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The significant growth in demand for electricity has led to increasing congestion on distribution networks. The challenge is twofold: it is needed to expand and modernize our grid to meet this increased demand but also to implement smart grid technologies to improve the efficiency and reliability of electricity distribution. In order to mitigate these congestions, novel approaches by using flexibility sources such as battery energy storage can be used. This involves the use of battery storage systems to absorb excess energy at times of low demand and release it at peak times, effectively balancing the load and reducing the stress on the grid. In this paper, two optimal power flow formulations are discussed: the branch flow model (non-convex) and the relaxed bus injection model (convex). These formulations determine the optimal operation of the flexibility sources, i.e., battery energy storage, with the objective of minimizing power losses while avoiding congestions. Furthermore, a comparison of the performance of these two formulations is performed, analyzing the objective function results and the flexibility operation. For this purpose, a real Spanish distribution network with its corresponding load data for seven days has been used. [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]

Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. This paper presents a comprehensive survey of the state-of-the-art technologies and models employed in the load forecasting process of BTM DERs in recent years. The review covers a wide range of models, from traditional approaches to machine learning (ML) algorithms, discussing their applicability. A rigorous validation process is essential to ensure the model's precision and reliability. Cross-validation techniques can be utilized to reduce overfitting risks, while using multiple evaluation metrics offers a comprehensive assessment of the model's predictive capabilities. Comparing the model's predictions with real-world data helps identify areas for improvement and further refinement. Additionally, the U.S. Energy Information Administration (EIA) has recently announced its plan to collect electricity consumption data from identified U.S.-based crypto mining companies, which can exhibit abnormal energy consumption patterns due to rapid fluctuations. Hence, some real-world case studies have been presented that focus on irregular energy consumption patterns in residential buildings equipped with BTM DERs. These abnormal activities underscore the importance of implementing robust anomaly detection techniques to identify and address such deviations from typical energy usage profiles. Thus, our proposed framework, presented in residential buildings equipped with BTM DERs, considering smart meters (SMs). Finally, a thorough exploration of potential challenges and emerging models based on artificial intelligence (AI) and large language models (LLMs) is suggested as a promising approach. [ABSTRACT FROM AUTHOR]

The pursuit of economic growth has implications for carbon emissions and climate change. Achieving low carbon development is important for attaining the targets of the sustainable development goals. Africa is often described as a largely import-dependent continent. The continent also requires significant investment in information and communication technologies (ICT) and renewable energy to achieve low-carbon economic growth. However, empirical evidence on the joint impacts of imports of goods and services, clean energy use, ICT, and economic growth on carbon emissions in Africa is scanty and mixed. This paper investigated the impacts of information and communication technologies, renewable energy consumption, import and economic growth on carbon emissions by using rich data on total per capita carbon dioxide (CO2) emissions, economic growth, import of goods and services, renewable energy consumption, fixed telephone subscriptions, mobile cellular subscriptions, and individuals using the internet in Africa (2001 ─ 2020) obtained from the World development indicators (WDI) database. Using the Panel autoregressive distributed lag model (PARDL), we found that mobile cellular subscriptions, and level of economic growth significantly increased per capita CO2 emissions in Africa in the long run while renewable energy consumption and technologies and import of goods and services significantly decreased per capita CO2 emissions in the long run. We conclude that information and communication technologies, level of economic growth, import of goods and services, and renewable energy consumption exert impacts on carbon emissions in Africa. Highlights: Achieving low-carbon development in Africa is possible. This research investigated the impacts of information and communication technologies, renewable energy consumption, import and economic growth on carbon emissions in Africa. This study utilized the Panel autoregressive distributed lag model and panel data from 31 African countries. The results shed new light on the emission reduction potential of renewable energy consumption and import of goods and services. The study also shows that ICTs increase carbon emissions in Africa. This article offers recommendations concerning carbon emissions reduction. [ABSTRACT FROM AUTHOR]

There is a tremendous growth in fabrication from small scale integration (SSI) to giant scale integration (GSI). It however raises a question of sustainability of Moore's law due to almost intolerable levels of power consumption. Researchers have invented a lot of methods to reduce power consumption and recent technologies are switching to reversible logic. Reversible logic has various applications in fields of computer graphics, optical information processing, quantum computing, DNA computing, ultra low power CMOS design and communication. ALU is considered to be the basic building block of a CPU in the computing environment and portability in computing system highly demands reversible logic based ALU. Modern processors usually have a word length of 32 or 64 bits. Divide and conquer approach principle cascades n number of 1 bit ALU to implement n bit ALU. Several researchers have proposed 1-bit ALU design using various reversible logic gates. This paper aims at categorizing various ways of implementation in VHDL using Xilinx ISE design suit 14.2 tool and comparative analysis of existing 1 bit ALU designs in terms of optimization metrics like power consumption, number of gates, number of constant inputs, number of garbage outputs and quantum cost. ALU realized using carry save adder block is found to be most optimum design in terms of gate count and quantum cost. [ABSTRACT FROM AUTHOR]

This paper aims to generate electricity, by decreasing fossil fuels consumption and conserving electricity for further use. In this project, the method is to produce pollution-free electricity using the technique i.e. piezoelectric effect technique. By applying mechanical stress electric charge is generated known as the piezoelectric effect. A sufficient amount of energy is produced as it can reduce the damage of pollution caused by power plants. So to make the use of moving vehicles on road, power generated helps the environment last longer. Different shape, proof mass geometries are analyzed and plotted a graph between frequency versus voltage and electrical energy. Among them, it is identified that stacked cylindrical block has a good Voltage of 2.04mV (milliVolts = 10-3V) and Energy of 21.2fJ (femtoJoule = 10-15J). [ABSTRACT FROM AUTHOR]

