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The subsector of pulp and paper presented in 2013 an electric power consumption of 9.3% of electricity consumption in the industrial sector in Brazil. It experienced a significant growth of production in the last 20 years, exceeding 160%, and their consumption of electricity increased by 130%, reaching the level of 19,594GWh. Due to its characteristics, with a known structure and production processes, and its importance in the country's industrial sector, this subsector was selected for study in this paper, to evaluate the behavior of its long-term annual electricity demand, at the horizon of 20 years ahead, in energy efficiency scenarios through bottom-up approach. This approach is data intensive and requires data with good quality; it consists of a hierarchical structure that enables to check how specific actions can affect the results at each level and its effect on higher levels. [ABSTRACT FROM AUTHOR]

This study considered the possibility of reducing the environmental footprint of paper and pulp industry by producing bioenergy from paper sludge by using process wastewater instead of fresh water, and reclaiming water trapped in paper sludge. Experimental studies are conducted with streams from three different pulp and paper mills (virgin pulp mill (VP), corrugated recycling mill (CR), tissue printed recycling mill (TPR)) for sequential bioethanol and biogas production with simultaneous reclamation of water from paper sludge (PS). Total energy yields of 9215, 6387, 5278 MJ/tonne dry PS for VP, CR and TPR, respectively, were obtained for ethanol-biogas production. Virgin pulp paper sludge gave the highest yield for ethanol and biogas in stand-alone processes (275.4 kg and 67.7 kg per ton dry PS respectively) and also highest energy conversion efficiency (55%) in sequential process compared with CR and TPR. Energy and environmental case study conducted on virgin pulp mill has proven the possibility of using paper sludge bioenergy to reduce energy demand by 10%, while reclaiming 82% of the water from the PS, reducing greenhouse gas emissions (GHG) by 3 times and producing solids suitable for land spreading. Integration of a sequential bioprocessing technology into the pulp and paper industry treats wastewater, produces bioenergy and eliminates the need to landfill paper sludge. [Display omitted] • Process wastewater substituted clean water in fermentation and anaerobic digestion. • Sequential bioprocessing reclaimed 65%−85% of entrapped water from paper sludge. • 80% of GHG emissions from landfills is avoided in paper sludge bioprocessing. • Integration of paper sludge bioprocessing in pulp mills can reduce energy demand by 10%. [ABSTRACT FROM AUTHOR]

Abstract: Paper production is an energy-intensive process and accounted for about 9% of industrial energy demand in Germany in 2008. There have only been slow improvements in energy efficiency in the paper industry over the past twenty years. Policies can accelerate the progress made, but knowledge about the remaining efficiency potentials and their costs is a prerequisite for their success. We assess 17 process technologies to improve energy efficiency in the German pulp and paper industry up to 2035 using a techno-economic approach. These result in a saving potential of 34 TJ/a for fuels and 12 TJ/a for electricity, which equal 21% and 16% of fuel and electricity demand, respectively. The energy savings can be translated into mitigated CO2 emissions of 3 Mt. The larger part of this potential is found to be cost-effective from a firm''s perspective. The most influential technologies are heat recovery in paper mills and the use of innovative paper drying technologies. In conclusion, significant saving potentials are still available, but are limited if we assume that current paper production processes will not change radically. Further savings would be available if the system boundaries of this study were extended to e.g. include cross-cutting technologies. [Copyright &y& Elsevier]

A new process for the production of bioethanol from paper mill sludge (PMS) is described in this work. PMS biomass feedstock was processed via the simultaneous saccharification and fermentation (SSF) with and without accelerants. The enzymatic hydrolysis and fermentation were first evaluated, and the energy demand was 2.2 MJ/L of produced ethanol. When the enzymatic hydrolysis and fermentation were combined, the energy demand was reduced to 1.0 MJ/L ethanol, the sugars production increased, and the overall capital cost of the process decreased. The sugar yield was improved by adding accelerant and selecting the optimal fiber recovery method. The accelerant improved the enzymatic hydrolysis via a pathing/bridging mechanism. The SSF with the chemical fiber recovery method coupled with accelerant addition would be the best process configuration. Upon this combination, the glucose profile was enhanced from 9.8 g/L to 17.0 g/L. The sludge fiber conversion by SSF was improved by selecting an efficient fiber recovery method combined with the accelerant addition. SSF in chemical fiber recovery with accelerant addition was the best process by a 10% improvement of ethanol yield. The proposed process configuration offers a lower cost and sustainable process and contributes to the circular economy of zero waste discharges. • A combined SSF process using PMS feedstock to produce bioethanol was developed. • The energy saving in an integrated process was 1.2 MJ/L ethanol. • The optimal fiber recovery from paper mill sludge was achieved via acid treatment. • The optimal fiber recovery increased fractional glucose by 27%. • The cationic accelerant improved the enzymatic hydrolysis by 10% due to the patching mechanism. [ABSTRACT FROM AUTHOR]

A direct-readout photoelectrochemical (PEC) lab-on-paper device based on coupled an electricity generating system and paper supercapacitors was established for highly sensitive detection of adenosine triphosphate (ATP). Concretely, CdSe quantum dots (QDs) decorated ZnO networks assembled sensing surface provided outstanding photoelectric properties, on which glucose oxidase (GOx) labeled aptamer was subsequently immobilized via the hybridization chain reaction. With analytes present, specific recognition was stimulated by aptamer, resulting in labeled GOx released. Such released GOx could flow to electrochemical cell to conduct electrochemical redox reactions, which could effectively produce electricity that was stored by capacitor I. Sequentially, photoactive material produced an outstanding voltage due to the decrease of steric hindrance on the sensing interface, which was utilized for charging an external capacitor II. The two instantaneous current was acquired along with the discharge of capacitor I and II by digital multimeter (DMM) readout, respectively. The summational current values performed an increment in pace with the addition of target ATP concentration with the dynamic working range from 10 nM to 3 μM and a detection limit of 6.3 nM attained. Significantly, the signal amplified strategy utilizing as-generated electricity from electrochemical redox reactions were isolated from the photoelectrodes, which was beneficial for amplifying the signal response in the PEC matrices and the development of more efficient signal performance. Image 1 • A Photoelectrochemical lab-on-paper device based on coupled electricity generating system and paper supercapacitors. • High sensitivity and selectivity for ATP detection. • Use of electricity generated from redox electrochemical reactions for signal amplification. • A portable digital multimeter for signal readout. [ABSTRACT FROM AUTHOR]

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

Insulators are vital components used in electrical transmission lines. It's crucial to keep their production cost low since they are widely used in electricity transmission. Instead of expensive alumina-based insulators, we can make cheaper ones using clay and bauxite found in nature. Adding more alumina makes them stronger, which is important for high voltage transmission. This paper aims to study the production process of bauxite-based insulators. We examined the insulators' structure using SEM images and EDAX spectrum analysis to see their formations, porosity ratio, and density. This research helps us learn more about these insulators and how they can be used effectively for high voltage transmission lines. [ABSTRACT FROM AUTHOR]

