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Increasing access to power, enhancing clean cooking fuels, decreasing wasteful energy subsidies, and limiting fatal air pollution are just a few of the sustainable development goals that all revolve around energy (E). Energy-specific sustainable development objectives were a turning point in the global shift towards a more sustainable and just system. By understanding energy resources, markets, regulations, and scientific studies, the country can progress more quickly towards a sustainable economy (SE). Investment in renewable energy industries is hampered by institutional obstacles such as market-controlled procedures and inconsistent supporting policies. Power plant building is currently incompatible with existing transmission and distribution networks, posing significant risks to investors. Deep neural networks (DNN) are specifically investigated in this article for energy demand forecasting at the individual building level. Other relevant information is supplied into fully connected layers along with the convolutional output. A single customer's power usage data were used and analyzed for the final fuel and electricity consumption by various energy sources and consumer groups to test the DNN-SE technique. The energy intensity and labor productivity indexes for several economic sectors are displayed. A wide range of economic activities are examined to determine their impact on environmental pollution indicators, greenhouse gas emissions, and other air pollutants. A more effective and comprehensive energy efficiency strategy should be implemented to lower emission levels at lower prices. Research-based conclusions must be enhanced to help policymaking. The results of the experiment using the proposed method show that it is possible to predict 98.1%, grow at 96.8%, meet 98.5% of electricity demand, use 97.6% of power, and have a renewable energy ratio of 96.2%. [ABSTRACT FROM AUTHOR]

The estimation of electric power utilization, its baseload, and its heating, light, ventilation, and air-conditioning (HVAC) power component, which represents a very large portion of electricity usage in commercial facilities, are important for energy consumption controls and planning. Non-intrusive load monitoring (NILM) is the analytical method used to monitor the energy and disaggregate total electrical usage into appliance-related signals as an alternative to installing multiple electricity meters in the building. However, despite considerable progress, there are a limited number of tools dedicated to the problem of reliable and complete energy disaggregation. This paper presents an experiment consisting in designing an electrical system with electrical energy receivers, and then starting NILM disaggregation using machine learning algorithms (MLA). The quality of this disaggregation was assessed using dedicated indicators. Subsequently, the quality of these MLA was also verified using the available BLUED data source. The results show that the proposed method guarantees non-intrusive load disaggregation but still requires further research and testing. Measurement data have been published as open research data and listed in the literature section repository. [ABSTRACT FROM AUTHOR]

Intermittent renewable energy is becoming increasingly popular, as storing stationary and mobile energy remains a critical focus of attention. Although electricity cannot be stored on any scale, it can be converted to other kinds of energies that can be stored and then reconverted to electricity on demand. Such energy storage systems can be based on batteries, supercapacitors, flywheels, thermal modules, compressed air, and hydro storage. This survey article explores several aspects of energy storage. First, we define the primary difficulties and goals associated with energy storage. Second, we discuss several strategies employed for energy storage and the criteria used to identify the most appropriate technology. In addition, we address the current issues and limitations of energy storage approaches. Third, we shed light on the battery technologies, which are most frequently used in a wide range of applications for energy storage. The usage and types of batteries are described alongside their market shares and social and environmental aspects. Moreover, the recent advances in battery state estimation and cell-balancing mechanisms are reviewed. [ABSTRACT FROM AUTHOR]

In Japan, mechanical plastic recycling has been widely practiced. In recent years, the chemical recycling method has been gaining interest, especially due to its high-quality products similar to virgin materials. Understanding the environmental impact of both methods from the energy consumption standpoint is crucial so that attempts to preserve plastic resources can be based in the most energy-sustainable way. This research aims to determine the environmental impact of mechanical recycling and two types of chemical recycling technologies (coke oven and gasification) by analyzing their energy usage and environmental loads. The results relating to the electricity consumption and water usage show that mechanical recycling results in a 17% share of global warming potential (GWP), coke oven 51%, and gasification 32%. Although mechanical recycling results in a lower GWP, chemical recycling yields highly valuable products and byproducts that can be reused in its processes, such as steam and industrial water, reducing the overall environmental load. These recovered materials are also potentially useful for other industrial processes in an industrial symbiosis ecosystem. [ABSTRACT FROM AUTHOR]

The growth of microgrids to fulfil electricity demand could lead to arguments or conflicts between the private microgrid system owners and the existing national distribution authority, especially in overlapping sales areas. The core issue identified in this paper is a distribution system management model based on the cooperative concept of unifying the multi-owned networks as a single system to ensure the natural monopoly function. This model can avoid complex wheeling charges across multi-owned grids within overlapping sales areas, in which all network users pay at the expense of the merged system charge. This paper proposes the model and shows numerical examples using a suitable distribution network pricing model to recover existing costs and plan for future system expansion. The proposed model and charging algorithm were tested on a modified IEEE 13 bus to examine their impacts. The result demonstrates that the model resolves the issues with the overlapping sales area and creates a fair scenario for the network users regarding the usage charges. Thailand is chosen as the primary reference area, as it is a country with an actual sample case for the argument. [ABSTRACT FROM AUTHOR]

The development of a country's economy is directly related to the use of energy in that country's economic sectors. Therefore, the energy–environmental Kuznets curve (EEKC) is often used when analysing a country's potential and challenges in sustainable development, green economy, and green growth. This hypothesis tests whether there is an inverse "U"-shaped relationship between energy use and economic growth and is especially important when analysing developing countries to assess if, at a certain point, energy use begins to drop, resulting in fewer greenhouse gas emissions, environmental degradation, and the consumption of fossil-based fuels. This study aims to examine the relationship between energy consumption and economic growth in the Baltic States from 1995 to 2019, with a focus on the agriculture sector. The study uses the non-linear autoregressive distributed lag (NARDL) model for individual and panel time series. Total energy use, as well as electricity use, is included in the study, whereas gross value added is employed as a measure of economic growth. Research data analysis reveals that energy use in all three Baltic countries stabilises as gross value added increases. However, there is insufficient evidence to show that after a certain point, energy use begins to drop; thus, the hypothesis for the inverse "U"-shaped energy–environmental Kuznets curve (EEKC) is rejected. Research results have important practical implications regarding countries' policies toward energy, including the use of electricity and sustainable development. [ABSTRACT FROM AUTHOR]

This study aims to develop statistical and machine learning methodologies for forecasting yearly electricity consumption in Saudi Arabia. The novelty of this study include (i) determining significant features that have a considerable influence on electricity consumption, (ii) utilizing a Bayesian optimization algorithm (BOA) to enhance the model's hyperparameters, (iii) hybridizing the BOA with the machine learning algorithms, viz., support vector regression (SVR) and nonlinear autoregressive networks with exogenous inputs (NARX), for modeling individually the long-term electricity consumption, (iv) comparing their performances with the widely used classical time-series algorithm autoregressive integrated moving average with exogenous inputs (ARIMAX) with regard to the accuracy, computational efficiency, and generalizability, and (v) forecasting future yearly electricity consumption and validation. The population, gross domestic product (GDP), imports, and refined oil products were observed to be significant with the total yearly electricity consumption in Saudi Arabia. The coefficient of determination R 2 values for all the developed models are >0.98, indicating an excellent fit of the models with historical data. However, among all three proposed models, the BOA–NARX has the best performance, improving the forecasting accuracy (root mean square error (RMSE)) by 71% and 80% compared to the ARIMAX and BOA–SVR models, respectively. The overall results of this study confirm the higher accuracy and reliability of the proposed methods in total electricity consumption forecasting that can be used by power system operators to more accurately forecast electricity consumption to ensure the sustainability of electric energy. This study can also provide significant guidance and helpful insights for researchers to enhance their understanding of crucial research, emerging trends, and new developments in future energy studies. [ABSTRACT FROM AUTHOR]