To address the current problem of high energy consumption in data centers, this paper proposes a data center heat pipe air-conditioning system based on the latent heat of water evaporation, which uses the latent heat of water evaporation for cooling by creating a low pressure environment to evaporate large amounts of water. In order to verify the effect of the system, a heat pipe test bench based on the latent heat of water evaporation was designed and built. Compared with the traditional heat pipe in the data center for heat dissipation, the performance and economy of the water evaporation latent heat pipe system designed in this paper are analyzed experimentally. A multi-physics coupled model of water evaporation latent heat pipe air-conditioning based on COMSOL Multiphysics was established to simulate and study the temperature field and velocity field distribution of water evaporation latent heat pipe air-conditioning system in data centers. The research shows that: -- Under the designed test conditions, compared with the traditional heat pipe system, the water evaporation latent heat pipe air conditioner can conduct 2540 kJ more heat in one day in an outdoor environment of 24 °C. -- At an ambient temperature of 3 5°C and an indoor temperature of 25.8 °C, the cooling capacity of the heat pipe in the data center water evaporation latent heat pipe air-conditioning system is twice the cooling capacity of the air conditioner, and the heat pipe can work efficiently regardless of the outdoor ambient temperature. -- The energy-saving effect of the latent heat pipe of water evaporation in the data center has a significant effect on air conditioners with an energy efficiency rating (EER) lower than 2.5-4.4. It can improve the energy efficiency of level 5 with an EER of 2.5 to level 2 with an EER of 3.22, greatly reducing the power consumption of the data center air-conditioning system. When the EER of the air conditioner exceeds 4.4, the coefficient of performance of the data center water evaporation latent heat pipe air-conditioning system will be lower than that of the air conditioner itself. [ABSTRACT FROM AUTHOR]

Renewable energy resources can be deployed locally and efficiently using the concept of microgrids. Due to the natural uncertainty of the output power of renewable energy resources, the planning for a proper operation of microgrids can be a challenging task. In addition, the information about the loads and the power consumption of them can create benefits to increase the efficiency of the microgrids. However, electrical loads can have uncertainty due to reasons such as unpredictable behavior of the consumers. To exploit a microgrid, energy management is required at the upper level of operation and control in order to reduce the costs. One of the most important tasks of the energy management system is to satisfy the loads and, in other words, develop a plan to maintain equilibrium between the power generation and power consumption. To obtain information about the output power of renewable energy resources and power consumption, deep learning can be implemented as a powerful tool, which is able to predict the desired values. In addition, weather conditions can affect the output power of renewable energy-based resources and the behavior of the consumers and, as a result, the power consumption. So, deep learning can be deployed for the anticipation of the weather conditions. This paper will study the recent works related to deep learning, which has been implemented for the prediction of the output power of renewable energy resources (i.e., PVs and wind turbines), electrical loads, and weather conditions (i.e., solar irradiance and wind speed). In addition, for possible future directions some strategies are suggested, the most important of which is the implementation of quantum computing in cyber–physical microgrids. [ABSTRACT FROM AUTHOR]

Because the traditional methods do not select the best feature collection in feature analysis, the accuracy and effectiveness of user feature clustering are not high, and the accuracy of user feature classification is not high. Therefore, this paper proposes a customer feature analysis method based on power consumption feature selection and behavior portrait of different people. The optimal feature set is obtained according to the maximum correlation and minimum redundancy criterion, and the user portrait task is described. The spatial feature domain classification method is used to classify the user portrait information, and the user label database is constructed according to the classification results. The AP clustering algorithm is used to cluster the power user portrait information and complete the customer feature analysis. Experimental results show that this method effectively improves the accuracy and effectiveness of user feature clustering, and the accuracy of user feature classification is high, indicating that the application effect is good. [ABSTRACT FROM AUTHOR]

Avoiding heat stress in cows is an important condition for animal productivity and the maintaining of animal health. For this, it is necessary to provide an optimal microclimate in cowsheds using systems of air cooling. The paper analyzes one of these systems—an air humidification–cooling system. The research was carried out in a semi-insulated box-type cowshed containing 244 places. The changes in temperature, relative humidity, and temperature humidity index (THI) were studied for the air coming from outside and for the air inside the cowshed. Considering the fact that the cows were in the cowshed most of the time (51.5%) under heat stress, the use of a cooling system is appropriate. It was established that a cooling system is capable of compensating for heat released by animals. It was determined that with an increase in air temperature the relative efficiency of a cooling system increases. An intensive constant air exchange provided using fan operation avoids an excessive growth of relative humidity in a cowshed. To reduce the consumption of electricity and water, the paper suggests regulation of both the power of the fans of the system and the water supply to the nozzles not using temperature but using THI. Theoretically, when THI is used to regulate the operation of the cooling system, the consumption of electrical energy is reduced by 17.8%, and the consumption of water is reduced by 43.2% when compared to the option when the temperature is used to regulate the operation of a cooling system. [ABSTRACT FROM AUTHOR]

This paper describes research on the influence the technology of zinc-coated steel wire manufacturing has on the energy and force parameters of the drawing process, energy consumption and zinc expenditure. In the theoretical part of the paper, the theoretical work and drawing power were calculated. Calculations of the electric energy consumption have shown that usage of the optimal wire drawing technology results in a 37% drop in energy consumption, which in the course of a single year translates to savings equal to 13 TJ. This, in turn, results in the decrease of CO2 emissions by tons and a total decrease of the eco-costs by approximately EUR 0.5 mln. Drawing technology also influences the losses of the zinc coating and CO2 emissions. Properly adjusted parameters of the wire drawing technology allow obtaining a zinc-coating that is 100% thicker, translating to 265 tons of zinc, whose production generates 900 tons of CO2 and incurs eco-costs equal to EUR 0.6 mln. Optimal parameters for drawing, from the perspective of decreased CO2 emissions during the zinc-coated steel wire manufacturing, are as follows: usage of the hydrodynamic drawing dies, angle of the die reducing zone α = 5°, and drawing speed of 15 m/s. [ABSTRACT FROM AUTHOR]