Energy hubs (EHs) represent a pivotal paradigm in achieving optimal resource utilization across various energy domains. This paper presents an advanced framework for the optimal management of a smart island, leveraging the synergies within the water-electricity nexus. By integrating diverse resources, including electricity, water, heat, and hydrogen, the proposed EH model aims to meet the multifaceted demands of consumers on the island. To enhance operational efficiency, this study delves into the nuanced impacts of key EH components, elucidating their roles in meeting demand profiles and minimizing operational costs. Formulated as a mixed integer linear programming (MILP) model, the EH optimization problem is addressed using the GAMS optimization tool. The overarching objective is to fulfill consumer demand while concurrently optimizing resource utilization, considering factors such as storage degradation costs and emissions from fossil-fuel-based units. In addition to strategic optimization, this study pioneers a novel approach to stochastic parameter forecasting, integrating convolutional neural networks (CNNs) and long-short-term memory networks (LSTMs). By harnessing the capabilities of these advanced forecasting techniques, the EH model can anticipate dynamic changes in demand patterns with heightened accuracy and precision. The empirical results underscore the transformative potential of the proposed EH framework, showcasing significant reductions—up to 30%—in emission costs. Moreover, the study underscores the pivotal role of EHs as enablers for scaling up renewable energy penetration, offering a robust foundation for sustainable energy transitions in island communities and beyond. Additionally, implementing a load-shifting demand response program can lower total costs by approximately $257 per day, offering significant savings for EHs over extended periods. [Display omitted] • Modeling a mixed integer linear programming for energy management of an energy hub. • Comprehensive study on hydrogen and electric storage systems. • Uncertainty modeling using convolutional and long short-term memory networks. • Modeling water–electricity nexus to meet water and electricity demand. [ABSTRACT FROM AUTHOR]

• A compression-assisted absorption refrigeration cycle using R32/DMAC is developed. • Enable the implementation of solar absorption refrigeration in refrigerated warehouses. • Using 60∼80 °C solar hot water obtains a low evaporation temperature of −20∼0 °C. • There is an 18.2 % reduction in electricity consumption compared to traditional method. Solar absorption refrigeration technology is considered an ideal alternative to energy-intensive refrigerated warehouses due to its decarbonization and environmental friendliness. However, it faces challenges in competing with vapor compression refrigeration, such as high driving heat source temperature (>100 °C) and a long payback period. In this paper, we address these challenges by focusing on cycle structure and working pair, and propose a compression-assisted absorption refrigeration cycle (CARC) using R32/dimethylacetamide (DMAC) as working pair. The aim is to significantly reduce driving heat source temperature by adjusting absorption pressure utilizing an auxiliary compressor, enabling the cycle to be connected to low-cost and efficient non-concentrating solar collectors, thereby effectively shortening its payback period. This cycle includes two operating modes: CARC mode powered by solar energy during the daytime and vapor compression refrigeration cycle mode driven by off-peak electricity at night, ensuring 24-hour cooling capacity for refrigerated warehouses. The results indicate that this cycle can flexibly utilize 60∼80 °C solar hot water easily obtained as driving heat source and its evaporation temperature decreases to -20 °C. To assess its performance benefits, a compression-assisted absorption refrigeration system is developed and implemented. By utilizing the limited roof area of a refrigerated warehouse, it can achieve an 18.2 % reduction in electricity consumption and a 24.2 % reduction in carbon emissions compared to the vapor compression one annually. Additionally, during peak electricity demand periods in summer, the system exhibits significant energy-saving effects, and its payback period is about 4.2 years. Ultimately, this cycle provides a viable solution for energy-saving and emission reduction in refrigerated warehouses. [ABSTRACT FROM AUTHOR]

To reflect the future electricity demand variations in the industrial sector of western China under the "carbon peaking" and "carbon neutral" strategies, that the traditional methods of electricity consumption forecasting are no longer effective, this paper proposes a new hierarchy of electricity demand influencing factors based on the transition path of low-carbon and CO 2 emission intensity constraints of the industrial sector. Then, the feedback equations of the influencing factors based on CO 2 emission intensity is constructed, and a long-term electricity forecast model based on system dynamics is established. Finally, taking Ningxia Province as an example, the electricity demand and carbon emissions of the industrial sector are predicted under different policy constraint scenarios. The results show: (1) In the baseline scenario, electricity demand will tend to saturate with about 140.1TWh in 2030, and the CO 2 emission level of electricity demand will peak at 72.52 million tons in 2029. (2) Compared with the system dynamics model without considering CO 2 emission intensity, the mean absolute percentage error is reduced about 3.16 % by inputting the influence factors and regression coefficients designed in this paper. (3) Compared with other forecasting methods, the model proposed in this paper has the lowest mean absolute percentage error of 2.75 %. The model improves the accuracy and provides a reference for planning departments to develop energy transition plans, where the prediction results reflect the reality of the industrial sector in western China. Correspondingly, this model has reference value for relevant institutions in forecasting the electricity demand under the low-carbon development path in industrial sectors of different regions. • Influencing factors are screened according to the low-carbon transition paths. • The distributed power installed capacity and load interaction coefficient are considered. • Multiple regression equations are developed between CO 2 emission targets and influencing factors. • A long-term electricity demand forecast model driven by CO 2 constraints is constructed. • The multi-scenario electricity demand in western China is forecasted in response to carbon peaking and carbon neutral targets. [ABSTRACT FROM AUTHOR]

China has witnessed 556 million people migrating to urban areas over the past three decades with the process of urbanization, which has brought enormous pressure on electricity consumption and urban sustainable development. This paper establishes the generalized Divisia Index Decomposition Method (GDIM) model, and aims to reveal the electricity consumption mechanism of urban residents in China between 2000 and 2018. The results show that residential income, per capita electricity consumption and residential appliances are the main driving factors for the growth of urban residential electricity consumption, while the energy-saving willingness and expenditure structure have the inhibiting effect. With the expansion of urban residential income, the increase in income and air-conditioning numbers will continue to promote the growth of urban residential electricity consumption. Then, this paper puts forward relevant suggestions to alleviate the pressure of electricity demand and sustainable development from urban residents in China. [Display omitted] • The growth of power consumption in residential sector mainly comes from urban areas. • Establish GDIM model to decompose urban residential electricity consumption in China • Income has the greatest driving effect on urban residential electricity consumption. • Air-conditioning is the main source of the effect of household appliances. • Energy-saving willingness inhibits urban residential electricity consumption. [ABSTRACT FROM AUTHOR]

This work aimed at solving the environmental problem of the hottest greenhouse gas reduction. The paper identifies the energy electricity demand, proposes solutions, and calculates the greenhouse gases emitted in each of the interconnected networks available in Cameroon. For a total production estimated at 1292 MW, the country's electricity production depends heavily on hydropower plants (57%), thermal power plants (42%), and only 1% of biomass. Specifically, biomass would contribute to 40% of the supply of the demand if it is sufficiently exploited. Hydroelectricity needs transmission for long distances. The electricity demand in the median economic scenario is the basis of this study. GHGs were calculated using the tool MESSAGE. The results of this study show that it will be possible, without taking into account the fact that biomass is clean energy, to avoid 462 kt of CO 2 eq and 14 kt of CO 2 eq for South Interconnected Network and East Interconnected Network respectively. The amounts of CH 4 , SO 2 , and NO x are not statistically significant. This study takes into account the main existing energy sources in the country. The specificity of this study is to treat the three interconnected networks of the country separately. The use of biomass in local power plants was proposed to reduce dependence on fossil fuels. This study shows that to reduce the GHG emission and the loss of electricity due to its transmission in the country, it is better to use biomass instead of fossil fuels for the production of electricity in Cameroon. [Display omitted] • Electricity demand of the country was identified. • The quantity of GHG emitted from electricity generation in Cameroon was estimated. • Biomass power plants were introduced in the electricity mix of the country to promote local production. • Local realities were taken into account and dependence of imported fossil fuels was reduced. • Biomass instead of fossil fuels was to avoid emission of 462 kt of CO 2 eq. [ABSTRACT FROM AUTHOR]

