Search

With the energy structure transition and the development of the green power market, the role of electricity retailers in multi-market green power trading has become more and more important. Particularly in China, where aggressive green energy policies and rapid market transformations provide a distinct context for such studies, the challenges are pronounced. Under demand-side response, electricity retailers face the uncertainty of users' electricity consumption and incentives, which complicates decision-making processes. The purpose of this paper is to explore the optimization decision-making problem of multi-market green power trading for electricity retailers under demand-side response, with a special focus on the Chinese market due to its leadership in implementing substantial green energy initiatives and its potential to set precedents for global practices. We first construct a two-party benefit optimization model, which comprehensively considers the profit objectives for electricity retailers and utility maximization for users. Then, the model is solved by the Lagrange multiplier method and distributed subgradient algorithm to obtain the optimal solution. Finally, the effectiveness of the incentive optimization strategy under the multi-market to promote green power consumption and improve the profit of electricity retailers is verified by arithmetic simulation. The results of this study show that the incentive optimization strategy under multi-market, particularly within the Chinese context, is expected to provide a reference for electricity retailers to develop more flexible and effective trading strategies in the green power market and to contribute to the process of promoting green power consumption globally. [ABSTRACT FROM AUTHOR]

A reasonable allocation of production schedules and savings in overall electricity costs are crucial for large manufacturing conglomerates. In this study, we develop an optimization model of off-site industrial production scheduling to address the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's power supply, and the difficulty in promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and variations in electricity prices in different places to optimize the scheduling of industrial production in various locations. The model is embedded with two sub-models, i.e., an electricity price prediction model and a distributed photovoltaic power cost model to complete the model parameters, in which the electricity price prediction model utilizes a Long Short-Term Memory (LSTM) neural network. Then, the particle swarm optimization algorithm is used to solve the optimization model. Finally, the production data of two off-site pharmaceutical factories belonging to the same large group of enterprises are substituted into the model for example analysis, and it is concluded that the optimization model can significantly reduce the electricity consumption costs of the enterprises by about 7.9%. This verifies the effectiveness of the optimization model established in this paper in reducing the cost of electricity consumption on the demand side. [ABSTRACT FROM AUTHOR]

This paper takes a low-energy building in Changchun, China, as an object to test and study the characteristics of two heating modes, AC/DC (Alternative current/Direct current) switching and AC/DC synthesis, from the perspectives of temperature change, irradiation intensity, power generation, electricity consumption, etc. Firstly, the experimental research was conducted under two heating cable modes by establishing mathematical models and a test rig, and it was found that the photoelectric conversion efficiency on sunny, cloudy, and overcast days was 18%, 14.5%, and 12%, respectively. A simulation model was established by TRNSYS to run an ultra-low-energy building throughout the year. It was found that the highest and lowest monthly power generation occurred in February and July, respectively. The annual power generation of the system was 6614 kWh, and the heating season power generation was 3293.42 kWh. In the current research, the DC electricity consumption was slightly higher than the AC electricity consumption. Under conditions of similar radiation intensity and power generation, the indoor temperature of the AC/DC synthesis cable heating mode were 1.38% higher than the AC/DC switching heating able mode, and the electricity consumption were 10.9% and 4.76% higher, respectively, than those of the AC switching heating cable mode. This is of great significance for clean-energy heating, energy savings, and emissions reduction in northern China. [ABSTRACT FROM AUTHOR]

Adoption of clean electric energy depends not only on administrative regulations, but also on public support, in particular, the public is willing to pay for environmental improvements. However, the increase of solar photovoltaic power generation willingness to pay (WTP) associated with higher education attainment and the identification of their causality has been missing. Present paper used the enactment of the Compulsory Schooling Law as an instrumental variable to solve the causal relationship between education and willingness to pay for photovoltaic power generation. The results are as follows:Heckman two-stage model and instrumental variable both confirmed that higher education has a positive impact on WTP for solar photovoltaic power generation. For each level of public education in the east, the WTP of photovoltaic power generation will increase by 7.540 CNY, 8.343 CNY and 8.343 CNY respectively, the central public will increase by 9.637 CNY, 10.775 CNY and 11.758 CNY, and the western public will increase by 12.723 CNY, 15.740 CNY and 17.993 CNY respectively. The positive influence of education level is smaller among the people who know the ladder price better, but it is bigger among the people who are male, older than 45 years old, healthier, higher income and stronger awareness of safe electricity use. The total socio-economic value of photovoltaic power generation is significantly different in eastern, central and western region China. [ABSTRACT FROM AUTHOR]

