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Electrochemical carbon dioxide (CO2) reduction can be used to convert CO2 into various compounds at room temperature and ambient pressure using electricity generated from renewable energy sources. This technology is indispensable in establishing an environmentally responsible and sustainable society. However, further improvements in the activity and selectivity require the development of electrocatalysts that can directly serve as the actual reaction sites. In recent years, metal nanoclusters, which are metal particles with a size of approximately 1 nm, have been reported to be capable of electrochemical CO2 reduction with high activity and selectivity, owing to their unique geometric/electronic structure. This review summarizes the synthesis methods of atomically precise metal nanoclusters and their application in electrochemical CO2 reduction. We expect that this review will help clarify the current status of these studies and further accelerate the research on highly active and selective CO2 reduction catalysts using metal nanoclusters. [ABSTRACT FROM AUTHOR]

In this research, a computationally-inexpensive stochastic MILP model is proposed for the optimal placement of hydrogen fuel stations (HFSs) in power systems with high penetration of renewables, while power system operator sells its extra hydrogen in a day-ahead local hydrogen market. In the developed model, a linearisation strategy is proposed to transform the nonlinear binary terms into linear terms and the uncertainties of electricity and hydrogen demands, photovoltaic (PV) generation and hydrogen market prices are modeled as scenarios. Any available HFS includes an electrolyzer and a hydrogen storage system. As the penetration of renewables in the studied power system is high, most of the produced hydrogen is green. According to the results, system operator uses the potential of hydrogen storage systems and responsive electricity demands to sell more hydrogen to the local hydrogen market and increase its profit. In times with higher hydrogen prices, system operator commands both shift-down in electricity demands and discharge mode for batteries to be able to sell more electricity and make more profit; on the other hand, in times with lower hydrogen prices, system operator commands shift-up in electricity demands and charge mode for batteries. The results show that demand response program increases expected profit of system by 4.7%. The results confirm that addition of HFSs strongly decreases PV curtailment. The impact of the participation in hydrogen market on system profit is assessed. The sensitivity of HFS profit to the number of HFSs, size of electrolyzers and demand response participation factor is assessed. • Optimal location of hydrogen fuel stations (HFSs) is found in power systems. • A linearisation strategy is used to transform bi-linear terms into linear terms. • Uncertainties of demands, PV generation and hydrogen market price are considered. • The sensitivity of the power system profit to model parameters is assessed. • Impact of the participation in hydrogen market on power system profit is assessed. [ABSTRACT FROM AUTHOR]

Liquid hydrogen station shows a great advantage in the future larger market of fuel cell vehicles. However, the energy-saving potential of the liquid hydrogen station remains underexplored. Therefore, a novel asynchronous refueling process (HRP-as) to refuel the vehicle and the high-pressure storage system is proposed based on a conventional synchronous hydrogen refueling process (HRP-s). Moreover, the imperfect compression of the liquid pump and different initial storage pressure are considered. Accordingly, the thermodynamic models of both processes were established, and the evolution of critical parameters was analyzed. Results indicate that the electricity consumption of the liquid pump rises along with the initial storage pressure increase. Compared to the HRP-s, HRP-as exhibits the ability to achieve electricity savings of up to 9.5% within the restrictions of pump capacity and total refueling time. Therefore, HRP-as provides an excellent option for liquid station design without extra expenditure. • An asynchronous refueling process for liquid hydrogen station was proposed. • Electricity consumption increase with the initial storage pressure. • The novel process saves electricity by 9.5% within capacity and time restrictions. [ABSTRACT FROM AUTHOR]

The study has as its original database the decarbonization process initiated in Mexico by the National Commission for the Efficient Use of Energy (CONUEE) as part of its "Savings Program of Electric Power in Buildings of the Federal Public Administration" (PAEIAPF) of 1999. The primary purpose of PAEIAPF was to reduce the levels of electric power consumption in Federal Government buildings. The program has operated for 20 years; however, its scope only reaches operational carbon. Since 90% of existing buildings will be in use by 2050, the Retrofit Models will be the base to determine solutions for a more resilient living environment that fortifies and extends the grid's capacity and meets climate change mitigation targets. Secondly, significant socioeconomic and profound environmental impacts are not calculated explicitly in existing tools and are often referred to as "secondary" or Non-Energy Benefits (NEB). "The goal is to give them a measurement value to be considered in the decision-making calculus. It is assumed that soon; such factors will enter the general climate change economy, not unlike carbon in the past decade." In this context, the proposed research aims to develop a value-based framework that will support a Building Stock Model and subsequent Retrofit Models, documented in a web-tool platform. The framework has three main steps: A) Building Stock Model: Mapping of selected buildings of the program PAEIAPF in a GIS system. Documentation of the baseline energy consumption and embodied CO2-eq of the existing building. B) Retrofit Models: Involving a Whole Life Cycle Assessment (WLCA) and Non-Energy Benefits (resilience coefficient, health, productivity). C) Web Tool Platform: Application and toolset that allows for consistent documentation, environmental impact evaluation of existing building stock, and solution design in identifying energy reduction concepts. [ABSTRACT FROM AUTHOR]

