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In recent years, due to the increase in electricity consumption and environmental problems, power system expansion planning requires new technologies. In this regard, the incorporation of renewable energy sources (RESs) and utilization of demand response (DR) programs need disruptive variations in the present power system configurations. This paper proposes a mixed-integer linear robust multiobjective model for generation and transmission expansion planning (GEP-TEP) taking into account wind farms (WFs) and a DR program based on time-of-use pricing. The suggested model is presented via mixed-integer nonlinear programming (MINLP) at the first stage and then transformed into mixed-integer linear programming (MILP) using the Big M linearization technique. Moreover, long- and short-term uncertainties of load demand and WFs are incorporated into the recommended model to achieve more accurate results. The interval-based method is applied for taking into account long-term uncertainties while the scenario-based stochastic model is applied for modeling short-term uncertainties in the recommended GEP–TEP model. Lastly, the suggested model is investigated on various standard test systems to evaluate the effectiveness of the GEP-TEP model. [ABSTRACT FROM AUTHOR]

The paper investigates the interplay between clean energy, environmental pollution reduction, and economic growth in the Gulf Cooperation Council (GCC) countries from 1980 to 2019, utilizing the autoregressive distributed lag (ARDL) method. The study underscores the global transition towards cleaner energy sources and its implications for the region. In assessing the determinants of economic growth, the findings reveal a positive and significant short- and long-term impact of energy production, with energy consumption exhibiting a positive and significant effect solely in the long term. The absence of a confirmed relationship in the short term is notable. Causality tests demonstrate a significant link from both energy consumption types to environmental pollution, alongside causal relationships from urbanization, energy production, and consumption to economic growth. Moreover, the results include the pivotal role of energy production in driving GDP growth, caution in short-term energy consumption effects, and significant causal links between energy consumption and environmental pollution. The recommendations to incentivize sustainable energy production, engage in long-term energy consumption planning, and adopt integrated urbanization policies provide actionable insights for policymakers. These suggestions are aimed at guiding the GCC countries in balancing the pursuit of economic growth with environmental sustainability—a delicate equilibrium that necessitates careful consideration of various factors. [ABSTRACT FROM AUTHOR]

High electricity consumption in Sarawak's cities is a concern, driven by residential, industrial, and commercial users. Greater demand for electrical energy requires more electrical resources, affecting grid stability. To prevent blackouts and enhance grid resilience, electric vehicles can serve as distributed energy storage in microgrids. The biggest problem facing energy storage is that not all batteries within energy storage are the same. Every battery cell in the battery pack has a different rate of self-discharge, capacity, internal resistance, and aging, even though they have the same chemical and physical characteristics. These variations lead to power imbalances in energy storage. Therefore, to address this issue, a unique voltage equalization method using a modular flyback converter is presented. Its core objective is to correct voltage imbalances among batteries. The proposed method is modelled using MATLAB, and the performance of the system has been evaluated. Furthermore, the developed model is implemented in a microgrid and tested for two different cases (with and without energy storage for electric cars). From the simulation outcomes, it has been verified that the developed cell balancing technique performs well in equalizing the voltage of cells than the conventional methods. [ABSTRACT FROM AUTHOR]

Carbon-free thermally driven district cooling systems (DCS) can effectively mitigate the excessive electricity consumption and carbon emissions associated with the cooling sector. This study proposes a DCS that employs nuclear heat as the primary energy source. The system comprises three main subsystems: heat station, heat transmission, and cooling station. A heat-only small modular reactor called Teplator, gas boilers, and heat storage are considered to supply the heat required to drive absorption chillers; cold storage and compression chillers are the supplementary units. The technoeconomic aspects of the system are formulated, and an algorithm is developed to determine the optimal design and operation. The method is examined for supplying a typical cooling demand profile with a peak of 2050 MWc. The resulting optimized design includes 11 nuclear plants (150 MWt each), 20 000 MWth heat storage, and 1.9 m diameter heat supply/return pipes. Absorption chillers with a total capacity of 1424 MWc are determined, covering 92% of the total cooling demand, and 244 MWc of compression chillers and 20 000 MWch of cold storage are found to cover the peak and enhance the load following. This system saved 69% of the electricity consumption and carbon emissions and 34% of the costs compared with an electric-based scenario. [ABSTRACT FROM AUTHOR]

Summary. The dynamically rising demand for electricity must be satiated by both conventional and unconventional power sources. PV electricity generation is essential. The installed PV sources are not used to their full potential due to low irradiance and poor weather. The goal of many research publications was to maximize PV power using different MPPT approaches. In order to extract the most power under the aforementioned circumstances, this study introduces a novel notion of using a voltage boosting PV panels. Additional PV panels are used in the suggested way to provide enough voltage and current. Using a voltage-boosting PV panel, a 20 kW panel is examined for low irradiance and unfavorable weather conditions. In a proposed method, results are compared with the previous INC MPPT method using a 20-kW panel. Through the power analysis, the proposed method extracted 1277.5 kW of power per year that is more than the INC MPPT method, and Rs. 10,220 per year was benefited during low irradiation alone. The power extraction and cost benefits are similar in cloudy and misty conditions. With power, cost, and efficiency analyses, the new method is contrasted with the traditional incremental conductance method and perturb and observe methods. Simulations were run using the MATLAB/Simulink programme, and experimental results were assessed using the appropriate hardware setup. [ABSTRACT FROM AUTHOR]

In the present study, a solar-assisted heat pump dryer (SAHPD) has been designed, fabricated, and tested its performance on drying tobacco leaves. The hot air generated from the solar collector and condenser unit of the heat pump was used as a source of heat in the drying chamber. In this study, we investigated the influence of three duct configurations (open, partially closed, and completely closed) on the thermal performance of SAHPD to establish the best configuration for drying tobacco leaves. The average drying temperature was found to be 66, 64, and 60°C; the coefficient of performance of the heat pump was 3.4, 3.2, and 3.0; the heat energy contribution from the solar collector was 6.6%, 5.0%, and 5.1% while for the condenser was 93.4%, 95.0%, and 94.9%, and electrical energy consumption was 2.3, 2.8, and 2.6 kWh, for the open, partially closed, and completely closed duct system, respectively. Based on these results, the open system demonstrated the best performance. According to the study's findings, SAHPD has been shown to be an energy-efficient method of drying tobacco leaves and is environmentally friendly as opposed to the conventional use of wood fuel, which results in environmental pollution, desertification, and deforestation. [ABSTRACT FROM AUTHOR]