Industrial Kitchens are very energy-intensive businesses, consuming between five and seven times more energy per square meter than other commercial spaces like office spaces and retail stores. Still, very little research has been carried out on improving the energy efficiency of this industry. This paper presents the FIKElectricity dataset, a collection of electricity data from three Portuguese restaurant kitchens during their daily operation. The duration of the datasets spans three to four weeks in each industrial kitchen and comprises aggregated and individual appliance consumption, collected at 1 Hz and 1 5 Hz, respectively. The public release of FIKElectricity is expected to draw more attention from the research community to this overlooked industrial sector. The data collection and post-processing methods are thoroughly described in this paper, as well as the dataset organization. Examples showing the dataset's quality and instructions for its reuse are also provided. [ABSTRACT FROM AUTHOR]

The critical transformation of the energy sector demands innovative approaches to ensure the reliability and efficiency of energy systems. In this pursuit, this study delved into the potential of Deep Recurrent Neural Networks (DRNNs) for forecasting energy demand, using a comprehensive dataset detailing Kazakhstan's electrical consumption over a span of two years. Traditional statistical models have historically played a role in energy demand prediction, but the growing intricacy of the energy landscape calls for more advanced solutions. The paper presented a comparison of the DRNN with other traditional and machine learning models and highlighted the superior performance of DRNNs, especially in capturing complex temporal relationships. The energy sector is confronting unprecedented challenges due to population growth and the integration of diverse energy sources, leading to increased demand and system strains. Accurate energy demand prediction is essential for system reliability. Traditional models, though widely used, often overlook intricate variables like weather patterns and temporal factors. Through rigorous methodology, encompassing exploratory data analysis, feature engineering, and hyperparameter optimization, an optimized DRNN model was developed. The results demonstrated the DRNN's exceptional capability in processing complex time-series data, as evidenced by its attainment of an R-squared value of 83.6%. Additionally, it achieved Mean Absolute Errors and Root Mean Squared Errors of less than 2%. However, there were noticeable deviations in some predictions, suggesting areas for refinement. This research underscores the significance of DRNNs in energy demand prediction, highlighting their advantages over traditional models while also noting the need for ongoing optimization. The findings underscore DRNN's promise as a robust forecasting tool, pivotal for the energy sector's future resilience and efficiency. [ABSTRACT FROM AUTHOR]

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The increased use of refrigerants with low global warming potential GWP over the past decade indicates that manufacturers are already making gradual replacements with refrigerants with reduced environmental impact. Thus, in addition to HFC gas phase-out requirements, most EU countries are pursuing a trend to reduce the energy consumption of refrigeration appliances. Such countries restrict the import of equipment that does not meet the energy efficiency requirements. This paper presents the ecodesign of refrigeration equipment in relation to the requirements of the sales market using the Regulation on Conformity Assessment Procedure. HVAC&R equipment contributes to GHG emissions in two ways: directly through refrigerant leakage or venting, and indirectly through electricity use, the latter accounting for up to two-thirds of GHG emissions. The paper compares the energy and environmental characteristics of refrigerants R404a and R290 in freezer M200V. The experimental results show that the highest COP values in the system for R404a and R290 are 3.02 and 3.19, respectively. Possibilities of complete transition from F-gases to natural analogues with GWP ≈ 0 are shown. At application of R290 its advantages on energy efficiency are obvious - reduction of electric power consumption of freezing chests on 8,9 %, influence on environment R404A – GWP=3260, R290 – GWP=3. The calculation of energy efficiency class for freezing chest was made. To improve energy efficiency, a number of design modifications were made: the density of the thermal insulation material and the compressor were changed. The energy consumption of the modified version at the second climatic class was measured at 1.23 kW/24h, and the energy efficiency coefficient according to the calculation EEI =34.904, which corresponds to the energy efficiency class «C». [ABSTRACT FROM AUTHOR]

The growing demand for electricity in long-duration space missions has become a pressing concern. The space nuclear closed-Brayton-cycle (CBC) power generation system offers advantages in power output, operational lifespan, and range. However, a significant speed disparity exists between its compressor and alternator. To address this challenge, this paper proposes a double-rotor CBC configuration. A corresponding dynamic model that couples the nuclear reactor and radiator is formulated, and dynamic analysis is conducted to facilitate system control. The study delves into the dynamic start-up process of the double-rotor CBC system and examines how various component parameters impact its power generation performance. The findings indicate that through the introduction of suitable reactivity to regulate reactor power and the incorporation of a PID controller to manage flow distribution between two turbines, the system can achieve start-up within 5200 s. Moreover, the innovative double-rotor structure suggested in this paper enables the separation of compressor and alternator speeds. Consequently, the compressor and alternator can operate within their optimal speed ranges independently, which is a feature that holds potential benefits for the system's practical implementation. In addition, the steady-state operation of the system showcases the recuperator's heat transfer power at around 1127.60 kW, a parameter of significant importance. Following steady-state operation, the double-rotor CBC system demonstrated an electrical power output of 175.99 kW and a thermal efficiency of 32.38%. [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]

The measurement of electricity consumption at 15-minute granularity, including for households, is increasingly mandated in the EU and this also allows, once sufficient data have been collected, the prediction of future consumption at the same time intervals. In this paper, we present preliminary results of the industry project that aims to build AI models for next-day electricity consumption at 15-minute granularity. We have identified the main influencing factors, developed scripts and databases to collect data about these features and about the past electricity consumption at 15-minute granularity for each measuring point, and, finally, developed three AI models to predict the future electricity consumption for each 15-minute interval and each measurement point. We provide descriptive analyses for all measuring points that were in the database in April 2022 and show that for computing the prediction of accumulated electricity consumption at 15-minute granularity, it is much more accurate (in terms of mean absolute percentage error – MAPE) to compute the prediction for each measuring point and accumulate these predictions. An evaluation of the models on the list of the 10 outstanding measuring points (according to the data provider) shows that our predictions achieve very good MAPE. Additionally, we have provided an evaluation of possible ways of parallelization within R, and laid out results of a computational study using parallel, doParallel, and foreach R libraries. [ABSTRACT FROM AUTHOR]

