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During the past two decades, a number of two-terminal switching devices have been demonstrated in the literature. They typically exhibit hysteric behavior in the current-to-voltage characteristics. These devices have often been also referred to as memristive devices. Their capacity to switch and exhibit electrical hysteresis has made them well-suited for applications such as data storage, in-memory computing, and in-sensor computing or in-memory sensing. The aim of this perspective paper is to is twofold. Firstly, it seeks to provide a comprehensive examination of the existing research findings in the field and engage in a critical discussion regarding the potential for the development of new non-Von-Neumann computing machines that can seamlessly integrate sensing and computing within memory units. Secondly, this paper aims to demonstrate the practical application of such an innovative approach in the realm of cancer medicine. Specifically, it explores the modern concept of employing multiple cancer markers simultaneously to enhance the efficiency of diagnostic processes in cancer medicine.

Abstract The insulation performance of oil paper insulation is significantly affected by moisture such that monitoring moisture content is important. However, it is difficult to obtain the moisture content accurately due to its dynamic change under multi physical fields. Studying the moisture transportation in oil paper insulation under multi physical fields becomes a vital method in solving the problem. A multi physical model describing moisture migration was proposed, which considered the effects of temperature on moisture in different existing states in oil‐immersed paper (OIP). The temperature distribution formed a vapour pressure gradient to drive moisture migration and affected migration speed. Then, experiments and simulations of moisture migration were performed, which showed that the experiments were in good agreement with simulation. The results revealed that the temperature gradient caused uneven moisture distribution and the increased temperature reduced OIP moisture content. The established model could fully characterise moisture migration under temperature gradient, which provided a theoretical reference for predicting the risk of partial dampness and evaluating insulation performance.

Abstract Various dielectric response processes in the oil‐paper insulation are sensitively affected by the insulating states (ageing degrees and moisture contents). However, the existing research is still incomplete in revealing the microscopic mechanisms of various dielectric response processes in oil‐paper insulation with different insulating states. Given this issue, the genetic algorithm is first adopted to extract the Dissado–Hill (D–H) model parameters by simulating the frequency domain spectroscopy (FDS) of oil‐paper insulation. Then, the change laws of the extracted D–H model parameters are adopted to reveal the microscopic mechanisms of various dielectric response processes. Microscopic mechanisms of four dielectric response processes are studied, which are quasi‐dc relaxation, loss peak relaxation, optical frequency relaxation, and DC conductance. Meanwhile, due to the dielectric response processes being dominated by various polar particles (methanol, ethanol, furfural, and water molecule), the contents of various ageing by‐products dissolved in the insulating oil are measured to support the above analysis. In this respect, a dielectric theoretical reference for the FDS technique to research the insulating states of oil‐paper insulation is provided.

Abstract The main part of oil‐immersed power equipment is oil‐paper insulation. At lower ambient temperatures, transformer oil will become viscous. As a result, compared to those at room temperature or operating temperature, the frequency domain dielectric properties of oil‐paper insulation at low temperatures are drastically different. For evaluating the insulation condition of electrical equipment, it is essential to accurately determine their dielectric properties. This paper described the FDS test, the DC conductivity test, and the transformer oil viscosity test, which was carried out in a laboratory environment for different ageing of oil‐immersed pressboard and transformer oil at 233–373 K. The effects of temperature on the dielectric properties at the competing mechanisms of polarisation and conductivity loss were clarified based on variations of the FDS curves. Considering the viscosity change of ageing transformer oil at low temperatures, the Arrhenius and VFT viscosity equations were used to achieve a fitting calculation in the different temperature zones. Based on the molecular/ion transition model in the force field and electric field, the characterisation relationships between the dynamic viscosity, DC conductivity, and test temperature of transformer oil were established. The limitations of conventional transformer oil equivalent dielectric relaxation models over a wide temperature range were finally clarified by combining the test findings of the DC conductivity over a wide temperature range with observed and simulated calculations. This paper provides an essential theoretical reference when using the FDS test to diagnose the insulation performance of oil‐immersed power equipment in extremely cold regions.

Abstract Oil‐paper insulation material is an essential component for oil‐filled transformers, and polyimide (PI) insulation paper is a novel material in the field of electricity. The improvement of insulation class for insulation paper has gained significant attention in recent years. In this study, SiO2 nanoparticles were modified with a silane coupling agent (KH550), and the KH550‐SiO2 was added to the fibre membrane through in situ polymerisation. The chemical composition and microcosmic characteristics of the PI porous fibre membrane were characterised using FT‐IR and SEM FT‐IR and SEM. Consequently, the dielectric constant of the composite paper was reduced to 2.3 upon adding 3 wt% of KH550‐SiO2, and the dielectric strength reached its maximum value of 85 kV/mm. The results demonstrate that the insulation performance of the KH550‐SiO2/PI composite was significantly improved due to the incorporation of nano‐SiO2. This establishes an efficient approach for achieving excellent electrical and thermal properties in practical applications using the KH550‐SiO2/PI fibre membrane.