The increased electricity demand amidst inadequate electricity generation in South Africa has plunged the country into frequent power outages and load shedding. However, the country still has the lowest electricity transmission and distribution losses in sub-Saharan Africa. Despite low losses, there is still an opportunity to reduce losses further and reduce power outages and load shedding. This study examines the determinants of electricity transmission and distribution losses in South Africa. The results will inform policymakers on avoiding higher electricity transmission losses to alleviate the current electricity shortfall. Using the time-series data from 1971–2020 and the autoregressive distributed-lag (ARDL) bounds testing approach to cointegration, the study confirmed a long-run relationship between electricity transmission, distribution losses, and income, price, investment, political regime, and economic integration. Regression analysis from the ARDL methods revealed that investments, political administration, and economic integration positively influence electricity transmission and distribution losses. At the same time, income reduces electricity transmission and distribution losses in the long run. However, income, price, and economic integration minimize electricity transmission losses in the short run while the remaining variables maintained their positive effects. The implication is that without proper checks in place, an expansion in South Africa's economic integration, investment, and democracy may negatively affect the electricity sector of the country through an increase in electric power losses, while higher income will help the industry via lower electric power losses. The paper, among other things, recommends building a robust economy to ensure lower levels of electricity transmission and distribution losses. [ABSTRACT FROM AUTHOR]

Environmental pollution has stimulated cleaner and sustainable energy (CSE) resource consumption which fuels low-carbon electric-power growth. This consumption is particularly affected by two factors. One of them is the power grid's capital size in the electricity supply chain (ESC) and the other is the carbon emission reduction level elasticity of electricity consumption. Further, the financial strategy directly exerts quantifiable influence on one of the factors of capital size, while the procurement strategy plays a role in another factor. However, the impact of strategic behavior on all participants in the ESC remains unexplored. Thus, we construct a game model for the ESC consisting of two heterogeneous power plants and a strategic power grid with financial constraints, in order to analyze the purchasing and financing strategies for low-carbon electric-power consumption and examine the profit scenario from the credit and option hedging. We find that for the power grid, high self-owned funds level or affluent credit line encourages procurement. And when considering the power grid funds are uniformly distributed on or are constant, this funds property variation surely imposes influence on the wind electricity purchases. We find price elasticity of demand shows monotonic on the purchase when the power grid funds are constant but not necessarily when funds are uniformly distributed. The two power plants pursue the most favorable scenario for profit-maximizing by means of credit interest rate. Whether the power plants increase the financial credit interest is contingent upon option portfolios, the grid's fund property, and wind yield conditions. We also find whether the relations between the two power plants are competitive or complementary depending on the wind yield rate. Numerical study shows that when the power grid funds are uniformly distributed, executing the double option seems to be the most profitable choice for the power grid and the traditional energy power plant, whereas with the uniform distribution of the grid's funds, executing the call option but abandoning put is the most profitable to the traditional energy power plant. Moreover, under the grid's fund of uniform distribution, it can both motivate power user consumption for clean energy generation and expand the power grid's capital size. [ABSTRACT FROM AUTHOR]

In the marine engineering industry, turbocharged diesel engines are often used to generate electricity, and hot oil can be extracted after generating electricity. However, marine diesel engine heat recovery can be distinguished from gas heat recovery for turbocharged and nonturbocharged diesel engine systems. The ideal air model Brayton cycle is used to evaluate the feasibility of turbocharged/nonturbocharged cogeneration retrofits in turbocharged diesel engine systems, and the paper is designed to evaluate the effect of pressure and temperature and cooling ratio of exergy efficiency. The results show that the performance of turbocharged and nonturbocharged work increases with increasing pressure ratio until it reaches a maximum value and decreases with increasing pressure ratio at a constant temperature. If an electric generator is selected first, the heat recovery after the turbocharger can be used to improve the contract for heating and electricity needs. While the exergy efficiency is selected priorly, cogeneration retrofit for heating and electricity demands can be used for heat recovery without the turbocharger system. [ABSTRACT FROM AUTHOR]

The analysis of load characteristics is the basis and premise of load actively participating in power grid regulation. This paper proposes a multi-factor load classification method considering the load of clean energy power generation, the rapidity of load classification, and various subjective and objective factors that may affect the behavior of load consumption. First, it describes the characteristic index of load consumption behavior and analyzes the subjective and objective factors that affect the power grid consumption behavior. The effect of clean energy generation on load side is considered. Based on the load characteristics, the K-means algorithm is used for main clustering. Then, the confidence level of the uncertainty of the actual load adjustable capacity is analyzed by quantifying the load adjustable potential index and the fuzzy C-means clustering method was used for secondary clustering of the adjustable capacity. Finally, DBI and SC indexes are used to evaluate the clustering results, standard values of evaluation indexes are set, and unqualified clustering results are recalculated and corrected. 31 industrial users in a province are selected as research objects, and the load data of the past 365 days are collected to verify the effectiveness and practicability of the proposed method. The classification results show that the classification accuracy is still good when the noise is 30%, and the maximum deviation between the clustering results and the actual load regulation potential is 12%. It can meet the actual engineering error standard. [ABSTRACT FROM AUTHOR]

Typically, diesel or other fossil fuel-based related applications meet electricity demand. Nonetheless, as the cost of fossil fuels rises, as do their harmful emissions, there is a sudden transition toward stand-alone hybrid renewable energy systems (HRESs). The proposed project proposes electrifying VIT University in Vellore, Tamil Nadu, India, using an off-grid HRES. The goal of enhancing hybrid energy system (HES) regulation, size, and component selection is to provide society with a cost-effective power supply. This article's primary purpose is to show how to utilize HOMER Pro Software to reduce total net present cost, cost of energy, and CO2 emissions. After that, the findings were compared to four different HRES configurations. The efficient solution for transmitting power at the lowest energy cost is configuration 1 (solar + fuel cell + battery + wind + diesel generator) which is found to be the optimal solution for supplying energy with 0% unmet load at the least cost of energy, which is at 24.91 Rs/kWh. These data are used to calculate the optimal size of energy storage components based on long-term system behavior using HOMER and to forecast short-term generation and demand changes while maintaining system dependability and grid voltage. Using and incorporating model predictive control (MPC) for an interlinking converter (ILC), the proposed HRES design is validated for the study area in a novel way that sequentially applies HOMER and MATLAB simulations. [ABSTRACT FROM AUTHOR]

Most field scientific observation and research stations are located at the end of power grids which are usually not extended to such areas. Consequently, the power supply of equipment in field observation stations cannot be guaranteed. Meanwhile, regions with poor ecosystem stability are relatively sensitive to environmental changes and thus prone to degradation and succession due to external interference. In this paper, the photovoltaic (PV) power generation system of a grassland ecohydrological field scientific observation and research station was taken as the research object. Two kinds of distributed PV power generation systems were simulated and analyzed by use of PVsyst software. The total power of laboratory equipment, PV power generation efficiency, and system cost of the field observation station were calculated and analyzed. The design scheme and scale of PV power generation systems suitable for field observation stations were determined. Finally, a PV power generation test system was set up, and PV power generation data were sorted out. The feasibility of the design scheme of PV power generation systems was verified by analyzing the relationship between the simulated and actual power generation of systems and that between the daily energy use proportions of field observation stations. Besides, the environmental benefits of PV systems were analyzed, and their amount of energy saving and emission reduction was calculated. This study can solve the issue of the low power supply guarantee rate of field observation stations, provide a design basis and beneficial reference for the construction of environment-friendly field laboratory stations, and realize green energy saving and the sustainable use of energy while protecting the ecosystem from being destroyed. [ABSTRACT FROM AUTHOR]