This work used a time-integrated performance index to significantly address the low cost of the Electrocoagulation process to strengthen its tetracycline removal performance. The main goal was controlling the time-depending tetracycline removal efficiency of the electrocoagulation process was searched by choosing a better combination of the electrode material and electrolyte species types for getting a stabilization condition of the total electric voltage. The response surface methodology was included to address, compare and rescue the best results of the time-integrated performance index. The electrolyte type, pH of the aqueous media, electric conductivity, and electric voltage were assessed to better respond to the performance of tetracycline removal. In this regard, an iron-based electrocoagulation reactor was operated over 40 to 67 A m−2 of electric current density supported by 5 to 10 mS cm−1 electric conductivity values, testing a 10-to-90 min electrocoagulation time range. A 10-to-60 mol m−3 concentration range of Cr, Zn, Ca, and K cationic species was tested to set 5-to-10 mS cm−1 electric conductivity values and assess their influences on EC performance due to each one separately. In combination with Fe electrodes, Ca2+ electrolytes have driven to a stable electric conductivity in time besides pH buffering at the alkaline region with the best electrocoagulation performance, resulting getting a low and stable total electric voltage (less than 10 V). Using Fe electrodes and Ca2+ electrolytes, a Box-Behnken experimental design was performed regarding ranges of 4-to-8 solution pH, 40-to-94 A m−2 electric current density, and 10-to-30 min time. A time-integrated performance index as the response variable was proposed for addressing a better tetracycline removal. Finally, a 40 A m−2 electric current density allowed the maximal time-integrated performance index value over 30 min, removing above 97% tetracycline with minimal electric power consumption. [ABSTRACT FROM AUTHOR]

Climate change is having a significant impact on weather variables like temperature, humidity, precipitation, solar radiation, daylight duration, wind speed, etc. These weather variables are key indicators that affect electricity demand and consumption. Hence, understanding the significance of weather elements on energy needs and consumption is important to be able to adapt, strategize, and predict the effect of the changing climate on the required energy of an organization. This study aims to investigate the relationship between changing weather elements and electricity consumption, employing Multivariate Linear Regression (MLR), Support Vector Regressions (SVR), and Artificial Neural Network (ANN) models to predict the effect of weather changes on energy consumption. The following approaches were engaged for this study: Creating a catalog of weather elements and parameters of energy need or its consumption; analyzing and correlating electrical power consumption to weather factors; and developing prediction models--MLR, SVR, and ANN to predict the significance of the change in the variables of weather on the electrical energy consumption. Among the weather variables considered, temperature emerged as the most influential factor affecting electricity consumption, displaying the highest correlation. The monthly total pattern for electricity use for the case study area followed a similar pattern as the mean apparent temperature. Of the three models (MLR, SVR, and ANN) developed in this study, the ANN model yielded the best predictive performance, with Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of 2.733%, 1.292%, and 4.66%, respectively. Notably, the ANN model outperformed the other models (MLR and SVR) by more than 20% across the predictive performance metrics employed. [ABSTRACT FROM AUTHOR]

At present, electricity demand and pollution are the main concerns all over the world. With the help of this paper, a small contribution to energy production and pollution in the environment can be reduced. Exhaust Fan Cum Micro Wind Turbine (EFCMWT) is used for power generation. In this paper, I study a novel approach of EFCMWT for power generation. It has been installed at a height of 14.11 m at the windows near the toilet in a high rise building at Kosi Hostel NIT Patna. The EFCMWT generates power at a height of 14.11 m in the building. With respect to wind speed, various parameters are analyzed, such as how much DC voltage is generated, how much DC current is generated and also it is analyzed that how much EFCMWT power is generated and tip speed ratio variation with respect to wind speed. On windows where EFCMWT is installed, place anemometer near EFCMWT and analyzed wind power. The power coefficient with respect to wind speed is also analyzed for EFCMWT. Maximum DC voltage is 16 V, maximum DC current is 0.076 A, tip speed ratio is 0.45 and maximum EFCMWT power is 1.216 W which is achieved at wind speed of 7 m/s. [ABSTRACT FROM AUTHOR]

This study investigates the practicality of integrating renewable energy systems to meet the electricity needs of a self-contained hydroponic greenhouse that functions as an environmentally friendly facility with reliable air conditioning and electrical infrastructure. The first step is to assess the greenhouse’s annual electricity consumption and design a renewable energy system that can sustain this year-round. The results show that daily electricity consumption is between 0.75 and 2 kWh, with thermal air conditioning accounting for over 50% of the total consumption. To address this issue, a simulation model is developed to evaluate the performance of a hybrid photovoltaic/wind system connected to an electric storage battery and to optimize its integration into the greenhouse. This causes the battery’s voltage to generally increase during charging (to 350.5 V) and its state of charge to change non-linearly depending on the battery’s chemical makeup. An Energy Management System (EMS) is then developed for the Hybrid Renewable Energy System (HRES), which works in two modes to ensure the energy balance for uninterrupted power supply. An economic and environmental analysis of three scenarios is developed and concludes that the systems are effective and sustainable, with an estimated payback period of approximately 6 to 8 years. [ABSTRACT FROM AUTHOR]