In this paper is developed an environmental comparison analysis associated with electrolytic process of hydrogen production using renewable electricity generated by solar photovoltaic (PV), wind energy, and hydroelectric systems. The electrolytic process and the electricity generation are particularly explained, and the global warming potential (GWP) and greenhouse gases (GHG) emissions are analysed, highlighting advantages and disadvantages associated with the production of green hydrogen. The hydrogen production processes are compared considering those different scenarios, enabling the determination of pollutant emissions factors, as the equivalent dioxide carbon and ecological efficiency. As a conclusion, the sustainability study using environmental analysis showed that electricity from renewable sources (wind, solar and water) have ecological impacts on hydrogen production. Comparatively, the best level of ecological efficiency obtained was in the production of green hydrogen from electricity from hydroelectric plants, followed by wind and photovoltaic (PV) plants. • Environmental comparison analysis associated with three electrolytic process of hydrogen production. • Electrolysis hydrogen produced with electricity from renewable sources: solar photovoltaic (PV), wind and hydropower systems. • Generation of green hydrogen. • Determination of pollutant emissions factors, as the equivalent dioxide carbon and ecological efficiency. [ABSTRACT FROM AUTHOR]

This paper has sorted out the general logic of the impact of COVID-19 on energy consumption. In the short term, the epidemic has forced governments to adopt different levels of lockdown measures. The total electricity consumption decreased and the share of renewable energy has increased. Based on the International Energy Agency's (IEA) data on electricity production and consumption, this paper analyzes the long-term effects: The COVID-19 pandemic does affect overall electricity demand and market structure in the short term, but in the long term, the impact of the pandemic is detrimental to the proportion of electricity generated from renewable sources. Increased, especially in hard-hit countries. The impact on countries with different levels of development is different, and countries with higher levels of economic development are better able to resist such exogenous shocks. The pandemic is still ongoing, how to steadily promote sustainable development? In this paper we suggest that to cope with climate change, to accomplish energy transformation and upgrading or even sustainable development, we should not only rely on reducing human activities, but give more support to countries with low development levels in realizing sustainable development. [ABSTRACT FROM AUTHOR]

The ultra-short-term electricity demand forecast is very crucial to the operation of power grid. This paper analyses the hourly electricity demand in a functional data framework and uses the functional nonparametric prediction method to forecast the hourly electricity demand of the next day. The results indicate functional nonparametric prediction method performs better than traditional time series model (seasonal-ARMA). This paper expands the application field of functional data analysis. This paper also introduces some future research directions under this topic. [ABSTRACT FROM AUTHOR]

The total electricity consumption (TEC) reflects the operation condition of the national economy, and the prediction of the total electricity consumption can help track the economic development trend, as well as provide insights for macro policy making. Nowadays, novel neural networks provide a new perspective to predict the total electricity consumption. In this paper, a time series forecasting method based on Transformer model, Trans-T2V model, is proposed and applied to TEC forecasting. Transformer is the first network structure that completely relies on self-attention to calculate input and output. In this paper, the Time2vec method is used to improve the existing Transformer model, as embedding the month sequence more efficiently in the Transformer model. By comparing with the existing Transformer models and other intelligent algorithm models, the robustness and superiority of the proposed method framework are verified, and the highest accuracy reaches 97.36%. The method presented in this paper provides valuable insights in the field of time series prediction. [ABSTRACT FROM AUTHOR]

• Critical challenge in developing countries. • Limitations of demand flexibility. • Innovative multi-strategy approach with community engagement. • Quantified benefits and sensitivity analysis. Electricity shortage remains a critical challenge in developing countries striving to achieve the United Nations' universal electricity access goal by 2030. Limited fuel availability and limited funds for renewable energy investments, while demand flexibility is implemented to regulate electricity access, it faces limitations due to voluntary participation, rotating load-shedding schedules, and lack of electrification in certain communities. This research paper proposes a multi-strategy approach as an alternative solution by scheduling essential appliances and optimizing power flow in distribution grids. The article aims at community engagement and utility collaboration to improve electricity access. This paper proposes a multi-strategy framework for regulating social organization and utility operations, and attaining energy conservation, efficiency, and incentives. Optimization tool is developed to extend benefits of optimal scheduling of end user devices under power supply shortage, uncertainty of demand and grid operational codes, using multiple strategies regarding to consumers' fairness of electricity access in a community, and maximum electricity access per customer and region. Such strategies are also compared with the case of customized appliance priority of preference. The results demonstrate that energy efficiency and economic sustainability indices can achieve 94 % and 29.5 % improvement, respectively, compared to conventional methods. [ABSTRACT FROM AUTHOR]

The defossilization of industry has far-reaching implications regarding the future demand for hydrogen and other forms of energy. This paper presents and applies a fundamental bottom-up model that relies on techno-economic data of industrial production processes. Its aim is to identify, across a range of scenarios, the most cost-effective low-carbon options considering a variety of production systems. Subsequently, it derives the hydrogen and electricity demand that would result from the implementation of these least-cost low-carbon options in process industries in Germany. Aligning with the German government's target year for achieving climate neutrality, this study's reference year is 2045. The primary contribution lies in analyzing which hydrogen-based and direct electrification solutions would be cost-effective for a range of energy price levels under climate-neutral industrial production and what the resulting hydrogen and electricity demand would be. To this end, the methodology of this paper comprises the following steps: selection of the relevant industries (I), definition of conventional reference production systems and their low-carbon options (II), investigation and processing of the techno-economic data of the standardized production systems (III), establishment of a scenario framework (IV), determination of the least-cost low-carbon solution of a conventional reference production system under the scenario assumptions made (V), and estimation of the resulting hydrogen and electricity demand (VI). According to the results, the expected industrial hydrogen consumption in 2045 ranges from 265 TWh for higher hydrogen prices in or above the range of onshore wind-based green hydrogen supply costs, to up to 473 TWh for very low hydrogen prices corresponding to typical blue hydrogen production costs. Meanwhile, the direct electricity consumption of the process industries in the results ranges from 147 TWh for these rather low hydrogen prices to 338 TWh for the higher hydrogen prices in the region of or above the hydrogen supply costs from the electrolysis of energy from an onshore wind farm. Most of the break-even hydrogen prices that are relevant to the choice of low-carbon options are in the range of the benchmark purchase costs for blue hydrogen and green hydrogen produced from offshore wind power, which span between €40 per MWh and €97 per MWh. [Display omitted] • Derivation of the future hydrogen and power demand of the process industry in Germany. • Bottom-up, process-level model to find the least-cost low-carbon production method. • Reference year is 2045, and the achievement of climate-neutral production is assumed. • Model determines the hydrogen and power demand under varying hydrogen prices. • Expected hydrogen use between 265 TWh and 473 TWh, depending on scenario assumptions. [ABSTRACT FROM AUTHOR]