Abstract The moisture generated by the ageing of the oil‐paper insulating system can easily lead to the deterioration of the performance of the insulating paper. In order to alleviate the impact of moisture on the performance of the insulating paper, we developed a kind of lipophilic hydrophobic self‐repairing cellulose insulating paper (new insulating paper) based on the ternary system of polytetrafluoroethylene, fluorocarbon surfactant and fluorinated alkyl silane, and accelerated thermal ageing test for 45 days at 130°C was carried out. The experimental results show that the new insulating paper with excellent lipophilic property can realise hydrophobic self‐repairing through heat treatment after physical damage. The tensile strength and elongation at break of the new insulating paper increased by 5%–8% and 6.25%–27% respectively compared with the original cellulose insulating paper. At the end of ageing, the relative dielectric constant and dielectric loss factor of new insulating paper are 10.4% and 9.9% lower than that of the original cellulose insulating paper, while the breakdown voltage is 6.8% higher. The acid and moisture content produced during the ageing process of the insulating oil impregnated with new insulating paper are also less, indicating that the coating can delay the ageing of the insulating paper.

Abstract The ionic transport process is an important counterpart to charge transport in oil‐paper insulation, and it significantly impacts oil flow electrification at the oil‐paper interface. Despite this, the dynamics of this phenomenon and the underlying mechanisms remain unclear, particularly at the molecular level. To understand this fundamental aspect, we conduct Molecular Dynamics study on the transport behaviour of an impurity ion in different oil‐paper insulation models under various external electric fields. Different influence factors, such as external electric fields, temperatures, and local structural characteristics, are investigated in relation to the corresponding ionic mobility in different polymer models. According to the simulations, ionic mobility and its response to electric fields are higher in weaker electrostatic models. As a result, mineral oil exhibits the highest ionic mobility and the most substantial enhancement of ionic mobility by external electric fields, followed by vegetable oil, oil‐paper blends, and insulating paper. This significant deviation in ionic mobility between oil and paper leads to the formation of an electric double layer near the oil‐paper interface. The underlying physical mechanisms of different ionic mobility and its response to the electric field in different polymer models could be explained by the different polymer structural influences in terms of interaction energy and coordination numbers in a static manner, as well as by the interactions between the impurity ion and its surrounding atoms in terms of lifetime correlation functions and velocity autocorrelation functions in a dynamic manner. In addition, the influence of temperature on ionic mobility in mineral and vegetable oil is examined, and their activation energies are calculated. Advancing in the fundamental understanding of the dynamics of the ion transport process in oil‐paper insulation is vital to improving their insulating properties for oil‐impregnated power transformers.

Under the background of cap-and-trade mechanisms, this article constructs a game model of the electricity supply chain, which is dominated by electricity generators and followed by electricity sellers, taking into account the situation of electricity generators investing in renewable energy and energy storage under the grandfathering mechanism (GM) and benchmarking mechanism (BM). By comparing the equilibrium solutions in different cases, the research finds that (1) compared with the grandfathering mechanism, the benchmarking mechanism has more investment in renewable energy and higher energy storage quality; (2) in the consumer market, compared with GM, the electricity price and the electricity demand are higher under BM; (3) an increase in the renewable energy preference coefficient or carbon price will lead to an increase in the electricity wholesale price, renewable energy investment, energy storage quality, electricity price, and electricity demand. Further, an increase in the energy storage cost coefficient or renewable energy investment cost coefficient will result in lower electricity wholesale price, renewable energy investment, energy storage quality, electricity price, and electricity demand; and (4) the profit of the generator under GM is higher than that under BM when the total carbon quota is larger, while the profit of the generator under BM is higher than that under GM when the unit carbon quota is larger., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Although, Spain does not obtain an official arctic strategy, it was mentioned in the paper for Spanish Polar Strategy that the Arctic region has become one of the country's national interests. The government of Spain is also deeply concerned about the problem of climate change and how it affects economic and social aspects of life. When addressing the Arctic energy issue we must make a prior delimitation, since it does not propose to study in detail, and only in general description, the internal functioning of the Arctic energy systems, that is, the one that serves to supply their populations with lighting, water, gas or electricity. [ABSTRACT FROM AUTHOR]

From a spatial perspective, this paper constructs a spatial Durbin model based on the adjacent weight matrix to analyze the impact of environmental regulation on the development of renewable energy in 30 provinces (autonomous regions and municipalities directly under the Central Government) in China from 2007 to 2020. The results show that (1) both environmental regulation and renewable energy development have positive spatial autocorrelation and form relatively similar spatial agglomeration areas, specifically showing the characteristics of "high-high" agglomeration in resource-rich areas and "low-low" agglomeration in resource-scarce areas. (2) Environmental regulation significantly promotes the development of local renewable energy and has a significant spatial spillover effect on the development of renewable energy in neighboring provinces. (3) The moderating effect results show that renewable energy installed capacity and electricity demand play an inverse moderating role between environmental regulation and renewable energy development. (4) From the perspective of different regions, the impact of environmental regulation on the development of renewable energy is heterogeneous. Based on the research conclusions, this paper finally provides corresponding policy suggestions from three aspects: regional cooperation, technical support, and differentiation., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