Electricity consumption is an important cost in businesses operating in the service sector such as marinas. Although the use of solar power for electricity has become widespread in various fields in Türkiye, marinas have yet to fully utilize this technology. The aim of this study is to examine the feasibility of supplying the electricity needs of a marina with a photovoltaic (PV) system. For this purpose, a marina in Muğla (Bodrum) was selected, and monthly/yearly electricity needs were determined. In this study, the PV installed capacity needed to meet the marina's electricity demand was selected. Simulations were performed for three different options using the Photovoltaic Geographical Information System (PVGIS). According to the calculations, a 2012 kW single-axis tracking PV system generated 4,469,618 kWh/year of electricity, which is sufficient to meet the annual electricity demand. It has been determined that the installation cost payback period is 3.91 years for the single-axis tracking PV system. In addition, it was observed that both the dual-axis tracking and single-axis tracking PV systems were more efficient than the fixed options. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) is crucial for the daily operation of power grids. However, the non-linearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. Various forecasting approaches have been proposed for improving STLF, including neural network (NN) models which are trained using data from multiple electricity demand series that may not necessarily include the target series. In the present study, we investigate the performance of a special case of STLF, namely transfer learning (TL), by considering a set of 27 time series that represent the national day-ahead electricity demand of indicative European countries. We employ a popular and easy-to-implement feed-forward NN model and perform a clustering analysis to identify similar patterns among the load series and enhance TL. In this context, two different TL approaches, with and without the clustering step, are compiled and compared against each other as well as a typical NN training setup. Our results demonstrate that TL can outperform the conventional approach, especially when clustering techniques are considered. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive analysis of vehicle ownership, energy consumption, and carbon emissions in Guangdong Province, China, from 2020 to 2035 under different scenarios. Key findings highlight the province's pursuit of carbon peak goals and provide valuable insights into strategies to achieve them. Vehicle ownership in Guangdong is projected to exceed 48 million by 2035, which represents a doubling from 2020. Under both scenarios, internal combustion engine vehicle ownership will peak around 2030 and then gradually decline, while under the enhanced scenario, electric vehicle ownership will exceed 40% by 2035. Enhanced vehicle energy efficiency and reduced annual mileage will lead to a 17% reduction in gasoline and diesel consumption by 2035 in both scenarios. At the same time, there will be a substantial five- to six-fold increase in electricity consumption for vehicles compared to 2020. Both scenarios peak in carbon emissions before 2030, with the enhanced scenario achieving this peak a year earlier. The enhanced scenario outperforms the baseline, reducing carbon emissions by about 21.2% from the peak and 8% relative to 2020. Pure electric vehicles exhibit a significant advantage in reducing carbon emissions per vehicle compared to their internal combustion engine counterparts. Encouraging new energy vehicles, especially pure electric ones, accelerates the carbon emissions peak and lowers overall peak emissions. Accelerating the adoption of electric vehicles, reducing per-vehicle fuel consumption and annual average mileage, and optimizing transportation modes are crucial for carbon peaking from the vehicle fuel cycle. Policy recommendations focus on promoting new energy vehicles, optimizing transportation, and advancing research and technology. [ABSTRACT FROM AUTHOR]

In 1998, the red-green Schröder government implemented the Environmental Tax Reform (ETR), raising taxes on petrol, diesel, natural gas and heating oil and introducing a new duty on electricity in Germany. At the same time, it cut non-wage labour costs by reducing public pension contributions. The goal was to achieve Germany's Kyoto Protocol emissions targets and to reduce a level of unemployment unprecedented since World War II while avoiding the burden on the public budget through revenue recycling. Employing microdata from household budget surveys of 1998 and 2003, this article analyses whether increased duties on motor fuels and electricity lead to a substantial reduction in households' consumption of these goods. Considering the ETR as a natural experiment, it uses the difference-in-differences approach in a European context with Germany as the treatment group and Italy, Spain and the UK as the control group. Ordinary least square regressions reveal that motor fuel demand is price inelastic, while electricity consumption increased despite the substantial rise in prices. Quartile regressions show that the effect of the motor fuel tax is slightly higher at the bottom than at the upper tail of the distribution supporting the notion that low-level consumers are more likely to find alternative substitutes. [ABSTRACT FROM AUTHOR]

• BECCS technologies operated on waste feedstock present a negative carbon footprint. • Technology and market readiness levels quantify the risk for commercial exploitation. • Climate change impact is highly dependent on the choice of functional unit. • WtH 2 has higher emissions than WtE with CCS, but these are offset by higher credits. • Temporal and geographical aspects can change the ranking order of different technologies. A growing global population and rising living standards are producing ever greater quantities of waste, while at the same time driving ever-larger demand for energy, especially electricity, or new emerging markets, such as hydrogen in more industrialised countries. A key solution to these challenges of waste disposal, rising energy and hydrogen demand is BECCS (Bioenergy with Carbon Capture and Storage); the generation of bioenergy – in the form of electricity (WtE) or hydrogen (WtH 2), as well as heat – from the thermochemical processing of waste. The addition of carbon capture and storage (CCS) to WtE or WtH 2 has the potential to make waste a zero or even negative emissions energy source, thus contributing to the removal of greenhouse gases from the atmosphere. This work undertakes a pre-screening of different BECCS configurations based on state of the art technologies and then performed an assessment of representative cases in UK for WtE and WtH 2 , necessary to understand if novel waste thermal treatment processes may become potential alternatives or improvements to current WtE plants when retrofitted with CCS. A systematic and comprehensive examination of different key Life Cycle Assessment methodological aspects reveals the importance of the functional unit and allocation approach in determining the preferred pathway in a specific context. [ABSTRACT FROM AUTHOR]

