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Large-scale wind power integration has raised concerns about the reliability and stability of power systems. The rotor circuit of a doubly fed induction generator (DFIG) is highly vulnerable to unexpected voltage dips, which can cause considerable electromotive force in the circuit. Consequently, the DFIG must fulfil the fault-ride through (FRT) criteria to ensure the system's performance and contribute to voltage regulation during severe grid outages. This paper provides a hybrid solution for DFIG wind turbines with FRT capabilities, using both a modified switch-type fault current limiter (MSFTCL) and a direct current (DC) chopper. The proposed system has the merit of keeping the rotor current and the DC-link voltage within the permissible limits, enhancing the FRT capability of generators. Moreover, the boundness of supply voltage into its reference value ensures dynamic stability during symmetric and asymmetric grid failures. Further, electromagnetic torque variations are significantly reduced during fault events. Finally, the performance validation of the proposed scheme is performed in a simulation setup, and the results are compared with the existing sliding mode control (SMC) and proportional-integral (PI) controller-based approaches. The comparison results show that a hybrid strategy with advanced controllers provides superior performance for all critical parameters.

In this paper, a systematic literature review is presented, through a survey of the main digital databases, regarding modelling methods for Short-Term Load Forecasting (STLF) for hourly electricity demand for residential electricity and to realize the performance evolution and impact of Artificial Intelligence (AI) in STLF. With these specific objectives, a conceptual framework on the subject was developed, along with a systematic review of the literature based on scientific publications with high impact and a bibliometric study directed towards the scientific production of AI and STLF. The review of research articles over a 10-year period, which took place between 2012 and 2022, used the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) method. This research resulted in more than 300 articles, available in four databases: Web of Science, IEEE Xplore, Scopus, and Science Direct. The research was organized around three central themes, which were defined through the following keywords: STLF, Electricity, and Residential, along with their corresponding synonyms. In total, 334 research articles were analyzed, and the year of publication, journal, author, geography by continent and country, and the area of application were identified. Of the 335 documents found in the initial research and after applying the inclusion/exclusion criteria, which allowed delimiting the subject addressed in the topics of interest for analysis, 38 (thirty-eight) documents were in English (26 journal articles and 12 conference papers). The results point to a diversity of modelling techniques and associated algorithms. The corresponding performance was measured with different metrics and, therefore, cannot be compared directly. Hence, it is desirable to have a unified dataset, together with a set of benchmarks with well-defined metrics for a clear comparison of all the modelling techniques and the corresponding algorithms. [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]

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]

A reduction in greenhouse gases has become an inescapable requirement. An effective scenario for achieving carbon neutrality is to develop a hydrogen economy. Its success, however, requires strict control of the different processes involved in planned hydrogen chains. The energy chain considered in this paper is a stationary application which involves the production of hydrogen by electrolysis (a power-to gas process) and its combustion in gas turbine combined cycles to generate electricity (a gas-to-power process). In such applications, the need is twofold: (i) to control the risk of explosive atmospheres by performing safe gas detection in the presence of hydrogen and (ii) to secure the reliability of all chain processes using hydrogen-rich gases by achieving reliable analyses of these gases. This paper is dedicated to the development of hydrogen energy to decarbonize the thermal production of electricity. We will first describe the hydrogen chain that would best suit the power generation sector. Then, we will highlight the properties of hydrogen that are critical for its reliable operation. Finally, we will review the sensing technologies suitable for hydrogen-containing fuels. This review paper was published as part of a Joint Industrial Project (JIP) aimed at enabling the safe and reliable deployment of hydrogen energy. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of determining the optimal capacity of concentrated solar power (CSP) plants, especially in the context of hybrid solar power plants. This work presents an innovative analytical approach to optimizing the capacity of concentrated solar plants. The proposed method is based on the use of additional non-dimensional parameters, in particular, the design factor and the solar multiple factor. This paper presents a mathematical optimization model that focuses on the capacity of concentrated solar power plants where thermal storage plays a key role in the energy source. The analytical approach provides a more complete understanding of the design process for hybrid power plants. In addition, the use of additional factors and the combination of the proposed method with existing numerical methods allows for more refined optimization, which allows for the more accurate selection of the capacity for specific geographical conditions. Importantly, the proposed method significantly increases the speed of computation compared to that of traditional numerical methods. Finally, the authors present the results of the analysis of the proposed system of equations for calculating the levelized cost of electricity (LCOE) for hybrid solar power plants. The nonlinearity of the LCOE on the main calculation parameters is shown. [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]

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]