In the grey models, the GM(1,1) model is an important type of prediction model. The traditional grey GM(1,1) model has good prediction results in the case the original data show exponential variations at a slow rate. However, in practical problems, although showing exponential variations or approximately exponential variations, original data vary very fast sometimes. In these cases, the traditional grey GM(1,1) model tends to have poor prediction accuracy, mainly because the data fails to meet the laws presented by the traditional model. Therefore, the paper makes improvements in the following two aspects: first, the paper transforms the traditional accumulated generating sequence of original data; second, the paper extends the traditional grey model's structure, i.e., building a grey EGM(1,1,Σe^(ck)) model. The paper offers the parameter optimization method of the grey EGM(1,1,Σe^(ck)) model. Using the novel modeling method proposed, the paper builds the grey EGM(1,1,Σe^(ck)) models for China's total electricity consumption and China's GDP per capita, respectively, in the final section. Results show that the models built with the proposed modeling method have high simulation precision and prediction precision. [ABSTRACT FROM AUTHOR]

Solar-heating technology is a promising solution to help China achieve the "3060 double carbon" target as soon as possible. Seasonal thermal storage (STS) can effectively solve the mismatch problem of solar-heating systems between the supply and demand of thermal energy. Due to the instability of solar radiation resources and the heat demand, it is necessary to analyze the dynamic response characteristics and operation strategy optimization of the system in different operation stages. Yet, related studies are still scarce. The aim of this paper is to study the switching mechanism of the operation modes and the transitive relation of the system energy in different operation stages based on a pilot solar-heating system with STS in Huangdicheng, northern China. The impacts of different heating strategies on the system performance were also analyzed with a dynamic simulated method in TRNSYS. The results showed that the solar fraction of the system reached 89.4% in the third year, which was 3.6% higher than that in the first year. The quality–quantity heating operation strategies are effective ways to improve the discharge efficiency of the STS and the system performance without a heat pump. The electricity consumption of the pump on the heating side could be significantly reduced by 44.6% compared with the quality control. Ultimately, the findings in this paper are valuable for the optimization of the operation of solar-heating systems. [ABSTRACT FROM AUTHOR]

Electric power is the basic industry of a modern country. The rapid development of the power industry has promoted economic development and social progress. With the establishment of the carbon neutrality target of carbon peak, China's power industry is also facing new situations and new challenges. This paper innovatively introduces the concept of allometric growth in biology, and uses the data of provincial electricity consumption and economic development from 1972 to 2017, to study the allometric growth relationship between electricity consumption and economics in China at multiple scales. Combined with the spatial autocorrelation analysis method, the temporal and spatial characteristics of allometric growth between electricity consumption and economic development were researched based on the analysis of spatial correlation of GDP electricity consumption intensity. The results show that: (1) The provincial GDP electricity consumption intensity shows an aggregate distribution, and the local aggregate pattern changed significantly from 1972 to 2017. (2) The High-High Cluster gradually disappeared, and the Low-Low Cluster moved southward, appearing in the middle and lower reaches of the Yangtze River. (3) The differential growth coefficient of electricity consumption and economic development increased initially and decreased from 1972 to 2017. (4) GDP would increase by 7.4%, 20.1%, and 15.6% in the simulation of electricity consumption, increasing 10% at the three stages of China's economic development. (5) The allometric growth coefficients of electricity consumption and economic development are very different in the three economic belts, eight economic zones, and provinces with different characteristics at the different stages of economic development. Formulating differentiated regulation policies for regional economic development and electricity consumption will be conducive to the coordinated development of electric power production and the national economy. [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]