Generally, the main focus of the grid-linked photovoltaic systems is to scale up the photovoltaic penetration level to ensure full electricity consumption coverage. However, due to the stochasticity and nondispatchable nature of its generation, significant adverse impacts such as power overloading, voltage, harmonics, current, and frequency instabilities on the utility grid arise. These impacts vary in severity as a function of the degree of penetration level of the photovoltaic system. Thus, the design problem involves optimizing the two conflicting objectives in the presence of uncertainty without violating the grid's operational limitations. Nevertheless, existing studies avoid the technical impact and scalarize the conflicting stochastic objectives into a single stochastic objective to lessen the degree of complexity of the problem. This study proposes a stochastic multiobjective methodology to decide on the optimum allowable photovoltaic penetration level for an electricity grid system at an optimum cost without violating the system's operational constraints. Five cutting-edge multiobjective optimization algorithms were implemented and compared using hypervolume metric, execution time, and nonparametric statistical analysis to obtain a quality solution. The results indicated that a Hybrid NSGAII-MOPSO had better convergence, diversity, and execution time capacity to handle the complex problem. The analysis of the obtained optimal solution shows that a practical design methodology could accurately decide the maximum allowable photovoltaic penetration level to match up the energy demand of any grid-linked system at a minimum cost without collapsing the grid's operational limitations even under fluctuating weather conditions. Comparatively, the stochastic approach enables the development of a more sustainable and affordable grid-connected system. [ABSTRACT FROM AUTHOR]

Complex systems, such as the power grid, are essential for our daily lives. Many complex systems display multifractal behavior, correlated fluctuations and power laws. Whether the power-grid frequency, an indicator of the balance of supply and demand in the electricity grid, also displays such complexity remains a mostly open question. Within the present article, we utilize highly resolved measurements to quantify the properties of the power-grid frequency, making three key contributions: First, we demonstrate the existence of power laws in power-grid frequency measurements. Second, we show that below one second, the dynamics may fundamentally change, including a suddenly increasing power spectral density, emergence of multifractality and a change of correlation behavior. Third, we provide a simplified stochastic model involving positively correlated noise to reproduce the observed dynamics, possibly linked to frequency-dependent loads. Finally, we stress the need for high-quality measurements and discuss how we obtained the data analyzed here. [ABSTRACT FROM AUTHOR]

Due to high rate of electrical device usage of all types and high consumption of electricity, it is necessary to search for alternative way. There are so many models of nature describing many phenomena in energy, magnetic field, heat transfer, and so on. These models are reflected in the delicate nonlinear partial differential equations. The system of coupled Konno-Oono equations is one of those models. We extract some vital solutions for this model, which describe some vital phenomena in applied science. Namely, we applied unified technique in order to perform this mechanism in a completely unified way. Finally, some simulations are performed by utilizing Matlab 18 to exhibit the behaviour of these solutions. [ABSTRACT FROM AUTHOR]

With the development of the economy and society, electricity demand is increasing. However, there are some problems on the power grid side, such as the excess capacity of power installed, abandoned power of renewable energy, low overall utilization rate, and high pressure of stable operation. The problem stems from the inconsistency between the peak of electricity production and consumption and the lack of obvious power price signals. In this article, the market development objectives are combined with the time-divided transaction, and two objectives of market development are proposed: (1) Reduce the peak-valley difference of power supply and demand; (2) Expand the peak-valley price difference in the market. Combined with the physical properties of the power market, a multiperiod coupling trading mechanism is designed. The results show that the speculative and profit-seeking behavior of power producers will be stopped under the proposed trading mode, the potential of users for peak-valley filling will be effectively developed, the peak-valley gap of the power system can be controlled within a reasonable range, and the peak-valley gap of power trading can be further amplified. [ABSTRACT FROM AUTHOR]

As a result of the green IoT, application layer users are increasingly seeking more services, and it is particularly important to reduce the energy consumption of an IoT monitoring center while ensuring service reliability. In this paper, the max-min ant colony algorithm is proposed to effectively solve the problems of user service reliability and energy consumption optimization of an IoT monitoring center. First, the service reliability of the application layer users is modeled based on a Markov chain, and the quantitative reliability performance indicators are given. Second, an optimization model between the user service reliability and the energy consumption of an IoT monitoring center is established. Finally, the optimization problem is solved based on the improved max-min ant system. Simulation experimental results show that compared with the improved FFD algorithm, the average power consumption of the max-min ant colony algorithm can be reduced by up to 7.14%. [ABSTRACT FROM AUTHOR]

Microgrids are considered to be smart power grids that can integrate Distributed Energy Resources (DERs) in the main grid cleanly and reliably. Due to the random and unpredictable nature of Renewable Energy Sources (RESs) and electricity demand, designing a control system for microgrid energy management is a complex task. In addition, the policies of microgrid agents are changing over time to improve their expected profits. Therefore, the problem is stochastic and the policies of the agents are not stationary and deterministic. This paper proposes a fully decentralized multiagent Energy Management System (EMS) for microgrids using the reinforcement learning and stochastic game. The microgrid agents, comprising customers, and DERs are considered as intelligent and autonomous decision makers. The proposed method solves a distributed optimization problem for each self-interested decision maker. Interactions between the decision makers and the environment during the learning phase lead the system to converge to the optimal equilibrium point in which the benefits of all the agents are maximized. Simulation studies using a real dataset demonstrate the effectiveness of the proposed method for the hourly energy management of microgrids. [ABSTRACT FROM AUTHOR]

A potential and developing green technology for producing renewable energy and treating wastewater is the microbial fuel cell (MFC). Despite several advancements, there are still several serious problems with this approach. In the present work, we addressed the problem of the organic substrate in MFC, which is necessary for the degradation of metal ions in conjunction with the production of energy. The utilization of fruit waste as a carbon source was strongly suggested in earlier research. Hence, the mango peel was used as a substrate in the current study. Within 25 days of operation, a 102-mV voltage was achieved in 13 days, while the degradation efficiency of Cr3+ was 69.21%, Co2+ was 72%, and Ni2+ was 70.11%. The procedure is carried out in the batch mode, and there is no continuous feeding of the organic substrate. In addition, a detailed explanation of the hypothesized mechanism for this investigation is provided, which focuses on the process of metal ion degradation. Lastly, future and concluding remarks are also enclosed. [ABSTRACT FROM AUTHOR]