This paper presents a literature review in which methodologies employed for the forecast of the price of stock companies and raw materials in the fields of electricity, oil, gas and energy are studied. This research also makes an analysis of which data variables are employed for training the forecasting models. Three scientific databases were consulted to perform the present research: The Directory of Open Access Journals, the Multidisciplinary Digital Publishing Institute and the Springer Link. After running the same query in the three databases and considering the period from January 2017 to December 2021, a total of 1683 articles were included in the analysis. Of these, only 13 were considered relevant for the topic under study. The results obtained showed that when compared with other areas, few papers focus on the forecasting of the prices of raw materials and stocks of companies in the field under study. Furthermore, most make use of either machine learning methodologies or time series analysis. Finally, it is also remarkable that some not only make use of existing algorithms but also develop and test new methodologies. [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]

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]

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]

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]

Achieving clean energy goals will require significant advances in regard to addressing the computational needs for next-generation renewable-dominated power grids. One critical obstacle that lies in the way of transitioning today's power grid to a renewable-dominated power grid is the lack of a faster-than-real-time stability assessment technology for operating a fast-changing power grid. This paper proposes an artificial intelligence (AI) -based method that predicts the system's stability margin information (e.g., the frequency nadir in the frequency stability assessment and the critical clearing time (CCT) value in the transient stability assessment) directly from the system operating conditions without performing the conventional time-consuming time-domain simulations over detailed dynamic models. Since the AI method shifts the majority of the computational burden to offline training, the online evaluation is extremely fast. This paper has tested the AI-based stability assessment method using multiple dispatch cases that are converted and tuned from actual dispatch cases of the Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. The results show that the AI-based method could accurately predict the stability margin of such a large power system in less than 0.2 milliseconds using the offline-trained AI agent. Therefore, the proposed method has great potential to achieve faster-than-real-time stability assessment for practical large power systems while preserving sufficient accuracy. [ABSTRACT FROM AUTHOR]

This paper focuses on the possible roles of aggregators in the European electricity markets and the challenges and opportunities they face in participating in different market segments. Demand response (DR) is becoming increasingly important with the growth of renewable energy, and aggregators can play a critical role in balancing supply and demand in real time. This paper provides an overview of prices in electricity markets in which DR aggregators can participate and provides recommendations for aggregators regarding which markets to focus on. However, the regulatory framework for aggregators is still evolving in Europe, creating challenges for them to navigate different market designs, regulatory frameworks, and pricing mechanisms. Through a combination of literature review and data analysis, this paper aims to provide insights for aggregators on how to maximize profits and minimize risks in the European electricity market. The article achieves this by conducting an extensive analysis of various markets, comparing their essential attributes relevant to the functioning of aggregators. [ABSTRACT FROM AUTHOR]

This paper explores the role of electricity in the transition to renewable energy to mitigate climate change. A systematic literature review with the Scopus database identified 92 papers relevant to the renewable electricity transition. A PESTLE (Political, Economic, Sociological, Technological, Legal, and Environmental) review of the papers provided a multidisciplinary analysis. The Paris Agreement created a global movement for carbon neutrality to address the threats of climate change, calling for a transition to renewable electricity to lead the way and expand into new sectors and regions. Although smaller renewable technologies are ramping up, complexities thwarting the transition include locked-in assets, high upfront costs, variability of solar and wind energy, infrastructure, difficulty in decarbonizing transportation and industry, material resource constraints, and fossil fuel support. This research found that renewables are not replacing fossil fuels to date but adding further energy demands, so that greenhouse gas emissions rose in 2021 despite an increased renewable electricity share. Without a major shift in the trillions of dollars of subsidies and investment away from fossil fuels to renewables, catastrophic climate change is predicted. This paper found that the Paris Agreement's commitment to net-zero carbon and the transition to renewable electricity are undermined by record-high levels of subsidies and financing for fossil fuel industry expansion. Transitioning to a climate-neutral economy requires an investment away from fossil fuels into renewable energy ecosystems. Renewable electricity provides possibilities to realize sustainable development goals, climate stabilization, job creation, a green economy, and energy security with careful planning. [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]

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]

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]

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]