There has been a growing demand for energy consumption statistics in the manufacturing industry to establish national energy and greenhouse gas policies. Despite its importance, the Korean government faces significant challenges in collecting energy data at a facility level in a precise and timely manner. To address the lack of timely data, this paper employs machine-learning models to predict the annual total energy consumptions of each manufacturing facility. We first designed four prediction models that take into account the characteristics and energy consumption behaviors of industry sub-sectors. As input variables, these prediction models mainly included electricity consumption, employee size, energy types, gas consumption and other accessible data. Finally, we conducted numerical experiments on approximately 100,000 facilities and evaluated the prediction performance of various machine-learning algorithms such as linear regression, decision tree regression, random forest regression, gradient boost regression, and extreme gradient boosting regression. The numerical experiments provided insights into which model and algorithm offer the best prediction performance for each industry sub-sector. In addition, we identified the important variables for predicting total energy consumption, revealing that not only electricity but also various other energy sources and variables representing industry-specific characteristics play a crucial role in improving prediction performance. • This paper predicts the annual total energy consumption of individual manufacturing facilities in Korea. • Several prediction models capable of considering industry-specific features are presented. • This paper employs machine-learning models. • The numerical experiments provided insights into which model and algorithm offer the best prediction performance. • Not only electricity but also variables representing industry-specific characteristics play a crucial role. [ABSTRACT FROM AUTHOR]

The load data features of some electricity-theft consumers during the theft period are similar to those of normal consumers, making these electricity-theft consumers outliers from the cluster of electricity-theft. The current classification method, which uses the mean value to determine the cluster centers, is vulnerable to the influence of outliers. Therefore, this paper proposes a self-decision ant colony clustering algorithm for electricity theft detection method that is targeted to self-decision which samples are used to update the cluster centers. The method constructs a dynamic weighting approach to determine the cluster centers based on the idea of Backpropagation, and updates the weights of each sample in the clusters to reflect the different importance of different samples, thus reducing the influence of outlier samples. A new activation function, Odd, is proposed to enhance the ability of the proposed method to solve linearly indistinguishable problems. A self-decision dropout mechanism is proposed which evolves the mechanism of randomly stopping the work of samples in clusters into a targeted and self-decision mechanism that stops the work of redundant or non-active samples as well as improves the contribution of outlier samples with positive effects. In this paper, the proposed method is tested by the electricity consumption data provided by the State Grid Corporation of China (SGCC) and the Smart* Data Set for Sustainability (SDSS) provided by the UMass Trace Repository, and the experimental results show that the proposed method effectively solves the above problems with higher detection accuracy, it has certain advantages over other current studies. [ABSTRACT FROM AUTHOR]

Electricity theft has emerged as a notable concern for smart grids, as fraudulent users illicitly access electricity from utilities without a contractual agreement or manipulate meter readings to evade bill payments. Consequently, the importance of electricity theft detection (ETD) in preserving the cost-effectiveness of smart grids has increased. In recent years, the utilization of deep learning models in electricity theft detection has surged in popularity, owing to their capacity to capture the periodic patterns within time-series data on electricity consumption. Nonetheless, deep learning techniques for electricity theft detection encounter several challenges, such as data imbalance, higher false positive rates, and the determination of periodic cycle timings. In this paper, we present an innovative approach to tackle these challenges: the Multi-Objective Evolutionary Hybrid Deep Learning (MOE-HDL) architecture, specifically designed to identify electricity theft. In MOE-HDL techniques, first, a novel multi-objective evolutionary algorithm based on ranking scheme (RMOE) is proposed to optimize architectural model parameters and the detection window size, with precision and recall as conflicting objectives. Next, an optimized dynamic detection window is generated to effectively capture the periodic patterns in consumption data and convert the one-dimensional data into a grid structure. Finally, a hybrid deep learning model is designed with the help of optimized model parameters to identify abnormal patterns in electricity consumption data. We conducted a comprehensive set of experiments, comparing our approach with eight baseline methods for electricity theft detection. The experimental results demonstrate that the proposed MOE-HDL approach outperforms these alternatives in terms of performance. • In this paper, a novel multi-objective evolutionary hybrid deep learning approach was developed for theft detection in smart grids. • Precision and recall are two conflicting yet effective objective functions designed to optimize the model parameters and detection window. • A dynamic detection window is obtained through optimization to transform electricity consumption data. • The hybrid deep learning model is designed according to the model parameters obtained through evolutionary multi-objective optimization. [ABSTRACT FROM AUTHOR]

[Display omitted] • We considered two strategies of electricity purchasing for industries: TB and NTB. • We applied an evolutionary game theory approach to examine industries' behavior. • We studied three real-world cases of Iran: cement, steel, and paper industries. • We mainly focused on finding the population ratio convergence to TB/NTB strategies. Electricity shortages severely affect the production chain and cause damage to manufacturing productivity. This study examines how the industrial sector can transform technological innovations toward renewable electricity procurement. We consider two strategies for manufacturers: purchasing electricity from a technology-based supplier (TB strategy) and purchasing it from a non-technology-based supplier (NTB strategy). Manufacturers need technological transformation, such as upgrading their electrical system toward digitalization to manage power outages. We examine manufacturers' pricing decisions and their long-term behavior to adopt the TB and NTB strategies using a one-population evolutionary game-theoretic approach. Also, we investigate the majority of manufacturers' population that eventually choose the best strategy. Moreover, we apply the present model to real-world cases of Iran's cement, steel, and paper industries. The results show that the evolutionary behavior of sectors with high annual electricity consumption, such as steel, converges to the TB strategy faster than cement and paper industries. In contrast, the cement and paper industries are more sensitive to subsidy allocation than the steel industry with respect to electricity procurement strategies. [ABSTRACT FROM AUTHOR]

Frequent power outages constrain households' electricity consumption and lead to additional backup expenditures on top of their electric utility bills in many developing countries. This study analyzes how residential electricity consumers of different income levels would change their electricity consumption and backup decisions in response to electricity reliability improvements. Using a nationally-representative sample of Nepalese households, the findings point out that improved reliability is positively correlated with the probability of electric appliance ownership. The interaction of income and reliability-constraint indicators suggests that the unreliable electricity supply constrains households equally at all income levels. However, the results from an ordered probit model with three off-grid backup decision alternatives indicate that the improved reliability matters more for poorer households, for whom the combined ownership cost of both appliances and backup equipment may be prohibitive. Putting this paper's findings into an energy-policy perspective, a connection to the grid by itself does not necessarily translate to realized benefits from electricity consumption. The reliability of the service plays a critical role, especially for poorer households. • This paper investigates how households respond to reductions in power outages. • The probability of electric appliance ownership increases at all income levels. • Household income constrains both appliance ownership and backup decisions. • Therefore, poorer households benefit relatively more from a reliable grid supply. [ABSTRACT FROM AUTHOR]

The price of photovoltaics (PV) has been steadily decreasing over the last decade, and many reports suggest that PV has become considerably cheaper than conventional electricity sources. In this paper, we critically evaluate the PV grid parity and use China as a case study. China is an interesting case study due to the wealth of data combined with the recent decrease in financial subsidies. Electricity costs are commonly compared in the literature using levelized costs of electricity (LCOE). However traditional LCOE analyses neglect important cost factors that are specific to PV, in particular the cost of grid integration. Here, we demonstrate that system LCOE calculation more accurately estimates the grid parity of PV. We find that the integration costs account for 15% of the total system costs, which cannot be neglected with the higher penetration of PV in the electricity system. Further, provincial grid parity indexes are greater than 1, indicating that the grid parity from a system LCOE perspective has not yet been achieved. The learning rate of Chinese PV is still relatively small, and we estimate that grid parity for PV in China will be achieved between 2020 and 2032, depending on the region. These findings shed new light on assessing grid parity of PV systems by considering relevant local parameters and thereby offer an assessment method framework and prediction modeling for assessing grid parity of PV installations. • This paper assesses PV's commercial feasibility in the current electricity market. • Grid parity is estimated using a new approach of system LCOE and learning curve. • The impacts of system LCOE and electricity price on grid parity are investigated. • The additional grid integration costs amount for 15% of total PV system costs. • Grid parity from a system LCOE perspective will be achieved between 2020 and 2032. [ABSTRACT FROM AUTHOR]