This paper shows the technical–economic assessment of two power systems based on renewable electricity to cover the energy consumption requirements of a drinking water treatment plant in the town of Pile, Ecuador, with a planning horizon of 15 years. A stand-alone and a grid-connected solar PV system were proposed to power this plant, which was designed considering the maximum daily potable water supply condition. This plant operated under two scenarios: (1) 12 h during daylight hours and (2) 24 h. Both schedules were proposed to assess the impact of PV power systems on plant operation. We modeled and optimized a total of four scenarios, where each scenario consisted of one of the proposed PV power systems and the plant with one of its operating schedules. Homer Pro software was used to size and find an optimal solution in each scenario, considering the net present cost (NPC) as the main criterion for optimization. The results showed that the change in the plant operation schedule significantly influenced the parameters of each scenario, such as component sizing, electricity production, initial capital, NPC, and electricity purchase/sale capacity from the plant as a grid power service user to the electric utility company. [ABSTRACT FROM AUTHOR]

Theft of electricity poses a significant risk to the public and is the most costly non-technical loss for an electrical supplier. In addition to affecting the quality of the energy supply and the strain on the power grid, fraudulent electricity use drives up prices for honest customers and creates a ripple effect on the economy. Using data-analysis tools, smart grids may drastically reduce this waste. Smart-grid technology produces much information, including consumers' unique electricity-use patterns. By analyzing this information, machine-learning and deep-learning methods may successfully pinpoint those who engage in energy theft. This study presents an ensemble-learning-based system for detecting energy theft using a hybrid approach. The proposed approach uses a machine-learning-based ensemble model based on a majority voting strategy. This work aims to develop a smart-grid information-security decision support system. This study employed a theft-detection dataset to facilitate automatic theft recognition in a smart-grid environment (TDD2022). The dataset consists of six separate electricity thefts. The experiments are performed in four different scenarios. The proposed machine-learning-based ensemble model obtained significant results in all scenarios. The proposed ensemble model obtained the highest accuracy of 88%, 87.24%, 94.75%, and 94.70% with seven classes including the consumer type, seven classes excluding the consumer type, six classes including the consumer type, and six classes excluding the consumer type. The suggested ensemble model outperforms the existing techniques in terms of accuracy when the proposed methodology is compared to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Pumping systems, especially those used in the water supply sector and in industrial and hydroelectric facilities, are commonly infested by the golden mussel. This causes an increase in maintenance operations (e.g., system shutdowns for cleaning) that can generate an increased energy cost. The geographical expansion of the golden mussel in Latin America presents an economic risk, not only to the ecosystem in general, but also to the energy sector. The imminence of its spread in the Amazon region, one of the main river basins in South America, is cause for concern with regard to the problems that bioinvasion of this species can cause. Given the absence of studies on the loss of energy efficiency in pumping systems impacted by the golden mussel, this study proposes a methodology to estimate the increase in energy consumption and costs of pumping under such bioinfestation. For the standardization of the methodology and development of mathematical calculations (both novel and improved equations), data from the literature (the growth of the golden mussel as a function of infestation time) and an analysis of the dimensions (length and height) of a sample of mussels available in the laboratory were considered. These data were used to calculate the roughness generated by the mussel infestation in the pumping suction and discharger pipe, which was necessary to determine the loss of energy efficiency (load loss, power consumption, and cost of pumping) resulting from the increase in energy consumption for pumping. This methodology was applied to a pumping station representative of the Brazilian Amazon as a case study. The results show an average increase in economic indicators (consumption and cost of pumping) after the system undergoes bioinfestation. This total increase corresponded to 19% and 44% in the first and second years, respectively, achieving a stabilization of the increase in the cost of pumping at 46%, in the 30 months of operation. Our results demonstrate the pioneering nature of the proposal, since these are the first quantitative data on the energy efficiency of pumping systems associated with bioinfestation by the golden mussel. These results can also be used to estimate the increase in costs caused by golden mussel bioinfestation in the raw water pumping systems of other facilities. [ABSTRACT FROM AUTHOR]

Plate Heat Exchanger (PHE) and Shell and Tube Heat Exchanger (STHE) with identical heat transfer areas and material characteristics are proposed and a comparative thermal and economic comparative analysis is carried out on both exchangers. Ag-water nanofluid is used at low concentrations (0, 2.5, 5, 10 mg/L), flow rates (2, 5, and 8 L/min), and inlet temperatures (36, 46, and 56 °C) as hot flow and the heat transfer coefficient (U), electrical power consumption of the pump, and costs per unit of average U value are considered as the calculated parameters for each heat exchanger in co-current and counter-current flows. The results revealed that PHE generates a higher U value compared to the STHE under different Ag-water nanofluid concentrations. This is due to the existence of grooves on the plates of PHE which generates turbulent flow. The impact of nanofluid concentration on U is negligible for lower concentrations in both PHE and STHE. It is also found that the nanofluid flow rate has the highest impact on the U value, just like conventional fluid. Besides, even though counter-current flow increases the U values for both PHE and STHE, the flow pattern has a higher impact on the U value of PHE than that of STHE. For both PHE and STHE, increasing the nanofluid flow rate enhances the amount of U. However, the effect of flow rate on the U value of PHE is greater than that of the STHE. It is also shown that throughout the entire experimental temperature domain, PHE has had higher performance than STHE, and as the fluid temperature increased from 36 to 56 °C, there was a slight increase in the overall heat transfer of both PHE and STHE. Furthermore, for the same flow rate, both PHE and STHE had almost the same pump power consumption, and increasing the nanofluid flow rate from 2 L/min to 8 L/min promoted the electrical power consumption of the pump. Finally, we found that the costs per unit of heat transfer coefficient for PHE are significantly lower than STHE. The presented results also indicated that using a vortex generator at the inlet of STHE tubes, to form turbulent flow, increases the U values of STHE for both co-current and counter-current flows but these U values are lower than the corresponding U values of PHE. Small plates gap in PHE structure cause higher fluid flow velocities and create a chain-like structure of nanoparticles (NPs) between PHE's plates (especially at higher nanofluids concentrations). [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the energy consumption of production in selected branches of the food industry in Poland and to identify its changes after Poland's accession to the EU. This issue is particularly important in the period of energy transformation and soaring energy prices. The novelty of this article is the determination of changes in the energy efficiency of various branches of the food industry. The main source of data was mass statistics data and unpublished data from the Central Statistical Office for 2004–2020. Descriptive statistics, comparative analysis, and strategic group mapping were used in the data analysis. The research shows that the production of foodstuffs is one of the most energy-intensive processing sectors. This results, among others, from many active enterprises in this sector and a large variety of industries. Individual food-processing industries are characterized by large differences in the energy consumption of production, which determines the different levels of electricity costs and affects the competitiveness of enterprises. In 2004–2019, the average electricity consumption in the food industry in Poland decreased by 31.5%. A greater increase in the value of sold production compensated for the higher energy consumption. This indicates an improvement in production efficiency and contributes to greater environmental protection. In the food sector, simple comparative advantages disappear in the form of lower production costs. This situation encourages processing companies to look for energy savings. The research results can be useful not only in Poland but also in other countries in shaping economic policy. The energy crisis caused by the war in Ukraine may require different actions to be taken against various sectors of the food industry. [ABSTRACT FROM AUTHOR]