Smart grids are a combination of traditional power system engineering as well as information and communications technology. Smart grid terminals provide convenient services to users by aggregating their data in real time. However, terminals can derive user privacy information from real-time data on smart devices. Therefore, security data aggregation has been widely studied in the field of smart grid. Most existing schemes are one-dimensional data aggregation or rely on a trusted third party. In reality, multidimensional data (such as a user's electricity consumption or user's main usage time, etc.) makes sense for terminals to flexibly adjust supply and demand strategies. In this paper, we propose an efficient and secure multidimensional data aggregation scheme that supports batch validation without a trusted third party. Firstly, we apply the Chinese remainder theorem to encode the user's multidimensional data and realize the independence of each dimension in terminal decryption. Secondly, we adopt a secure key negotiation protocol that does not require a trusted third party. Finally, based on paillier homomorphic encryption and bilinear pairing, we construct an encryption scheme that can reuse the key and blind factor and support batch verification. The analysis results show that our scheme is secure for users' privacy protection. Experimental results show that, compared with existing 1 dimensional aggregation schemes, our scheme has almost no growth in computational overhead for terminal decryption. [ABSTRACT FROM AUTHOR]

Since housing electrical work is essential in the aspect of electricity use and electrical accidents, it is necessary to properly design, construct, and manage it to avoid quality problems. However, disputes are intensifying in Korea due to numerous quality problems after the house is handed over to the owner. Although related standards have been established and some studies have been conducted, there is a lack of discussion on housing electrical work because the housing quality problem is varied. In this study, the cases of quality problems that occurred in housing electrical works were investigated, while 52 quality control indices were presented and classified according to detailed work category, object, and problematic occurrences. In addition, the number of cases of quality problems for each quality management index and the repair cost ratio were used as a scale to understand the status of quality management. Finally, 11 quality indices were suggested as candidates of important indices which needed to be controlled to focus on the housing electrical work by comparing the above 52 indices. The comparison was made on which one was best suited to the Pareto principle, a quality management strategy, among the candidates for the critical index and the indices suggested by relevant standards and previous studies. The comparison results showed that the candidates of the essential indices suggested in this study conformed to the Pareto principle, while other indices hardly conformed to it. Therefore, if the quality control strategy of choice and concentration for housing electrical works is used, focusing on these vital index candidates can effectively assist in handling the occurrence of quality problems. [ABSTRACT FROM AUTHOR]

Summary: Shading is one of the cases that has effectivity on the household energy consumption. Shadings can be used either in horizontal or vertical form adjacent to the building's window in order to control the sunlight. In this paper, a multi‐objective optimization is conducted to simultaneously reduce the annual electricity consumption and augment the electricity production with the application of photovoltaic shadings installed over the building window. To initiate the multi‐objective optimization, four design variables based on the positioning and geometry of photovoltaic shading are selected and test samples are simulated through EnergyPlus software. The model is three‐floor building located in Tehran, Iran. Next, the Morris sensitivity analysis (MSA) is implemented to calculate the extent to which each design variable affects the objective functions. Afterwards, the relation between the design variables and objective functions is extracted through the regression modeling using GMDH type‐ANN and the equations extracted are then utilized as inputs for genetic algorithm to conduct a Pareto‐based optimization. The results demonstrate that the maximum sunlight reception is attributed to the PV shading in southern direction of the building and its optimal tilt angle is 19.6 considering an annual period. There are slight changes to the tilt angle while seasonal period is considered for the calculations. The results also show that the use of solar shadings can reduce electricity consumption by approximately 4 to 11, 1 to 4, and 10 to 22% for an annual period, winter and summer season, respectively. At the end, the numerical data for the city of Tehran is compared to the cities of Canberra and Macapa. [ABSTRACT FROM AUTHOR]

The grey model, which is abbreviated as GM (1, 1), has been widely applied in the fields of decision and prediction, particularly in the prediction of time series with few observations, referred to as the poor information and small sample in the literature related to grey model. Previous studies focus on improving the accuracy of prediction but pay less attention to the robustness of the grey model to outliers, which often occur in practice due to an incorrect record by chance or an accidental failure in equipment. To fill that void, we develop a robust grey model, whose structural parameters are obtained from the least trim squares, to forecast Chinese electricity demand. Also, we use the last value in the first-order accumulative generating time series as the initial value, according to the new information priority criterion. We name the novel grey model, proposed in this paper, the novel robust grey model integrating the new information priority criterion, which could be abbreviated as NIPC-GM (1, 1). In addition, we introduce a novel approach, that is, the bootstrapping test, to investigate the robustness against outliers for the novel robust grey model and the classical grey model, respectively. Using the data on Chinese electricity demand from 2011 to 2021, we find that not only does the novel robust grey model integrating the new information priority criterion have a better robustness to outliers than the classical grey model, but it also has a higher accuracy of prediction than the classical grey model. Finally, we apply the novel robust grey model integrating the new information priority criterion to forecasting the future values in Chinese electricity demand during the period 2022 to 2025. We see that Chinese electricity demand would continue to rise in the next four years. [ABSTRACT FROM AUTHOR]

Summary: In this study, a solar‐based integrated system is designed and analyzed thermodynamically using the first‐ and second‐law approaches. The sustainable energy island concept is developed to meet the energy and commodity demands of emerging countries in a sustainable manner. The proposed system utilizes an energy island concept where solar energy is the main source and provides electricity, freshwater, and hydrogen by integrating a floating photovoltaic system, a parabolic through collector system, the Cu‐Cl cycle, a multi‐effect desalination unit, and a fuel cell unit in a sustainable fashion. The hydrogen‐based system is used to store energy in the chemical form to compensate for the time when the solar radiation is not available. A transient analysis is carried out to realistically determine the performance of the proposed system in a case study where Northern Cyprus is considered with its annual electricity demand. The proposed system averagely achieves an overall energy efficiency of 14.06% and an overall exergy efficiency of 14.61% while producing a total of 7.1 ton/h of hydrogen, a total of 115.4 ton/h of freshwater, and a total of 362.4 MWp of electricity, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a prediction model based on fuzzy logic applied to anticipate electricity production and consumption in a building equipped with photovoltaics and connected to the grid. The goal is a smart energy management system able to make decisions and to adapt the consumption to the actual context and to the future electricity levels. The interest is to use as much electricity as possible from own production. The surplus is captured by an energy storage system or is sent to the grid. When no electricity is available from self‐production, the grid is used to cover the necessities. The evaluations are performed on a data set collected in a real household. The proposed method is compared in terms of mean absolute error with other existing methods. The method developed based on fuzzy logic has an error of about 67 W, which places it among the most efficient models. [ABSTRACT FROM AUTHOR]