Electricity is fundamental for supporting economic development in emerging economies. Power generation from clean energy sources may help to meet societal needs such as increasing electricity demand while complying with contemporary global challenges (e.g. climate change). This paper aims at qualitatively describing the development of clean energy technologies in Mexico by using a multi-perspective analytical framework. By integrating a meta-theoretical framework for national energy transitions and core concepts from policy mixes for sustainability transitions literature, a series of stylised facts are developed to qualitatively describe the evolution of clean energy technologies in Mexico from 1879 to 2021. Interactive governance theory is used as an analytical framework to synthesise these storylines by highlighting the role and interactions among technologies, actors, and policy instruments in governance processes. The case study revealed that the ever-growing electricity demand in Mexico have called for exploring different available energy sources and technologies over time. Diminishing oil and gas domestic reserves, increasing concerns around environmental issues, and worldwide agreements on greenhouse gas emission reductions have been the main drivers for clean energy and energy efficiency development in Mexico. However, different barriers to a clean energy transition in Mexico have been identified and discussed in this study: (1) access to a vast oil and gas resources in the past reinforced the position of fossil-fuel incumbent regimes, displacing clean energy technological niches; (2) lack of investment in clean energy research, development, and innovation; (3) policy paradigms have shifted based on (dynamic) societal needs and socio-political interests in favour of fossil fuel power generation. To overcome these barriers and allow a transition towards a clean energy electricity system in Mexico, multiple policy instruments have been identified and discussed. • The paper describes the evolution of clean energy technologies in Mexico. • A multi-perspective analytical framework is used to create micro-logics. • The interactive governance approach is used to synthesise these micro-logics. • Barriers and drivers for a clean energy transition in Mexico are discussed. • Energy policy mixes towards a clean energy transition are discussed. [ABSTRACT FROM AUTHOR]

The entering sunlight from the building's windows mainly affects the heating and visual comfort of the occupants. The applications of Venetian blinds are a solution to improve the heating and visual comfort of the occupants. However, reducing the sunlight that enters the space can result in a rise in the building's electricity consumption. While most studies focus on the electricity production of solar panels, present study aims to examine the effect of solar venetian blinds on the indoor visual and thermal comfort of the occupants and optimize their geometry considering different geographical specifications. In the present paper, efforts are made to numerically install solar panels on Venetian blinds and analyze the effect of changing the geometrical parameters of solar Venetian blinds and the building's window dimensions on visual comfort and net electricity. Therefore, the target functions in the present paper are an improvement percentage in the daylight glare index and an improvement percentage in the net electricity costs for the analyzed building. As a result, five cities in Iran that have different climatic conditions are targeted to model the building. EnergyPlus software is employed to conduct the energy-based calculations, and the design variables and target functions are defined using JEPLUS software. The outputs are next inserted in JEPLUS+EA software to process a multi-objective optimization using the NSGA-II algorithm. The results demonstrate that the visual comfort and net electricity can be optimized by ranges of 10–100% and 1.5–10%, respectively. Furthermore, Venetian blinds are proven to have higher reception of sun radiations and better efficiency in southern cities and they can have a more proper performance while being installed for windows of southern building wall. [ABSTRACT FROM AUTHOR]

• The center of gravity of China's EPC is located in Luoyang City, Henan Province. • During the study period, China's EPC in general exhibits an east-high and west-low trend. • During the period 2000–2019, there are significant regional differences in the strength of the spatial linkage of EPC in China. • In 2000–2019, the decoupling status of EPC in china is of two types of decoupling: weak decoupling and expansive coupling. Exploring the evolution of spatial and temporal patterns of electricity power consumption in China is of great significance and value for the promotion of Carbon Peaking and Carbon Neutrality Strategy and the scientific allocation of electricity resources. Therefore, this paper takes China as an example and innovatively applies nighttime light data from a combination of two remote sensing satellites. And using spatial analysis methods to analyze the evolution of spatial and temporal patterns of electricity power consumption over a longer study time series. Meanwhile, this paper also uses the Tapio decoupling model to study the decoupling effect of electricity power consumption and economic growth in China. The results of the study show that, during the period 2000–2019, China's electricity power consumption was in an unbalanced state, with the eastern coastal region dominating. It also formed a gradient distribution from the coastal area to the inland area. In terms of spatial connection, it shows an unbalanced distribution pattern of "low in the west and high in the east". In addition, the spatial linkage of electricity power consumption among provinces is more than 93 % of weak linkage, and gradually increases to 97 %, indicating that the spatial linkage of electricity power consumption among regions is gradually weakening. The decoupling state of electricity power consumption in China is characterized by two types of decoupling: Weak decoupling and Expansive coupling. The decoupling status has experienced a significant change from 84 % of provinces being Expansive coupling to 100 % of provinces being Weak decoupling. [ABSTRACT FROM AUTHOR]

The carbon dioxide (CO 2) emissions have reached their highest levels since measurements began in recent years. This is widely regarded as one of the primary factors contributing to the increasing frequency of extreme climate events. Consequently, accurate and reliable CO 2 emission data becomes the benchmark in shaping policies and setting targets aimed at reducing emissions, which are essential for carbon emission-related research. Among all industries, the electric power sector takes the highest proportion of CO 2 emissions, making the carbon emission accounting for this sector a crucial component of overall carbon emissions accounting. Therefore, the objective of this paper is to provide a comprehensive review of the applied technologies and research progress in electricity carbon emissions accounting. The paper begins by introducing the fundamental concept of electricity carbon emission accounting. Electricity carbon emissions are categorized into two types: direct carbon emissions from electricity production and indirect carbon emissions from electricity consumption. Then, the current applied approaches for accounting for these two types of emissions are reviewed separately. After analyzing the shortages of existing applied carbon emission accounting approaches, the paper proceeds to review the research on the improved approaches of direct and indirect carbon emission accounting. Finally, the paper outlines the future development prospects of carbon emission accounting technology. • Application status of carbon emission accounting approaches in the electric power industry is analyzed. • Direct carbon emission accounting approaches based on big data technologies are introduced. • Research exploration in electricity indirect carbon emission accounting is reviewed and compared. [ABSTRACT FROM AUTHOR]