The continuous increase in the demand for electricity makes it necessary to modernize or build new transmission lines. This, in turn, results in research that is still being carried out on the optimal use of power cables. In the paper, an improved analytical method for the determination of the current rating of power cables was proposed. The method for determining the ampacity of the power cable presented in the IEC standard assumes that power losses in the phase conductors and screens are determined by taking into account skin and the proximity effects on the basis of tabulated coefficients. The methodology proposed in the paper is based on the method presented in the IEC standard, but the power losses in the conductive elements of the cable are determined analytically, which offers higher accuracy. In order to validate the analytical method proposed in this paper, numerical calculations based on the finite element method with very fine mesh were also performed. Exemplary calculations carried out for three types of cables with use of the proposed method, IEC standard and finite elements showed very good agreement in the results. The proposed method requires more computational effort, but it offers more accurate results than the IEC standard and can be used when higher accuracy is required. It can also serve as a reference point for simplified calculations. [ABSTRACT FROM AUTHOR]

Pig farming in mechanically ventilated barns requires much electricity for ventilation or exhaust air purification. Furthermore, thermal energy is needed to fulfill the animals' temperature requirements, especially in piglet rearing. Electrical and thermal energy input leads to CO2 emissions and operating costs. Up to 90% of heat losses are due to the exhausted air. Heat exchangers can recover thermal energy from the warm exhaust air and transfer it to the cold fresh air. This study aimed to investigate energy consumption, efficiency, CO2 emissions, and energy costs when using heat exchangers in a German piglet rearing barn under practical conditions in combination with exhaust air purification. The following parameters were obtained for a two-year period: air temperatures, air flow rates, and electricity and liquefied natural gas consumption; the latter were used to calculate CO2 emissions and energy costs. In total, 576,042 kWhel,th and 616,893 kWhel,th (years 1 and 2) of energy were provided, including 290,414 kWhth and 317,913 kWhth of thermal energy recovered. Using heat exchangers reduced CO2 emissions by up to 37.5% and energy costs by up to 19.7% per year. The study shows that piglet rearing can increase its ecological and environmental sustainability by using heat recovery. [ABSTRACT FROM AUTHOR]

The planning of efficient policies based on forecasting electricity demand is essential to guarantee the continuity of energy supply for consumers. Some techniques for forecasting electricity demand have used specific procedures to define input variables, which can be particular to each case study. However, the definition of independent and casual variables is still an issue to be explored. There is a lack of models that could help the selection of independent variables, based on correlate criteria and level of importance integrated with artificial networks, which could directly impact the forecasting quality. This work presents a model that integrates a multi-criteria approach which provides the selection of relevant independent variables and artificial neural networks to forecast the electricity demand in countries. It provides to consider the particularities of each application. To demonstrate the applicability of the model a time series of electricity consumption from a southern region of Brazil was used. The dependent inputs used by the neural networks were selected using a traditional method called Wrapper. As a result of this application, with the multi-criteria ELECTRE I method was possible to recognize temperature and average evaporation as explanatory variables. When the variables selected by the multi-criteria approach were included in the predictive models, were observed more consistent results together with artificial neural networks, better than the traditional linear models. The Radial Basis Function Networks and Extreme Learning Machines stood out as potential techniques to be used integrated with a multi-criteria method to better perform the forecasting. [ABSTRACT FROM AUTHOR]

This work aims to provide an energy and critical analysis of the new "ecological scenario" that sees the world shift the transport sector from the classic fossil-fueled traction towards a purely electric traction. Is it a transition to a cleaner and more environmentally friendly world? This is the central question we need to answer. In this context of forced electrification of passenger vehicles, the authors estimated the electricity surplus needed in Italy to face this transition with a projection up to 2050, considering the electricity demand of purely electric vehicles and for the production of green hydrogen for the fuel supply of fuel cell vehicles. Throughout this investigation, the authors discovered that, in 2050, the surplus of electricity to be produced compared to the current production in Italy (year 2021) is equal to +27.6% (1). This value increases if two limit scenarios are considered: it becomes +40.0% within a limit scenario in which the entire private car fleet is made up of pure electric traction vehicles only (2). It becomes +100.6% in the hypothesis that the entire fleet of passenger cars is made up of fuel cell vehicles powered exclusively by green hydrogen (3). [ABSTRACT FROM AUTHOR]

The stochastic nature of renewable energy resources and consumption has the potential to threaten the balance between generation and consumption as well as to cause instability in power systems. The microgrid operators (MGOs) are financially responsible for compensating for the imbalance of power within their portfolio. The imbalance of power can be supplied by rescheduling flexible resources or participating in the balancing market. This paper presents a robust optimization (RO)-based model to maintain the balance of a portfolio according to uncertainties in renewable power generation and consumption. Furthermore, load reduction (LR) and battery energy storage (BES) are considered flexible resources of the MGO on the consumption side. The model is formulated based on the minimax decision rule that determines the minimum cost of balancing based on the worst-case realizations of uncertain parameters. Through the strong duality theory and big-M theory, the proposed minimax model is transformed into a single-level linear maximization problem. The proposed model is tested on a six-node microgrid test system. The main contributions of the proposed model are presenting a robust model for portfolio management of MGO and using BES and LR to improve the flexibility of microgrid. Simulation results demonstrate that using LR and BES could decrease the balancing cost. However, the optimal portfolio management to compensate for the imbalance of power is highly dependent on the risk preferences of MGO. [ABSTRACT FROM AUTHOR]

Fossil fuels, which are widely used in Poland, have negative environmental and health consequences. Photovoltaics, used especially by households, contribute to a reduction in CO2 emissions and indirectly reduce the problem of smog. However, the high cost of photovoltaic installations forces the use of economic incentives, such as direct subsidies to the installation, under the "My Electricity" program (hereinafter referred to as the program). The development of photovoltaics is hindered by the ineffective functioning of transmission grids. In the current edition of the "My Electricity" program, the main emphasis was placed on the use of electricity at the place of generation through the use of energy storage facilities and the transition to a new billing system, so-called net billing. The program indirectly contributes to the growing popularity of heat pumps as an installation that uses electricity generated on-site. Significant changes in the program inspired the authors of the study to plan and conduct nationwide research; they assessed investment plans for energy storage and heat pumps by potential individual investors, i.e., owners of single-family houses. Based on the results obtained, the authors of the article concluded that the new version of the "My Electricity" program will almost exclusively affect the energy storage market. This impact will stimulate the development and application of this technology. In the case of the heat pump market, the current demand will remain at the same level. The program will contribute to achieving the assumed goals, especially energy consumption at the place of production and reducing so-called low emissions. The program will not affect the demand for heat pumps. [ABSTRACT FROM AUTHOR]