Reducing demand coincidence of customers with distribution system peak hours is very essential in the modern world energy sector. There is a great scope for peak demand coincidence reduction at the customer level, especially in the residential sector through Residential Demand Response programs. Through smart meter installations along with IT-enabled technologies, many of the distribution company's initiatives like residential demand response programs can be taken to domestic customers with ease, less cost, and less technology deployment. Rate design is one such effective approach. Even though time-varying rates like Time of Use have been used as an effective approach for reducing peak electricity demand in different sectors around the world, the residential sector has not gained much attention due to a few challenges like externality problems, high on-peak and low off-peak prices, improper pricing mechanisms. Hence, considering the above challenges and constraints, a rate design, named consumer-centric time of use tariff is proposed in this article for domestic customers. The tariff is consumer-centric such that each customer gets a unique on-peak unit price and off-peak unit price based on their consumption during peak hours both at the house-level and utility level rather than common and fixed on-peak and off-peak prices for all the customers, thus addressing the above-mentioned constraints. For this, customers are classified into different clusters using the Machine Learning Algorithm K-Means. The proposed rate design model has been analyzed on synthetic smart meter data of 10 houses, and it is observed that the proposed tariff shows an increase in the monthly revenue by 4.3% for the utility and a variation of -0.4% to 7% of energy charge for different customers. This study and analysis show that the proposed consumer-centric time of use rate design provides a better pricing mechanism with a win-win strategy for both customers and utility, thereby avoiding windfall gains or losses to both. Furthermore, the proposed tariff influences each residential customer of different consumption levels to reduce peak demand coincidence as well as energy consumption for the power sector. [ABSTRACT FROM AUTHOR]

The increasing growth of electricity consumption, the need for its continuous supply, and the impossibility of storing this vital energy are among the primary and essential reasons for preventing any interruption in supply and proper planning regarding energy consumption management, especially in industry. The innovation of this research is the design of a decision-making system to help decision-making in energy management in small- and medium-sized industries using the knowledge of experts in small- and medium-sized industries and the electricity industry in the field of energy consumption management and extracting relevant expertise rules to create a knowledge base. Therefore, the main advantage of the research is to provide an expert system for the correct management of energy consumption in small and medium industries using soft computing. Another advantage of the research is the use of uncertain decision-making variables in this field; instead of using words and linguistic restrictions in this research, modeling them in the system using the principles of fuzzy logic is used. The main objectives of this research include determining the effective variables in determining the amount of energy production for small and medium industries, explaining the existing tacit knowledge and turning it into explicit knowledge, and providing an expert system for managing energy consumption in this field. Based on the results of field studies and the research literature, effective variables in determining the amount of electricity generation were identified. By extracting the knowledge of industry experts, the tacit knowledge available as explicit knowledge in the storage knowledge base and the system was designed to manage electrical charge consumption. One of the achievements of this system is its creation and measurement using various data and results obtained from small and medium industries. Finally, the results obtained from the expert system in comparing the output of expert opinions and the system's performance in managing the amount of energy production showed that the usefulness of the expert system was designed. The system uses the knowledge base to make decisions such as an expert human. According to the main results of the research based on comparing the performance of the system with the Bayesian inference networks, it showed that there is an acceptable correlation between the data and the prediction system. Also, to evaluate the acquisition of data using a statistical test, it has been determined that the total number of blackout hours in different areas of Tehran in the base year was more than the current year. [ABSTRACT FROM AUTHOR]

The underspecification product obtained from crab stick production is considered as a crab stick by‐product. To extend its shelf life, frozen storage is required but this process is costly due to electricity consumption. Hence, the process for producing a shelf‐stable crab stick by‐product was focused in this study to reduce the cost of storage. For this experiment, the by‐product (73% wet basis) was dried using different drying methods such as freeze drying and convective hot air drying at 50, 60, and 70°C. The qualities of dried products including physicochemical, morphological, and rehydration properties, and adsorption characteristics were assessed. The results found that freeze drying was the best drying method to preserve the qualities of dried sample. Moreover, convective hot air drying at 70 and 60°C gave better qualities (higher porosity, % reconstruction, rehydration properties, and less hardness) compared to 50°C. Adsorption isotherm types II and III were observed in this study. Novelty impact statement: In a private company, approximately 10–15 metric tons per day of crab stick by‐product is obtained, meaning that high cost for frozen storage is required. Freeze drying is considered as the best drying method but high production cost, time‐consuming, and more complex in terms of operation are the limitations of this technique. In this work, the influence of drying conditions on physicochemical, morphological, and rehydration properties, and adsorption characteristics of dried crab stick by‐product were evaluated and drying at 60 and 70°C were the effective conditions due to the superior product qualities compared to drying at 50°C. [ABSTRACT FROM AUTHOR]

We present HydroFlow, a novel deep generative model for predicting the electricity generation demand of large‐scale hydropower stations. HydroFlow uses a latent stochastic recurrent neural network to capture the dependencies in the multivariate time series. It not only utilizes the hidden state of the neural network, but also considers the uncertainty of variables related to natural and social factors. We also introduce an end‐to‐end approach based on generative flows to approximate the posterior distribution of time series with exact likelihoods. Our model is powerful as adding stochasticity to different factors (e.g., reservoir capacity and water‐flow measurements) and thus overcomes the expressiveness limitations of deterministic prediction methods. It also enables trainable latent transformations that can improve the model interpretability. We evaluate HydroFlow on the data collected from the hydropower stations of a large‐scale hydropower development company. Experimental results show that our model significantly outperforms the state‐of‐the‐art baseline methods while providing explainable results. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy, power demand has been closely linked with economic development in order to analyze and predict the future power situation in China. Based on the historical data of China's electricity consumption, this paper analyzes the data characteristics of China's electricity consumption by using Eviews software. The long-term trend of power consumption sequence is eliminated by fitting the regression curve, and then the residual sequence is analyzed and identified according to the relevant theory of time series. Using the data of China's power demand from 2004 to 2019, the ARMA time series model is used to analyze China's future power situation finally according to the test statistics of the model. Finally, the exponential regression ARMA model is selected to predict China's electricity consumption. The index regression analysis method extracts the long-term characteristic information of the sequence, which is conducive to the stability processing and empirical analysis of long-term prediction. The result predicts China's power demand in 2020 more accurately, which can provide a reference for the future productivity layout of China's power. [ABSTRACT FROM AUTHOR]

Based on a literature review of influencing factors and forecasting methods for grain production, the empirical analysis of the influencing factors of China's grain output is performed using the full subset regression method, the ridge regression method, and the LASSO regression method. The results show that (1) the increase in the sown area of grain crops is the main reason for the increase in grain output, (2) the use of agricultural fertilizers and the increase in rural electricity consumption are the driving factors for the increase in grain output, (3) the impact of total power of agricultural machinery is limited, and (4) natural disasters have a certain negative impact on food production. [ABSTRACT FROM AUTHOR]