Rapid growth in population and economic development in Iran and its neighboring countries has resulted in a dramatic increase in electricity demand over the past few years. A substantial amount of electricity must be produced to meet this growing demand in this region. By installing conventional fossil power plants, using renewable energy, and interconnecting with neighboring countries, this growing demand can be accomplished. The main aim of this research is to identify prospects and challenges in the electrical sector around Iran. To this end, this paper evaluates the existing and future state of the electrical sector in Iran and its neighboring countries. Despite the fact that there is still a long way to go in terms of renewable energy in this region, there are some promising targets for the future that will allow fossil fuels to be replaced effectively. Notwithstanding this, we should point out that a number of cross-border interconnection projects are being pursued by Iran and its neighbors in order to integrate their networks and supply this growing demand. Finally, this study presents an overview of the future prospects and challenges of electricity capacity expansion, cross-border interconnections, and electricity transmission corridors in this region. The review results provide comprehensive background knowledge to all the researchers in order to investigate further into the field. This paper assists energy policy makers regarding the development of sustainable energy planning. • •In addition to providing a general overview of the geographical location, population growth, and economic indicators of Iran and its neighboring countries, this paper presents the current state of the Iranian electricity sector and its neighboring countries in light of the trends in electricity production, energy mix, price, and exports and imports. • •This paper explores the existing and future states of electricity capacity expansion in the region, specifically focusing on renewable energy sources. • •The existing and future states of cross-border electricity interconnections are examined to present a comprehensive overview of the cross-border interconnections in this region. • •This region's potential electricity transmission corridors are proposed to create the opportunity to transmit electricity across countries. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between financial development and access to electricity for rural and urban India by incorporating economic growth, trade, and foreign direct investment in energy production function. In doing so, we have applied autoregressive distributed lag bounds testing to examine long run cointegration between the variables by accommodating structural break(s) stemming in the series. Our empirical results indicate the presence of cointegration association between access to electricity and its determinants. We note that financial development enhances access to electricity in rural India but declines it in urban India. Economic growth has positive effect on access to electricity. Trade openness and foreign direct investment lower access to electricity in rural India. In urban India, trade openness and foreign direct investment increase access to electricity. The association between financial development and access to electricity is inverted-U shaped for rural and urban India. This paper would present new policy direction for Indian government to enhance access to electricity by using financial development as an economic tool. • Financial development and access to electricity have a strong linkage. • In India financial development enhances access to electricity in rural area but has adverse effect in urban area. • Rise in economic growth of India has increased the access to electricity. • Growth of Trade Openness and Foreign Direct investment increase access to electricity. [ABSTRACT FROM AUTHOR]

• Establish an optimization scheduling strategy for electrothermal joint operation. • Increase wind power consumption with electric boiler & heat storage tank. • Simulation shows 87 % wind power absorption rate with electric boiler & heat storage tank. • Combined use of electric boiler & heat storage lowers system operating costs. The intermittency and volatility of wind power and the characteristics of the operating mode of cogeneration units pose certain challenges to the wind power consumption of the power grid. To optimize the performance of the power and heat combined operation system and maximize the wind power consumption, this paper establishes a mathematical model for each component of the cogeneration system, including the thermal dynamic characteristics of the heat network, buildings, cogeneration units, electric boiler, and heat storage tank. At the same time, a scheduling optimization model is proposed, which considers the power load demand, and wind power output of the regional heating system. The research results provide insights and optimal dispatching strategies for ensuring the reliable operation of the power system and heating system and realizing the efficient utilization of wind power. This paper verifies the effectiveness and feasibility of optimizing the cogeneration system and provides a practical solution to the challenge of cogeneration economics. [ABSTRACT FROM AUTHOR]

Nowadays, climate change is a major global societal challenge that significantly increases environmental stress. Most international organizations and policies have promoted initiatives to minimize emissions, reduce fossil fuel dependence and increase renewable energy resource integration into different sectors. An energy transformation towards more renewable systems is thus a priority. Under this scenario, the present paper describes and evaluates an alternative energy conversion matrix-based model to combine sector electrification, power generation units from renewables, and new clean technologies. The proposed green matrix-based model allows analyzing future scenarios, including electricity participation in end-use consumption and electric power generated by renewables – potentially integrated into different sectors –. The proposed model is evaluated in the state of Maine (United States). This case study is focused on decarbonizing both residential heating and transport sector through the integration of large offshore wind power plant. Results and discussion is also included in the paper, providing expected energy demand reductions and decreasing emissions through the integration of renewables. This energy transition integration case study is proposed in three road-maps with different penetration rates and time scales. The proposed green matrix-based model can be also applied to other areas and energy resources, as an alternative way to analyze and estimate renewable integration into different sectors. • Green Electrical Matrix to link electricity supply and end-use for energy transition. • Assesses renewable potential and electrification tech for end uses, e.g., transport. • Applies model to Maine, USA to show reductions in fossil fuels and emissions. • GIS and MCDM for optimal offshore wind location, maximizing electricity generation. • Short, medium, and long-term projections of renewable integration into energy system. [ABSTRACT FROM AUTHOR]

Solar Home Systems (SHSs) have become a popular means of electrification for millions of people living in rural regions of developing countries with no access to the national grid. As the electrical demand grows, however, there are economic and technical challenges that exist in scaling-up SHSs. At present, up to 70% of the energy generated by SHSs is surplus and effectively goes to waste, due to high solar resource peaks aligning with low demand periods and restricted storage capacity. Interconnecting existing SHSs to form 'DC village' microgrids, comprised of distributed photovoltaic generation and battery storage, enables communities to unlock this surplus energy for more productive uses. This may be to service small businesses with higher energy demand than can be provided by a typical SHS (e.g. to shops or barbers), or to allow domestic consumers to connect higher powered appliances. Meanwhile, people without their own SHS can also connect to the system introducing basic lighting and phone charging for lower capital costs than those with SHSs. This paper presents the results of a field trial conducted in Murambi Village, in the Northern Province of Rwanda, which involved the interconnection of seven households with their own SHS and one with no previous access to electricity. An innovative smart controller developed by the authors to facilitate SHS interconnection was deployed across this village. Using a mix of survey and focus group methods, the paper examines how people experienced the interconnected SHS system; the opportunities and benefits (social, economic) this presented; how the system enabled the use of new (higher power) appliances and new practices by trialing a novel shared refrigeration unit; and the potential of SHS interconnection in the broader context of the 'bottom-up electrification' paradigm. • Introduction of Interconnected Solar Home Systems Networks in Sub-Saharan Africa. • Stimulating productive use of electricity in the Developing World. • Engaging highly distributed prosumers to form local energy networks. • Increasing penetration of high-power appliances in rural Sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

Nowadays, different types of dispatchable and non-dispatchable distributed energy resource (DER) units, combined heat and power (CHP) plants as well as heat only units are integrated into a micro-grid (MG) platform. These facilities can help MG operators to satisfy the required electricity and heat demands of controllable and non-controllable loads even with no power exchange between the external main grid and the MG. This paper introduces a network constrained optimal operation model based on an integrated active-reactive and heat powers scheduling in MG for both grid-connected and isolated modes. The DERs like fuel cell (FC) units, diesel generators (DGs) and micro turbines (MTs) are motivated to take part in providing reactive power to the MG based on a reactive power payment function. Meanwhile, the dependency of produced heat and active power by CHP units through feasible operating region as well as the dependency of produced active and reactive powers by DER units through capability curves are taken into account in the optimal economic dispatch of the MGs. The main goal of this paper is to find the optimal economic dispatch of active and reactive powers as well as heat energy for DER and CHP based micro-grids in which network constraints such as AC power flow limitations are taken into consideration. The proposed model is examined on a 33-bus test MG for grid-connected and isolated modes and the simulation results are presented to investigate and analyze the different features of the proposed method. [ABSTRACT FROM AUTHOR]

Water and electricity distribution networks are two highly interdependent and critical infrastructures in the world today. This paper investigates the coupling of water and electricity distribution networks through the electrical energy demand of water pumps. Further, with additional variable renewable energy (VRE) sources in the distribution system, utilities face significant challenges stemming from the VRE stochasticity. In this paper, a coordinated operational model of urban water and electricity distribution networks is proposed. Stochastic VRE generation is handled by using a data-driven probability efficient point (PEP) method based on historical wind and solar datasets without the need for any probability distribution function. Case studies are performed on a modified Institute of Electrical and Electronics Engineers (IEEE) 13-node electricity distribution network coupled with a 10-node water distribution network, typical of a small-town setting. Results show the impact of coordinating water and energy networks on the cost of operation and the distribution locational marginal prices (DLMPs). The inclusion of water tanks as alternative storage devices in the electricity distribution network are shown to slightly reduce voltage violations, line congestion, and VRE curtailments in a case with high VRE penetration. [Display omitted] • Propose coordinated water and electricity distribution model. • Stochastic renewable generation is handled by probability efficient point method. • Water tank storage improves network condition, distribution locational marginal price. [ABSTRACT FROM AUTHOR]