In communities with a high tourist influx and warm climate, such as Mediterranean small islands, the energy demands for space cooling and domestic hot water are expected to significantly increase during summer. Considering the current energy context, for succeeding energy transition and reducing the dependency on not endogenous fossil fuels, it is paramount to increase the deployment of renewable sources of energy, especially wind and solar which, however, are aleatory and unpredictable. Hence, to reduce the high costs for energy supply in these contexts, the analysis of the variation of energy consumption is fundamental. Moreover, mapping the spatial distribution of energy profiles can be useful to have an overview at a large scale of the considered building stock. Within this frame, a Geographic-Information-System-based procedure was implemented to estimate the residential buildings energy demand profiles, focusing on the seasonal variation. The adopted method can provide a valid supporting tool for decision makers that have to implement smart energy strategies in contexts with a high variation of the energy demand and evident electricity summer peaks. The method for mapping the energy demand profiles, implemented on the small island of Pantelleria, can be applied to other similar contexts, also supporting energy policies in the implementation of renewable energy communities. [ABSTRACT FROM AUTHOR]

In order to effectively carry out the heavy overload monitoring and maintenance of public transformers in the distribution network, ensure the reliability of the distribution network power supply, and improve customer satisfaction with electricity consumption, this paper presents a short-term heavy overload forecasting method for public transformers based on the LSTM-XGBOOST combined model. The model extracts heavy overload feature variables from four dimensions, including basic parameter information, weather, time, and recent load, and constructs a short-term second highest load prediction model based on the LSTM algorithm to obtain the predicted value of the second highest load rate. After aggregating the heavy overload feature variables and the predicted second highest load rate, the XGboost algorithm is employed to construct a short-term heavy overload prediction model for public transformers to judge whether the public transformers display heavy overload. The test results show that this method has high accuracy in short-term heavy overload forecasting, and can effectively assist in the key monitoring and control of heavy overload in public transformers. [ABSTRACT FROM AUTHOR]

Historically, the combination of generous subsidies along with extreme climate has led to unsustainable domestic electricity consumption in Saudi Arabia. The residential sector constitutes a significant portion of this consumption. Amid the economic challenges, the country enforced a new electricity tariff for residential consumers in 2018. This study thus leverages change in 2018–2020 by collecting and analyzing the electricity consumption data of 73 households in the Eastern Province of Saudi Arabia. The energy consumption is modeled based on the households' attributes (e.g., dwelling type, ownership, number of residents, rooms, ventilation type, etc.) and applied tariffs using a machine learning technique. The extreme learning machine (ELM) is employed in solving the overfitting problem due to low-volume data. The correlation matrix is also constructed to determine the relationship between the household attributes. The ELM model developed in this study extracts the correlation between the input variables in determining energy consumption and also predicts the energy consumption related to low consumption data. The findings indicated that the electricity consumption between the pre-revised tariff year and the revised tariff enforcement year saw a reduction which was consistent in the subsequent years. This was also validated by the paired sample t-test, which showed a significant decrease in electricity consumption for the study period. The analysis also revealed that several household attributes had a relatively high impact on the reduction in the electricity consumption level following the revised tariffs, whereas the majority of the attributes had a moderate impact. In addition to these key findings, the demonstrated pathway adopted in this study is itself a methodological contribution that provides critical information about the sensitivity of the impacts of tariffs on energy consumption with respect to different household attributes. Economic factors being the critical stress need to be blended with existing energy consciousness for positive changes in favor of energy-saving behavior of the household members. The study does not attempt to represent the population of concern, but demonstrates a methodology that would help unleash inherent energy consciousness in favor of sustainable and energy-efficient behavior. [ABSTRACT FROM AUTHOR]

The purpose of this work is to develop a model for balancing the processes of the generation and consumption of electricity, taking into account the random nature of these processes. The subject of the study is hybrid power systems that use traditional and renewable energy sources and have the properties of a local network. Such systems are sensitive to variable generation modes, and the presence of rapid changes in power requires short time intervals. The presence of wind and solar power plants makes it difficult to ensure a balance of power, which increases the need for intermediate energy storage. The research method is a mathematical modeling of random processes of energy consumption and generation, which allows for the analysis of the current power balancing and the obtaining of the integrated characteristics of the state of energy storage and reuse. The unique goal of the study is to take into account the power gradients and the state of charge of the batteries. The results of the study allow for the comparison of the different configurations of the power system in terms of balance, storage needs, and energy loss. It has been shown that the increase in battery capacity and speed limitations are nonlinearly related to the possibilities of energy conservation and the probability of the incomplete use of the capabilities of the energy storage system. [ABSTRACT FROM AUTHOR]

Electricity consumption and government policy are two vital elements for economic growth. Thus, this study explores the roles of electricity use and government policy in the economic growth of the selected four South Asian countries over the period from 1980 to 2014. The study includes the government policy variable in the extended Cobb–Douglas production function of the electricity driven growth model, which was absent in earlier studies. The pooled mean group-based panel autoregressive distributed lag (P-ARDL) method is used for empirical investigation, while fully modified ordinary least squares (FMOLS) and dynamic ordinary least square (DOLS) methods are used for checking the sensitivity of the P-ARDL estimates. Our results reveal that the effects of electricity, government spending, financial development and capital formation have significant positive effects on the economic growth of South Asia. However, exports and imports are found to have detrimental effects. Causality test reveals a unidirectional causality from electricity consumption to economic growth that supports the growth hypothesis. Following the findings, important policy recommendations are made to foster the economic growth in the South Asian countries. [ABSTRACT FROM AUTHOR]

Energy efficiency is one of the most powerful strategies to mitigate the impacts of climate change by reducing energy demand, which in turn reduces the emissions of Green House Gases (GHG), as well as reducing the burden on the supply side renewable generation expansion. Because electric motors systems represent such a large share of the overall electricity consumption (over 50%), large savings potential could be made available by the use of energy-efficient motor systems both in new installations and by accelerating the replacement of old inefficient motors. Since electric motors are very reliable, their lifetime is long (according to recent studies it may exceed 20 years) which translates into a very inefficient existing stock despite worldwide policy efforts. This paper analyzes the current efficiency of the installed stock and the causes for its low efficiency, possible policy options to increase its the efficiency, the role of new technologies and improvements possible by targeting the entire motor system at the time of motor replacement. The paper presents an innovative analysis of the estimated impact of increasing the uptake of high-efficiency motors and motor systems; effective policies could translate into 100 TWh/year in the European Union if additional measures, such as addressing oversizing, proper controls (VSDs) and digitisation, are also implemented. If similar measures were adopted globally, the savings triggered could be at least tenfold reaching over 1000 TWh/year. [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]