Bearing in mind European Green Deal assumptions regarding a significant reduction of green house emissions, electricity generation from Renewable Energy Sources (RES) is more and more important nowadays. Besides this, accurate and reliable electricity generation forecasts from RES are needed for capacity planning, scheduling, managing inertia and frequency response during contingency events. The recent three years have proved that Machine Learning (ML) models are a promising solution for forecasting electricity generation from RES. In this review, the 8-step methodology was used to find and analyze 262 relevant research articles from the Scopus database. Statistic analysis based on eight criteria (ML method used, renewable energy source involved, affiliation location, hybrid model proposed, short term prediction, author name, number of citations, and journal title) was shown. The results indicate that (1) Extreme Learning Machine and ensemble methods were the most popular methods used for electricity generation forecasting from RES in the last three years (2020–2022), (2) most of the research was carried out for wind systems, (3) the hybrid models accounted for about a third of the analyzed works, (4) most of the articles concerned short-term models, (5) the most researchers came from China, (6) and the journal which published the most papers in the analyzed field was Energies. Moreover, strengths, weaknesses, opportunities, and threats for the analyzed ML forecasting models were identified and presented in this paper. [ABSTRACT FROM AUTHOR]

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

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]

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]

The achievement of the carbon peaking and carbon neutrality targets requires the adjustment of the energy structure, in which the dual-carbon progress of the power industry will directly affect the realization process of the goal. In such terms, an accurate demand forecast is imperative for the government and enterprises' decision makers to develop an optimal strategy for electric energy planning work in advance. According to the data of the whole social electricity consumption in Jiangsu Province of China from 2015 to 2019, this paper uses the improved particle swarm optimization algorithm to calculate the fractional-order r of the FGM (1, 1) model and establishes a metabolic FGM (1, 1) model to predict the whole social electricity consumption in Jiangsu Province of China from 2020 to 2023. The results show that in the next few years the whole social electricity consumption in Jiangsu Province will show a growth trend, but the growth rate will slow down generally. It can be seen that the prediction accuracy of the metabolic FGM (1, 1) model is higher than that of the GM (1, 1) and FGM (1, 1) models. In addition, the paper analyzes the reasons for the changes in the whole society electricity consumption in Jiangsu Province of China and provides support for government decision making. [ABSTRACT FROM AUTHOR]

In recent years, northern China has suffered from the severe haze in autumn and winter, a phenomenon to which coal-fired heating appliances contribute significantly. To reduce pollution and achieve sustainability in the electricity industry, government departments have proposed the "Electricity Substitution" strategy, and the State Grid Corporation has also proposed the "coal-to-electricity" transformation movement. Against this background, our study aims to identify the impact factors of electricity consumption behavior among customers who have completed this transformation. Based on customers' monthly electricity consumption data in Shaanxi from 2017 to 2019 and on-site survey results, this paper constructs a multiple regression model and utilizes SPSS25.0 to analyze the relationship between residents' electricity consumption and related factors. The research results revealed that after the implementation of the "coal-to-electricity" transformation, residential customers' electricity consumption tended to rise. Consequently, winter temperature, appliance power, and heating area negatively correlate with residents' electricity consumption. Simultaneously, customers' habits, subsidies, and electricity pricing positively correlate with residents' electricity consumption. Finally, this paper offers some practical management suggestions for power companies based on the analysis. [ABSTRACT FROM AUTHOR]

Based on the counted power system emission factors of North China Power Grid, a community carbon emissions sample database is constructed. The support vector regression (SVR) model is trained to forecast the power carbon emissions, which is optimized by genetic algorithm (GA). A community carbon emission warning system is designed according the results. The dynamic emission coefficient curve of the power system is obtained by fitting the annual carbon emission coefficients. The time series SVR carbon emission prediction model is constructed, while the GA is improved to optimize its parameters. Taking Beijing Caochang Community as an example, a carbon emission sample database is generated based on the electricity consumption and emission coefficient curve to train and test the SVR model. The results show that the GA–SVR model fits well with the training set and the testing set, and the prediction accuracy of the testing set reaches 86%. In view of the training model in this paper, the carbon emission trend of community electricity consumption in the next month is predicted. The carbon emission warning system of the community is designed, and the specific strategy of community carbon emission reduction is proposed. [ABSTRACT FROM AUTHOR]