To enhance the accuracy of theft detection for electricity consumers, this paper introduces a novel strategy based on the fusion of the dual-time feature and deep learning methods. Initially, considering electricity-consumption features at dual temporal scales, the paper employs temporal convolutional networks (TCN) with a long short-term memory (LSTM) multi-level feature extraction module (LSTM-TCN) and deep convolutional neural network (DCNN) to parallelly extract features at these scales. Subsequently, the extracted features are coupled and input into a fully connected (FC) layer for classification, enabling the precise detection of theft users. To validate the method's effectiveness, real electricity-consumption data from the State Grid Corporation of China (SGCC) is used for testing. The experimental results demonstrate that the proposed method achieves a remarkable detection accuracy of up to 94.7% during testing, showcasing excellent performance across various evaluation metrics. Specifically, it attained values of 0.932, 0.964, 0.948, and 0.986 for precision, recall, F1 score, and AUC, respectively. Additionally, the paper conducts a comparative analysis with mainstream theft identification approaches. In the comparison of training processes, the proposed method exhibits significant advantages in terms of identification accuracy and fitting degree. Moreover, with adjustments to the training set proportions, the proposed method shows minimal impact, indicating robustness. [ABSTRACT FROM AUTHOR]

Harvesting renewable mechanical energy is envisioned as a promising and sustainable way for power generation. Many recent mechanical energy harvesters are able to produce instantaneous (pulsed) electricity with a high peak voltage of over 100 V. However, directly storing such irregular high‐voltage pulse electricity remains a great challenge. The use of extra power management components can boost storage efficiency but increase system complexity. Here utilizing the conducting polymer PEDOT:PSS, high‐rate metal‐free micro‐supercapacitor (MSC) arrays are successfully fabricated for direct high‐efficiency storage of high‐voltage pulse electricity. Within an area of 2.4 × 3.4 cm2 on various paper substrates, large‐scale MSC arrays (comprising up to 100 cells) can be printed to deliver a working voltage window of 160 V at an ultrahigh scan rate up to 30 V s−1. The ultrahigh rate capability enables the MSC arrays to quickly capture and efficiently store the high‐voltage (≈150 V) pulse electricity produced by a droplet‐based electricity generator at a high efficiency of 62%, significantly higher than that (<2%) of the batteries or capacitors demonstrated in the literature. Moreover, the compact and metal‐free features make these MSC arrays excellent candidates for sustainable high‐performance energy storage in self‐charging power systems. [ABSTRACT FROM AUTHOR]

Purpose: On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data. Design/methodology/approach: First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption. Findings: Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability. Originality/value: This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability. Highlights: The stability of electricity consumption is important to the stable operation of the grid. An improved FP-growth algorithm is employed to explore the influencing factors. The improved algorithm enables the mining of rules containing specific attribute labels. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) energy trading has attracted a lot of attention and the number of electric vehicles (EVs) has increased in the past couple of years. Toward sustainable mobility, EVs meet the standard development goals (SDGs) for attaining a sustainable future in the transport sector. This development and increasing number of EVs creates an opportunity for prosumers to trade electricity. Considering this opportunity, this review article aims to provide an in-depth analysis of P2P energy trading of EVs using blockchain in centralized and decentralized networks, which enables prosumers to exchange energy directly with one another. The paper is aimed to provide the reader with a state-of-the-art review on the P2P energy trading for EVs, considering different blockchain algorithms that are practically implemented or still in the research phase. Moreover, the paper presents blockchain applications, current trends, and future challenges of EVs' energy trading. P2P energy trading for EVs using blockchain algorithms can be successfully implemented considering real-time scenarios and economically benefits smart sustainable societies. [ABSTRACT FROM AUTHOR]

With growing concerns about power and environment issues, smart grid technology, such as meter data management systems, can be viewed as a great solution to handle issues of the environment and energy efficiency. Based on the push–pull–mooring (PPM) model, this research views dissatisfaction, personal innovativeness, information quality, locatability, economic benefits, and perceived value as predictors to examine their impact on switch intention to an electricity management system. The analysis results display that dissatisfaction has no significant effect on switch intention. As a mooring variable, personal innovativeness not only influences switch intention positively, but also moderates the relationship between perceived value and switch intention. Moreover, through perceived value, locatability and economic benefits influence switch intention positively. This research fills the gap in government policies as well as energy companies and provides ponderable suggestions if a government and energy companies would like to change citizens' behavior from paper-based bill to electricity management system. The government and energy companies should not overlook the effect of personal innovativeness and perceived value and put more effort to publicize the advantages of locatability and economic benefits. [ABSTRACT FROM AUTHOR]