With the high demand for basic resources such as electricity, communication, water supply, and gas in cities, most cities in my country are vigorously developing underground integrated pipe gallery. When constructing pipe gallery in coastal areas, it is necessary to consider the impact of groundwater level on structural calculations. There is still a lack of systematic research in domestic and foreign literature. Relying on the Qingdao West Coast New Area Project, this paper uses a combination of laboratory tests and MIDAS GTS/NX numerical simulation to study the influence of groundwater in the coastal area on the stress and deformation characteristics of the integrated pipe gallery. The results show that (1) the Mises stress on the roof and bottom of the integrated pipe gallery increases continuously with the rise of the groundwater level. Due to the effect of the effective stress, the Mises stress on the side wall shows a decreasing trend. (2) Due to the influence of the construction form of the cross-section interface, the maximum principal stress of each part of the M side is always smaller than that of the N side; with the continuous increase of groundwater level, the maximum principal stress at each part of the N and M sides of the pipe gallery structure joint gradually increases. (3) The overall structure of the pipe gallery floats up. Under the action of the buoyancy force, the bottom plate moves upward, and the surface displacement rises continuously with the rise of the groundwater level. (4) When the groundwater depth is the same, the analysis of the test results and the numerical simulation calculation results are basically consistent, which further verifies the validity of the numerical simulation calculation and has reference value. (5) The engineering example shows that in order to reduce the service life of the integrated pipe gallery, the impact of groundwater on the integrated pipe gallery cannot be ignored. [ABSTRACT FROM AUTHOR]

The distribution line network is the electric power infrastructure directly facing the users, with the characteristics of large coverage and complex network, and its operation safety is directly related to the stability and reliability of the power supply system, which is the key link to ensure the safety of power supply and the reliability of residential electricity consumption. In order to realize the autonomous obstacle avoidance and autonomous navigation of the live working manipulator for inspection and maintenance of the power grid equipment, a mobile manipulator intelligent control method combining SumTree-weighted sampling and deep deterministic policy gradient (DDPG) is proposed. Firstly, the traditional DDPG algorithm is improved to optimize the action value function of Q-learning to get a better control strategy, and the weighted sampling technique is used to add priority to each sample in the replay buffer, which improves the learning speed and accelerates the convergence speed. The corresponding environmental state space is designed, and simulation experiments are conducted to verify the proposed manipulator control method. Simulation results demonstrate that the proposed method performs better than traditional DDPG and DQN methods in obstacle avoidance and navigation tasks, with faster convergence, better path planning ability, and lower offset cost, which can provide theoretical and technical references for realizing fully autonomous power grid inspection operations. [ABSTRACT FROM AUTHOR]

Summary: Electrification of private transport is a fundamental step for decarbonizing mobility. Electric vehicles (EV) can be a burden for the power system if vehicle‐grid integration is not implemented by design. Market‐based smart charging projects are effective, but their massive diffusion is limited. A fundamental instrument toward a large adoption of smart charging is the inclusion of smart charging‐oriented measures in regulatory sandboxes, conveniently acting on electricity tariff. This paper presents a set of possible toolboxes for smart charging to show the potential that regulatory measures can have on steering the infrastructure deployment and the charging activity. Each proposed toolbox addresses a specific charging mode, including domestic, workplace, and public access charging. Proposed measures are target‐oriented and evaluated based on their environmental, technical, and economic impacts. These include the carbon footprint of the electricity used for EV charging, the impact in terms of peak power withdrawal from the public grid and the charging cost born by EV users. Additionally, the assessment about the impact of prospected measures on the electricity tariffs' income is provided. Results show the possibility of reducing the evening EV‐related peak load by 30% to 50% via home smart charging. Also, a 10% decrease in carbon footprint is achieved by valley‐filling with work charging. Charging at the destination can reduce the system cost for the new distribution infrastructure, dropping the number of new dedicated connection points for public charging. The cost of incentives is partially repayable considering the additional EV penetration fostered by the reduced charging costs. [ABSTRACT FROM AUTHOR]

In order to solve the problem of abnormal data identification for key indicators with the deepening and development of power enterprise reform, this paper proposes a method of dangerous trend identification model based on a statistical analysis of abnormal power generation behavior data. The method includes a data access scheme, feature extraction scheme, and anomaly detection algorithm. The experimental results show that the proportion of users whose electricity consumption behavior conforms to the peak period electricity consumption > normal period electricity consumption > valley period electricity consumption exceeds 90%. More than 85% of users' electricity consumption behavior is in line with the proportion of electricity consumption that is less than 0.25 in millet hours. The proportion of users whose fluctuation coefficient of electricity consumption in the valley period is less than 1 exceeds 85%, and 99.9% of users' fluctuation coefficient of electricity consumption in the valley period is less than 5, which proves that abnormal power generation behavior data and abnormal power consumption data can bring early warning to some dangerous power consumption behaviors. The statistical analysis model of abnormal power generation behavior data can play a positive role in the identification of dangerous trends. [ABSTRACT FROM AUTHOR]

The high density of H-CRAN associated with frequent UE handover may degrade the throughput. The infrastructure equipment like RRHs and BBUs consumes more energy to reduce UE energy consumptions. In this paper, we propose a utility-based joint power control and resource allocation (UJPCRA) algorithm for heterogeneous cloud radio access network (H-CRAN). In this framework, the power consumption of baseband units (BBUs), remote radio heads (RRHs), and macrocell base station (MBS) are estimated by predicting their dynamic loads. The data rate achievable for UE associated with each RRH and MBS on resource block RBk is then estimated. The user wishing to connect to a RRH or MBS then checks the corresponding utility with minimum expected energy consumption and the maximum expected data rate. If any UE with high priority traffic connected to MBS could not achieve its desired data rate requirements, then it can cooperatively seek the assistance of any RRH for assigning the balance RBs. The throughput may be enhanced by the high density of H-CRAN and frequent UE handover. Inter- and intracell interference causes the H-CRAN macrocells' improved data rate to diminish. To lower UE energy consumption, infrastructure devices like RRHs and BBUs need more energy. As a result, there is a trade-off between operators and UE energy conservation. It is possible to determine the power consumption of BBUs, RRHs, and MBS using predictions of their dynamic loads. The UE may then forecast the data rate for each RRH and MBS on the resource block. When a user wishes to connect to an RRH or MBS, they look at the utility with the highest expected data rate and the least predicted energy usage first. A UE with high priority traffic connected to the MBS can cooperatively ask any RRH for assistance in allocating the remaining RBs if it is unable to achieve its intended data rate needs. Experimental results have shown that the proposed JRAUA algorithm achieves higher throughput, resource utilization, and energy efficiency with reduced packet loss ratio, when compared to the existing techniques. [ABSTRACT FROM AUTHOR]

Whether the traditional water equipment is equipped with remote transmission unit depends on whether the power consumption of the installed equipment can meet the expected life cycle. In the paper, by using intelligent strategies to save the power consumption of sending data, more sites can be selected to install remote transmitting units. In the process of transmitting data packet sequence, the remote transmission device needs to perceive the environment, interact with the environment and make decisions, and adjust the strategy according to the effect of the decision actions. Therefore, in this paper, the transmission process is modeled as Markov sequence decision model. And the real time signal interference noise ratio of the channel and the transmission delay of the data packet are defined as the state space. The decision action space consists of immediate transmission and delayed one, and the minimum total power consumption is taken as the objective function. The model is solved by Proximal Policy Optimization (PPO) algorithm, and the optimal decision sequence of site selection threshold is obtained. [ABSTRACT FROM AUTHOR]