Challenges brought on by the rapid rate of economic growth include the depletion of natural resources (NAR) and environmental deterioration. Due to its abundant mineral resources and rapid economic growth, China has gained attention from both the developing and developed worlds. Using data from 1970/Q1 to 2020/Q4, this paper evaluates the time‐frequency nexus between CO2 emissions and natural resources, coal consumption, and economic growth. The study employs the Wavelet Local Multiple Correlation (WLMC) to explore this nexus. This approach enhances the capacity to comprehend the fundamental relationships between these indicators at different frequencies. To the best of the investigator' knowledge, this is the first empirical investigation to explore the time‐frequency nexus between natural resource, economic growth, coal use, and CO2 emissions in China, thus filling a gap in the literature. Furthermore, wavelet coherence is employed as a robustness check. The WLMC bivariate cases revealed that economic growth, coal consumption, and natural resources drive CO2 emissions positively at all frequencies, that is, in the short and long‐term, thus leading to environmental degradation. Furthermore, the wavelet coherence results support the outcomes of the WLMC bivariate cases. This study offers significant implications for policy and insights that are supported by the findings. [ABSTRACT FROM AUTHOR]

The introduction of the new round of electricity reform policies has made the electricity sales companies' trading environment increasingly complex. In the medium- and long-term market and spot market, following the new policy-oriented optimization of trading decisions is the focus of electricity sales companies. The main objective of this study is to consider the impact of the latest policies of China's current electricity reform on each subject of electricity trading and to propose a method for electricity sales companies to make optimal decisions on renewable energy source (RES) power and conventional energy source (CES) power mixed with power trading in the medium- and long-term and spot markets to improve the efficiency of electricity market trading, promoting the consumption of renewable energy and helping the synergistic development of the electricity market and the tradable green certificate (TGC) market. This paper first discusses the impact of the new electricity reform policies on the transactions of various subjects in the electricity market and constructs the model of the consumer utility function, the profit model of an electricity sales company, and the profit model of power generators with energy storage. Considering the complex power supply and demand relationship among the various subjects of the electricity market, a game model is established for the decision on mixed trading between the medium- and long-term market, the spot market, and the tradable green certificate market to minimize the comprehensive power purchase cost of an electricity sales company. To reduce the decision-making risk caused by the uncertainty of spot price, the prophet model is used to predict the spot price; finally, through the analysis of the decision-making model of the electricity sales companies, the optimal transaction decisions of the electricity sales companies in different trading periods and different scenarios are solved. The test results show that the proposed model can significantly improve the profitability of the electricity sales companies and provide a decision-making reference for electricity sales companies to participate in the medium- and long-term market and spot market. [ABSTRACT FROM AUTHOR]

Based on the emission factor method and LMDI-LEAP model, this paper systematically studies the current situation, influencing factors and changing trend of carbon emissions from rural buildings in a typical village located in southern China. The results showed that (1) the per capita carbon emissions generated by the energy consumption of rural buildings is 2.58 tCO2/a. Carbon emissions from electricity consumption in buildings account for about 96.07%; (2) the per capita building area, building area energy intensity, population size, population structure and carbon emission coefficient affect rural building carbon emissions, with contribution rates of 70.13%, 31.27%, 0.61%, −1.21% and −0.80%, respectively; (3) from 2021 to 2060, the carbon emissions of rural buildings are expected to increase first and then decrease. In 2021, the base year, carbon emissions from buildings were 2755.49 tCO2. The carbon emissions will peak at 5275.5 tCO2. Measures such as controlling the scale of buildings and improving the utilization rate of clean energy can effectively reduce carbon emissions, in which case the peak can be reduced to 4830.06 tCO2. Finally, the countermeasures and suggestions about rural building energy saving and emission reduction are proposed, including improving the construction management, raising energy efficiency standards in buildings, increasing the proportion of clean energy and raising residents' awareness of energy conservation. [ABSTRACT FROM AUTHOR]