Electricity is used in educational buildings, and there are now numerous attempts to reduce consumption, achieve sustainability, and protect the environment. This paper aims to study energy consumption, identify opportunities to rationalize energy consumption, and propose solutions at Sulaiman Al-Rajhi University in the Kingdom of Saudi Arabia in order for the university campus to become sustainable. The results showed that total annual electrical consumption totaled 13,859 MWh in 2021. The air conditioning system, other devices, and lighting have the percentage of consumption 79, 14, and 7% of the total, respectively. Electricity consumption intensity was approximately 145–155 kWh/m2, and the per capita intensity was approximately 12,987–16,351 kWh, in the period 2017–2021. The price of the electrical energy generated on the university campus ranged between 0.3 and 0.53 SR/kWh in 2021, while the electricity tariff from the grid for educational buildings was 0.18 SR/kWh. This means that the public grid was 50% cheaper than campus-generated electricity. At the price of energy generated by generators, the total simple payback time (SPBT) for the suggested energy conservation measures (ECMs) is 4.13 years, whereas the SPBT is 8.96 in the case of the consumption of the electricity supplied by the national grid. The environmental benefits of the proposed ECMs were also identified. [ABSTRACT FROM AUTHOR]

The necessity of a flow express control of oil dispersed system (ODS) properties, such as crude oil, oil products, water–oil emulsions, and polluted waters, is substantiated. This control is necessary for the production and preparation of oil for transportation through the pipeline and oil refining, oil products, and wastewater treatment systems. A developed automatic measuring complex (AMC) is used to implement the concept of digital oil deposits. The primary measuring device is a relaxometer developed by us based on nuclear (proton) magnetic resonance (PMR). The design and operation algorithm of the AMC and the relaxometer are described. Equations have been developed to determine the ODS characteristics using the measured PMR parameters. This makes it possible to determine the flow rates of crude oil, the concentration of water in the oil, the concentration of asphaltene, resins, and paraffins in the oil, as well as the density, viscosity, and molecular weight of the oil. Additionally, it is possible to determine the dispersed distribution of water droplets in emulsions in oil production and treatment units. Data on this distribution will improve the management of separation processes. It has been established that the implemented control of multiphase ODS using PMR parameters (relaxation times, populations of proton phases, and amplitudes of spin-echo signals) makes it possible, using AMC, to assess the consumption of electricity in technological processes at the digital oil deposits, as well as during the transportation of oil and oil products through pipelines. AMC makes it possible to reduce electrical energy consumption in technological installations and reduce pollution emissions into wastewater. The advantages of using the developed AMC are shown in examples of its application. Such as an assessment of the influence of the gas factor on electricity consumption during oil transportation through pipelines or compensation for the additional moment of resistance on the shaft of the submersible motor, which is caused by surface tension forces at the interface of water droplets in the emulsion. [ABSTRACT FROM AUTHOR]

Negawatt trading is expected to improve energy efficiency via the prediction of peak demands and power-saving requests. However, the amount of power saved by consumers is not stable. The accurate prediction of demands and making appropriate requests with respect to power saving are difficult obstacles that need to be overcome in order to attain useful negawatt trading processes. To increase the accuracy of predictions and requests, earlier research suggests some methods or linear problems. On the other hand, the investigation of factors that affect consumption is important for correcting the current instability. In this research study, weather changes including temperature, time passing, and the consciousness of consumers are considered as important factors against electricity demands. In this paper, we propose a behavioral model of consumers using weather data. By using this behavioral model, the effectiveness of the suggested methods in earlier research for improving negawatt trading and the uncertainty of negawatts caused by weather changes is investigated. [ABSTRACT FROM AUTHOR]

Many developing countries subsidise energy (petroleum fuel products, natural gas and electricity), which was reflected in an extra pressure on the national budget, and this will support inefficient use of energy. In this study, the effects of electrical energy subsidies on the total electrical energy consumption in the residential sector were examined. Data on more than 260,000 Jordanian ordinary customers were collected, and the energy consumption of more than 1000 energy-extra subsidised Irbid District Electricity Distribution Company (IDECO) staff members was recorded over a 2-year period (2017 and 2018). These two groups were compared to examine the consequences of subsidising energy on the energy consumption and the consumption behaviour in the residential sector. The analysis revealed that ordinary householders consume around 296 kWh/month, while for the subsidised group 615 kWh/month was noted. Energy consumption increased during the summer and winter months, especially in the subsidised group, due to the heavy reliance on mechanical systems for cooling and heating. Electricity full price (without any subsidies) can be a very effective way to control the demand profile. It can be structured to encourage customers (generally those that have significant electricity demand) to reduce their total usage as well as peak demand (thus reducing the pressure on the grid and the power plant) by charging them full electricity prices. [ABSTRACT FROM AUTHOR]

Non-intrusive load monitoring (NILM) can obtain fine-grained electricity consumption information of each appliance by analyzing the voltage and current data measured at a single point on the bus, which is of great significance for promoting and improving the efficiency and sustainability of the power grid and enhancing the energy efficiency of users. NILM mainly includes data collection and preprocessing, event detection, feature extraction, and appliance identification. One of the most critical steps in NILM is signature extraction, which is the basis for all algorithms to achieve good state detection and energy disaggregation. With the generalization of machine learning algorithms, different algorithms have also been used to extract unique signatures of appliances. Recently, the development and deployment of the voltage–current (V-I) trajectory signatures applied for appliance identification motivated us to present a comprehensive review in this domain. The V-I trajectory signatures have the potential to be an intermediate domain between computer vision and NILM. By identifying the V-I trajectory, we can detect the operating state of the appliance. We also summarize existing papers based on V-I trajectories and look forward to future research directions that help to promote the field's development. [ABSTRACT FROM AUTHOR]

Hybrid concentrated solar thermal power (CSP) and photovoltaic (PV) plants are gaining relevance because they combine their advantages: easy installation and low cost of PV plus dispatchability of CSP. This paper presents results of a techno-economic modelling of this hybrid approach for sites in Jordan, Tunisia and Algeria. Local boundary conditions such as meteorology, cost and electricity demand have been considered to determine the best configurations for these three sites. Different CSP technologies with thermal energy storage have been selected. Hybridization with natural gas has also been included. The optimization is done towards minimizing the LCOE while covering the electrical demand 24/7. Results are presented for different CO2 emissions ranges, as the use of fossil fuel has a strong impact on the LCOE and for environmental reasons, it may be preferred to be kept to a minimum. For most of the cases analyzed, the fraction of energy from PV that leads to minimum LCOE is lower than the energy from CSP. It is shown that for countries with a high fuel price, the use of natural gas reduces the LCOE until a share from this source of about 20%. A higher integration of fossil fuel for sites rich in solar irradiation is considered not advantageous if the price of natural gas is above EUR 40/MWh. [ABSTRACT FROM AUTHOR]

