks can be made less significant by combining interventions in the system with energy-efficiency measures applied to the building envelope. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) play a crucial role in the global transition towards decarbonization and renewable energy resources (RERs). As EVs gain popularity, this has resulted in various challenges for the power grid, such as an intensified peak-to-valley load differential, causing transformer overloading. Vehicle-to-grid (V2G) technology has emerged as a promising solution due to its controllable charging and discharging capabilities. Mature business schemes can incentivize the development of V2G technology. However, the business schemes of V2G technology are still unclear. Therefore, this paper provides a comprehensive review of the business schemes associated with V2G technology, especially focusing on its feasibility and challenges with respect to the electricity market. In this paper, several business schemes with respect to the electricity market are explored by conducting extensive literature reviews, including peak-to-valley arbitrage, the spot market, demand–response (DR), frequency regulation, voltage regulation, spinning reserve, and black start. Next, application scenarios and real-world use cases of the V2G technology's business schemes are investigated. Furthermore, the challenges faced by the V2G technology's business schemes are assessed by considering the technical, economical, and social aspects. By identifying these challenges, it is important to highlight the existing shortcomings and areas of interest for V2G technology's research and development. This review contributes to a deeper understanding of V2G technology and its implications for the energy sector. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RESs) offer key transformative potential from a societal point of view due to their modularity and ability to generate energy at the local level, allowing for the development of grassroots democratic and participatory initiatives. The paper aims to share insights into the processes of creating RES cooperatives in Poland. One of the first cooperatives to be established in the Upper Silesian region in Poland was the energy cooperative (EC) "Our Energy". This study presents an in-depth empirical analysis of a community-based renewable energy cooperative. The study employed a case study methodology, including a SWOT analysis framework, to describe the research subject and identify its strengths, weaknesses, opportunities, and threats. Key findings indicate that members benefit from stable energy prices and full recovery of the energy produced, and the cooperative is at the forefront of energy-sharing practices that minimize costs through direct transactions with the local municipality. The strategic goals of the EC focus on expanding membership, increasing the number of photovoltaic installations, implementing energy balancing, combating energy poverty, and reducing emissions. Challenges such as financial constraints and a lack of real-time monitoring of energy distribution are acknowledged, and carbon footprint reduction innovations and stakeholder engagement are highlighted as forward-looking approaches. The study highlights the role of cooperatives as a model for community-led sustainable energy initiatives. However, the study acknowledges the limitations of its small sample size, suggesting the need for broader research to understand the impact of collaborative energy on decarbonization. Future research directions are proposed, focusing on the long-term sustainability and socioeconomic impacts of energy cooperatives. This study contributes to the scholarly discourse on renewable energy cooperatives by offering insight into their potential to bridge the gap between energy producers and consumers and support sustainable community development. The main novelty of this paper lies in its detailed examination of a specific renewable energy cooperative, incorporating SWOT analysis, stakeholder perspectives, quantitative assessments, and a forward-thinking approach. This multifaceted analysis contributes to the existing literature on renewable energy initiatives, providing a valuable reference for researchers, policymakers, and practitioners in the field. [ABSTRACT FROM AUTHOR]

This paper examines the use of an Uninterruptible Power Supply (UPS) to enhance the operational efficiency of data centers. It focuses on developing an optimal energy scheduling strategy for a data center equipped with UPS, using the Markov decision process (MDP) framework. The MDP framework simulates the decision-making process involved in minimizing energy costs. Each unit's available power output in the data center is treated as a Markov state, taking into account the uncertainty associated with renewable distributed generation. This uncertainty drives the system to transition to other Markov states in subsequent decision times. A recursive optimization model is established for each Markov state at each decision time to guide state-based operations, which includes determining the unit output while considering both current and future costs. The challenge of dealing with high dimensionality, arising from a substantial number of states and actions in the model, is effectively addressed by adopting an approximate dynamic programming (ADP) method. This approach incorporates decision-state and forwards dynamic algorithms to tackle the complexity of the MDP-based model. By employing ADP, the computational burden is reduced, enabling efficient and practical solutions to be obtained. [ABSTRACT FROM AUTHOR]