One of the secrets for China's economic growth miracle is continuous investment in infrastructure, and the role of power infrastructure is irreplaceable. However, previous studies mostly focused on generation-side indicators rather than grid-side indicators, ignoring the importance of the mega-grid established in China. As a result, this paper is committed to exploring the economic growth pulling effect of power grids with different voltage levels in the nonlinear framework. On the premise of determining the robustness of estimation parameters by instrumental variable method, the panel smooth transition regression (PSTR) model is used to capture panel heterogeneity. To be exact, the regional economic growth driven by extra-high voltage (EHV) and ultra-high voltage (UHV) power grids presents a regime transition accompanied by the growth of regional power generation and electricity consumption per capita. This means that the mega-grid is more conducive to the economic growth of energy bases and load centers. To further build and improve the state-leveled mega-grid into a nationwide basic platform for trans-regional renewable energy transmission and distribution is a key measure to enhance China's sustainable economic growth under the constraints of carbon peak and carbon neutrality targets. [ABSTRACT FROM AUTHOR]

Cellular gravel based on alumino-silicate wastes including residual clay brick recovered from construction demolition and fly ash resulted from coal burning in energy industry was experimentally made. The expanding process made with glycerin as a liquid organic material together water glass was obtained by direct electromagnetic wave warming without affecting the structural integrity of the product. The special effectiveness of the direct irradiation of the material led to extremely low specific electricity consumption (0.53-0.64 kWh/kg). The optimal version had the following characteristics: bulk denseness of 0.21 g·cm-3, heat conductivity of 0.063 W·m-1 ·K-1, compression resistance of 3.9 MPa, flexural strength of 2.4 MPa, and cell dimension within the limits of 0.2-0.5 mm. The use of alumino-silicate wastes in association with direct microwave warming keeping the physical, thermal, mechanical and microstructural properties unchanged (almost similar to industrial products) constitutes the paper originality. [ABSTRACT FROM AUTHOR]

This paper examines the effects of time-of-use (TOU) pricing on distributed renewable energy (DRE) investment for a non-power generating firm. We develop an electricity consumption cost-minimization model by considering the intermittent generation as well as the firm's electricity consumption. It has been found that implementing full retail prices compensation for the surplus renewable electricity is probably not good as it may lead to DRE over-investment. Moreover, we find that the firm's optimal investment strategy is not necessarily sensitive to the price signal of TOU pricing (i.e., the ratio of peak to off-peak price). Particularly, when the service-level difference in meeting a firm's electricity consumption between peak and off-peak periods by adopting DRE technology is above a critical threshold in relation to the peak time, a strong price signal will not promote the firm's optimal DRE capacity investment. This paper yields a policy insight that "getting the time right" may be more important than "getting the price right" in terms of enabling DRE investment for TOU pricing design. [ABSTRACT FROM AUTHOR]

This paper aims to reconsider prosumers' role in the power markets in the early stages of their decentralization, accounting for rising self-supply trends, security threats, and economic and regulatory barriers. The development of prosumerism envisages finding the ratio between retail market sales under the feed-in tariff and the net billing mechanism. Within the methodology section, the indicator of prosumer efficiency for electricity generation (EUR/kWh) is proposed based on average consumption/production ratios and consumption/delivery incentives. To support household prosumers, the mentioned incentives on the renewable energy market consider the self-supply cost of electricity, the levelized cost of electricity for small-scale green energy facilities (solar photovoltaic and wind), and transaction costs. This paper evaluates prosumer efficiency under three consumption/production ratio scenarios for Ukrainian households (self-consumption of 40%, 20%, and 100% of green electricity annually generated by a household and selling the leftovers via the feed-in tariff) for 2023. The gradual movement from fixed tariffs for households toward market-based prices promotes the emergence of new related market players and their consolidation in the market. Participation in the organized power market segments is relevant for day-ahead market prices above 130 EUR/MWh, disregarding the households' tariff rate. The low price caps inhibit the prosumer's participation in the market, while the transition from the feed-in tariff to net billing significantly promotes their development only under high price caps. [ABSTRACT FROM AUTHOR]

Clustering-based reactive voltage partitioning is successful in reducing grid cascading faults, by using clustering methods to categorize different power-consuming entities in the power grid into distinct regions. In reality, each power-consuming entity has different electrical characteristics. Additionally, due to the irregular and uneven distribution of the population, the distribution of electricity consumption is also irregular and uneven. However, the existing method neglects the electrical difference among each entity and the irregular and uneven density distribution of electricity consumption, resulting in poor accuracy and adaptability of these methods. To address these problems, an enhanced density peak model-based power grid reactive voltage partitioning method is proposed in this paper, called EDPVP. First, the power grid is modeled as a weighted reactive network to consider entity electrical differences. Second, the novel local density and density following distance are designed to enhance the density peak model to address the problem that the traditional density peak model cannot adapt to weighted networks. Finally, the enhanced density peak model is further equipped with an optimized cluster centers selection strategy and an updated remaining node assignment strategy, to better identify irregular and uneven density distribution of electricity consumption, and to achieve fast and accurate reactive voltage partition. Experiments on two real power grids demonstrate the effectiveness of the EDPVP. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence and computer technology, grid corporations have also begun to move towards comprehensive intelligence and informatization. However, data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data. The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’ needs and their habits, providing better services for users. Nevertheless, users’ electricity consumption data is sensitive and private. In order to achieve highly efficient analysis of massive private electricity consumption data without direct access, a blockchain-based federated learning method is proposed for users’ electricity consumption forecasting in this paper. Specifically, a blockchain system is established based on a proof of quality (PoQ) consensus mechanism, and a multilayer hybrid directional long short-term memory (MHD-LSTM) network model is trained for users’ electricity consumption forecasting via the federal learning method. In this way, the model of the MHD-LSTM network is able to avoid suffering from severe security problems and can only share the network parameters without exchanging raw electricity consumption data, which is decentralized, secure and reliable. The experimental result shows that the proposed method has both effectiveness and high-accuracy under the premise of electricity consumption data’s privacy preservation, and can achieve better performance when compared to traditional long short-term memory (LSTM) and bidirectional LSTM (BLSTM). [ABSTRACT FROM AUTHOR]