The quality of electricity is one of the most discussed issues in the regulatory context, structured by guidelines that allow the assessment of efficiency in electricity supply. Regarding electricity distribution systems, it is essential to highlight the aspects associated with the definition of continuity indicators to measure the service provided by the utilities. The parameters used to calculate the indicators are set by specific agencies in each country based on studies and their characteristics. With the growth in demand for electricity, debates associated with reducing greenhouse gas emissions in the atmosphere, and the increase in Distributed Energy Resources (DERs) connected to the grid, a new trend related to the energy supply is verified. Given these new scenarios, this paper presents a literature review in the regulatory field of existing distribution systems in South American countries. The research proposes to analyze, compare, and expose the current models to evaluate their methods and indicators, including a complete case study in Brazil. This paper seeks to illustrate and point out perspectives, challenges, and future perspectives through the collected and analyzed data. [ABSTRACT FROM AUTHOR]

Highlights: What are the main findings? The calculated HI, UTCI and THW indices show that thermal difference between the city center and the peri-urban area is greater in terms of perceived temperatures (thermal comfort indices) than in terms of air temperatures (UHI) at night in summer, especially during the heat wave period. A correlation between temperature distribution and electrical energy consumption was found. In fact, During the hot period, the overconsumption of electricity can reach 400% in the downtown area. What is the implication of the main finding? The improvement of thermal comfort is possible by promoting natural ventilation. reducing the UHI would reduce the consumption of electrical energy in summer. This study follows on from the work on the urban climate of Sfax and its energy repercussions. The spatial configuration of the urban heat island (UHI) and the spatial distribution of the thermal comfort indices, the heat index (HI), the universal thermal climate index (UTCI) and the temperature humidity wind index (THW), resulting from the car survey method, essentially show the impact of land use and the distance from the coastline on the spatial distribution of air temperatures. A maximum difference of 7 °C between the city centre and the countryside is measured in very hot summer weather. During the night phase, the apparent temperatures are oppressive, particularly in the city centre, where HI and THW comfort indices exceeding 40 °C were calculated. Compared to the HI and UTCI, the THW demonstrates a much stronger variation due to the influence of wind between open areas and the downtown area. The intensity of thermal discomfort decreases as one moves towards the rural area (−15 °C compared to the centre), hence the impact on the consumption of electrical energy dedicated to air conditioning. The central part is the most energy-intensive area compared to the peripheral areas, especially during hot days when the use of air conditioners becomes constant, day and night. [ABSTRACT FROM AUTHOR]

The integration of EU markets is still limited due to a variety of factors diminishing the role of cross-border flows in inducing greater competition in the electricity markets, a development which would lower electricity prices for end-consumers [[4]]. Here, we seek to review the relevant literature and published articles on the subjects of electricity markets, electricity prices and green energy transition. The Polish power exchange is analyzed by [[10]], providing specific information about the Polish electrical energy system and its installed capacity, achievable capacity, production and consumption of electric power, electricity market and its nature, in addition to the prices and functioning of the day-ahead Polish market. Electricity markets and electricity prices are important, both for supply and demand of electricity, in decentralized electricity generation markets and relevant prices. [Extracted from the article]

This article provides a solution based on statistical methods (ARIMA, ETS, and Prophet) to predict monthly power demand, which approximates the relationship between historical and future demand patterns. The energy demand time series shows seasonal fluctuation cycles, long-term trends, instability, and random noise. In order to simplify the prediction issue, the monthly load time series is represented by an annual cycle pattern, which unifies the data and filters the trends. A simulation study performed on the monthly electricity load time series for 35 European countries confirmed the high accuracy of the proposed models. [ABSTRACT FROM AUTHOR]

The publication presents heat and electricity management in the Polish steel industry. The paper is based on actual data on heat and electricity consumption and intensity by processes in the steel industry in Poland in Industry 4.0 conditions. Two steel production processes are used in Poland: EAF Electric Arc Furnace and BOF Basic Oxygen Furnace. The analysis is an analysis of actual data is used to characterise the electricity and heat consumption by processes in the Polish steel industry. The analysis shows that the EAF technology is always more electricity intensive and the BOF technology more heat intensive. On the basis of conducted analysis, it can be concluded that pro-environmental innovations in the steel industry should first aim to reduce the electricity consumption of EAF technology and the heat consumption of BOF. An analysis of data for Poland for the period 2004–2020 shows that both cases occurred. The study shows that the heat consumption of BOF technologies has been steadily decreasing since 2010, and the electricity consumption of EAF technologies has been decreasing throughout the period under review. It can be concluded from this that the Polish steel industry is adapting to pro-environmental requirements and, through the introduction of technological innovations, is moving towards the concept of sustainable steel production according to green steel principles. The decrease in energy intensity (means electricity) of steel produced according to EAF technology is an important issue, as the high energy intensity of EAF processes affects the overall energy intensity of the steel production in Poland. In the future, the use of new innovative technological solutions, including solutions based on Industry 4.0 principles, should help the Polish steel industry to further reduce the level of electricity and heat consumption. The driving force behind the investment is the boom in the steel market. The authors made a short-term forecasts of steel production (2022–2025). The annual forecasts determined and analyses made were used to determine the heat and energy consumption of the Polish steel industry up to 2025. [ABSTRACT FROM AUTHOR]

Recently, thanks to various support mechanisms, residential photovoltaics (PVs) for self-consumption are proliferating at a rapid pace. The net-metering scheme, one of the prevailing support mechanisms for self-consumption PVs, contributes to the proliferation of residential PVs by enhancing the economic benefits of PV adopters, but it suffers from certain disadvantages, such as missing network revenue of utilities and spread of cross-subsidies between the customers. This paper analyzes the cross-subsidy effect of residential PV proliferation across customer groups segmented according to their PV adoption and electricity consumption level under the net-metering scheme in the Korean electricity market. The results show that missing network revenue of utilities increase by about 0.83% for every 1% increase of residential PV penetration, and customers in the lowest usage tier provide more cross-subsidies toward customers in the higher usage tiers as with higher proliferation of residential PVs. In addition, this paper suggests that the cross-subsidy effect between customers can be reduced by introducing a new network charge design that is more consistent with the cost-causality principle as well as targeted deployment policies for self-consumption PVs. [ABSTRACT FROM AUTHOR]

Due to the increasing demand for electricity around the world, different technologies have been developed to ensure the sustainability of each and every process involved in its production, transmission, and consumption. In addition to ensuring energy sustainability, these technologies seek to improve some of the characteristics of power systems and, in doing so, make them efficient from a financial, technical, and environmental perspective. In particular, solar photovoltaic (PV) technology is one of the power generation technologies that has had the most influence and development in recent years due to its easy implementation and low maintenance costs. Additionally, since PV systems can be located close to the load, power losses during distribution and transmission can be significantly reduced. However, in order to maximize the financial, technical, and environmental variables involved in the operation of an electrical system, a PV power generation project must guarantee the proper location and sizing of the generation sources. In the specialized literature, different studies have employed mathematical methods to determine the optimal location and size of generation sources. These methods model the operation of electrical systems and provide potential analysis scenarios following the deployment of solar PV units. The majority of such studies, however, do not assess the quality and repeatability of the solutions in short processing times. In light of this, the purpose of this study is to review the literature and contributions made in the field. [ABSTRACT FROM AUTHOR]