• City level data in China was used to examines the effects and mechanisms of digital finance on residential carbon emissions. • In terms of the structural composition of carbon emissions, digital finance mainly increases residential carbon emissions by affecting electricity consumption and transportation in China. • Digital finance can increase residential carbon emissions through the consumption effect and employment effect. Reducing residential carbon emissions is important for all countries to achieve carbon neutrality. For such task, the impact of digital development of finance on residential carbon emissions cannot be ignored. This paper estimates residential carbon emissions in 186 Chinese cities from 2011 to 2019 and examines the effects and mechanisms of digital finance on residential carbon emissions. The results show that digital finance exacerbates residential carbon emissions in China. In terms of the structural composition of carbon emissions, digital finance mainly increases residential carbon emissions by affecting electricity consumption and transportation in China. Mechanism analysis shows that digital finance can increase residential carbon emissions through the consumption effect and employment effect. [ABSTRACT FROM AUTHOR]

The world's key renewable power markets are generally challenged by wind and solar power curtailment. Research on the influencing factors of curtailment improvement can provide a reference for the further penetration of renewable energy (RE) into the power system. Based on the logarithmic mean Divisia index (LMDI) method, this paper analyzes the factors influencing the wind and solar power consumption rate in China from 2015 to 2018, and four typical provinces are discussed in detail. By decomposing the consumption rate into six factors (transmission structure, generation proportion, power self-sufficiency, resource development, resource utilization, and power consumption), and focusing on the transmission structure factors, this paper further clarifies the contributions of local use, ultra-high-voltage (UHV) transmission, and conventional transmission to the improvement of curtailment. The results reveal the following. (1) At the national level, the power mixing effect and the resource development effect have played major roles in the change of the consumption rate, but the influences of these two effects have been decreasing. (2) The UHV power lines provide a robust resource for the inter-provincial deployment of renewable power (such as the RE power transfer in Inner Mongolia and Gansu), but the role of UHV transmission has not yet been fully utilized. (3) The impact of the electricity consumption effect on renewable power consumption is increasing year by year, but the local consumption capacity remains insufficient (both at the national level and in the four typical provinces analyzed), so it is necessary to further increase the share of renewable electricity in UHV power transmission and the local consumption capacity. (4) The expanding development of wind and solar power may be the reason for the curtailment in Shaanxi, and a reasonable renewable power development plan must be further formulated. [ABSTRACT FROM AUTHOR]

To achieve the goal of achieving carbon-neutral by 2060, the government of China has put forward higher requirements for energy conservation and consumption reduction in the energy industry. Therefore, it is necessary to reduce energy consumption in the process of transporting oil. In this paper, an optimization model that minimizes the total energy consumption of the entire pipeline system is proposed and the squirrel search algorithm (SSA) is used to solve the optimization model. Meanwhile, to improve the performance of the SSA, two strategies are proposed. One is the adaptive inertia weight strategy, and the other is the multi-group co-evolution strategy. The adaptive inertia weight can adjust the step size of the flying squirrels according to the difference of the objective function value and multi-group co-evolution is introduced to improve population diversity. The improved SSA is named multigroup coevolution-adaptive inertia weight SSA (MASSA). A total of 20 benchmark functions are used to test the performance of MASSA, including unimodal functions and multimodal functions. Compared with the other four algorithms, MASSA has better performance and convergence capabilities. In the case study experiment, an optimization model of the oil pipeline is built, which takes the minimum energy consumption of the whole pipeline as the objective function. Compared with the actual operating conditions, the electricity consumption optimized by MASSA decreases by 399,018.94 kgce, and the thermal energy dissipation decreases by 113,759.25 kgce. The total energy consumption is reduced by 512,778.19 kgce, which is 9.62%. These results indicate that the two improvement strategies are significant, and optimizing the operating parameters can reduce energy consumption. [ABSTRACT FROM AUTHOR]