Power consumers can obtain authoritative green environmental value certification through green electricity trading, which plays an important role in improving the production competitiveness of enterprises, especially for international product trade affairs. However, the credibility of green electricity transactions faces serious challenges in the enterprise green authentication affairs, especially the user's identity authentication, the traceability of green electricity transactions, and the standardization of green electricity transactions. Aiming to solve the certification and traceability problem of tradable green certificates, this paper proposes an integrated green certificate trading protocol, which solves its double-trading problem and helps to improve the credibility of renewable energy use. The main contribution is providing a solution based on the consortium blockchain technology to solve the main challenges mentioned above. The main solved scheme designs a series of protocols, which includes a purchase protocol, payment protocol, and non-transferable protocol. The whole process ensures the credibility, traceability, and non-transferability of green certificate trading. Multiple verification measures are adopted to address security and privacy challenges in green certificate management. Through security analysis, the protocol effectively defends against attacks such as double payments, transaction rollback, and transaction replays while ensuring users' privacy. [ABSTRACT FROM AUTHOR]

Understanding electrical load profiles and detecting anomaly behaviors are important to the smart grid system. However, current load identification and anomaly analysis are based on static analysis, and less consideration is given to anomaly findings under load change conditions. This paper proposes a deep variational autoencoder network (DVAE) for load profiles, along with anomaly analysis services, and introduces auto-time series data updating strategies based on sliding window adjustment. DVAE can help reconstruct the load curve and measure the difference between the original and the newer curve, whose measurement indicators include reconstruction probability and Pearson similarity. Meanwhile, the design of the sliding window strategy updates the data and DVAE model in a time-series manner. Experiments were carried out based on datasets from the U.S. Department of Energy and from Southeast China. The results showed that the proposed services could result in a 5% improvement in the AUC value, which helps to identify the anomaly behavior. [ABSTRACT FROM AUTHOR]

The paper examines the status of energy decentralization in three key Arab League countries, namely, Egypt, the United Arab Emirates (UAE), and Saudi Arabia. It specifically focuses on decentralization in their electricity markets. The paper analyzes the regulatory environment in these three countries and then looks at how things stand regarding new tools and technologies to facilitate decentralization. It explores the progress in deployment of these tools and technologies, and the specific regulatory barriers in the three nations. The paper also offers recommendations for how regulations can be more encouraging and discusses electricity interconnections as a vital step towards decentralization that will boost energy security and energy efficiency. [ABSTRACT FROM AUTHOR]

Definition: In young Maxwell's eyes, electricity, magnetism and fluid mechanics present analogies that let the first be mathematically grasped in a unitary way similarly to the latter. This entry sketches the first steps of James Clerk Maxwell's unitary view of electromagnetism and fluid mechanics, with long-lasting effects on understanding the physical world. In his very first paper on the subject, Maxwell interpreted Faraday's concept of lines of force of both electricity and magnetism as filaments of a flowing fluid. That is, Maxwell suggested to unify the previously distinct fields of electricity and magnetism, considering them as different aspects of the same entity, the 'electromagnetic field', which is mathematically described by four partial differential equations. In the literature there are several comprehensive works on Maxwell's mature exposition of his theory; however, the aim of this entry contribution is rather more limited. Indeed, it is restricted to examining the contributions of Maxwell's first paper towards developing the idea of electric current as the motion of an incompressible fluid. Thus, this entry highlights the strong connections of Maxwell's epistemological view with the 'mechanistic' approach of the time to every aspect of physical phenomenology. For this purpose, this entry mentions some historical context surrounding the emergence of Maxwell's innovative concepts. [ABSTRACT FROM AUTHOR]

Safeguarding the data collected by smart meters is essential because the disclosure of this information may threaten the privacy of the consumer. By obtaining them, hackers can find out the behavior of the person and use that information for malicious purposes. Therefore, the anonymity of such information can prevent the occurrence of risks. Given the paramount significance of user privacy and data integrity, this paper primarily investigates the confidentiality, integrity, and anonymity of messages. This paper aims to develop a platform for determining dynamic pricing to coordinate supply and demand, thereby maximizing the efficiency of facilities. In the previous research, the operation center was not authenticated for the customer in the first step, and they also had a heavy computational cost. But this paper has endeavored to develop an efficient and comprehensive privacy-preserving solution for the smart electricity network. Also, it has tried to cover all the required security objectives by dealing with authenticity, confidentiality, and irrefutability. The method of the research is that two entities mutually authenticate each other and reach a key agreement so that if the operation center wants to send a control command, it can send control commands directly to the meter with less time complexity. The power company sends control commands and requests to the smart meters until the analyzed and collected energy consumption data are transmitted. The data aggregator node gathers the data from the meters. The results showed that the proposed method reduced the computational complexity and communication overhead to a satisfactory level and is also resistant to various attacks. [ABSTRACT FROM AUTHOR]

Traditional load prediction methods are unable to effectively predict the loads according to the spatial topology of each electricity consumer in neighboring areas and the load dependency correlations. In order to further improve the load prediction accuracy of each consumer in the region, this paper proposes a shortterm prediction method of electric load based on multi-graph convolutional network. First, the input data are selected with maximum information coefficient method by integrating multi-dimensional information such as load, weather, electricity price and date in the areas. Then, a gated convolutional network is used as a temporal convolutional layer to capture the temporal features of the loads. Moreover, a physical-virtual multi-graph convolutional network is constructed based on the spatial location of each consumer as well as load dependencies to capture the different evolutionary correlations of each spatial load. Comparative studies have validated the effectiveness of the proposed model in improving the prediction accuracy of power loads for each consumer. [ABSTRACT FROM AUTHOR]