The article presents the results of the analysis of parameters describing electricity consumption in individual phases of the production cycle on a poultry farm. One full broiler rearing cycle on the farm was analyzed. Electrical parameters were tested during the broiler rearing process using a power quality analyzer with a measurement interval of 1 min. During the tests, the analyzer recorded the active and reactive power, phase and line voltage, power factor, and frequency. On the basis of collected data, original indicators describing electricity consumption per chicken wkA, wkQ, and per unit area wiA, wiQ were determined. The regression curves of active and reactive power consumption in particular stages of the cycle were also determined, and the minimum and maximum values of active and reactive power consumption were determined. The accomplished research can be used in planning electricity demand in energy-self-sufficient areas. The presented original indicators can be used to determine power demand on broiler farms, depending on the planned production volume or size of farm buildings. [ABSTRACT FROM AUTHOR]

For several decades, the design and manufacture of electrical machines has been considered a technically mature area and, as a result, research and development in the area has been extremely limited, even though this is a crucial technology in the application of electrical energy. Electrical machines are used in over 80% of the world's energy conversion processes—first to create electrical energy, which can be easily transmitted, and second to convert that energy into mechanical form for applications ranging from dishwashers to transportation, and from medical devices to those used for industrial processes. Today, two technologies are changing this. The first is the development of power electronic drives and the second is the introduction of additive manufacturing technology. The latter technology has opened up new areas for innovation and research, and many conventional processes are likely to become obsolete. Considering the overall consumption of electricity by electrical machines, the design freedom granted by the novel production technology gives the opportunity for even more efficient, object-oriented machines to be built, with a lower environmental impact and less raw material consumption. If this technology can be developed to maturity, it would have a significant positive impact on the desired green transition that is being pursued all over the world. [ABSTRACT FROM AUTHOR]

Forecasting energy consumption in Saudi Arabia for the period from 2020 until 2030 is investigated using a two-part composite model. The first part is the frontier, and the second part is the autoregressive integrated moving average (ARIMA) model that helps avoid the large disparity in predictions in previous studies, which is what this research seeks to achieve. The sample of the study has a size of 30 observations, which are the actual consumption values in the period from 1990 to 2019. The philosophy of this installation is to reuse the residuals to extract the remaining values. Therefore, it becomes white noise and the extracted values are added to increase prediction accuracy. The residuals were calculated and the ARIMA (0, 1, 0) model with a constant was developed both of the residual sum of squares and the root means square errors, which were compared in both cases. The results demonstrate that prediction accuracy using complex models is better than prediction accuracy using single polynomial models or randomly singular models by an increase in the accuracy of the estimated consumption and an improvement of 18.5% as a result of the synthesizing process, which estimates the value of electricity consumption in 2030 to be 575 TWh, compared to the results of previous studies, which were 365, 442, and 633 TWh. [ABSTRACT FROM AUTHOR]

Distributed electric power production by small-scale customers is increasing continuously. Photovoltaic production is a popular method of producing self-energy for customers. Additionally, power systems require more flexibility when weather-dependent renewable energy production increases. Small-scale customers can increase the self-consumption of self-produced energy by using batteries or a demand response operation. However, batteries require high investment, and demand response operations induce a loss of comfort. Customers who heat their buildings using electric heaters are a good target for demand response operations because their heating can be controlled with limited changes in the indoor temperature. The demand response potential of a building can be defined by simply using customer load profiles and knowledge of the outdoor temperature. Any other information is not required in the proposed novel method. A tolerable variation in indoor temperature corresponds to considerably smaller battery capacity, though it is still a significant amount. With an optimally sized photovoltaic system, it is possible to use both methods simultaneously to increase self-consumption. Maximal benefits can be attained from both methods if the battery system is used as a primary control and the demand response is used as a secondary control. The defined novel method for determining the demand response potential of small-scale customers can also be used when estimating the flexibility of a large customer group. Small-scale customers together can provide significant flexible capacity when their electrical heating is centrally controlled. [ABSTRACT FROM AUTHOR]

A smart grid (SG), considered as a future electricity grid, utilizes bidirectional electricity and information flow to establish automated and widely distributed power generation. The SG provides a delivery network that has distributed energy sources, real-time asset monitoring, increased power quality, increased stability and reliability, and two-way information sharing. Furthermore, SG provides many advantages, such as demand response, distribution automation, optimized use of electricity, economical energy, real-time grid status monitoring, voltage regulation or VAR control, and electricity storage. In this survey, we explore the literature on smart Grid enabling technologies until 2022. We dig out four major systems: (1) the smart grid's prominent features and challenges; (2) the smart grid standard system and legislations; (3) smart grid energy subsystem; and (4) the smart grid management system and protection system for new researchers for their future projects. The research challenges and future recommendations are also presented in the conclusion section to explore the new paradigm. [ABSTRACT FROM AUTHOR]

This work proposed an optimal design of PV-system-based water-pumped energy storage for both electricity and water supply. A case study was considered in a rural community in Cameroon. The parameters of the assessment of the system were reliability, represented in the present work by the system supply deficiency (SSD), and economic accessibility, represented by the levelized cost of energy (LCOE). The obtained results showed that for 0% SSD, the optimal configuration of the system was composed of 438 PV modules of 235 W, an immersed solar motor pump of 35 kW, a hydroelectric turbine of 51.7 kW, an upper reservoir of 2307.1 m3, an inverter of 25.27 kW, and a total dynamic head of 88 m. The corresponding LCOE to this configuration is 0.224 USD/kWh. The economic accessibility of the designed system was evaluated by comparison with a PV-system-based battery energy storage. The optimal design configuration of the studied PV-system-based battery energy storage was a PV generator (120 PV modules of 235 W), solar motor pump (15 kW), upper reservoir (590.4 m3), battery capacity (351.78 kWh), inverter (25.27 kW), and total dynamic head (81 m). The corresponding LCOE to this configuration was 0.1857 USD/kWh. Although the PV-system-based battery storage appeared to be economically more cost-effective than the PV-system-based water-pumped energy storage, the sensitivity analysis revealed that there was the possibility for the PV-system-based water-pumped energy storage to be economically more profitable than the PV-system-based battery energy storage. This economic outperformance occurred when the project lifetime was a multiple of 7.5 years or when the costs of the storage components were reduced from 20% to 60%. [ABSTRACT FROM AUTHOR]