The objective of this paper is to study smart home network systems and the application of LabVIEW to develop a human machine interface (HMI), so that traditional instrument panels can be replaced with virtual panels to reduce the consumption of hardware resources. For energy efficiency, MATLAB's fuzzy toolbox is used as the computing center, which is applied to the lighting system and air-conditioning system. This paper also uses LabVIEW's common gateway interface (CGI) tools to develop remote monitoring functions, as well as to embed the network-related syntax into the web pages. The user can not only use computer equipment, but can also use their mobile devices to connect to the networks and conduct remote monitoring, which enhances convenience and security. This paper is finally tested with actual cases, and the electricity consumption with and without fuzzy logic control is compared. The test results show that fuzzy logic control can reduce electricity consumption. As well as using computers to test the remote monitoring functions, cell phones and pads were used. [ABSTRACT FROM AUTHOR]

According to the World Health Organization, between 2000 and 2050, the proportion of the world's population over 60 will double, from 11% to 22%. In absolute numbers, this age group will increase from 605 million to 2 billion in the course of half a century. It is a reality that most of them prefer to live alone, so it is necessary to look for mechanisms and tools that will help them to improve their autonomy. Although in recent years, we have been living in a veritable explosion of domotic systems that facilitate people's daily lives, it is also true that there are not many tools specifically aimed at this sector of the population. The aim of this paper is to present a potential solution to the monitoring of activity of daily living in the least intrusive way for people. In this case, anomalous patterns of daily activities will be detected by analysing the daily consumption of household appliances. People who live alone usually have a pattern of daily behaviour in the use of household appliances (coffee machine, microwave, television, etc.). A neuronal model is proposed for the detection of abnormal behaviour based on an autoencoder architecture. This solution will be compared with a variational autoencoder to analyse the improvements that can be obtained. The well‐known dataset called UK‐DALE will be used to validate the proposal. [ABSTRACT FROM AUTHOR]

To cope with the coronavirus disease (COVID-19), national or sub-national regions have carried out many powerful anti-pandemic measures such as locking down, which may improve their regional air quality. This paper examines the relation between regional air pollution and work resumption from a novel post-pandemic perspective. Using a unique panel dataset on China's detailed industrial electricity consumption, this paper does not find a positive relation between post-COVID-19 work resumption and regional air pollution during China's early-stage recovery. This result is obtained after controlling for province and date fixed effects, as well as local weather conditions. However, the positive relations are found in a particular sub-sample of large industrial enterprises and a particular sub-sample of April. These findings indicate that large industrial enterprises may recover first, and the resumption is progressing gradually. Finally, several policy implications are provided, which are essentially helpful for other countries' post-pandemic recovery. [ABSTRACT FROM AUTHOR]

The energy crisis is a problem that countries all over the world pay more and more attention to, and a series of ecological problems caused by it have become increasingly prominent. It is difficult for traditional fossil fuels to maintain a healthy and coordinated sustainable development of society and economy. The establishment of a sustainable energy system has become the development trend of various countries to solve energy problems. Electric energy is a secondary energy that all primary energy can be converted into, and an irreplaceable consumable for all industrial technologies and people's lives. Electric power data has the characteristics of large rate span, numerous data sources, complicated interaction methods, and various types of data. The existence of abnormal data in the power system will greatly reduce the accuracy of the system state estimation and the state estimation convergence rate. This paper introduces the power grid industrial control system, combines the data flow of power big data, and analyzes the abnormal information detection process in detail. It takes the data stream acquired by the acquisition unit PMU of the wide area measurement system as the research object. The rapid development of the Hadoop big data platform provides important technical support for the research of power grid big data. Based on the Hadoop platform, the clustering algorithm is used to complete the anomaly detection of real-time data. The LOF algorithm has poor performance when dealing with a large amount of high-dimensional data, and has high time and space complexity. In order to make up for the shortcomings of the LOF algorithm, this paper uses the K-means clustering algorithm to propose an improved algorithm K-LOF of the density-based local abnormal factor detection algorithm LOF, and optimizes the neighborhood query process. It is verified by experiments that the K-LOF algorithm can effectively reduce the time complexity of the anomaly detection algorithm and improve the detection accuracy by 2–4.2%. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between geothermal energy consumption, economic growth, and foreign direct investments in countries where geothermal energy production is possible. Panel Fourier Granger causality and panel Fourier Toda–Yamamoto causality tests (2020–2021) were applied, which can take into account smooth transitional structural breaks with trigonometric functions using quarterly data for the period 2016 Q1–2020 Q3. Data were obtained from the International Energy Agency (IEA), Federal Reserve Economic Data (FRED), and the OECD official website. According to the results obtained based on panels, there is one-way causality from economic growth to geothermal energy and one-way causality from geothermal energy consumption to foreign direct investments. The results obtained based on individual countries indicate that one-way causality from foreign direct investment to geothermal energy consumption was found for Mexico and Portugal, and one-way causality from geothermal energy consumption to economic growth was found for Italy and Mexico. On the other hand, causality from economic growth to geothermal energy consumption was observed for Germany, Japan, and USA. No significant results were found for Turkey and New Zealand, and it is understood that the macroeconomic structures of these countries are not affected by geothermal energy. The difference in the results reveals that the application recommendations on this subject should also be different. [ABSTRACT FROM AUTHOR]