This paper will explore policy shifts in Nigeria's oil and gas, solar, nuclear, and mineral energy sectors. This policy shift by way of a transition, indigenization, and Nigerianization, has given way to deregulation, decentralization, and de-indigenization of many industries, most notably in the oil and gas sector, through the Petroleum Industry Act (PIA) of 2021 and the Local Content Act of 2010 (LCA). The paper recommends, amongst others, the establishment of a new legal regime that grants resource-based and property rights to resource-bearing communities and incorporates principles of international law, energy diplomacies, International Environmental Law, and international best practices. [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]

The electric power sector is the primary contributor to carbon emissions in China. Considering the context of dual carbon goals, this paper examines carbon emissions within China's electricity sector. The research utilizes the LMDI approach for methodological rigor. The results show that the cumulative contribution of economies scale, power consumption factors and energy structure are 114.91%, 85.17% and 0.94%, which contribute to the increase of carbon emissions, the cumulative contribution of power generation efficiency and ratio of power dissipation to generation factor are -19.15% and -0.01%, which promotes the carbon reduction. The decomposition analysis highlights the significant influence of economic scale on carbon emissions in the electricity industry, among the seven factors investigated. Meanwhile, STIRPAT model, Logistic model and GM(1,1) model are used to predict carbon emissions, the average relative error between actual carbon emissions and the predicted values are 0.23%, 8.72% and 7.05%, which indicates that STIRPAT model is more suitable for medium- to long-term predictions. Based on these findings, the paper proposes practical suggestions to reduce carbon emissions and achieve the dual carbon goals of the power industry. [ABSTRACT FROM AUTHOR]

The integration of gas and electricity networks has emerged as a promising approach to enhance the overall flexibility of energy systems. As the transition toward sustainable and decarbonized energy sources accelerates, the seamless coordination between electricity and gas infrastructure becomes increasingly crucial. This paper presents a comprehensive review of the state-of-the-art research and developments concerning the flexibility in the operation of low-carbon integrated gas and electricity networks (IGENs) as part of the whole system approach. Methods and solutions to provide and improve flexibility in the mentioned systems are studied and categorized. Flexibility is the system's ability to deal with changes and uncertainties in the network while maintaining an acceptable level of reliability. The presented review underscores the significance of this convergence in facilitating demand-side management, renewable energy integration, and overall system resilience. By highlighting the technical, economic, and regulatory aspects of such integration, this paper aims to guide researchers, policymakers, and industry stakeholders toward effective decision-making and the formulation of comprehensive strategies that align with the decarbonization of energy systems. [ABSTRACT FROM AUTHOR]

In order to improve the consumption of renewable energy and reduce the carbon emissions of integrated energy systems (IESs), this paper proposes an optimal operation strategy for an integrated energy system considering the coordination of electricity and hydrogen in the context of carbon trading. The strategy makes full use of the traditional power-to-gas hydrogen production process and establishes a coupling model comprising cogeneration and carbon capture equipment, an electrolytic cell, a methane reactor, and a hydrogen fuel cell. Taking a minimum daily operating cost and minimal carbon emissions from the system as objective functions, a mixed-integer nonlinear optimal scheduling model is established. This paper designs examples based on MATLAB R2021b and uses the GUROBI solver to solve them. The results show that compared with the traditional two-stage operation process, the optimization method can reduce the daily operation cost of an IES by 26.01% and its carbon emissions by 90.32%. The results show that the operation mode of electro-hydrogen synergy can significantly reduce the carbon emissions of the system and realize a two-way flow of electro-hydrogen energy. At the same time, the addition of carbon capture equipment and the realization of carbon recycling prove the scheduling strategy's ability to achieve a low-carbon economy of the scheduling strategy. [ABSTRACT FROM AUTHOR]

Integrating energy systems with information systems in smart grids offers a promising avenue for combating electricity theft by leveraging real-time data insights. Suspicious activity indicative of theft can be identified through anomalous consumption patterns observed in smart networks. However, a smart model is required for capturing and analysing the data intelligently to accurately detect electricity theft. In the paper, electricity theft has been detected using an encoder-decoder-based classifier that integrates two models of convolutional neural networks (CNN). The aim is to scan the strength of the data and built a smart model that analysed the connections in complex data and determine the pattern of theft. The model comprises three compartments: the auto-encoder, the wide convolutional neural network (1-D CNN model), and the deep convolutional neural network (2-D CNN model). The auto-encoder has been trained on the complex and in-depth linkage between the theft data and the normal data as it removes noise and unnecessary information. The 1-D CNN model gathers relevant connections and general features, while the 2-D CNN model determines the rate at which energy theft occurs and differentiates between the energy-stealing consumers and normal consumers. The efficacy of the approach is underscored by its superiority over traditional deep learning and machine learning techniques. This paper elucidates the distinct advantages and applications of the proposed model in combating electricity theft within smart grid environments. [ABSTRACT FROM AUTHOR]