With the growth of the cloud computing market, the number and scale of cloud data centers are expanding rapidly. While cloud data centers provide a large amount of computing power, generating tremendous energy consumption has become a fundamental issue in the financial and environmental fields. Improving quality of service and reducing energy costs are fundamental challenges for next-generation cloud data centers. Task scheduling in cloud data centers grows increasingly complex due to the heterogeneity of computing resources, intricate dependencies of jobs and rising expenses resulting from high energy consumption. Efficiently utilizing computing resources is crucial, so it is necessary to develop optimal strategies for job scheduling. This paper proposes a reinforcement learning-based task scheduler (E2DSched) for online scheduling of randomly arriving directed acyclic graph jobs in cloud data centers. E2DSched divides the scheduling process into three layers: task selection layer, server selection layer and frequency control layer. It achieves joint optimization of energy consumption and quality of service through three-layer cooperation. Finally, we compare E2DSched with various other algorithms, and the results show that E2DSched can provide excellent service with less energy consumption. [ABSTRACT FROM AUTHOR]

The paper describes a new method for determining the energy efficiency of the life cycles of wind farms by the aggregated data of energy costs. The rationale for the use of aggregated data to determine energy costs during the life cycle of the wind farms is given. The classification of wind turbines and wind farms elements by the parameters and technical characteristics of the elements with subsequent division into groups for which aggregated data of energy costs are determined is given. Within the framework of the method have been developed an algorithm for determining energy costs on the production of elements of wind turbines and wind farms and formulas for their calculation during the life cycle of wind farms. In order to test the method, energy cost was calculated during the life cycle for two wind farms with wind turbines that differ parameters and technical characteristics of the elements and their energy efficiency was determined. It has been shown that the use of a hydrogen storage unit as part of a wind turbine makes it possible to efficiently use energy during down periods and increase the efficiency of the installation by 25–30%. It is noteworthy that a wind-hydrogen farm allows not only to accumulate excess energy during lean periods, but also to save the resource of wind turbines. When the energy reserve in the hydrogen battery reaches close to full, part of the wind turbines of the wind-hydrogen farm can be automatically temporarily stopped in a given order. This technology can provide an extension of the warranty service life for each wind turbine. [ABSTRACT FROM AUTHOR]

The report of the 20th National Congress of the Communist Party of China pointed out that it is necessary to accelerate the construction of digital China, and actively develop equipment intelligence, digitalization, and high‐end equipment manufacturing. With the construction of a new round of digital China, the safety of digital technology and high‐end equipment is particularly important for power grid. Transformer is an important link of power grid operation. The destructive thermal failure of transformer has become a hot research issue in the power industry. At the same time, there are more and more power electronic equipment in the power grid, which makes the regulation and control more and more complicated. Taking a 1000 kVA oil‐immersed transformer as an example, the magnetic, current, and thermal multi field coupling numerical analysis method is first used to simulate and analyse the operation status of the transformer. By comparing the simulation results with the monitoring data results, the error of the results is controlled within 5%. Finally, the multi‐state characteristic parameters of the transformer are monitored through sensors, and the numerical simulation analysis results are integrated with the state monitoring results to build a transformer thermal life loss assessment system. The method in this paper can evaluate and analyse the running state of transformer in real time, which is of great significance for the power company to formulate the treatment measures. [ABSTRACT FROM AUTHOR]

Energy is vital to international trade, social security, and financial markets. Crude oil, as a non-renewable resource, is affected by complex factors. To better capture this influence, we introduce stochastic differential equations (SDEs) to depict crude oil prices. This paper establishes time-dependent linear and space-dependent nonlinear SDEs respectively. Time-dependent linear SDEs are established by recovering the drift and diffusion terms based on point estimation and sliding window. Space-dependent nonlinear SDEs are established by sparse Bayesian learning. Empirical results show that time-dependent linear SDE can better describe the actual fluctuations of historical crude oil prices, and can predict more accurately compared with constant coefficient linear SDE, namely geometric Brownian motion. The space-dependent nonlinear SDE can achieve the effect of time-dependent linear SDEs on historical data, while the former is more accurate in predicting. In addition, the former gained an in-depth understanding of the intrinsic dynamics. In summary, the models proposed in this study provide powerful tools for better understanding and predicting crude oil prices, which make a positive impact on the stability of the global economy and energy markets. [ABSTRACT FROM AUTHOR]

Institutions have significant implications for whether and how energy systems restructure, evolve, and successfully transition. Yet, literature analyzing energy sector reforms often approach transitions from economic or technical perspectives, with much less attention to the underlying roles and influences of institutions. This paper explores the roles and influence of institutions on the speed, direction, timing, and sequence of energy transitions. A conceptual framework integrating the hierarchy of institutions with an historical institutionalist approach is developed and applied to explore transitions in Norway's electricity sector as a case study. Results show that conversion followed by layering emerge as the dominant modes of institutional change in Norway's electricity sector reform, illustrating the importance of alignment between institutions in creating the conditions for large‐scale energy transitions and the importance of boundaries to maintain alignment between levels of institutions. Governments can minimize potential gaps between transition intentions and outcomes through effective conversion and layering of institutional arrangements, but layering challenges emerge when institutional change introduces new actors or energy arenas to existing policy paradigms. [ABSTRACT FROM AUTHOR]