The noticeable rise in electricity demand, environmental concerns, and the intense land burden has led to installing PV systems on water bodies to create floating photovoltaic (FPV). Of all market niches, FPV is the one developing the fastest. Along with some of its well-documented merits comes a claim that FPV modules operate at a lower temperature than their ground-mounted counterparts (GPVs). This claim is essential due to the performance loss of PV modules at high operating temperatures. Some literature claims that FPVs are so well-cooled that they maintain around 10% higher efficiencies. However, this cooling is poorly quantified, and the root cause remains unclear in the industry. In this paper, an extensive review of all the latest published literature and white paper advertisements was analyzed. The gains in energy yield coming from different root causes range from 0.11% to 31.29%! This proves the point of lack of clarity of potential gain of FPV. The paper then analyses four possible explanations for this cooling effect and its root causes. The FPV performance parameters are isolated and systematically investigated through physics-based finite element modeling. The impacts of wind velocity, wind direction, water temperature, relative humidity, air temperature, proximity to water, tilt angle, and others are evaluated and explained. The outcomes dictate that FPV is cooled largely through wind convection. But the increase in efficiency is below the anticipated values, ranging from 0.5% to 3%. [ABSTRACT FROM AUTHOR]

This paper studies the feasibility of intelligent power design mode, and proves that the intelligent power design mode has significant advantages over the traditional mode by using the method of comparative study. Comparative studies are more distinctive in the breadth and depth of content. With the further acceleration of the urbanization process and the popularization of electronic equipment, people's demand for electricity is gradually increasing. The overall operation of the transmission line is actually highly related to the design mode of the line. The quality of electrical design will be closely related to people's electricity consumption. The stability of the electrical design is directly linked to the sustainability, and it will also be linked to the reputation of the power design company and the safety of public life. The use of a variety of new technologies in the electrical design process to guide the design scheme and identify design problems as soon as possible is the latest trend in the electrical design of current transmission lines. In the context of the rapid development of data science, the functions of comprehensive sorting and providing suggestions provided by big data technology have become the mainstream choice for solving problems in various industries. At present, big data technology has begun to be associated with the power industry, and big data technology has been applied to transmission lines. Electrical design can also solve various dilemmas that exist in current design. Artificial intelligence technology enables computers to have the ability to think like ordinary people through algorithms and training data. Applying artificial intelligence technology to electrical design of transmission lines can make scheme design more intelligent. In order to analyze the current status of electrical design of transmission lines, this paper conducts a questionnaire survey on professionals and practitioners of electrical design of transmission lines. Through a detailed analysis of the questionnaire data, it is found that the electrical design of current transmission lines mainly includes unreasonable line path arrangement, line support towers unreasonable models and insufficient consideration in safety protection design, and combined with big data and artificial intelligence technology, the intelligent arrangement of line paths, tower-shaped intelligent matching and intelligent safety protection strategies are designed. The respondents of the questionnaire conducted a second survey and found that the efficiency, cost, intelligence, sustainability and safety of the electrical design of transmission lines were improved after combining big data and artificial intelligence technology. The intelligent circuit design scheme constructed by big data and artificial intelligence technology can improve the quality of circuit transportation, solve the problems of unreasonable route layout, unreasonable line support tower type and insufficient consideration of safety protection design. Intelligent circuit design scheme compared to the traditional circuit design scheme in the design efficiency, cost, sustainability is significantly improved. [ABSTRACT FROM AUTHOR]

It was last estimated that in 2020, data centers comprised approximately 2% of total US electricity consumption, with an estimated annual growth rate of 4%. As our country increasingly relies on information technology (IT), our data centers (DCs) will need to increase their energy efficiency (EE) to stabilize their energy consumption. The task of studying EE in DCs is complicated by the interconnected nature of humans and mission-critical technical systems. Moreover, the literature tends to focus on technology solutions such as improvements to IT equipment, cooling infrastructure, and software, without addressing organizational and psychological drivers. Our research demystifies the complex interactions between humans and DCs, by asking What non-technical barriers impede EE investment decision-making and/or implementing energy management strategies? To begin to answer this question, we perform a literature review of 86 resources, ranging from peer-reviewed journal publications to handbooks. We also consider related fields such as organizational behavioral management and energy intensive buildings. We develop a public Zotero library, perform content coding, and complete a rudimentary network analysis. Our findings from the literature review suggest that (1) technological solutions are abundant in the literature but fall short of providing practical guidance on the pitfalls of implementation, (2) making energy efficiency a priority at the executive level of organizations will be largely ineffective if the IT and facilities staff are not directly incentivized to increase EE, and (3) there is minimal current understanding of how the individual psychologies of IT and facilities staff affect EE implementation in DCs. In the next phase of our research, we plan to interview data center operators/experts to ground-truth our literature findings and collaboratively design decarbonization policy solutions that target organizational structure, empower individual staff, and foster a supportive external market. [ABSTRACT FROM AUTHOR]

The UK net‐zero target requires the integration of significant renewable energy resources into the electrical grid. This, together with the projected growth in electricity demand imposes practical challenges on the power transfer capability of existing transmission feeders. For environmental and planning reasons, construction of new overhead lines is problematic, and where possible upgrading of existing substations and lines is preferred. This paper investigates the feasibility of transforming an existing three‐phase (3Φ) double‐circuit 400 kV line into a six‐phase (6Φ) feeder for a simulated future GB scenario. Results indicate a 6Φ feeder, operating at a 'phase‐adjacent phase' voltage of 400 kV can effectively solve the constraints of boundaries in transmission networks. When considering the various operating contingencies, associated with an important UK network boundary and its expected near‐future winter‐peak power transfer requirements, the thermal overload issues are mitigated by transforming a feeder from 3Φ to 6Φ. The paper demonstrates upgrading to 6Φ technology is a potential solution to certain future boundary constraints and in these cases avoids the need to build new underground or overhead AC/DC lines. [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]