Since the government implemented the supply-side structural reform, the growth of electricity consumption in energy-intensive manufacturing industries has been contained in an all-round way, which poses greater challenges to overcapacity in the power sector. It is still a mystery that how to restrain the electricity consumption of energy-intensive manufacturing industry affects the installed capacity of power generation. Thereupon, this paper empirically studies the relationship between electricity consumption of the six energy-intensive manufacturing subsectors and provincial power generation capacity. The empirical results with line loss rate as instrumental variables indicate that the electricity consumption of the six energy-intensive manufacturing subsectors will increase or decrease the installed capacity of power generation by a one-to-one elastic coefficient. In terms of manufacturing subsectors, the power sector's own electricity consumption has the highest capacity enhancement effect, followed by non-metallic minerals and chemical raw materials manufacturing subsectors. The resolution of overcapacity for power generation is fundamentally to reduce the electricity consumption intensity of the six high-energy-consuming manufacturing subsectors. [ABSTRACT FROM AUTHOR]

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

As one of the most significant carbon emission departments in China, the power industry will gradually become the core hub of reducing carbon emissions in the process of undertaking carbon emissions transferred from other industries. Therefore, it is of vital importance to predict the power consumption in China's end energy consumption to achieve the carbon peak goal on time. This paper firstly uses the gray relational analysis model to study the relationship between power consumption indicators of the three major industries and some social and economic indicators and obtains the influencing factors with the greatest correlation with the power consumption of the three industries. Then, based on the analysis of socio-economic factors, considering different growth rates, the GMCN(1,N) model of electricity consumption in China's three major industries is established. Forecast data under different scenarios have important practical significance for formulating active and effective energy policies. The data indicate that the secondary and tertiary industries consume the greatest amount of electricity. It is estimated that the power consumption of China's three major industries will reach 10.15 trillion kWh (kilowatt hours) by 2030. [ABSTRACT FROM AUTHOR]

Accurate electricity consumption forecasting, especially monthly electricity consumption forecasting, plays an essential role in the stable and efficient operation of power systems. To improve forecasting performance and practicality, we propose a dynamic decomposition-denoising-ensemble model to predict monthly electricity consumption in this paper. Firstly, seasonal-trend decomposition procedure based on loess (STL) incorporated with a dynamic mechanism is used to decompose the original monthly electricity consumption series into trend component, seasonal component and residual component. Secondly, empirical wavelet transform (EWT) is introduced to denoise the residual component and obtain the denoised residual component. Thirdly, autoregressive integrated moving average (ARIMA), Holt-Winters (HW) and extreme learning machine (ELM) are used to predict the trend component, the seasonal component and the denoised residual components, respectively. Finally, the forecasting results of each component are added to obtain the final forecasting results. Furthermore, we take two real datasets from China as examples to verify the performance of our proposed model. The empirical results show that the proposed model performs better than the benchmark models and denoising the residual component can enhance the forecasting performance. Specifically, compared with benchmark models, the mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean squared error (RMSE) values of the proposed model decrease by 5.81%–46.02% (12.15%–51.03%), 4.80%–42.24% (12.75%–53.53%) and 1.98%–41.02% (10.44%–55.27%). [ABSTRACT FROM AUTHOR]

To reflect the trend of electricity consumption (EC) in China's Pearl River Delta (PRD) under a balanced environment and economy, this paper proposes an EC forecasting framework from the perspective of optimal economic growth. The historical GDP statistics of the PRD are calibrated with data from the new "NPP-VIIRS-like" nighttime light dataset from 2000 to 2020, and two simulation scenarios of optimal economic growth are constructed according to the carbon emission reduction rate. Finally, based on the electricity data from 2000 to 2017, an error correction model is constructed to predict and compare the trend of EC in the PRD under different carbon emission scenarios. The results demonstrate the following. (1) The economic growth path under low-carbon constraints is more closely aligned with the actual economic development of the PRD. (2) The electricity demand required to sustain optimal economic growth in the PRD under low-carbon constraints is projected to reach saturation around 2037, approximately a decade earlier than the scenario without carbon constraints. The results of the projections are expected to guide future work on power system planning and economic development assessment in the context of carbon reduction. • The balance between environmental resources and economic development is considered. • The optimal economic growth path under the low-carbon goal is measured. • Calibration of regional statistics with the nighttime light dataset "NPP-VIIRS-like". • The error correction model is built as a short-term regulation mechanism for power prediction. • Predicted electricity demand for optimal economic growth in the PRD under different scenarios. [ABSTRACT FROM AUTHOR]