Transmission line silicon rubber insulators are frequently subjected to harsh environmental conditions that can change their surface characteristics and result in a transient or permanent loss of hydrophobicity. Algae, fungi, mould, and lichen are examples of biological growth that can compromise the power system's ability to operate safely by lowering the insulator's flashover voltage. We evaluated and identified the major microorganisms that could be responsible for the flashovers of insulators in South Africa. Due to the difficulty of accessing insulators currently in use within the electricity network, only two insulators, from two provinces in South Africa, were used in the study. Although algae were not found on the insulators, two major filamentous fungi identified as Curvularia sp. and Aspergillus sp. were isolated. The absence of algae could be attributed to the weather pattern of the two locations where the insulators were placed during their use as part of the network. According to previous studies worldwide, the high occurrence of these fungi could be linked to the reduction of flashover voltage of the composite insulators. Although a larger survey including more insulators from all provinces in South Africa should be conducted, the current study demonstrates the need for a cleaning programme for insulators using cost-effective eco-friendly antimicrobial agents. Significance: * Degradation and loss of hydrophobicity on the overhead line composite due to biological growth were found to be an impediment to the safe operation of high-voltage insulators. Fungi were found to be the main factor causing the degradation of overhead transmission lines. * With these data, a model can be developed to predict the rate of biological growth on a given insulator, to estimate the degradation of the surface conditions with time for the utility to know when to remove them from the line. Such a model could lead to a more reliable and efficient power system and reduce the engineer's workload. [ABSTRACT FROM AUTHOR]

In recent years, global warming and environmental problems have become more serious due to greenhouse gas (GHG) emissions. Harvesting solar energy for production and logistic activities in supply chains, including factories and distribution centers, has been promoted as an effective means to reduce GHG emissions. However, it is difficult to balance the supply and demand of solar energy, owing to its intermittent nature, i.e., the output depends on the daylight and season. Moreover, the use of large-capacity solar power generation systems and batteries incurs higher installation costs. In order to maintain low costs, demand-to-supply management of solar energy, based on appropriate seasonal analysis of power generation and consumption and the capacity planning for power generation and the storage battery, is necessary. In this study, the on-demand cumulative control method is applied to actual power consumption data and solar power generation data estimated at a distribution center. Moreover, the monthly, seasonal, and temporal characteristics of power generation and consumption at the distribution center are analyzed. Additionally, the total amount of power purchased is investigated for solar energy demand-to-supply management. [ABSTRACT FROM AUTHOR]

The reliability and security of an electric power supply have become pivotal to the proper functioning of modern society. Traditionally, the electric power supply system has been designed with the objective of being able to adequately meet present and future demand, with efforts to maintain supply reliability being focused primarily on the supply side. Over the decades, however, the value of demand-side management—efforts focused on enhancing the efficient and effective use of electricity in support of the power system and customer needs—has been widely acknowledged as being able to play a greater role in ensuring that the key objectives of power system operation are satisfied. This article presents a study of demand-side management and opportunities for incorporating it into network planning as an effective means of addressing supply capacity constraints in the South African electric grid. The main drivers, benefits and potential barriers to the effective implementation of demand-side management are examined, along with the main enabling technologies. The key finding of the study is that the effective integration of demand-side management into network planning requires a shift from the traditional network planning approach to one that is more suited to fully exploiting the flexibility resources available on the demand side of the network. [ABSTRACT FROM AUTHOR]

The research into new approaches to shift from fossil fuels to renewable energy sources (RES) has surged as environmental issues are on the rise, and fossil fuel sources are becoming scarce. The flexibility potential of cold supply systems has been discussed widely in the literature, firstly due to their high share of electricity consumption worldwide and secondly because of their potential to store thermal energy in the form of cold energy. However, finding a clear definition of flexibility and a concise approach for its quantification is still under progress. In this work, a comprehensive definition of the flexibility of energy systems and a novel methodology for its quantification are introduced. The methodology was applied on a cold warehouse with real data regarding its cold energy demand. The cold warehouse was first modeled via oemof, which is a modular open source framework developed in Python 3.8 using a mixed integer linear programming (MILP) optimization approach. The operation optimization of the cold warehouse was conducted for three goals, namely "minimization of electricity costs", "minimization of CO2 emissions", and "minimization of maximum used electric power (peak load minimization)". Additionally, the effect of using different types of refrigeration compressors on the optimized operation of the cold warehouse was investigated. The results suggest that a cold warehouse possesses a high level of flexibility potential, which can be taken advantage of to reduce the electricity cost by up to 50%, the CO2 emissions between 25% to 30%, and the maximum used electric power by 50%. Different compressor types produced very similar results, although their flexibility level may vary. [ABSTRACT FROM AUTHOR]

Accurate electricity demand forecasting is essential for global energy security, reducing costs, ensuring grid stability, and informing decision making in the energy sector. Disruptions often lead to unpredictable demand shifts, posing greater challenges for short-term load forecasting. Understanding electricity demand patterns during a pandemic offers insights into handling future disruptions. This study aims to develop an effective forecasting model for daily electricity peak demand, which is crucial for managing potential disruptions. This paper proposed a hybrid approach to address scenarios involving both government intervention and non-intervention, utilizing integration methods such as stepwise regression, similar day selection-based day type criterion, variational mode decomposition, empirical mode decomposition, fast Fourier transform, and neural networks with grid search optimization for the problem. The electricity peak load data in Thailand during the year of the COVID-19 situation is used as a case study to demonstrate the effectiveness of the approach. To enhance the flexibility and adaptability of the approach, the new criterion of separating datasets and the new criterion of similar day selection are proposed to perform one-day-ahead forecasting with rolling datasets. Computational analysis confirms the method's effectiveness, adaptability, reduced input, and computational efficiency, rendering it a practical choice for daily electricity peak demand forecasting, especially in disrupted situations. [ABSTRACT FROM AUTHOR]