The development of renewable energy in China has made remarkable achievements, but the problem of renewable energy consumption has become increasingly prominent. This paper establishes a power market trading system for renewable energy, with the aim of promoting large-scale renewable energy consumption and increasing the enthusiasm of renewable energy producers and users to participate in market transactions. First, according to the power generation cost, the backup cost of renewable energy power plants and the possible quotation strategies of other renewable energy producers, a quotation model of renewable energy producers is established. In the clearing of the spot market by renewable energy producers, the independent market operator conducts the first-stage clearing of the electricity market with the goal of maximizing social welfare. After the announcement of the clearing results, the renewable energy producers that did not win the bid will revise their quotations and carry out the second stage clearing to realize the consumption of renewable energy. In this paper, the particle swarm algorithm combined with the CPLEX solver is used to solve the problem, and finally, different scenarios are analyzed through example analysis. The results show that, compared with the conventional power market trading mechanism, the energy abandonment rate of the power market trading mechanism for renewable energy proposed in this paper drops from 8.2% to 2.1%, and the profit margin of renewable energy producers increase by 6.6%. It is demonstrated that the proposed electricity market mechanism can effectively promote the consumption of renewable energy and increase the income of renewable energy producers. [ABSTRACT FROM AUTHOR]

Unmediated charging of a battery using solar radiation is a very attractive project of solar energy conversion and storage. In the present work, solar energy was converted into electricity using a photocatalytic fuel cell operating with a chalcogenide-semiconductor-sensitized nanoparticulate titania photoanode and an air-cathode functioning by oxygen reduction. This cell produced sufficient energy to directly charge a vanadium redox battery functioning with a VOSO4 electrolyte and carbon paper electrodes. The whole system is characterized by ease of construction and simplicity of conception; therefore, it satisfies conditions for practical applications. [ABSTRACT FROM AUTHOR]

[Display omitted] • The state of art of wet substrate materials is presented. • Five materials were tested and compared to Kraft paper as wet substrate. • Absorption, evaporation and moisture-wicking capacities were determined according to norm NF ISO 20158, NF EN ISO 9073-6 and AATCC 198. • Taking into account the cost, our results suggest that Fibre fabrics (tight straight weave) is a good choice for evaporative cooling Pad purpose. Due to the ever-growing demand for air-conditioning to bring the indoor air temperature to a comfortable level despite the unreasonable electricity consumption, research is more oriented towards techniques in connection with new methods enabling more energy savings and less adverse environmental impacts. Dew point evaporative cooling systems hold among the most promising. Their ability to use the evaporation of water to absorb heat from the air in order to lower its temperature below its wet bulb level without adding humidity, is an extremely low-energy and eco-friendly cooling principle. The properties of the material used in the construction of the wet channel surface in an evaporative cooler, i.e, its moisture wicking ability, diffusivity and evaporation capacity, can alter cooling efficiency and performance considerably. The irregular fibres help to divert moisture and enlarge the wet area, thus promoting evaporation. A measuring device was mounted to test the capillary rise of evaporative fabrics, which is one of the techniques used to assess the liquid moisture transmission performance of fabrics. A variety of materials (fabrics) woven from different types of fibres have been experimentally tested and compared to Kraft paper, which is commonly used as a wet surface medium in evaporative cooling systems. The wicking rate of fabrics obtained from the test device correlated well with the NF EN ISO 9073-6. Consequently, the test device clearly demonstrated differences between the fabrics used in the study, and can be used to determine vertical wicking behaviour of fabrics. Most textile fabrics have been found to have superior properties and qualities in terms of moisture wicking ability than the Kraft Paper. Also, that the type of weaving and the compactness of the fabric improved the capillary rise. Compared to Kraft paper, the absorbency of some fabrics has been found 160% to 355% higher. An estimation evaluation regarding both moistures transfer and mechanical properties revealed that two of the fabrics were the most suitable for evaporative cooling applications. [ABSTRACT FROM AUTHOR]

Since 2008, the South African economy has experienced several power cuts (unplanned or as part of a load-shedding schedule), presumably because of the inability of the electricity supply to cover the demand. This paper examines the impact of such a demand-supply mismatch on the country's economic growth within a production function framework. To do so, we use an Autoregressive Distributed Lag Model (ARDL) for the period 1985 to 2019. The paper finds that a positive mismatch (or surplus) of electricity (supply>demand) boosts economic growth in the long run. This finding provides evidence that supports the necessity of electricity supply expansion and the promotion of energy efficiency measures that both will create a mismatch (surplus) conducive to economic growth. [ABSTRACT FROM AUTHOR]

The Smart Grid operates autonomously, facilitating the smooth integration of diverse power generation sources into the grid, thereby ensuring a continuous, reliable, and high-quality supply of electricity to end users. One key focus within the realm of smart grid applications is the Home Energy Management System (HEMS), which holds significant importance given the fluctuating availability of generation and the dynamic nature of loading conditions. This paper presents an overview of HEMS and the methodologies utilized for load forecasting. It introduces a novel approach employing Quantum Support Vector Machine (QSVM) for predicting periodic power consumption, leveraging the AMPD2 dataset. In the establishment of a microgrid, various factors such as energy consumption patterns of household appliances, solar irradiance, and overall load are taken into account in dataset creation. In the realm of load forecasting in Home Energy Management Systems (HEMS), the Quantum Support Vector Machine (QSVM) stands out from other methods due to its unique approach and capabilities. Unlike traditional forecasting methods, QSVM leverages quantum computing principles to handle complex and nonlinear electricity consumption patterns. QSVM demonstrates superior accuracy by effectively capturing intricate relationships within the data, leading to more precise predictions. Its ability to adapt to diverse datasets and produce significantly low error values, such as RMSE and MAE, showcases its efficiency in forecasting electricity load consumption in smart grids. Moreover, the QSVM model's exceptional flexibility and performance, as evidenced by achieving an accuracy of 97.3% on challenging datasets like AMpds2, highlight its distinctive edge over conventional forecasting techniques, making it a promising solution for enhancing forecasting accuracy in HEMS.The article provides a brief summary of HEMS and load forecasting techniques, demonstrating and comparing them with deep learning models to showcase the efficacy of the proposed algorithms. [ABSTRACT FROM AUTHOR]