Due to climate change, emission balancing is a relevant tool to quantify the environmental impact of a building system. The electrification of energy production at a national level, as well as energy supply at a building level, shifts the focus to the emission factor (EF) of the electricity grid. Currently, static EFs are used for calculating the emission balance. However, the electricity grid already shows fluctuations in power generation and EF due to renewable energies. The paper reviews recent literature outlining the research gap and presents the development of a simulation setup and concept, in which the emission balance of the building operation changes, using dynamic EFs that map fluctuations at an hourly resolution. In the first step, we simulate the thermal building and radiance performance. The data are than used in a second step to conduct a system simulation, which analyzes the effects of the dynamic EFs. The results show that the dynamic balance approach for different building system variants deviates considerably from the static approach. By comparing different concepts for the loading strategy, the predictive strategy outperforms a common control strategy, when considering the energy prices and/or the emissions. This is especially true for systems with inert storage units, where charging times significantly influence the balance, as well as for systems with PV integration. This paper outlines the potential of the EFs-optimized control increases when evaluating a potential scenario for the year 2040, factoring in increased seasonal and daily fluctuations in electricity generation. [ABSTRACT FROM AUTHOR]

In the field of renewable energies, the logistical intricacies of production, as well as the use and storage of photovoltaic energy, have become critical issues. In addition to sheer quantity, the stability of this type of energy is a crucial factor in ensuring the reliability and consistency of power generation. This paper defines Solar Irradiance Stability Factors (SISFs) as indicators complementing electricity production. When planning solar energy production in each geographical area, both the quantity and stability of solar irradiance are necessary for exploitation and determining the quality of solar irradiance. While the average production of solar energy per unit area in each time interval is a widely used parameter in daily practice, the observation of the amplitude of solar irradiance and its influence on energy production in the observed time interval is currently still rare. The SISFs defined in this article are new metrics that mainly depend on the meteorological variability in an area, and the observed time intervals should be in the range of seconds, minutes, or even hours. Larger time intervals are not helpful for the stability of solar irradiance in energy production and logistics from the source to the destination. They provide a complementary and more accurate measure of how suitable a particular geographical area is for producing solar energy. [ABSTRACT FROM AUTHOR]

With the explosive development of power edge equipment and the continuous improvement in power inspection performance, the requirements of substations and terminal equipment, such as drones with limited resources, cannot meet the strict delay and energy consumption requirements. This paper proposes an adaptive partitioning strategy for heterogeneous substation inspection systems. First, a layer delay prediction model and layer energy consumption prediction model are established on each heterogeneous node, and nonlinear characteristics related to delay and energy consumption are trained. On this basis, a deep neural network (DNN) hybrid partitioning strategy is proposed. The DNN task is divided into synchronous cooperative reasoning between terminal devices and multi-heterogeneous edge nodes. The experimental results show that the average absolute percentage error (MAPE) of the delay model was reduced by 31.49% on average. On drones and mobile edge nodes, the energy consumption model MAPE reduced the average by 21.92%, and the DNN end-to-end latency was reduced by 31.48%. The total cost of the system was reduced and the efficiency of UAV inspection was improved. [ABSTRACT FROM AUTHOR]

The noticeable dynamic development of electromobility poses new challenges for the energy industry and users of electric vehicles. One of these challenges is coping with the change in the way we refuel. In the case of electric vehicles, the batteries can also be charged via home electrical installations. The presence of a new, non-linear load with significant power may affect the quality parameters of electricity in this installation and therefore indirectly affect the operation of other loads. In order to investigate the possible impact of the electric vehicle charging process on these parameters, a number of measurements were carried out. This paper presents the results of observing voltage distortions in the installation as a result of the harmonics of the current supplying the AC/DC converter in the vehicle. The test results confirm the compliance of the voltage with the requirements of the standards; however, the large share of current harmonics requires the analysis of the anti-shock protections existing in the installation to ensure that they are effective when currents at higher frequencies flow. The research results may be a guide for designers and users of home electrical installations. [ABSTRACT FROM AUTHOR]

Wind energy is a clean energy source that is characterised by significant uncertainty. The electricity generated from wind power also exhibits strong unpredictability, which when integrated can have a substantial impact on the security of the power grid. In the context of integrating wind power into the grid, accurate prediction of wind power generation is crucial in order to minimise damage to the grid system. This paper proposes a novel composite model (MLL-MPFLA) that combines a multilayer perceptron (MLP) and an LSTM-based encoder–decoder network for short-term prediction of wind power generation. In this model, the MLP first extracts multidimensional features from wind power data. Subsequently, an LSTM-based encoder-decoder network explores the temporal characteristics of the data in depth, combining multidimensional features and temporal features for effective prediction. During decoding, an improved focused linear attention mechanism called multi-point focused linear attention is employed. This mechanism enhances prediction accuracy by weighting predictions from different subspaces. A comparative analysis against the MLP, LSTM, LSTM–Attention–LSTM, LSTM–Self_Attention–LSTM, and CNN–LSTM–Attention models demonstrates that the proposed MLL-MPFLA model outperforms the others in terms of MAE, RMSE, MAPE, and R 2 , thereby validating its predictive performance. [ABSTRACT FROM AUTHOR]