Day-ahead electricity price forecasting (DAEPF) holds critical significance for stakeholders in energy markets, particularly in areas with large amounts of renewable energy sources (RES) integration. In Japan, the proliferation of RES has led to instances wherein day-ahead electricity prices drop to nearly zero JPY/kWh during peak RES production periods, substantially affecting transactions between electricity retailers and consumers. This paper introduces an innovative DAEPF framework employing a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model designed to predict day-ahead electricity prices in the Kyushu area of Japan. To mitigate the inherent uncertainties associated with neural networks, a novel ensemble learning approach is implemented to bolster the DAEPF model's robustness and prediction accuracy. The CNN–LSTM model is verified to outperform a standalone LSTM model in both prediction accuracy and computation time. Additionally, applying a natural logarithm transformation to the target day-ahead electricity price as a pre-processing technique has proven necessary for higher prediction accuracy. A novel "policy-versus-policy" strategy is proposed to address the prediction problem of the zero prices, halving the computation time of the traditional two-stage method. The efficacy of incorporating a suite of multimodal features: areal day-ahead electricity price, day-ahead system electricity price, areal actual power generation, areal meteorological forecasts, calendar forecasts, alongside the rolling features of areal day-ahead electricity price, as explanatory variables to significantly enhance DAEPF accuracy has been validated. With the full integration of the proposed features, the CNN–LSTM ensemble model achieves its highest accuracy, reaching performance metrics of R 2 , MAE, and RMSE of 0.787, 1.936 JPY/kWh, and 2.630 JPY/kWh, respectively, during the test range from 1 March 2023 to 31 March 2023, underscoring the advantages of a comprehensive, multi-dimensional approach to DAEPF. [ABSTRACT FROM AUTHOR]

The non-technical losses caused by abnormal power consumption behavior of power users seriously affect the revenue of power companies and the quality of power supply. To assist electric power companies in improving the efficiency of power consumption audit and regulating the power consumption behavior of users, this paper proposes a power consumption anomaly detection method named High-LowDAAE (Autoencoder model for dual adversarial training of high low-level temporal features). High-LowDAAE adds an extra "discriminator" named AE3 to USAD (UnSupervised Anomaly Detection on Multivariate Time Series), which performs the same function as AE2 in USAD. AE3 performs the same function as AE2 in USAD, i.e., it is trained against AE1 to enhance its ability to reconstruct average data. However, AE3 differs from AE2 because the two "discriminators" correspond to the high-level and low-level time series features output from the shared encoder network. By utilizing different levels of temporal features to reconstruct the data and conducting adversarial training, AE1 can reconstruct the time-series data more efficiently, thus improving the accuracy of detecting abnormal electricity usage. In addition, to enhance the model's feature extraction ability for time-series data, the self-encoder is constructed with a long short-term memory (LSTM) network, and the fully connected layer in the USAD model is no longer used. This modification improves the extraction of temporal features and provides richer hidden features for the adversarial training of the dual "discriminators". Finally, the ablation and comparison experiments are conducted using accurate electricity consumption data from users, and the results show that the proposed method has higher accuracy. [ABSTRACT FROM AUTHOR]

This research paper explores the relationship between green resources, product recycling, CO2 emission, and clean environment, using time series data spanning from 1990 to 2021. The sample of the study was the green energy sector of Pakistan and clean energy was taken as the dependent variable while green energy resources, product recycling, and CO2 emissions were chosen as independent variables. Correlation matrix and multiple regression analysis were applied to determine the relationship between green energy resources, product recycling, CO2 emissions, and clean environment. The results of the study show that there is significant positive relationship between green energy resources and product recycling with clean environment but negative association between CO2 emissions and clean environment. These results provide valuable insight for policy makers to focus on development of green energy resources and encourage products recycling as an effective strategy to mitigate the depletion of natural resources and improvement of clean environment to attain sustainability in the energy sector. [ABSTRACT FROM AUTHOR]