Global energy consumption is growing rapidly, with the frequency and intensity of extreme events constantly increasing, posing a long-term threat to power supply and consumption. Therefore, analyzing the spatiotemporal characteristics of electricity consumption and quantitatively assessing the impact of extreme events on electricity consumption are of great significance. Based on fine-grained electricity consumption data from Europe for the years 2019–2022, this paper employs a data mining perspective and four methods including Z-score, Isolation Forest, Local Outlier Factor, and Autoencoder to detect abnormal electricity consumption during extreme events. Additionally, it combines indicators such as elastic loss, vulnerability, and duration to measure the impact of extreme events on electricity consumption. It is found that low temperatures could lead to abrupt changes in electricity consumption, with Northern Europe being more significantly affected by low temperatures. The COVID-19 pandemic had the most significant impact on electricity consumption in Europe, with the middle part of Europe being the hardest hit during the first wave of the pandemic. Electricity anomalies during the pandemic period were related to national pandemic control policies and exhibited some lag. High temperatures persisted for a longer duration in the middle part of Europe. [ABSTRACT FROM AUTHOR]

Natural-gas-fueled solid oxide fuel cell (SOFC) systems have the potential for high-efficiency conversion of carbon to power due to the underlying electrochemical conversion process while readily facilitating carbon capture through the separation of the fuel and oxidant sources. Compressed air energy storage (CAES) technology can potentially store significant quantities of energy for later use with a high round-trip efficiency and lower cost when compared with state-of-the-art battery technology. The base load generation capability of SOFC can be coupled with CAES technology to provide a potentially flexible, low-carbon solution to meet the fluctuating electricity demands imposed by the increasing share of intermittent variable renewable energy (VRE) production. SOFC and CAES can be hybridized through thermal integration to maximize power output during periods of high electrical demand and then store power when either demand is low or renewable generation reduces power prices. The techno-economics of a low-carbon hybrid SOFC and CAES system was developed and investigated in the present study. The proposed hybrid system was found to be cost-competitive with other power-generating base-load facilities when power availability was considered. The hybrid system shows increased resilience to changes in a high VRE grid market scenario. [ABSTRACT FROM AUTHOR]

This paper presents the concept of using electric vehicles (EVs) as a countermeasure to deal with the negative effects of power rationing when electricity demands become difficult to meet due to unfavorable electrical system operating conditions. At present, an energy storage is widely used to maintain the stability of electricity supply in facilities whose main source of energy is renewable energy sources (RESs). However, we must not forget that electric vehicles are also electricity storage facilities, but they are not always available due to their mobility. With properly developed strategies, they can be used in electricity management processes, for example, by reducing their consumption during charging using smart charging technology, or by providing electricity from their batteries using vehicle-to-building (V2B) technology. Thus, this article presents a research methodology that treats electric vehicles as a remedy for eliminating power constraints. It consists of five main steps, including two algorithms for deciding how to deploy EVs during power rationing periods. An efficiency factor for eliminating these constraints was also introduced. The results showed that the use of smart charging or V2B technology in EVs can reduce the number of potential hours in which certain power levels are exceeded by up to several tens of percent. This means that in the future, with the significant development of electromobility, such a way of dealing with power constraints could be an alternative to conventional solutions like diesel generators. [ABSTRACT FROM AUTHOR]

The rapid fall in the cost of solar photovoltaics and wind energy offers a pathway to the deep decarbonization of energy at an affordable price. Off-river pumped hydro energy storage and batteries provide mature and large-scale storage to balance variable generation and demand while minimizing environmental and social impacts. High-voltage inter-regional interconnection and dispatchable capacity (existing hydro and geothermal) can help balance supply and demand. This work investigates an Indonesian energy decarbonization pathway using mostly solar photovoltaics. An hourly energy balance analysis using ten years of meteorological data was performed for a hypothetical solar-dominated Indonesian electricity system for the consumption of 3, 6 and 10 megawatt-hours (MWh) per capita per year (compared with current consumption of 1 MWh per capita per year). Pumped hydro provides overnight and longer storage. Strong interconnection between islands was found to be unnecessary for Indonesia, contrary to findings from similar modelling in countries at higher latitudes. Storage requirements for power and energy were found to be smaller than three kilowatts and 30–45 kilowatt-hours per person, respectively. Introducing gas turbines (burning hydrogen or synthetic methane) contributing around 1% of annual generation reduced the levelized cost of electricity (LCOE) by 14% and halved the storage requirements by allowing the system to ride through prolonged cloudy periods at lower cost. This work showed that Indonesia's vast solar potential combined with its vast capacity for off-river pumped hydro energy storage could readily achieve 100% renewable electricity at low cost. The LCOE for a balanced solar-dominated system in Indonesia was found to be in the range of 77–102 USD/megawatt-hour. [ABSTRACT FROM AUTHOR]