Accurate energy consumption prediction is crucial for addressing energy scheduling problems. Traditional machine learning models often struggle with small-scale datasets and nonlinear data patterns. To address these challenges, this paper proposes a hybrid grey model based on stacked LSTM layers. This approach leverages neural network structures to enhance feature learning and harnesses the strengths of grey models in handling small-scale data. The model is trained using the Adam algorithm with parameter optimization facilitated by the grid search algorithm. We use the latest annual data on coal, electricity, and gasoline consumption in Henan Province as the application background. The model's performance is evaluated against nine machine learning models and fifteen grey models based on four performance metrics. Our results show that the proposed model achieves the smallest prediction errors across all four metrics (RMSE, MAE, MAPE, TIC, U1, U2) compared with other 15 grey system models and 9 machine learning models during the testing phase, indicating higher prediction accuracy and stronger generalization performance. Additionally, the study investigates the impact of different LSTM layers on the model's prediction performance, concluding that while increasing the number of layers initially improves prediction performance, too many layers lead to overfitting. [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]

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]

Net energy, that is, the energy remaining after accounting for the energy "cost" of extraction and processing, is the "profit" energy used to support modern society. Energy Return on Investment (EROI) is a popular metric to assess the profitability of energy extraction processes, with EROI > 1 indicating that more energy is delivered to society than is used in the extraction process. Over the past decade, EROI analysis in particular has grown in popularity, resulting in an increase in publications in recent years. The lack of methodological consistency, however, among these papers has led to a situation where inappropriate comparisons are being made across technologies. In this paper we provide both a literature review and harmonization of EROI values to provide accurate comparisons of EROIs across both thermal fuels and electricity producing technologies. Most importantly, the authors advocate for the use of point-of-use EROIs rather than point-of-extraction EROIs as the energy "cost" of the processes to get most thermal fuels from extraction to point of use drastically lowers their EROI. The main results indicate that PV, wind and hydropower have EROIs at or above ten while the EROIs for thermal fuels vary significantly, with that for petroleum oil notably below ten. [ABSTRACT FROM AUTHOR]

Microhydraulic turbines offer a promising solution for decentralized energy production, suitable for both grid-connected and standalone applications, due to their compactness and high efficiency. This paper introduces a control approach for such systems employing microhydraulic turbines as distributed generators (DGs), utilizing six-phase induction generators for electricity production. This study emphasizes control strategies for both grid-connected and standalone modes utilizing proportional-integral (PI) controllers. An integrated energy storage system based on Li-Ion battery technology is also implemented to store the excess energy and compensate for production deficits to meet demand. The results obtained using MATLAB/Simulink demonstrate efficient and reliable power management among production sources, the grid and the local load, highlighting the unique contribution of employing a six-phase induction generator with the energy storage system. [ABSTRACT FROM AUTHOR]

Featured Application: Featured Application: This research offers a practical methodology for designing biogas plants, improving renewable energy production, and promoting sustainable agricultural practices. This paper aims to enhance the efficiency of biogas plants for the production of electrical and thermal energy by optimizing substrate selection and digester heating techniques. The study presents a comprehensive project of a biogas plant, including all necessary installations and a detailed methodology for equipment selection. Specific substrates used include animal flour, corn silage, and molasses, each chosen for their high dry matter content and biogas production efficiency. The digester heating technique was analyzed to maintain optimal fermentation temperatures, ensuring a stable and efficient biogas production process. The projected daily biogas production is estimated to be 5688 m3. The analysis shows that maintaining a constant digester temperature significantly improves biogas yield. Seasonal variations in thermal energy requirements were identified, highlighting the need for effective insulation and heating mechanisms, particularly during colder months. Additionally, the implementation of advanced heating techniques demonstrated a reduction in overall energy consumption for maintaining the desired digester temperature. This research provides valuable insights for the design and operation of efficient biogas plants. By optimizing substrate mixtures and digester heating, the study contributes to sustainable energy production, reduced greenhouse gas emissions, and enhanced biogas plant efficiency, supporting both environmental protection and economic development. [ABSTRACT FROM AUTHOR]