The rapid deployment of electric vehicles (EVs) in Jordan requires massive efforts to prepare the infrastructures that serve the transportation sector. The lack of EV charging stations is the major obstacle that faces EV drivers. Utilizing renewable energy in EV charging stations contributes to the spread of these stations. Renewable energy technologies are environmentally friendly as they mitigate greenhouse gases that cause the global warming phenomenon. In this paper, an EV charging station, based on a PV-biodiesel-battery hybrid system, is investigated. The importance of this article is to discuss the hybrid system of PV and waste vegetable oil (WVO) along with storage that applies the maximum reliability supply, having an environmentally friendly supply and achieving the lowest energy cost of charging. This station is designed based on renewable and WVO utilization. In fact, WVO, coming from restaurants, is exploited to produce electricity by a diesel generator through direct burning after converting it into biodiesel. The capacity of each station is 14 cars/day with a medium-speed charger of 7 kW. The system is simulated and optimized using iHOGA software where multiobjective optimization is applied to achieve the minimum net present cost (NPC) and CO2 emissions considering three cases, PV-diesel, PV-biodiesel, and PV-WVO, all with battery-hybrid system. These values for the EV charging station that uses the PV-biodiesel-battery hybrid system are 624408 € and 15.4 tons/year, respectively. The corresponding values are 781473 € and 15.14 tons/year and 615310 € and 18.84 tons/year for the systems that work with diesel and WVO, respectively, and energy cost is achieved in best solution to be 0.11 Euro/kWh. Simulation results show that the proposed technique led to enhanced operational efficiency in terms of both NPC and annual CO2 emissions. [ABSTRACT FROM AUTHOR]

Energy markets are crucial for integrating Distributed Energy Resources (DER) into modern power grids. However, this integration presents challenges due to the inherent variability and decentralized nature of DERs, as well as poorly adapted regulatory environments. This paper proposes a medium-term decision-making approach based on a comprehensive suite of computational tools for integrating DERs into Colombian energy markets. The proposed framework consists of modular tools that are aligned with the operation of a Commercial Virtual Power Plant (CVPP). The tools aim to optimize participation in bilateral contracts and short-term energy markets. They use forecasting, uncertainty management, and decision-making modules to create an optimal portfolio of DER assets. The suite's effectiveness and applicability are demonstrated and analyzed through its implementation with heterogeneous DER assets across various operational scenarios. [ABSTRACT FROM AUTHOR]

To achieve sustainable development goals and meet the demand for clean and efficient energy utilization, it is imperative to advance the penetration of renewable energy in various sectors. Energy storage systems can mitigate the intermittent issues of renewable energy and enhance the efficiency and economic viability of existing energy facilities. Among various energy storage technologies, thermocline heat storage (THS) has garnered widespread attention from researchers due to its stability and economic advantages. Currently, there are only a few review articles focusing on THS, and there is a gap in the literature regarding the optimization design of THS systems. Therefore, this paper provides a comprehensive review of the recent research progress in THS, elucidating its principles, thermal storage materials, applications, and optimization designs. The novelty of this work lies in the detailed classification and analysis of various optimization designs for THS, including tank shape, aspect ratio, inlet/outlet configuration, thermal energy storage materials arrangement, operating strategies, and numerical model optimization approaches. The limitations of existing research are also identified, and future perspectives are proposed, aiming to provide recommendations for THS research and contribute to the development and promotion of THS technology. [ABSTRACT FROM AUTHOR]

Egypt has the potential to generate a significant amount of energy from renewable technologies, in particular solar PV, concentrated solar power (CSP), and onshore and offshore wind. The energy sector is reliant on fossil fuels, particularly natural gas, for electricity production and is at risk of locking itself into a high carbon pathway. Globally, reducing greenhouse gas (GHG) emissions associated with national energy sectors is a target outlined in the UN's Paris Agreement. To reduce carbon dioxide (CO2) emissions associated with a higher dependence on fossil fuels, Egypt must consider upscaling renewable energy technologies (RETs) to achieve a clean energy transition (CET). This research modelled six scenarios using clicSAND for OSeMOSYS to identify the technologies and policy target improvements that are needed to upscale RETs within Egypt's energy sector. The results showed that solar PV and onshore wind are key technologies to be upscaled to contribute towards Egypt's CET. The optimal renewable target is the International Renewable Energy Agency's (IRENA) target of 53% of electricity being sourced from RETs by 2030, which will cost USD 16.4 billion more up to 2035 than Egypt's current Integrated Sustainable Energy Strategy (ISES) target of 42% by 2035; it also saves 732.0 MtCO2 over the entire modelling period to 2070. Socio-economic barriers to this transition are considered, such as recent discoveries of natural gas reserves combined with a history of energy insecurity, political instability impacting investor confidence, and a lack of international climate funding. The paper concludes with policy recommendations that would enable Egypt to progress towards achieving a CET. [ABSTRACT FROM AUTHOR]