Under the new power system, a high proportion of new energy is widely connected to the power grid, and it is necessary to increase investment in centralized and distributed energy storage, flexible resource regulation, and transmission and distribution grids, resulting in an increase in power system costs. The current electricity capacity price does not reflect the economic value of the added system adequately, and flexible capacity is needed to ensure the safety of the power grid under the new power system. In the context of the transformation of the new power system and investment function, it is increasingly difficult to recover the costs according to the traditional electricity price. It is necessary to establish a coordinated development mechanism for the electricity capacity price and the electricity price that recovers the fixed costs according to the investment function to ensure a reasonable return on investment in flexible resources and to provide effective investment signals. Therefore, this work first studied and proposed a mechanism for the formation of capacity and electricity prices based on the proportion of allowed income and fixed costs at different voltage levels and calculates the level of electricity capacity (demand) prices and electricity consumption prices. Then, using the BARY curve, genetic algorithm, and clustering of user load rates, a collaborative mechanism between the electricity capacity price and the electricity price was designed. With the goal of optimizing the electricity capacity price and considering constraints such as the flexibility and reliability of the new power system, the ratio of the capacity cost allocated to the electricity capacity price by voltage level users to the capacity cost allocated to the electricity price was calculated, and the optimal combination of electricity capacity price and electricity price for different load‐rate intervals was determined. Finally, the effectiveness of the model was verified through numerical simulations. On the basis of the investment characteristics of the new power system, suggestions for a capacity pricing mechanism under the new power system were proposed from the perspectives of transfer capacity function, transfer electricity function, grid reliability, and environmental and social benefits. [ABSTRACT FROM AUTHOR]

In today's society, there is a growing demand for smart home solutions that not only enhance convenience but also make a significant impact on affordability and widespread adoption. This paper describes the proposed design of a Node MCU-based Home Automation system with an integrated Wi-Fi module that enables internet-based remote control of home devices. The design utilizes a basic code template to connect with the Tasmota Web interface to configure the ESP module. The system is integrated with Alexa, offering various integration options such as routines, schedules and alarms. Furthermore, it adds a manual control feature for situations where there is no internet connection and an additional level of safety through emergency controls in the event of electronic failure. A comparison of energy usage with and without the use of the proposed system is also reported. A noticeable decrease in daily electricity consumption is observed on using the designed automation system. To ensure costeffectiveness, locally available components are utilized in the design. Consequently, it ensures that enhanced comfort, convenience, and control over one's living environment are available to a wider audience. [ABSTRACT FROM AUTHOR]

Induction channel furnaces (ICF) are widely used for melting, holding and casting metals and alloys in many processing industries because it has higher overall efficiency, lower electric power consumption and operation costs, better degassing and homogenization of the melt, lower oxide and slag formation than other induction furnaces. However, thermal stresses in the refractory lining caused by high temperature and flow of molten metal may cause its premature erosion of the lining and, failure of the inductor, and so it has difficulty to repair the furnace. In addition, the furnace for steel melting often experiencing shortened operating life and quicker needs for a repair cycle due to high temperature of molten metal and severe erosion of lining, so it is little used. In this paper, we propose a new type of channel of three-phase ICF for steel melting, and study temperature distribution of the channel by coupled simulation for the electromagnetic-heat-fluid using COMSOL Multiphysics 5.4 and Taguchi method. Simulation result demonstrates that maximum local superheating temperature (28.6K) of channel in the three-phase ICF for steel melting is as low as that (27K) of blowing channel in the two phase ICF, so it can be satisfactorily employed for steel melting. [ABSTRACT FROM AUTHOR]

The All-Photonics Network (APN), which is the basis of the Innovative Optical and Wireless Network (IOWN), has the characteristics of high speed, large capacity, low latency, and low power consumption compared with conventional networks. We aim to provide services that take full advantage of these characteristics as early as possible and expand their use. To operate high-quality services with these capabilities, the role of controllers has become more important than in conventional networks. In this article, we explain the APN-control function and APN-information-collection/analysis function, which are the main pillars of the APN-controller technology, as well as the value-added functions to implement the IOWN service. [ABSTRACT FROM AUTHOR]

This study examines regional price elasticities in electricity consumption to offer valuable insights for policymaking in electricity demand-side management in Japan. By employing a partial adjustment model and utilizing data from regional electric utilities, the study estimates regional electricity demand functions. The analysis reveals that, within the residential sector, the size of houses plays a significant role in explaining regional variations in price elasticity. Likewise, in the industrial sector, the presence or absence of private power generation facilities in firms significantly influences regional disparities in price elasticity. These findings hold substantial implications for enhancing the cost-effectiveness of electricity consumption regulations and policies. [ABSTRACT FROM AUTHOR]

This article analyzes the dynamic power losses generated by various hardware implementations of the BLAKE3 hash function. Estimations of the parameters were based on the results of post-route simulations of designs implemented in Xilinx Spartan-7 FPGAs. The algorithm was tested in various hardware organizations: based on a standard iterative architecture with one round instance in the programmable array, various derived versions with pipeline processing were elaborated, which ultimately led to a set of 6 architectural variants of the cipher, from the iterative case (without pipeline) to one with maximum of 6 pipeline stages. Moreover, the results obtained for the iterative architecture were compared with analogous implementations of the BLAKE2 (direct predecessor) and KECCAK (the foundation of the current SHA-3 standard) algorithms. This case study illustrates the differences (or lack thereof) in the power requirements of these three hash functions when they are implemented on an FPGA platform, and illustrate the significant savings that can be achieved by introducing pipeline to the processing of the BLAKE round. [ABSTRACT FROM AUTHOR]