With the development of global clean energy and the implementation of carbon emission reduction policies, the direct purchase of electricity by large consumers has been increasingly promoted as a special form of electricity trading. Therefore, on the basis of the completion of low-carbon emission reduction targets in each country, how to rationalize the electricity purchase by large consumers in the electricity market so as to reduce their electricity purchase costs has become the main target of attention in each country. Currently, there are fewer studies in existing research on the direct electricity purchase strategy of large consumers under the consideration of the weight of consumption responsibility and risk. Based on this, this paper constructs a dual-game model for direct electricity purchase by large consumers based on the Stackelberg game and non-cooperative game theory. The concept of value at risk is further introduced, and the optimal strategy of direct electricity purchase by large consumers is proposed. The results of this study show that when market players make decisions on the purchase and sale of electricity, power suppliers will increase their biddings to obtain the highest returns, and large consumers can reduce the transaction costs by combining the medium- and long-term market with the spot market to purchase electricity. In the choice of electricity purchase market, with the increasing risk factor, large consumers shift from the risky spot market to the less risky medium- and long-term market and option market. This paper provides a reference for the issues of power suppliers' contract bidding and large consumers' electricity purchase strategy in the medium- and long-term contract transactions. [ABSTRACT FROM AUTHOR]

Within global debates, decentralization with respect to renewable electricity is positioned as pivotal in overlapping calls for decarbonization, energy access and just transitions. The promise is that technologies that are smaller and distributed will deliver a range of benefits, including enabling infrastructural innovations and expanding sustainable access, notably by empowering local authorities. While shifting to low-carbon systems is imperative, in this paper we call for attention to the complexity, opportunities and risks of commonly celebrated experiments in distributed electricity technologies when applied to African contexts. We draw on the case of Uganda, unpacking a series of four innovative electricity projects currently under way. For each case, we look at the actors involved and the imagined relationship between the projects and the incumbent grid. From these cases, we argue that, in this African context characterized by contested urban governance and fragmented networks, careful attention to supporting urban scale institutional and infrastructural development is necessary, although in many cases bypassed. [ABSTRACT FROM AUTHOR]

China's renewable energy industry is facing the challenge of overcapacity. The environmental management literature suggests that consumers' participation in the green electricity market holds immense potential in addressing renewable energy consumption concerns. However, the question of how payment policies influence China's consumers' willingness to pay for green electricity remains unresolved. Based on 2854 valid questionnaires from a survey conducted in China's four first-tier cities in 2023, our research findings reveal: (1) While 97.9% of consumers express a willingness to use green electricity, only 63.1% are willing to pay a higher cost, indicating the existence of a "value-action" gap between environmental awareness and actual willingness to pay. (2) China's consumers' willingness to pay for green electricity is approximately 38.4 RMB per month. This figure has decreased by 5.7 RMB compared to our survey in 2019. (3) Consumers' willingness to pay will be influenced by the attitudes of those around them. (4) The voluntary payment policy positively impacts consumers' willingness to pay for green electricity. (5) Male, younger, lower education level, higher income, and larger household size consumers exhibit a higher willingness to pay. (6) Electricity price sensitivity weakens the impact of payment policies on willingness to pay., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This study aims to investigate the effect of pulsed electric field (PEF) assisted OSA esterification treatment on the multi-scale structure and digestive properties of cassava starch and structure-digestion relationships. The degree of substitution (DS) of starch dually modified at 1.5-4.5 kV/cm was 37.6-55.3 % higher than that of starch modified by the conventional method. Compared with native starch, the resistant starch (RS) content of esterified starch treated with 3 kV/cm significantly increased by 17.13 %, whereas that of starch produced by the conventional method increased by only 5.91 %. Furthermore, assisted esterification at low electric fields (1.5-3 kV/cm) promotes ester carbonyl grafting on the surface of starch granules, increases steric hindrance and promotes the rearrangement of the amorphous regions of starch, which increases the density of the double-helical structure. These structural changes slow down starch digestion and increase the RS content. Therefore, this study presents a potential method for increasing the RS content of starch products using PEF to achieve the desired digestibility., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

With the continuous increase in air conditioning installations, the proportion of air conditioning electricity consumption to total electricity consumption is growing. Research on the decomposition of air conditioning electricity consumption is of great significance for assessing electricity usage, formulating electricity scheduling plans, and ensuring the stable operation of the power grid. Currently, typical day selection strategies for the decomposition of air conditioning electricity consumption often overlook the corresponding relationship between typical daily electricity consumption and temperature. Therefore, this paper proposes an air conditioning electricity consumption decomposition method based on the dependence between temperature and electricity consumption. This method filters typical days based on Copula-based dependence indicators and equiprobable ellipses, determines the baseline electricity consumption curve through model selection voting, and ultimately calculates the air conditioning electricity consumption. The effectiveness of the proposed method is validated by applying it to electricity consumption data in Fuzhou from 2019 to 2022., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Xun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