Sierra Leone is an electricity-poor country with one of the lowest electricity consumption per capita rates across sub-Saharan Africa. Yet, with ambitious targets to transform and stimulate its economy in the coming decades, energy demand forecasting becomes an integral component of successful energy planning. Through applying the MAED-D (version 2.0.0) demand software, this research study aims to generate Sierra Leone's electricity demand forecasts from 2023 to 2050. Three novel scenarios (baseline-, high-, and low-demand) are developed based on socio-economic and technical parameters. The baseline scenario considers the current electricity sector as business-as-usual; the high-demand scenario examines an ambitious development future with increased economic diversification and mechanisation, and the low-demand scenario examines more reserved future development. The modelled scenario results project an increase in electricity demand ranging from 7.32 PJ and 12.23 PJ to 5.53 PJ for the baseline-, high-, and low-demand scenarios, respectively, by 2050. This paper provides a base set of best-available data needed to produce an electricity demand model for Sierra Leone which can be used as a capacity-building tool for in-country energy planning alongside further integration into data modelling pipelines. [ABSTRACT FROM AUTHOR]

It is crucial to increase the resilience of the global food production and distribution systems against the growing concerns relating to factors that could cause global catastrophic infrastructure losses, such as nuclear war or a worldwide pandemic. Currently, such an event would result in the global loss of industry, including the ability to drill and refine crude oil. In such an event, the existing above-ground reserves of diesel and gasoline are likely to still be intact but would only be able to power the production and transportation of food between 158 days and 481 days with 80% confidence, where the mean is 195 days at current rates. This paper investigates a novel group of interventions in relation to the scenario of providing food under these conditions. It was found that by using a plausible combination of wood gasification, increasing vehicle utilisation rate, and reducing food consumption, the stockpile duration could increase to between 382 days and 1501 days with 80% confidence, where the mean is 757 days. This is an improvement in mean duration by a factor of 3.9. It was discovered that diesel is the limiting fuel in all scenarios due to wood gas only being a partial replacement for diesel fuel and also because of the prevalence of diesel engines in both the agricultural and trucking industries. A sensitivity analysis was completed identifying that reducing food consumption to minimum levels was the most effective method to prolong diesel reserves. The other factors that benefited from extending fuel reserves in terms of their effectiveness are reducing the lag time before gasification devices are installed, increasing the rate at which gasification devices are installed, and increasing the agricultural equipment utilisation rate. [ABSTRACT FROM AUTHOR]

Electricity theft comes with various disadvantages for power utilities, governments, businesses, and the general public. This continues despite the various solutions employed to detect and prevent it. Some of the disadvantages of electricity theft include revenue loss and load shedding, leading to a disruption in business operations. This study aimed to conduct a systematic literature review to identify what technology solutions have been offered to solve electricity theft and the effectiveness of those solutions by considering peer-reviewed empirical studies. The systematic literature review was undertaken following the guidelines for conducting a literature review in computer science to assess potential bias. A total of 11 journal articles published from 2012 to 2022 in SCOPUS, Science Direct, and Web of Science were analysed to reveal solutions, the type of theft addressed, and the success and limitations of the solutions. The findings show that the focus in research is channelled towards solving electricity theft in Smart Grids (SGs) and Advanced Metering Infrastructure (AMI); moreover, there is a neglect in the recent literature on finding solutions that can prevent electricity theft in countries that do not have SG and AMI installed. Although the results reported in this study are confined to the analysed research papers, the leading limitation in the selected studies, lack of real-life data for dishonest users. This study's contribution is to show what technology solutions are prevalent in solving electricity theft in recent years and the effectiveness of such solutions. [ABSTRACT FROM AUTHOR]

With the energy structure transition and the development of the green power market, the role of electricity retailers in multi-market green power trading has become more and more important. Particularly in China, where aggressive green energy policies and rapid market transformations provide a distinct context for such studies, the challenges are pronounced. Under demand-side response, electricity retailers face the uncertainty of users' electricity consumption and incentives, which complicates decision-making processes. The purpose of this paper is to explore the optimization decision-making problem of multi-market green power trading for electricity retailers under demand-side response, with a special focus on the Chinese market due to its leadership in implementing substantial green energy initiatives and its potential to set precedents for global practices. We first construct a two-party benefit optimization model, which comprehensively considers the profit objectives for electricity retailers and utility maximization for users. Then, the model is solved by the Lagrange multiplier method and distributed subgradient algorithm to obtain the optimal solution. Finally, the effectiveness of the incentive optimization strategy under the multi-market to promote green power consumption and improve the profit of electricity retailers is verified by arithmetic simulation. The results of this study show that the incentive optimization strategy under multi-market, particularly within the Chinese context, is expected to provide a reference for electricity retailers to develop more flexible and effective trading strategies in the green power market and to contribute to the process of promoting green power consumption globally. [ABSTRACT FROM AUTHOR]