The building sector consumes as much as 80% of generated electricity in the UAE; during the COVID-19 pandemic, the energy consumption of two sub-sectors, i.e., commercial (50%) and residential (30%), was significantly impacted. The residential sector was impacted the most due to an increase in the average occupancy during the lockdown period. This increment continued even after the lockdown due to the fear of infection. The COVID-19 pandemic and its lockdown measures can be considered experimental setups, allowing for a better understanding of how users shift their consumption under new conditions. The emergency health measures and new social dynamics shaped the residential sector's energy behavior and its increase in electricity consumption. This article presents and analyzes the identified issues concerning residential electricity consumers and how their behaviors change based on the electricity consumption data during the COVID-19 period. The Dubai Electricity and Water Authority conducted a voluntary survey to define the profiles of its residential customers. A sample of 439 consumers participated in this survey and four years of smart meter records. The analysis focused on understanding behavioral changes in consumers during the COVID-19 period. At this time, the dwellings were occupied for longer than usual, increasing their domestic energy consumption and altering the daily peak hours for the comparable period before, during, and after the lockdown. This work addressed COVID-19 and the lockdown as an atypical case. The authors used a machine learning model and the consumption data for 2018 to predict the consumption for each year afterward, observing the COVID-19 years (2020 and 2021), and compared them with the so-called typical 2019 predictions. Four years of fifteen-minute resolution data and the detailed profiles of the customers led to a better understanding of the impacts of COVID-19 on residential energy use, irrespective of changes caused by seasonal variations. The findings include the reasons for the changes in consumption and the effects of the pandemic. There was a 12% increase in the annual consumption for the sample residents considered in 2020 (the COVID-19-affected year) as compared to 2019, and the total consumption remained similar with only a 0.2% decrease in 2021. The article also reports that machine learning models created in only one year, 2018, performed better by 10% in prediction compared with the deep learning models due to the limited training data available. The article implies the need for exploring approaches/features that could model the previously unseen COVID-19-like scenarios to improve the performance in case of such an event in the future. [ABSTRACT FROM AUTHOR]

One of the research areas related to renewable energy sources is the search for new applications for currently used technologies. An important postulate is to achieve the synergy effect by including the existing infrastructure in this process. The innovation described in this article is the integration of solar cells into a modular sound barrier on the highway. The contribution of this article is mainly based on the introduction of the potential feasibility of the first Turkish solar highway, describing the installation of PV systems based on a multi-criteria evaluation (azimuth angle, loss of shade, and dirt). The aim of the study is to determine the economic dimension by examining the costs of implementing such an investment and answering the question concerning the efficiency of solar cells on sound barriers. The research took into account various scenarios regarding the shadow effect and inclination of solar panels and their impact on solar energy production in the example of a given case study, an identified urban area. The research is limited to the model for assessing the feasibility of solar cells on sound barriers based on NPV and the essence of the relationship regarding the LCOE averaged electricity costs. In this study, the function and technical parameters of solar cells are not specified, but the article is limited to the basic information and characteristics required to investigate the payback period and profitability of an investment. Research has shown that if the investment is carried out in accordance with the proposed model, the expected amount of energy produced is 62,257 kWh per year. This value corresponds to the average consumption of electricity in a household by 24 apartments for a given destination, or to providing energy for a selected electric vehicle for one year of its operation, at the same time leading to a significant reduction of CO2. The presented research can find practical application in the area of developing a strategy for the development and popularization of renewable energy sources while increasing the use of the existing road infrastructure, presenting an important postulate in promoting this type of solution for other geographic destinations. [ABSTRACT FROM AUTHOR]

In recent decades, the rapid rise in electricity demand has compelled transmission and distribution systems to operate at almost their maximum capacity. This can pose numerous technical challenges such as excessive power losses, voltage and transient instabilities, as well as reduced power quality and reliability. Employment of Flexible Alternating Current Transmission System (FACTS) devices can be an effective approach to obviate such challenges and reinforce the power system functionality. Nevertheless, FACTS devices require a high initial investment, and hence their optimal allocation in terms of various aspects such as type, size and location is of utmost importance. This cannot be achieved without the deployment of optimization techniques. The aim of this paper is to provide a comprehensive review of the existing proposals for the enhancement of power system performance adopting FACTS devices. Adhering to that, an in-depth analysis is carried out, in which the most pertinent options are classified into specific groups based on their optimization objectives. Finally, a comparative analysis is accomplished in which the main attributes and drawbacks of each optimization technique are presented. [ABSTRACT FROM AUTHOR]

The application of residential rooftop PV systems increased significantly in the last 10 years in many countries and became a major source of clean energy in dwellings besides traditional solar hot water technology. To optimise the performance of these green energy systems, the incorporation of PV/thermal hybrid systems is a future option for sustainable residential building designs. In this work, a novel design of PV/Thermal (PVT) hybrid panels, using heat pipe technology, is proposed with the aim of fulfilling the hot water and electricity demand of a house in Sydney. The heat pipe system is integrated into a traditional PV panel to transfer the heat stored within the PV panel material to a header that is connected to the household hot water cycle. A preliminary analysis of the test results for the proposed PVT system design under different weather conditions in Sydney is conducted, where the transient variation of the output water temperature as well as power production is investigated. The results show that the hot water temperature at the header outlet reaches around a maximum of 50 °C on a typical summer day and a minimum of 30 °C on a typical winter day. The daily heat delivered to the hot water tank is found to be in the range of 3.7–5.2 MJ per m2 of the PVT panel surface area. The results show that the energy efficiency of the adopted PVT panel design could reach more than 4 times higher than the traditional PV panel. [ABSTRACT FROM AUTHOR]

The European Union (EU) has committed to supporting the United Nations' efforts in line with the Paris Agreement for addressing climate change and has set ambitious targets to reduce primary energy consumption and emissions. Similar commitments have also been set by EU-27 member states. For this purpose, it is necessary to use a primary energy factor (PEF) for converting electricity use to primary energy units and for assessing energy conservation measures. Lower PEFs reflect efficiency improvements in power generation, an increased share of renewable energy sources in the fuel mix for electricity generation, and lower transmission and distribution losses. Over the past decades, there have been intensive efforts and notable progress in the EU-27 for increasing the use of renewables in the energy mix for electricity generation. However, the EU default PEF value for electricity was not regularly updated and remained at 2.5 for several years till it was finally recalculated at 2.1 in the 2018 recast of the Energy Efficiency Directive. This paper reviews different calculation options for estimating the PEF for electricity from official annual statistics, presents the historical evolution of the calculated conversion factors, and provides simple linear correlations for projecting the PEF values that can be used to facilitate more-realistic forward-looking calculations and assess national energy efficiency, climate change, or decarbonization plans in EU-27 member states. A more detailed analysis and case studies on the impacts of this work are illustrated for Greece and Poland. [ABSTRACT FROM AUTHOR]

10.3390/en15020590 20 Binyet E., Chiu M.-C., Hsu H.-W., Lee M.-Y., Wen C.-Y. Potential of Demand Response for Power Reallocation, a Literature Review. Dejamkhooy and Ahmadpour [[4]] applied a Gaussian process model to forecast electricity prices in a restructured power system. Shaqiri et al. [[5]] uses a dynamic regression model for a short-term forecast of the electricity consumption of industrial and institutional customers of an electricity provider. Kanté et al. [[2]] aimed to forecast and understand the long-term electricity demand of the Taoussa area for the sustainable development of the regions of northern Mali, using the Model for Analysis of Energy Demand (MAED) from the International Atomic Energy Agency. [Extracted from the article]