Aiming to address power consumption issues of various equipment in metro stations and the inefficiency of peak shaving and valley filling in the power supply system, this study presents an economic optimization scheduling method for the multi-modal "source-network-load-storage" system in metro stations. The proposed method, called the Improved Gray Wolf Optimization Algorithm (IGWO), utilizes objective evaluation criteria to achieve economic optimization. First, construct a mathematical model of the "sourcenet-work-load-storage" joint system with the metro station at its core. This model should consider the electricity consumption within the station. Secondly, a two-layer optimal scheduling model is established, with the upper model aiming to optimize peak elimination and valley filling, and the lower model aiming to minimize electricity consumption costs within a scheduling cycle. Finally, this paper introduces the IGWO optimization approach, which utilizes meta-models and the Improved Gray Wolf Optimization Algorithm to address the nonlinearity and computational complexity of the two-layer model. The analysis shows that the proposed model and algorithm can improve the solution speed and minimize the cost of electricity used by about 5.5% to 8.7% on the one hand, and on the other hand, it improves the solution accuracy, and at the same time effectively realizes the peak shaving and valley filling, which provides a proof of the effectiveness and feasibility of the new method. [ABSTRACT FROM AUTHOR]

In this research, proportional-integral (PI) and Fuzzy logic control (FLC) strategies are designed to control wastewater treatment processes at the whole plant level. Two control combination strategies are implemented to minimize the operational cost index (OCI) and effluent quality index (EQI). The benchmark simulation model (BSM2-P) is used as a working platform. In close resemblance to practical operations, the sensors are considered as nonideal with delay and also having measurement noise. Binary-level control topology is designed in which the lower-level PI controller is used to control the dissolved oxygen (DO) levels in the second aerobic reactor by manipulating the oxygen mass transfer coefficient (KLa) of the last three aerobic reactors and FLC is designed at the supervisory level based on Ammonia levels. Ammonia in the second aerobic reactor is taken as a feedback signal for FLC to compute the DO setpoints. In comparison with non-supervisory control structures, operational cost is improved in PI-Fuzzy by 6.5%. Compared to the open-loop control, the PI controller improves effluent quality by 2.1%. Nevertheless, the combination of PI and fuzzy control shows high greenhouse gas production rates. [ABSTRACT FROM AUTHOR]

The current work presents the design and modeling of a solar and hydrogen energy-based integrated energy system that provides the electricity demand of a stand-alone house located in Izmir, Türkiye. This system is mainly comprised of photovoltaic (PV) cells, battery banks, a PEM electrolyzer (PEM-El), a hydrogen (H 2) compressor, and a pressurized hydrogen tank. Electricity produced from PV cells was either used in the PEM-El for hydrogen production or stored in the batteries to meet the required energy during low solar radiation or night time. The H 2 produced by the electrolyzer was pressurized with a compressor and stored in pressure tanks at a pressure of 350 bar. A zero-dimensional (0-D) mathematical approach was applied for the system component modeling. An energy management strategy was incorporated into the model to investigate the dynamic behavior of the integrated system. Parametric studies were then conducted for different numbers of PV and batteries. It was found that the amount of hydrogen produced is higher in summer (28.58 kg with 20 PV-30 batteries in June, 47.69 kg with 30 PV-20 batteries in July, and 66.96 kg with 40 PV-30 batteries in August). On the other hand, hydrogen produced is lower in winter (11.60 kg with 20 PV-10 batteries in December, 21.82 kg with 30 PV-20 batteries in January, and 32.20 kg with 40 PV-30 batteries in February). In addition, an economic analysis was conducted for the hybrid system. • Hybrid system based on solar and hydrogen energy is investigated. • A new energy management strategy is determined for the hybrid system. • Minimum of 5 PV-7 batteries are needed to meet the electricity needs of the house. [ABSTRACT FROM AUTHOR]

Fruit and vegetable processing comes 6th in terms of energy consumption in the agri-food industry. At the same time, 88.4% of the industry's final energy consumption structure is thermal energy, which depends heavily on electricity consumption. In addition, fruit and vegetable processing has a significant impact on the environment due to consumption of significant amounts of water. Reducing these three indicators simultaneously would increase the efficiency of the process while improving environmental protection. This paper proposes neural models of thermal energy, electricity and water consumption for selected major fruit- and vegetable-processing plants in Poland. These models were the basis for formulating a multi-criteria optimization task. Optimization of thermal energy, electricity and water consumption was carried out using genetic algorithms. The optimization results in the sense of Pareto can be the basis for the use of sustainable technology in selected fruit- and vegetable-processing plants. [ABSTRACT FROM AUTHOR]

There is a significant demand for materials and energy throughout the manufacturing and construction of a solar power plant's component parts. Electricity and fossil fuels are used in enormous quantities during the industrial processes in the photovoltaic power plant's life cycle. It is then necessary to assess the energy needs, especially during production processes, to improve the efficiency of energy usage and management of natural resources from the global perspective. This will lead to a decrease in natural resource consumption and electricity demand. The main aim of this study was to assess the energy demand in the life cycle of the photovoltaic power plant and identify the most energy-intensive stages and components of this type of installation throughout its life cycle. The study of energy consumption in the whole life cycle was conducted for a 2 MW photovoltaic power plant situated in the northern region of Poland using the Life Cycle Assessment (LCA) methodology, particularly the Cumulative Energy Demand (CED) method. Two post-consumer management scenarios were investigated: recycling and landfilling. It was found that the life cycle of PV panels and the inverter station had the largest energy demand among all the components. This study revealed that, compared to recycling, the life cycle involving post-consumer management in the form of landfilling had a higher total energy demand of 4.09 × 107 MJ. The results of this investigation validate the benefits of recycling. Thus, recycling ought to be commonplace to improve the environment. [ABSTRACT FROM AUTHOR]