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represents a fundamental effort that can inform artificial intelligence applications in general. In this paper, a comprehensive study is reported regarding day-ahead electricity load forecasting. For this purpose, three sequence-to-sequence (Seq2seq) deep learning (DL) models are used, namely the multilayer perceptron (MLP), the convolutional neural network (CNN) and the ensemble learning model (ELM), which consists of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL, emphasizing the way the datasets are trained and fine-tuned for higher forecasting accuracy. In order to implement the forecasting strategies using deep learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1–24 h) for each month of the year for the Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. Four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only the Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for the Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) involves the training of the DL models using the Lesvos dataset, and the testing period is performed directly on the Chios dataset without fine-tuning. The fourth strategy is a multi-task deep learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. The results obtained demonstrate that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, DTL Case 1 and 2 outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it is concluded that the MLP model presents the best monthly forecasts with MAPE values of 6.24% and 6.01% for the first two cases, the CNN model presents the best monthly forecasts with MAPE values of 5.57% and 5.60%, respectively, and the ELM model achieves the best monthly forecasts with MAPE values of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve. [ABSTRACT FROM AUTHOR]

Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers compromise their smart meters (SMs) to downscale the reported electricity consumption readings. This problem costs electric utility companies worldwide considerable financial burdens and threatens power grid stability. Therefore, several machine learning (ML)-based solutions have been proposed to detect electricity theft; however, they have limitations. First, most existing works employ supervised learning that requires the availability of labeled datasets of benign and malicious electricity usage samples. Unfortunately, this approach is not practical due to the scarcity of real malicious electricity usage samples. Moreover, training a supervised detector on specific cyberattack scenarios results in a robust detector against those attacks, but it might fail to detect new attack scenarios. Second, although a few works investigated anomaly detectors for electricity theft, none of the existing works addressed consumers' privacy. To address these limitations, in this paper, we propose a comprehensive federated learning (FL)-based deep anomaly detection framework tailored for practical, reliable, and privacy-preserving energy theft detection. In our proposed framework, consumers train local deep autoencoder-based detectors on their private electricity usage data and only share their trained detectors' parameters with an EUC aggregation server to iteratively build a global anomaly detector. Our extensive experimental results not only demonstrate the superior performance of our anomaly detector compared to the supervised detectors but also the capability of our proposed FL-based anomaly detector to accurately detect zero-day attacks of electricity theft while preserving consumers' privacy. [ABSTRACT FROM AUTHOR]

Tajikistan holds the eighth position globally in terms of hydropower potential, estimated at 527 terawatt-hours (TWh), with a technically exploitable capacity of 317 TWh. Only 4–6 percent of this immense potential is currently utilized. In this paper, employing a combination of the Johansen cointegration test, vector autoregression, and the Granger-causality test on annual data from 1993 to 2021, we examine the causality relationship between electricity production and key macroeconomic variables, including gross domestic product (GDP), GDP per capita, exports, imports, final consumption, capital investment, and employment, in Tajikistan. The empirical findings reveal a positive unidirectional causality from electricity production to exports and imports. A positive bidirectional or feedback causality is found between electricity production and variables such as GDP, GDP per capita, final consumption, and employment. No causality relationship between electricity production and variables such as trade openness and capital investment is observed. The exploration of complex causal relationships between electricity production and key macroeconomic variables in Tajikistan, as revealed in this study, offers a modest yet meaningful addition to academic discourse. It presents insights that may inform policymakers and stakeholders, albeit with a recognition of the limitations inherent in the findings. These insights could potentially guide the formulation of sustainable development strategies and shed light on the underutilized potential of the country's hydropower resources. [ABSTRACT FROM AUTHOR]

Bearing in mind European Green Deal assumptions regarding a significant reduction of green house emissions, electricity generation from Renewable Energy Sources (RES) is more and more important nowadays. Besides this, accurate and reliable electricity generation forecasts from RES are needed for capacity planning, scheduling, managing inertia and frequency response during contingency events. The recent three years have proved that Machine Learning (ML) models are a promising solution for forecasting electricity generation from RES. In this review, the 8-step methodology was used to find and analyze 262 relevant research articles from the Scopus database. Statistic analysis based on eight criteria (ML method used, renewable energy source involved, affiliation location, hybrid model proposed, short term prediction, author name, number of citations, and journal title) was shown. The results indicate that (1) Extreme Learning Machine and ensemble methods were the most popular methods used for electricity generation forecasting from RES in the last three years (2020–2022), (2) most of the research was carried out for wind systems, (3) the hybrid models accounted for about a third of the analyzed works, (4) most of the articles concerned short-term models, (5) the most researchers came from China, (6) and the journal which published the most papers in the analyzed field was Energies. Moreover, strengths, weaknesses, opportunities, and threats for the analyzed ML forecasting models were identified and presented in this paper. [ABSTRACT FROM AUTHOR]