As the share of air conditioning electricity consumption within total grid electricity consumption grows, the decomposition of such consumption becomes increasingly crucial for assessing electricity usage patterns, devising consumption scheduling strategies, and maintaining the stability of the power grid. Although there is a strong correlation between apparent temperature and air conditioning electricity consumption, the literature currently available seldom explores the impact of apparent temperature on this consumption. Moreover, there is a scarcity of effective assessment indices to evaluate the efficacy of air conditioning electricity consumption breakdown. This study introduces a method for decomposing electricity consumption from air conditioning units, utilizing effective duration as a basis to tackle these issues. By employing an apparent temperature model as a constraint, this approach identifies the effective operating time of air conditioning and constructs a constrained convex optimization problem to estimate air conditioning power usage. Additionally, a novel evaluation index for the effectiveness of air conditioning electricity consumption decomposition is proposed, which includes penalties for negative decomposed consumption, alongside the traditional consistency index. Comparative experiments are conducted using real electricity consumption data from Fujian Province. Empirical results indicate that the methodology for air conditioning electricity consumption decomposition presented in this paper aligns more closely with actual conditions. Furthermore, the evaluation metrics introduced for the decomposition of air conditioning electricity consumption are adept at precisely gauging the quality of the air conditioning electricity consumption data., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Xun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Hydrogen, a clean and sustainable energy source, faces challenges from energy-intensive pre-processing technologies. This study explores the synergistic enhancement of active electric fields on enzymolysis of wheat straw and hydrogen production through dark fermentation. The active electric field enzymolysis system improved the adsorption capacity of wheat straw to cellulase, increasing cellulase activity by 18.0 %, causing a 39.1 % increase in reducing sugar content. In the active fermentation system, Clostridium&#95;sensu&#95;stricto&#95;1 activity was enhanced in the first stage, increasing hydrogenase activity by 23.0 %, prolonging the first hydrogen production peak. Elevated reducing sugars were observed in the second stage, with Prevotella&#95;9 and Bacteroides becoming the dominant hydrogen-producing bacteria in the third stage, leading to a second hydrogen production peak. Overall, cumulative hydrogen production was enhanced by 50.9 % compared to the control. The synergistic pretreatment with an active electric field and cellulase provides a novel approach for efficiently utilizing wheat straw., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Anoxygenic phototrophic bacteria is a promising catalyst for constructing bioanode, but the mixed culture with non-photosynthetic bacteria is inevitable in an open environment application. In this study, a Rhodopseudomonas-dominated mixed culture with other electrogenic bacteria was investigated for deciphering the differentiated performance on electricity generation in light or dark conditions. The kinetic study showed that reaction rate of OM degradation was 9 times higher than that under dark condition, demonstrating that OM degradation was enhanced by photosynthesis. However, CE under light condition was lower. It indicated that part of OM was used to provide hydrogen donors for the fixation of CO 2 or hydrogen production in photosynthesis, decreasing the OM used for electron transfer. In addition, higher COD concentration was not conducive to electricity generation. EIS analysis demonstrated that higher OM concentration would increase Rct to hinder the transfer of electrons from bacteria to the electrode. Indirect and direct electron transfer were revealed by CV analysis for light and dark biofilm, respectively, and nanowires were also observed by SEM graphs, further revealing the differentiate performance. Microbial community analysis demonstrated Rhodopseudomonas was dominated in light and decreased in dark, but Geobacter increased apparently from light to dark, resulting in different power generation performance. The findings revealed the differentiated performance on electricity generation and pollutant removal by mixed culture of phototrophic bacteria in light or dark, which will improve the power generation from photo-microbial fuel cells., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

To support increasing renewable capacity for a net-zero future, energy storage will play a key role in maintaining grid stability. In this paper, all current and near-future energy storage technologies are compared for three different scenarios: (1) fixed electricity buy-in price, (2) market-based electricity buy-in price, and (3) energy storage integrated into a fully renewable electricity system. In the first part of this study, an algorithm is devised to simulate strategic buy-in of electricity for energy storage. This analysis yields a qualitative decision-making tool for a given energy storage duration and size. Building upon the first part's findings, an integration study gives insight into expected power prices and expected storage size in a typical northwestern European fully renewable energy system. The integration study shows significant need for electricity storage with durations spanning from one to several days, typically around 40 h. Pumped Hydro Storage and Pumped Thermal storage surface as the best options. The overall levelized costs of storage are expected to be in the USD 200–500/MWh range. Integration of storage with renewables can yield a system-levelized cost of electricity of about USD 150/MWh. Allowing flexibility in demand may lower the overall system-levelized cost of electricity to USD 100/MWh. [ABSTRACT FROM AUTHOR]