The electricity consumption behavior of the inhabitants is a major contributor to the uncertainty of the residential load system. Human-caused uncertainty may have a distributional component, but it is not well understood, which limits further understanding the stochastic component of load forecasting. This study proposes a short-term load-interval forecasting method considering the stochastic features caused by users' electricity consumption behavior. The proposed method is composed of two parts: load-point forecasting using singular spectrum analysis and long short-term memory (SSA-LSTM), and load boundaries forecasting using statistical analysis. Firstly, the load sequence is decomposed and recombined using SSA to obtain regular and stochastic subsequences. Then, the load-point forecasting LSTM network model is trained from the regular subsequence. Subsequently, the load boundaries related to load consumption consistency are forecasted by statistical analysis. Finally, the forecasting results are combined to obtain the load-interval forecasting result. The case study reveals that compared with other common methods, the proposed method can forecast the load interval more accurately and stably based on the load time series. By using the proposed method, the evaluation index coverage rates (CRs) are (17.50%, 1.95%, 1.05%, 0.97%, 7.80%, 4.55%, 9.52%, 1.11%), (17.95%, 3.02%, 1.49%, 5.49%, 5.03%, 1.66%, 1.49%), (19.79%, 2.79%, 1.43%, 1.18%, 3.37%, 1.42%) higher than the compared methods, and the interval average convergences (IACs) are (−18.19%, −8.15%, 3.97%), (36.97%, 21.92%, 22.59%), (12.31%, 21.59%, 7.22%) compared to the existing methods in three different counties, respectively, which shows that the proposed method has better overall performance and applicability through our discussion. [ABSTRACT FROM AUTHOR]

Electricity consumption is closely linked to economic growth, social development, and carbon emissions. In order to fill the gap of previous studies on the decomposition of electricity consumption drivers that have not adequately considered carbon emission constraint, this study constructs the Kaya extended model of electricity consumption and analyzes the effects of drivers in industrial and residential sectors using the Logarithmic Mean Divisia Index (LMDI) method, and empirically explores the temporal and spatial differences in electricity consumption. Results show that: (1) During 2005–2021, the total final electricity consumption growth in Guangdong was much higher than that in Yunnan, but the average annual growth rate in Guangdong was lower, and the largest growth in both provinces was in the industrial sector. (2) The labor productivity level effect is the primary driver that increases total final electricity consumption (Guangdong: 78.5%, Yunnan: 87.1%), and the industrial carbon emission intensity effect is the primary driver that decreases total final electricity consumption (Guangdong: −75.3%, Yunnan: −72.3%). (3) The year-to-year effect of each driver by subsector is overall positively correlated with the year-to-year change in the corresponding driver, and declining carbon emission intensity is a major factor in reducing electricity consumption. (4) The difference in each effect between Guangdong and Yunnan is mainly determined by a change in the corresponding driver and subsectoral electricity consumption. Policy implications are put forward to promote energy conservation and the realization of the carbon neutrality goal. [ABSTRACT FROM AUTHOR]

As the scale of electricity consumption grows, the peak electricity consumption prediction of campus buildings is essential for effective building energy system management. The selection of an appropriate model is of paramount importance to accurately predict peak electricity consumption of campus buildings due to the substantial variations in electricity consumption trends and characteristics among campus buildings. In this paper, we proposed eight deep recurrent neural networks and compared their performance in predicting peak electricity consumption for each campus building to select the best model. Furthermore, we applied an attention approach capable of capturing long sequence patterns and controlling the importance level of input states. The test cases involve three campus buildings in Incheon City, South Korea: an office building, a nature science building, and a general education building, each with different scales and trends of electricity consumption. The experiment results demonstrate the importance of accurate model selection to enhance building energy efficiency, as no single model's performance dominates across all buildings. Moreover, we observe that the attention approach effectively improves the prediction performance of peak electricity consumption. [ABSTRACT FROM AUTHOR]

This study assessed hourly electricity consumption profiles in railway signal boxes located in Poland. The analyses carried out consisted of assessing the correlation among the hourly demand profile, weather indicators, and calendar indicators, e.g., temperature, cloud cover, day of the week, and month. The analysis allowed us to assess which indicator impacts the energy consumption profile and would be useful when forecasting energy demand. In total, 15 railway signal boxes were selected for analysis and grouped according to three characteristic repeatability profiles. On this basis, six of the signal boxes and one that did not fit into any of the groups were selected for further analysis. Four correlation research methods were selected for analysis: Pearson's method, Spearman's method, scatter plots, and distance covariance. The possibility of forecasting electricity consumption based on previously aggregated profiles and determining correlations with indicators was presented. The given indicators vary depending on the facility. Analyses showed different dependencies of the electricity demand profile. The ambient temperature and time of day have the greatest impact on the profile. Regarding the correlation with temperature, the results of the Pearson's and Spearman's coefficients ranged from approximately −0.4 to more than −0.8. The highest correlation coefficients were obtained when comparing the demand profile with the previous day. In this case, the Pearson's and Spearman's coefficients for all analysed objects range from approximately 0.7 to over 0.9. [ABSTRACT FROM AUTHOR]