With the development of Non-Intrusive Load Monitoring (NILM), it has become feasible to perform device identification, energy consumption decomposition, and load switching detection using Deep Learning (DL) methods. Similar to other machine learning problems, the research and validation of NILM necessitate substantial data support. Moreover, different regions exhibit distinct characteristics in their electricity environments. Therefore, there is a need to provide open datasets tailored to different regions. In this paper, we introduce the Transient Dataset of Household Appliances with Intensive Switching Events (TDHA25). This dataset comprises switch instantaneous data from 10 typical household appliances in China. The TDHA dataset features a high sampling rate, accurate labelling, and realistic representation of actual appliance start-up waveforms. Additionally, appliance switching is achieved through precise control of relay switches, thus mitigating interference caused by mechanical switches. By furnishing such a dataset, we aim not only to enhance the recognition accuracy of existing NILM algorithms but also to facilitate the application of NILM algorithms in regions sharing similar electricity consumption characteristics to those of China. [ABSTRACT FROM AUTHOR]

Accurate electricity demand forecasting serves as a vital planning tool, enhancing the reliability of management decisions. Apart from that, achieving these aims, particularly in managing peak demand, faces challenges due to the industry's volatility and the ongoing increase in residential energy use. Our research suggests that employing deep learning algorithms, such as recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU), holds promise for the accurate forecasting of electrical energy demand in time series data. This paper presents the construction and testing of three deep learning models across three separate scenarios. Scenario 1 involves utilizing data from all-day demand. In Scenario 2, only weekday data are considered. Scenario 3 uses data from non-working days (Saturdays, Sundays, and holidays). The models underwent training and testing across a wide range of alternative hyperparameters to determine the optimal configuration. The proposed model's validation involved utilizing a dataset comprising half-hourly electrical energy demand data spanning seven years from the Electricity Generating Authority of Thailand (EGAT). In terms of model performance, we determined that the RNN-GRU model performed better when the dataset was substantial, especially in scenarios 1 and 2. On the other hand, the RNN-LSTM model is excellent in Scenario 3. Specifically, the RNN-GRU model achieved an MAE (mean absolute error) of 214.79 MW and an MAPE (mean absolute percentage error) of 2.08% for Scenario 1, and an MAE of 181.63 MW and MAPE of 1.89% for Scenario 2. Conversely, the RNN-LSTM model obtained an MAE of 226.76 MW and an MAPE of 2.13% for Scenario 3. Furthermore, given the expanded dataset in Scenario 3, we can anticipate even higher precision in the results. [ABSTRACT FROM AUTHOR]

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS. [ABSTRACT FROM AUTHOR]

Demand response management (DRM) plays a crucial role in the prospective development of smart grids. The precise estimation of electricity demand for individual houses is vital for optimizing the operation and planning of the power system. Accurate forecasting of the required components holds significance as it can substantially impact the final cost, mitigate risks, and support informed decision-making. In this paper, a forecasting approach employing neural networks for smart grid demand-side management is proposed. The study explores various enhanced artificial neural network (ANN) architectures for forecasting smart grid consumption. The performance of the ANN approach in predicting energy demands is evaluated through a comparison with three statistical models: a time series model, an auto-regressive model, and a hybrid model. Experimental results demonstrate the ability of the proposed neural network framework to deliver accurate and reliable energy demand forecasts. [ABSTRACT FROM AUTHOR]

Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications. [ABSTRACT FROM AUTHOR]

The growing demand for energy, driven by rapid economic development, necessitates higher electricity consumption. However, conventional energy systems relying on fossil fuels present environmental challenges, prompting a shift towards renewable energy sources. In Kosovo, coal-fired power plants dominate electricity production, highlighting the need for cleaner alternatives. Worldwide efforts are underway to increase the efficiency of photovoltaic systems using sustainable materials, essential for ecological and human health. Solar and wind energy are emerging as sustainable alternatives to traditional fossil fuels. However, global concerns about energy security and environmental sustainability are driving countries to prioritize renewable energy development. In Kosovo, the integration of renewable energy sources, such as wind and solar energy, is progressing rapidly. However, challenges such as voltage stability and power losses need to be addressed. Distributed generation offers a solution by increasing energy reliability and reducing greenhouse gas emissions. Further research is needed to assess the technical, economic, and environmental implications of integrating renewable resources into Kosovo's energy system, focusing on power quality, system reliability, and voltage stability. The research focused on the eastern region of the country, operating at the 110 kV substation level. Challenges in energy quality arise due to the lack of 400 kV supply and the continuous increase in energy consumption, especially in the Gjilan area. This paper investigated integrating renewable energy, especially wind and solar sources, into the medium- and long-term plans at the Gjilan 5 substation to enhance energy quality in the area. Successful integration requires detailed analysis of energy flows, considering the impact of photovoltaics (PVs) on distribution system operation and stability. To simulate and analyze the effects of renewables on the transmission system, voltage profile, and power losses, a case study was conducted using ETAP software. The simulation results present a comparison between scenarios before and after integrating renewable systems to improve energy quality in the identified area. [ABSTRACT FROM AUTHOR]

The distributed direct current (DC) power system relies heavily on the cascaded DC–DC converter that employs a common bus to connect multiple DC–DC converters. The first stage of the cascaded DC–DC converter is responsible for injecting power from a renewable source or a battery into the DC bus. Conversely, the second stage connects a load to the DC bus, creating a constant power load (CPL) that consumes constant power regardless of the supply voltage. This behavior often causes disturbances and instabilities, leading to unwanted oscillations that adversely impact the quality of the input current. To address this issue, this paper proposes an active current ripple-damping technique that extracts the fundamental of the inductor current. When combined with the super-twisting sliding mode control, this approach effectively mitigates input current ripples and enhances the stability of the CPL. The key to this approach is the sliding surface selection, which requires a cleaned inductor current from the second boost converter. Experimental results are provided to demonstrate the effectiveness of the proposed method. [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]