As penetration of EVs in the transportation sector is increasing, the demand for the mandatory installation of charging infrastructure also is increasing. In addition, renewable energy and energy storage systems (ESSs) are being reviewed for use in electric vehicle charging stations (EVCSs). In this paper, we present an optimal electricity trading volume and an optimal installation capacity of ESSs to maximize the daily profit of the EVCSs equipped with solar power generation when the EVCSs are licensed to sell energy to the power supplier during a specific time period. By formulating and solving the optimization problem of the EVCSs, this paper analyzes validation results for the different useful lives of ESSs, the peak power of a PV generator, and weather conditions at the Yangjae Solar Station and the Suseo Station public parking lot, Seoul, Republic of Korea. Furthermore, this paper validates that the daily expected profit of EVCSs with the proposed method outperforms the profit of conventional EVCSs which do not utilize ESSs. [ABSTRACT FROM AUTHOR]

The electricity market is evolving from the traditional unidirectional model into a bidirectional one in which households also generate and sell energy. This new scenario requires technology able to manage the available energy and guarantee that all the participants pay or are paid appropriately. Unfortunately, fine-grained monitoring of energy production and consumption makes it possible to infer sensitive information about confidential aspects of people's private life. In this paper, we propose a system designed for privacy-preserving electricity trading in a connected microgrid. The system guarantees that at the end of a billing period, the distribution system operator can compute the quantity to be charged or paid to each household while being unable to trace its consumption details. [ABSTRACT FROM AUTHOR]

Delays and cost overruns in turnkey projects can lead to significant economic losses, disputes and even project abandonment. These facts negatively impact the environmental, social and governance (ESG) policies of companies involved in the project. In this paper, a bibliographic review was conducted to identify the leading causes of delays and cost overruns in turnkey industrial projects in the refining, gas and conventional electricity generation (RGE) sector and propose good practices to avoid or mitigate them. We identified 893 causes of delays and cost overruns and 147 mitigation measures. The causes and mitigation measures were grouped into eight categories based on the execution phases of an RGE project. A critical analysis was carried out to avoid duplication, and the result was evaluated by experts in turnkey project management, reducing the causes and mitigation measures to a final set of 103 and 49, respectively. The construction category showed the most significant influence on project delays and cost overruns, and this, together with the preliminary phase and project management categories, contributed to 60% of the identified causes. The findings of this study can help project managers improve the profitability of turnkey industrial projects, promoting innovation and sustainability within companies and society. [ABSTRACT FROM AUTHOR]

Constructing a new energy-based power system is not only an important direction for the transformation and upgrade of China's power system, but also a key to achieving peak carbon and carbon neutrality. How to fully utilize situation awareness technology to adapt to diverse and differentiated scenarios has become a crucial breakthrough point for ensuring the reliable, safe, high-quality, low-carbon, and economical operation of the new power system. Starting from the distribution network demand resources, this paper proposes a dynamic aggregation method for load aggregators considering the user deviation quantity, to deal with the current situation that the adjustable load-side resource points are multi-faceted and wide, and the operating subjects are complex and difficult to participate directly in the grid dispatch. First, considering there is subjectivity in the electricity behavior of users under the jurisdiction of the load aggregator, a deviation amount may be generated during the actual aggregation process, which reduces the profit of the load aggregator. Therefore, a load aggregator-level user deviation dynamic volume forecasting method based on the Markov chain is proposed, which is used to predict the deviation quantity of users during the dispatch cycle and achieve a dynamic status estimate on the load side of the new power system. On this basis, a dynamic aggregation model for load aggregators based on the deviation volume was constructed with the objective of maximizing the revenue of load aggregators. The examples, by comparing the aggregation results of users under three scenarios, show the proposed method can effectively guarantee the income of load aggregators, verify the effectiveness of the proposed dynamic aggregation strategy, and provide technical support for the operation situation awareness of the load side of the new power system. [ABSTRACT FROM AUTHOR]

Electricity sales is one of the important assessment indexes of a power grid company's operation. Since electricity sales is closely related to many factors, how to consider the influence of multiple factors and improve the accuracy of the calculation of electricity sales is a difficult problem that needs to be solved urgently. In this paper, we first analyze the six dimensions affecting electricity sales and select the key influencing factors that can be quantified statistically. Secondly, the key influencing factors are screened according to Pearson's correlation coefficient and then the calculation model of electricity sales is established based on the random forest algorithm. Finally, we validate the feasibility and validity of the proposed calculation method for electricity sales through a case study. [ABSTRACT FROM AUTHOR]

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Large amounts of data are created from sensors in Internet of Things (IoT) services and applications. These data create a challenge in directing these data to the cloud, which needs extreme network bandwidth. Fog computing appears as a modern solution to overcome these challenges, where it can expand the cloud computing model to the boundary of the network, consequently adding a new class of services and applications with high-speed responses compared to the cloud. Cloud and fog computing propose huge amounts of resources for their clients and devices, especially in IoT environments. However, inactive resources and large number of applications and servers in cloud and fog computing data centers waste a huge amount of electricity. This paper will propose a Dynamic Power Provisioning (DPP) system in fog data centers, which consists of a multi-agent system that manages the power consumption for the fog resources in local data centers. The suggested DPP system will be tested by using the CloudSim and iFogsim tools. The outputs show that employing the DPP system in local fog data centers reduced the power consumption for fog resource providers. [ABSTRACT FROM AUTHOR]