With the rapid development of renewable energy sources such as wind power and photovoltaics, the penetration rate of power electronic equipment represented by grid-forming (GFM) and grid-following (GFL) converters in the power system has been increasing. The development of modern power systems is gradually showing a trend of high proportion of renewable energy and high proportion of power electronic devices (double-high), which profoundly changes the dynamic characteristics of modern power systems. Most of the converters in the power system adopt the GFL control strategy, which lacks the ability to support frequency and voltage. The converter with GFM control have gradually been incorporated into high-proportion renewable energy systems due to their voltage support and inertial response capabilities. However, the proportion configurations of GFM converter in the GFM/GFL hybrid system still needs to be further investigated. Therefore, this paper compares the basic principles and control methods of converters based on GFM strategy and GFL strategy, establishes the small-signal model of 100% REI system, and comparatively analyzes the dynamic characteristics of GFM and GFL converters. Secondly, a proportional configuration method for the GFM converter in 100% renewable electricity independent system (100% REI system) is proposed, and then through the small-signal model, the influence of different proportions of GFM converters and control mode on the stability of 100% REI system is discussed, the minimum configuration proportion of GFM converter to satisfy the stable operation of the system is explored. Finally, the correctness of the proportional configuration of GFM converters proposed in this paper and the engineering feasibility of independent operation of 100% REI system is verified. [ABSTRACT FROM AUTHOR]

Traditional centrifuges, extensively employed in biology, chemistry, medicine, and other domains for tasks such as blood separation and pathogen extraction, have certain limitations. Their high cost, substantial size, and reliance on electricity restrict their range of application. Contemporary centrifuges, inspired by everyday items like paper trays and egg beaters, boast characteristics such as ease of operation, independence from electricity, and portability. These features offer unique advantages in specific situations, such as electricity shortages, inadequate infrastructure, and challenging medical conditions. Consequently, we designed a hand-powered portable centrifuge driven by pulling a rope. Our experiments revealed significant performance factors, including load capacity, rope length, and frequency of rope pulling. The results demonstrated that the revolutions per minute (RPM) of a hand-powered portable centrifuge were directly proportional to the length of the rope and the frequency of pulling, up to a certain limit, while inversely proportional to the load. When used for separating and washing polystyrene microspheres, the portable centrifuge's performance equaled that of traditional centrifuges. According to relevant calculations, this centrifuge could be capable of meeting the application of blood separation. Therefore, we believe this portable centrifuge will find meaningful applications in similar areas, particularly in resource-poor settings. [ABSTRACT FROM AUTHOR]

In an electricity and heat integrated energy system, the transmission of thermal energy encounters significant delays, and the delays are often not integer multiples of the dispatch interval. This mismatch poses challenges for achieving coordinated dispatch with the electric power system. To address this problem, the fictitious node method is proposed in this paper, offering a novel approach to calculating the quasi-dynamic characteristics of the heating network. Furthermore, to enhance the local consumption capacity of wind power, the heat storage capacity of the heat supply network was taken into consideration in this study, and a combined energy supply model equipped with electric boilers, incorporating combined heat and power (CHP) units and gas turbine units, was developed. This model effectively expands the operational range of CHP units and enables the decoupling of electricity and heat operations in gas turbine units. The analysis conducted demonstrated the effectiveness of the proposed method and model in achieving the coordinated dispatch of electricity and heat. Moreover, it highlighted the positive impact on the overall economy of system operation and the promotion of wind power consumption. The optimal configuration presented in this paper resulted in an 8.2% improvement in system operating economics and a 38.3% enhancement in wind power integration. [ABSTRACT FROM AUTHOR]