The overall operational costs of home energy systems (HES) has been considered as an objective function (OF) by many articles. This paper shows that considering the energy cost as OF can lead to a decrease in the load elasticity against the electricity price, which can lead to demand response programs failure. So the distribution system operator (DSO) must stop this decline. A new index is introduced to calculate the responsiveness of the PV-integrated HES, based on which a new OF for home energy management system is proposed. This index shows the load elasticity against the electricity price. The relation between energy cost and load responsiveness index is investigated, and several scenarios are analyzed to demonstrate the applicability of the proposed approach in real cases. The proposed method is implemented to a PV- and ESS- integrated HES. The results show the relation between the responsiveness of the loads and the operational costs of home. A sensitivity analysis is added to the paper to show the robustness of the proposed control scheme. • Considering the energy cost leads to a decrease in load elasticity against price. • A new index is proposed to calculate the responsiveness of HEMS, based on a new OF. • Home loads responsiveness index relation with the energy supply cost is discussed. • Roles of ESS to improving the responsiveness of residential loads are investigated. • ESS roles to reduce negative effect of PV in increasing PAR of HES are investigated. [ABSTRACT FROM AUTHOR]

Life Cycle Climate Performance (LCCP) is a widely accepted metric to evaluate the carbon footprint of air conditioning (AC) systems "from cradle to grave." This paper: (1) reviews the invention and evolution of LCCP, including a comprehensive timeline and bibliography; (2) documents the successful application of LCCP in the replacement of HFC-410A with HFC-32 in room air conditioners; (3) compares the conceptual frameworks and the operational approaches; and 4) reflects on the drawbacks of current LCCP research and points out possible future work. The major policy-relevant findings are: 1. The indirect emissions caused by energy consumption is 70 to 80 percent of the LCCP of AC systems in most countries but will decline in importance as electric power supply shifts rapidly from fossil fuel to renewable energy sources, which have near-zero carbon intensity; 2. The embodied greenhouse gas (GHG) emissions in refrigerant manufacture are, in most cases, negligible but the physical and chemical properties are crucial for system optimization for low carbon footprint; 3. The LCCP metric can be used for multiple purposes such as refrigerant selection and AC system architecture optimization; and 4. Data limitations in material manufacturing and the carbon intensity of electric power are the most significant challenges. Finally, this paper describes a variety of methods to fill in data gaps, including the correction factor method , the data-driven method , and the database searching method. The next-generation LCCP will be an enhanced evaluation process considering local climate, heat islands, and local power supply characteristics. [ABSTRACT FROM AUTHOR]

China's electric vehicle industry has gained momentum due to multiple factors, but there is still a gap in demand for charging stations. China's subsidy policy for EVs is gradually withdrawing from the market and increasing subsidies for charging facilities to stabilize the growth of EVs. Taking the number of EVs as the decision parameter, this paper proposes a multi-factor combination prediction model of grey correlation and long and short-term memory. The model predicts that the number of EVs in China will reach 1.02 billion by 2030. The importance of factors is discussed qualitatively through scenario setting analysis of EV electricity consumption and power supply. It is found that charging stations in China will show varying degrees of growth from 2021 to 2030. Finally, corresponding suggestions are put forward for charging stations, including charging subsidies, technology R&D innovation policies, and licensing quotas. • Influence degree of influencing factors on charging station construction. • Problems Faced by the Growth of Electric Vehicles. • Electric vehicle's demand for char station. • Policy Suggestions on Charging Station Construction. [ABSTRACT FROM AUTHOR]

This research investigates the asymmetric impact of renewable electricity energy usage and industrialization on the environmental sustainability of South Africa. The load capacity factor (LF) is a more extensive measure of environmental deterioration when compared to carbon emissions and the ecological footprint, which cover a range of both the demand on and supply of nature. The series dataset used for the empirical analysis spans between 1990 and 2019. The study also investigates the effect of human capital, industrialization, and economic growth on load capacity factor. The result of the NARDL estimator reveals that the increase in renewable electricity energy usage increases LF in the long and short term. However, the decline in renewable electricity energy usage has a neutral impact on LF. Furthermore, the results indicate that industrialization and economic growth impact LF negatively. Lastly, human capital improves environmental quality due to the increasing effect on LF. The spectral Granger causality approach outcomes show that all the parameters can forecast load capacity factors in the long term. Some policy implications, restrictions, and potential future study pathways have been explored in light of this paper's results. • The nonlinear autoregressive distributive lag is employed. • Asymmetric impact of renewable electricity consumption on environmental sustainability. • A broad environmental indicator is used, load capacity factor. • The positive change in renewable electricity consumption promotes environmental sustainability. [ABSTRACT FROM AUTHOR]

Pumped hydro energy storage (PHES) can effectively alleviate the renewable curtailment and resource waste caused by expansion of wind and solar-based renewable energy (RE) sources. However, the influences of regional hydrological characteristics, operational characteristics of PHES units, and power supply-demand balance on the regulating effect of PHES have been neglected. In this paper, an improved hybrid power system dispatch model (Dispa-set) that considers regional hydrological characteristics, unit operational characteristics, and RE curtailment penalty is proposed. The potential and limitations of PHES on reducing renewable curtailment and carbon emissions in four Northwest provinces (FNP) during the 14th Five-Year Plan (FYP) are evaluated through 6 sub-scenarios. Studies have shown that increasing PHES can effectively reduce renewable curtailment and carbon emissions, but uncoordinated development of PHES and RE results in resource waste and slows down carbon reduction. For example, a 10% increase in PHES during the 14th FYP could increase renewable curtailment in Qinghai by 1.1% and CO2 emissions in Gansu by 0.05%, due to uncoordinated development, demand for electricity supply, and regulated power sources. Therefore, combining PHES development with regional hydrological and operational characteristics is essential to achieve coordinated development with RE and unlock technology, economic, and environmental benefits. [ABSTRACT FROM AUTHOR]

Anomalies in electricity consumption can have a significant impact on the efficiency and dependability of power systems. Therefore, precise and prompt anomaly detection is essential for ensuring efficient system operation. This research paper presents an in-depth analysis of various machine-learning approaches for detecting abnormalities in Indian electricity consumption data. Specifically, the performance of three widely used machine learning algorithms, namely eXtreme Gradient Boosting, (XGBoost) Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), is evaluated. The efficacy of each technique is assessed using performance indicators, including mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2). The findings of this study provide valuable insights into the most effective machine-learning methods for identifying anomalies in Indian energy usage data. Moreover, this research contributes to the advancement of power grid anomaly detection technologies, leading to more effective systems. The anticipated outcome of this effort is an increase in the dependability and efficiency of the electrical supply system, benefiting both power system operators and end users. [ABSTRACT FROM AUTHOR]