Over the decades, a rapid upsurge in electricity demand has been observed due to overpopulation and technological growth. The optimum production of energy is mandatory to preserve it and improve the energy infrastructure using the power load forecasting (PLF) method. However, the complex energy systems' transition towards more robust and intelligent system will ensure its momentous role in the industrial and economical world. The extraction of deep knowledge from complex energy data patterns requires an efficient and computationally intelligent deep learning-based method to examine the future electricity demand. Stand by this, we propose an intelligent deep learning-based PLF method where at first the data collected from the house through meters are fed into the pre-assessment step. Next, the sequence of refined data is passed into a modified convolutional long short-term memory (ConvLSTM) network that captures the spatiotemporal correlations from the sequence and generates the feature maps. The generated feature map is forward propagated into a deep gated recurrent unit (GRU) network for learning, which provides the final PLF. We experimentally proved that the proposed method revealed promising results using mean square error (MSE) and root mean square error (RMSE) and outperformed state of the art using the competitive power load dataset.(Github Code). (Github code: https://github.com/FathUMinUllah3797/ConvLSTM-Deep_GRU). [ABSTRACT FROM AUTHOR]

A smart and intelligent energy management system (SIEMS) can make the existing system more reliable, robust, and cost-effective to deal with the present power scenario. This also initiates the necessity to develop a new dynamic tariff system and integrate renewable energy sources (RES) along with the conventional grid which in turn reduces carbon emission. Many countries have adopted different strategies for energy conservation based on energy conservation code, the main aim of which is to enforce design and development of household appliances for efficient power consumption. From data provided by utility sectors, it has been found that even in few countries 25% to 28% of the total energy is consumed by domestic sector and 45% of other residential loads also contribute a major part of the total domestic load consumed in any country. As a whole, residences, which consist of light electrical loads and home appliances constitute prosumers. The total energy consumed by prosumers is also intended by Bureau of energy efficiency, to make them fall under these codes (Energy Conservation Building Code) so that they can be treated on a scale of 1 star to 5 stars. This in turn will help in improving the energy performance index (EPI). In this paper, the optimal scheduling model of household appliances and energy storage system (ESS) has been designed based on the availability of renewable energy sources and newly proposed dynamic tariff system. To validate the efficiency of the model, we have comparatively evaluated the performance of the model with different feature selection methods and optimization techniques. A case study has been done and it has been found that the weighted K nearest neighbor method for feature selection and hybrid greedy particle swarm optimization (HGPSO) technique for optimization are performing better than other existing methods. The proposed model reflects the cost-effectiveness and improvement in EPI for prosumers. [ABSTRACT FROM AUTHOR]

A decentralized power grid is a modern system that implements demand response without requiring major infrastructure changes. In decentralization, the consumers regulate their electricity demand autonomously based on the grid frequency. With cheap equipment (i.e., smart meters), the grid frequency can be easily measured anywhere. Electrical grids need to be stable to balance electricity supply and demand to ensure economically and dynamically viable grid operation. The volumes of electricity consumed/produced (p) by each grid participant, cost-sensitivity (g), and grid participants' response times (tau) to changing grid conditions affect the stability of the grid. Renewable energy resources are volatile on varying time scales. Due to the volatile nature of these renewable energy resources, there are more frequent fluctuations in decentralized grids integrating renewable energy resources. The decentralized grid is designed by linking real-time electricity rates to the grid frequency over a few seconds to provide demand-side control. In this study, a model has been proposed to predict the stability of a decentralized power grid. The simulated data obtained from the online machine learning repository has been employed. Data normalization has been employed to reduce the biased behavior among attributes. Various data level resampling techniques have been used to address the issue of data imbalance. The results showed that a balanced dataset outperformed an imbalanced dataset regarding classifiers' performance. It has also been observed that oversampling techniques proved better than undersampling techniques and imbalanced datasets. Overall, the XGBoost algorithm outperformed all other machine learning algorithms based on performance. XGBoost has been given an accuracy of 94.7%, but while combining XGBoost with random oversampling, its accuracy prediction has been improved to 96.8%. This model can better predict frequency fluctuations in decentralized power grids and the volatile nature of renewable energy resources resulting in better utilization. This prediction may contribute to the stability of a decentralized power grid for better distribution and management of electricity. [ABSTRACT FROM AUTHOR]

Solar energy is the kind of alternative energy that a photovoltaic (PV) system may generate. The PV system depends on the environmental conditions present at the moment, such as the humidity level, surface temperature of the PV system, and wind speed. These are the most important characteristics to consider when calculating the amount of electricity generated by the PV system. Therefore, the calculation of power production is not a simple task. In the meanwhile, we are working on developing the machine learning algorithm that will be used to estimate electricity production using the fundamental characteristics that are now accessible. Now, we begin the process of machine learning by developing a time series model since the essential parameters will change over time. We get preliminary information from four distinct locations around the country. We arrived at this conclusion by using the ANN and regression modeling approaches for best to create the most significant overall performance across all cities. [ABSTRACT FROM AUTHOR]

With the widespread use of new energy sources and Internet of things, the power market landscape has become complex. In particular, new energy is more stochastic and volatile; it is prone to the problems of inaccurate forecasting on longer time scales, affecting electricity trading. This study proposes a new method for predicting medium-term load series data based on the transformer-lightGBM. The method first preprocesses electricity market data, including missing value processing, outlier processing, overall analysis, and correlation analysis, to extract features with a strong correlation to medium-term electricity consumption forecasts. Then, a transformer neural network is used to learn the complex patterns and dynamic time scales of the load series data to predict the day-ahead market series. Finally, lightGBM is used to combine power characteristics and time characteristics to forecast power consumption. The effectiveness of the proposed method is proved using the ISO-NE dataset. Experimental results indicate that the present method verified more accurate prediction than LSTM-based methods. [ABSTRACT FROM AUTHOR]

Under the influence of the low-carbon economy, the electric vehicles have been vigorously promoted. As the main place for charging, charging stations are classified as unstable loads in the power grid because of the large fluctuation of electricity demand. If a large number of charging stations in the system are not regulated and dispatched, the stability of the system will be greatly affected, so it is vital to further study the scheduling strategy of the cars. In this paper, a scheduling model based on the multiobjective algorithm is established, in which the PSO algorithm is used in the model. And the experimental analysis shows that the model can reduce the average charging time and improve the utilization rate of charging piles. [ABSTRACT FROM AUTHOR]