Electricity load forecasting is of great significance for the overall operation of the power system and the orderly use of electricity at a later stage. However, traditional load forecasting does not consider the change in load quantity at each time point, while the information on the time difference of the load data can reflect the dynamic evolution information of the load data, which is a very important factor for load forecasting. In addition, the research topics in recent years mainly focus on the learning of the complex relationships of load sequences in time latitude by graph neural networks. The relationships between different variables of load sequences are not explicitly captured. In this paper, we propose a model that combines a differential learning network and a multidimensional feature graph attention layer, it can model the time dependence and dynamic evolution of load sequences by learning the amount of load variation at different time points, while representing the correlation of different variable features of load sequences through the graph attention layer. Comparative experiments show that the prediction errors of the proposed model have decreased by 5–26% compared to other advanced methods in the UC Irvine Machine Learning Repository Electricity Load Chart public dataset. [ABSTRACT FROM AUTHOR]

Potassium (K) channels show the highest variability and most frequent alterations in expression in many tumor types, and modulation of K + channels may represent a new window for cancer therapy. In previous work, we found that a terahertz (THz) field incident along the z -axis with a frequency of 51.87 THz increased the ion flux through K + channels. In practice, it is difficult to ensure that the incident electromagnetic (EM) wave is strictly parallel to the direction of channel ion flow. In this paper, we found by changing the direction of the applied electric field that the EM wave of a specific frequency has the largest ion flux when the incident direction is along the ion flow, and the smallest ion flux when the incident direction is perpendicular to the ion flow, and that overall the EM wave of this frequency enhances the ion flow of the K + channel. Changes in the direction of the applied field at a specific frequency affect the stability of the φ dihedral angle of the GLY77 residue and alter the ion permeation mechanism in the selectivity filter (SF) region, thus affecting the ion flux. Therefore, this frequency can be used to modulate K + fluxes by THz waves to cause rapid apoptosis in potassium-overloaded tumor cells. This approach consequently represents an important tool for the treatment of cancer and is expected to be applied in practical therapy.

Utilizing renewable energy (RE) sources can provide a realistic answer to the problem of many nations' energy needs. This paper reviews the current status of using RE to produce electricity in the Kingdom of Saudi Arabia (KSA). The primary aim of the review is to identify and analyze the solar- and wind-energy utilization, problems, and future trends in KSA while taking into account all relevant aspects, associated development hurdles, and suggested solutions. All involved parties will receive useful results and conclusions from a thorough examination and analysis of the pertinent literature that can assist them in understanding the potential of solar and wind energy, considering all the linked issues. [ABSTRACT FROM AUTHOR]

The current formal common denominator of the electricity supply system in Europe has been the Harmonized Electricity Market Role Model (HEMRM) set up by ENTSO-E, ebIX, and EFET at the turn of the millennium; it introduced the concept of de-coupling and the vertical structuring of the system into the previously vertically integrated system. Since then, within demonstration projects, the system has been undergoing further changes in a controlled environment, generating bottom-up energy, caused by new technologies, business models, and new players, and extending the concept of the system to the level of energy communities and prosumers. Therefore, this paper proposes a coherent approach to the extension of HEMRM to the lowest levels in both the grid and market segments—full harmonization. This entails further structuring of both segments downwards and applying the principles of vertically nested subsystems—a system of systems approach—to a unit functional level of the electricity system, which can be the prosumer itself. At the lowest levels, the de-coupled system becomes coupled; additionally, it cross-sects with other energy vectors. Complete harmonization reduces the number of system and market segments and represents system standardization, leading to both subsystem and system-wide optimization. Prerequisites for it include the automated trading of flexibilities by the prosumers and implicit trading of energy transfer capacities along the distribution grids. The energy reservoirs, implicit and explicit, short-term, and long-term, play a vital role in techno-economic balancing. [ABSTRACT FROM AUTHOR]

Accurate electricity forecasting is essential for smart cities to maintain grid stability by allocating resources in advance, ensuring better integration with renewable energies, and lowering operation costs. However, most forecasting models that use machine learning cannot handle the missing values and possess a single point of failure. With rapid technological advancement, smart cities are becoming lucrative targets for cyberattacks to induce packet loss or take down servers offline via distributed denial-of-service attacks, disrupting the forecasting system and inducing missing values in the electricity load data. This paper proposes a collective intelligence predictor, which uses modular three-level forecasting networks to decentralize and strengthen against missing values. Compared to the existing forecasting models, it achieves a coefficient of determination score of 0.98831 with no missing values using the base model in the Level 0 network. As the missing values in the forecasted zone rise to 90% and a single-model forecasting method is no longer effective, it achieves a score of 0.89345 with a meta-model in the Level 1 network to aggregate the results from the base models in Level 0. Finally, as missing values reach 100%, it achieves a score of 0.81445 by reconstructing the forecast from other zones using the meta-model in the Level 2 network. [ABSTRACT FROM AUTHOR]