Energy consumption plays an important role in contemporary economies. Its significance extends beyond utilitarian value, impacting economic robustness, environmental protection, and residents' well-being. The escalating global energy requisites necessitate efficient energy utilization and a shift towards renewable sources to address climate change and strengthen energy independence. Developing accurate predictive models to forecast long-term energy costs and savings remains a complex problem. This paper aims to provide a methodology to identify the influence of building energy performance on real estate market efficiency, focusing on property maintenance costs. Real estate plays a crucial role in human life, serving both as a fundamental need and as a vehicle for achieving personal aspirations and secure financial investments, particularly during times of economic and social instability. Through interdisciplinary methodological architecture, this study addresses three key issues: the impact of rising energy costs on market efficiency, the responsiveness of the real estate market to energy price fluctuations, and the significance of property maintenance costs on market value. The research approach includes creating and applying AI algorithms capable of evaluating extensive datasets pertaining to real estate features. Utilizing machine learning methods, the algorithm determines the importance of energy efficiency measures as well as various other inherent and external attributes of properties. The suggested methodology provides a novel approach to improve the effectiveness of market efficiency analysis. [ABSTRACT FROM AUTHOR]

In the context of power systems with a high proportion of renewable energy, energy storage plays a significant role in facilitating the consumption of renewable energy and ensuring the operational safety of power systems. However, the current power spot market's predominant power bidding model does not fully consider the physical and cost-operational characteristics of energy storage, which is not conducive to further incentivizing investment and construction of energy storage, and may indirectly affect the flexibility of energy storage in peak shaving and valley filling. This paper summarizes the key issues that need to be addressed for energy storage to participate in the spot market from two aspects: the power bidding model does not meet the requirements of the physical and cost-operational characteristics of energy storage, and the real-time market under this model cannot achieve optimal allocation of energy storage. Considering the energy constraints and cost characteristics of energy storage, a charge and discharge bidding model is proposed, which is based on the stored energy value of energy storage and is in line with the physical and cost-operational characteristics and real-time optimization needs of energy storage. Subsequently, a market clearing model for energy storage participation in the spot market under the state of energy bidding method is constructed, and based on the IEEE 39-bus test case, a comparative analysis of the nodal electricity prices, energy storage revenue, and total system costs under the proposed market participation model and the traditional power bidding model is conducted. Simulation results show that the proposed energy storage participation model in the spot market can better utilize the value of energy storage in peak shaving and valley filling compared to the conventional power bidding model, reducing the extreme electricity prices by up to 10%, increasing single cycle revenue of energy storage by 46%, and reducing the total operating costs of the system in scenarios with significant deviations in system load in the day-ahead and real-time markets. [ABSTRACT FROM AUTHOR]

The value of artificial intelligence and machine learning applications for use in heritage research is increasingly appreciated. In specific areas, notably remote sensing, datasets have increased in extent and resolution to the point that manual interpretation is problematic and the availability of skilled interpreters to undertake such work is limited. Interpretation of the geophysical datasets associated with prehistoric submerged landscapes is particularly challenging. Following the Last Glacial Maximum, sea levels rose by 120 m globally, and vast, habitable landscapes were lost to the sea. These landscapes were inaccessible until extensive remote sensing datasets were provided by the offshore energy sector. In this paper, we provide the results of a research programme centred on AI applications using data from the southern North Sea. Here, an area of c. 188,000 km2 of habitable terrestrial land was inundated between c. 20,000 BP and 7000 BP, along with the cultural heritage it contained. As part of this project, machine learning tools were applied to detect and interpret features with potential archaeological significance from shallow seismic data. The output provides a proof-of-concept model demonstrating verifiable results and the potential for a further, more complex, leveraging of AI interpretation for the study of submarine palaeolandscapes. [ABSTRACT FROM AUTHOR]

This paper aims to develop a MAGDM model using single-valued neutrosophic credibility matrix (SVNCM) energy in a SVNCM scenario. To do it, first, SVNCM energy and its score function are presented as a conceptual extension of existing single-valued neutrosophic matrix (SVNM) energy. Then, a MAGDM model is developed in terms of SVNCM energy and its score function in a SVNCM scenario and also its decision algorithm is provided to solve MAGDM problems with SVNCMs. Finally, the developed MAGDM model is applied in the school site selection problem as an actual example, then the comparative investigation of the decision results in the SVNM and SVNCM scenarios indicates the superiority of the developed model over existing MAGDM model. [ABSTRACT FROM AUTHOR]