World electricity consumption has grown permanently since 1990, dramatically increasing the emission of greenhouse gases. The worldwide efforts to reduce them and address climate change are leading to the massive electrification of different end-users, increasing the power demand. Therefore, achieving Sustainable Development Goals regarding affordable and clean energy requires the extensive availability of capital investments in electricity generation from renewable sources. Project Finance (PF) is a suitable financing alternative for large projects. This paper presents the advantages of PF contracts in allocating risks to appropriate counterparts and reducing the likelihood of default. Additionally, this study analyzes the characteristics that favor PF financing for renewable energy projects. The results reveal that country's governance levels, well-developed financial systems, and project costs affect the financing probability of a PF initiative, particularly for low and middle-income countries. Therefore, public policies aimed at increasing the availability of resources to renewable projects must tackle these factors. • The world requires massive renewable energy investments to meet Sustainable Goals. • Project Finance is a widely financing structure for large investment projects. • We study the determinants of renewable energy Project Finance. • Low governance and financial development hinder renewable energy PF initiatives. • Policies to develop capital markets and PF will help to reach the net zero target. [ABSTRACT FROM AUTHOR]

The fundamental way for China to achieve carbon peak and carbon neutrality is to continuously improve the penetration of renewable energy (RE) in the power system and establish an electricity mix dominated by RE power. According to China's 14th Five-Year Plan, the proportion of non-fossil energy power generation should reach about 39% by 2025. However, the impact of the increase in share of RE power on carbon emissions of the electricity sector remains to be estimated. To this end, the purpose of this paper is to capture the nonlinear characteristics of RE on electricity-related carbon emissions by using panel smooth transition regression (PSTR) model and provincial panel data covering the period 1991–2020 under the background of provincial RE quota system. The estimation results indicate that, (i) in the four transition variables of PSTR model such as the installed capacity of power generation, substation equipment capacity, total electricity consumption and total power generation, the nexus between RE deployment and electricity-related carbon emissions was detected as nonlinear. (ii) The heterogeneous RE carbon abatement effect is reflected in its increasing penetration in provincial power systems. (iii) Carbon abatement performance of RE generation decreases in the negative range with the increase of transition variables. (iv) The estimation coefficient of RE is positive at the higher value of the transition variable in the PSTR model of carbon emissions per capita. As a result, the RE quota with the best emission abatement performance should be designed based on the provincial power resource endowment and the medium and long-term expected electricity consumption. [ABSTRACT FROM AUTHOR]

Real-time algorithms have to adapt and adjust to new incoming patterns to provide timely and accurate responses. This paper presents a new distributed forecasting algorithm for streaming time series called StreamWNN. StreamWNN starts with an offline stage in which a forecasting model based on tuples of information fusion is created with historical data. In particular, this model consists of the fusion of patterns composed of past values of the time series with the future values of their k-nearest neighbors. Afterwards, streaming data starts to arrive. The model is incrementally updated in the online stage using a buffer with streaming data that more accurately matches the current model patterns. The model can be updated daily, monthly, quarterly or based on error thresholds. The methodology has been applied to Spanish electricity demand time series providing more accurate results when the model is updated incrementally. The best error results are obtained with the daily update of the model, resulting in an error between 2% and 3.5% depending on the prediction horizon. The model provides better error results than other algorithms. • StreamWNN is a real-time forecasting algorithm for time series received in streaming. • The prediction model is based on the fusion of relevant information related to the nearest neighbors. • Incremental learning deals with the change of distribution of streaming data. • StreamWNN predicts accurately Spanish electricity consumption in real-time. [ABSTRACT FROM AUTHOR]

In recent years, along with the rapid development of economy and society, the quality of life of people has been greatly improved, the total amount of electricity consumption has increased rapidly, and the scale of power system has been expanding. With the rapid increase of China's electricity supply, the corresponding infrastructure construction cannot keep up with the development of electricity consumption, resulting in a huge electricity consumption gap. Especially in recent years, some regions of China are facing industrial and industrial transformation, urban re-planning and renovation, leading to the complication of the structure and operation of the power grid, as well as the problem of heavy load and overload of power-using equipment. This paper describes the concepts and principles of the data algorithm and the peak-limit method, and makes a comparison of the economic losses of eight regions before and after the optimization of the data mining (DM) algorithm through experimental analysis. The results verify the effectiveness of the algorithm in solving the peak-limit problem and prove that the algorithm is of great research significance. [ABSTRACT FROM AUTHOR]

In this paper, we explore the co-movements between renewable and non-renewable energy sources and CO 2 emissions and GDP per capita in Saudi Arabia, respectively. Using the spectral Granger causality approach, our results suggested that non-renewable energy sources lead to carbon emission expansion in the frequency domain. Taken together, a significant causal relationship from fossil to economic growth is perceived over the long-term horizon while renewable energy source drives economic growth over different frequencies and considerably contributes to carbon emissions reduction. The wavelet plots revealed strong discrepancies in all variables in the time-frequency space. While non-renewable energy sources indorsed the economic growth across short-and medium-term scales and contribute to a polluted environment, renewable energy consumption has a substantial effect on carbon emission drop and on economic growth in the short-term. The multiple wavelet outcomes show that renewable energy may not only stimulate the economic growth but also promote sustainable and cleaner environment. The combined effects of non-renewable energy sources rise the carbon emissions. Thus, Saudi's government is counselled to promote the green energy investments and accelerate the transition to renewable energy production as its use as a substitute of fossil fuels carries advantages and opportunities alternating from environment to social-economic. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, we investigate the effects of information and communication technology (ICT) on economic growth and energy consumption for a panel of 27 countries ranked top of the ICT Development Index, from 1990 to 2019. By employing the panel nonlinear autoregressive distributed lag (NARDL) model approach, we confirm the presence of a long-run asymmetric effect of ICT on economic growth and energy consumption. Specifically, negative changes (reduction) in the adoption and usage of ICT can have severe consequences on economic growth in these 27 countries in the long-run, erasing all previous gains from positive ICT changes. Additionally, positive or negative shocks to ICT in these countries increase energy consumption, indicating the rebound effect exists in these countries. Finally, the results also reveal that, while the economic growth in these countries is dependent on energy consumption in the short-run, it is energy independent in the long-run. Several important policy implications are drawn. [ABSTRACT FROM AUTHOR]

Efficient energy production and consumption are fundamental points for reducing carbon emissions that influence climate change. Alternative resources, such as renewable energy sources (RESs), used in electricity grids, could reduce the environmental impact. Since RESs are inherently unreliable, during the last decades the scientific community addressed research efforts to their integration with the main grid by means of properly designed energy storage systems (ESSs). In order to highlight the best performance from these hybrid systems, proper design and operations are essential. The purpose of this paper is to present a so-called model predictive controller (MPC) for the optimal operations of grid-connected wind farms with hydrogen-based ESSs and local loads. Such MPC has been designed to take into account the operating and economical costs of the ESS, the local load demand and the participation to the electricity market, and further it enforces the fulfillment of the physical and the system's dynamics constraints. The dynamics of the hydrogen-based ESS have been modeled by means of the mixed-logic dynamic (MLD) framework in order to capture different behaviors according to the possible operating modes. The purpose is to provide a controller able to cope both with all the main physical and operating constraints of a hydrogen-based storage system, including the switching among different modes such as ON, OFF, STAND-BY and, at the same time, reduce the management costs and increase the equipment lifesaving. The case study for this paper is a plant under development in the north Norway. Numerical analysis on the related plant data shows the effectiveness of the proposed strategy, which manages the plant and commits the equipment so as to preserve the given constraints and save them from unnecessary commutation cycles. • A hydrogen-based ESS model includes several degradation costs of the electrolyzer and fuel cell. • Degradation costs concern working cycles and life spans of the electrolyzer and fuel cell. • Features, like warm and cold start, are considered through continuous and logical states combined. • An MPC controller minimizes the logical state switching between different operating modes. • The controller meets local load demand and maximizes revenue in the electricity market. [ABSTRACT FROM AUTHOR]