We live in a world that is completely dependent on energy; thus, humankind can no longer live without power. With electricity being the main form of energy today, this has increased the complexity of our life today. In Uganda, electricity generation is mainly through hydropower, which puts the country in the bottleneck of overdependence on one source of energy. There are many energy systems out there that the country can use to diversify its electricity generation. Therefore, the need to understand the level of development and utilization of various energy systems has been the underlying question for this present study. A comprehensive literature survey was conducted using electronic databases, including ScienceDirect, Wiley, Sage, Scopus, Taylor & Francis, and Google Scholar. The publications in the form of reports, conference papers, working papers, discussion papers, journal articles, book sections, and textbooks were considered in this study. In total, 11 energy systems, including human and animal energy, solid biomass (firewood), hydropower, wind, geothermal, solar, nuclear, peat, coal, petroleum, and nonsolid biomass (methanol, hydrogen, ethanol, biodiesel, and biogas), are described. The current and future development and utilization of these energy systems have been described. The challenges for developing and utilizing these systems were elaborated on, and the solutions for their challenges were presented. Hydropower from the Nile River, being the main river for large hydropower plant construction, is the dominant energy system in Uganda. Nuclear energy will be the salvation for the country's electric energy supply in the near future. Therefore, Uganda needs to bet big on nuclear energy. [ABSTRACT FROM AUTHOR]

Integration technique is becoming effective due to the world's largest power requirement, which has imposed a considerable need for various techniques by which electricity can be generated or integrated, as well as the assumption that integrating solar energy into nonrenewable source materials is essential to minimize the relative to nonresource consumption and thus reduces fossil fuel consumption. Photovoltaic (PV) systems are at the forefront of this transformation, harnessing the sun's renewable electricity and converting this from DC to AC. Controlling the power grid utilizes power system photovoltaic energy production and the many ramifications of grid-scale PV energy module integration into energy systems. To completely integrate photovoltaic (PV) processes into a network, cost-effective and efficient technologies of energy storage must be used in conjunction with smart energy management systems. Electrical energy storage system (EESS) could have been used to improve a system's stability and the performance, to recent technology improvements and quick efficiency improvements. This research provides an understanding of EESS for the photovoltaic system as well as a complete evaluation of the developing penetration level of PV. As the worldwide photovoltaic solar market is expanding, growing onsite use of PV-generated energy would become increasingly critical to ensure the stability of the grid. This research study was the first one to provide such a comprehensive overview of all forms of energy storage devices which can be used in conjunction with PV, including both thermal and electrical energy storage systems. Finally, a simulation of optimization planning is executed. The result reveals that lowering peak loads and lowering electricity usage lowers the price of electricity. [ABSTRACT FROM AUTHOR]

The traditional reliability of power supply only focuses on the continuity of power supply and does not include the impact of power quality on customers in the assessment. In this paper, we use "the ability of the grid to provide continuously available power to customers" as the criterion to reflect more realistically the level of reliability perceived by customers. This reliability assessment, which is closer to the customer's perspective, can also be used as a basis for quality-based pricing in the electricity market that is acceptable to both the supplier and the consumer, and the difference between the reliability of electricity consumption and the reliability of electricity supply statistics can also be used by power supply companies to evaluate their own power supply quality and service level. This paper uses the AC algorithm to calculate the reliability indexes of the power system in combination and proposes to use the component sensitivity analysis to rank the system components to obtain the key component information for the combination of the power system reliability indexes. [ABSTRACT FROM AUTHOR]

Energy consumption is the key factor leading to high operating costs in data center. In this paper, considering the data center service level under the premise of using electricity and gas as energy supply of energy supply and energy consumption from two angles, the data center energy scheduling model is built. The lower model is calculation scheduling model for the data center, and the upper model is data center energy supply scheduling model. Then the particle swarm algorithm is used to simulate the schedule. The results show that using natural gas as supplementary energy supply can effectively reduce the overall energy consumption of data center in considering the service delay level of data center. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) emissions are one of the major problems that the world is facing nowadays. The transportation sector, where vehicles run on oil, contributes a large amount of GHG. The development of electric vehicles to meet the allowed GHG limits has recently been the main focus of research worldwide. Research in electric vehicles (EVs) has observed a tremendous upsurge in recent years. However, reviews that analyze and present the demand and development of EVs comprehensively are still inadequate, and this integrative review is an effort to fill that gap. This study has revealed many thought-provoking understandings related to specific developments, specifically global demand and growth of EVs along with electricity and battery demand, current technological developments in EVs, energy storage technologies, and charging strategies. It also details the next generation of EVs and their technological advancements, such as wireless power transfer. The development of a smart city concept by EV implementation added a new aspect to this review. The summary would be advantageous to both scholars and policymakers, as there has been a lack of integrative reviews that assessed EVs' global demand and development simultaneously and collectively. This review concludes the intuitions for investors and policymakers to envisage electric mobility. [ABSTRACT FROM AUTHOR]

Cryptocurrency mining and blockchain technology using renewable energy as the main electricity source has gained attention for sustainable development in financial areas. However, very few studies have been reported concerning the power usage of cryptocurrencies using renewable energy. In this article, we report the effect of overclocking and undervolting on power usage and the hash rate for mining dogecoin with solar energy as renewable energy. The mining rig used in this work consists of different graphics processing units (GPUs) and non-LHR (lite hash rate) cards. The UnMineable software has been used for mining dogecoin as well as for wallet integration. The results indicate that mining dogecoin with solar energy as renewable energy consumes 2000 Watts power with overclocking and 1700 Watts power with undervolting technique. This work implicates the potential future of crypto-mining with renewable energy and the hardware configuration associated with it, which is expected to reduce e-waste and improve sustainable development. To reduce the e-waste and high electricity consumption, we have introduced two important techniques named GPU optimization and use of renewable energy for mining, which helps the miners to reduce the e-waste and electricity consumption significantly at the same time getting most out of the GPU by not having any impact on the environment. [ABSTRACT FROM AUTHOR]

This paper proposes a study on the potential of waste-to-energy (WTE) possibility in the Arab Republic of Egypt. WTE is a viable option for municipal solid waste (MSW) management and a renewable energy source. The issue of waste spread is a chronic environmental challenge in the Arab Republic of Egypt. The MSW practices in Egypt are simply done by collecting the waste and dumping it in open landfill sites. This research aims to assess the potential contribution of the WTE facility to meet electricity demand in Cairo City, which is Egypt's capital and the biggest city in population as a sample, and then apply the results to all of Egypt. The paper introduced a step-by-step calculation for the electrical power that can be generated from Cairo MSW. Four scenarios for WTE utilization were developed: Mass Burn without any waste processing, Mass Burn with 25% recycling, Mass Burn excluding 50% of organic material, and Mass Burn excluding 50% of organic material with 25% recycling. The Mass Burn scenario implies full MSW stream incineration; the Mass Burn with recycling scenario considers a partial separation of reusable materials and the waste leftover for incineration; the Mass Burn with excluding 50% of organic material scenario considers a partial separation of organic materials and the waste leftover for incineration; the Mass Burn with excluding 50% of organic material with 25% recycling scenario considers a partial separation of organic materials and partial separation of reusable materials and the waste leftover for incineration. The analyses were done for the period 2011–2031 for Cairo with a total population of about 10 million in Nov 2021. The results show the huge potential of WTE conversion as a source of renewable energy, which is a key factor in Egypt's sustainable development strategy 2030. Available data have been theoretically processed to show the decision-makers in Egypt the amount of electrical power that can be generated by using the WTE options to manage the MSW in Egypt. [ABSTRACT FROM AUTHOR]