In the past 20 years, China has undergone a rapid process of electrification, which has contributed to the modernization of people's lives. This paper associated household electricity consumption, appliance service function, total cost of electricity consumption to life well-being for the first time, and explained the structural characteristics of these changes. The welfare index from appliance services is defined as the result of the interaction between electricity consumption and appliance operation, and is used as an indicator to assess the improvement of household well-being. We sorted out 9 types of service functions of appliances, estimated the structure of household electricity consumption, total electricity cost and welfares received, and analyzed the factors that enhance welfare and their contributions. The results show that the electricity consumed by 12 main household appliances per household per year increased from 358 kWh in 2000 to 1419.5 kWh in 2020, and the total household electricity cost increases from 1315.74 yuan (RMB) to 6772.68 yuan. The quantity and structure of both have changed greatly. The service functions provided by appliances are increasingly diversified, have expanded from the basic services such as lighting, watching TV, refrigerated food to nine aspects including communication and transportation. The quality of life of residents continues to be improved, and the welfare index has increased from 2.48 to 7.82. There is a good linear relationship between the index and the three independent variables. The unit contributions of household disposable income, total electricity cost and household population size to the welfare index were 0.0836, 0.1195 and −1.6161, respectively. To accelerate electrification and enhance people's well-being, China needs to further improve the power and communication infrastructure, promote the application of intelligent technology, and implement precise encouragement policies. • Electricity consumption and appliance services cloud be linked to residential welfare. • The changes of household electricity structure and cost are quantitatively measured. • The welfare index from home appliance services are estimated for the first time. • The well-being is influenced by ability to pay, willingness to pay and family size. • The domestic electrification has greatly improved people's quality of life. [ABSTRACT FROM AUTHOR]

The household energy consumption has been a hot field in the study of household energy consumption in recent years. With the increase of residents' income level and the pushing of urbanization, there is a complex nonlinear relationship between energy price and energy consumption. The purpose of this paper is to investigate the scenario effect of per capita income and regional differences in urbanization development on the relationship between electricity sales price and urban household electricity consumption. To this direction, based on the regional characteristics of economic development in China, with the residents' disposable income and the urbanization level as the conversion variables and the electricity sales price as the core explanatory variable, the panel smooth transition regression (PSTR) model of electricity sales price and urban household electricity consumption from the perspectives of income level and urbanization has been constructed in this paper. The empirical results show the following: (1) Under the consideration of regional difference of residents' income level, with the increase of residents' disposable income level, there is a significant negative correlation between electricity sales price and urban household electricity consumption in the whole country, the eastern region, and the central region, while such correlation is significantly positive in the western region. (2) Under the consideration of the difference of urbanization development level, the national regional electricity sales price and the urbanization level are positively related to the urban household electricity consumption, and the urbanization level in the western region plays the biggest role in promoting the urban household electricity consumption, followed by the eastern region and then the central region which plays the smallest role. This paper discusses the effect of electricity sales price on urban household electricity consumption from the perspective of regional difference in income and urbanization, which provides the decision-making basis and empirical support for developing regional electricity price policy and household energy consumption policy. [ABSTRACT FROM AUTHOR]

Due to the inherent uncertainty and the mismatch between renewable energy output and load demand, renewable energy-based system optimization is increasingly important in China. Therefore, electricity demand response (EDR) is critical to the stability and efficiency of an integrated renewable energy system (IRES). The customers' demand response under the time-of-use (TOU) mechanism is the key to the entire process. However, how to optimize integrated renewable energy system based on accurately identifying the electricity demand response participation of customers is one of the main issues at this stage. To solve the problem, this paper presents a novel approach for integrated renewable energy system optimization considering electricity demand response management and multistage energy storage systems from the perspective of Fujian Province, China. To enable large-scale renewable energy integration, the original output is processed using the Hampel identifier (HI)-Savitzky-Golay filter (SGF) technology. A machine learning (ML)-based identification model is developed for EDR to determine whether the customers are participating or not. Driven by the time-of-use mechanism, customers can make dynamic adjustments based on the Logistics function. After that, an integrated renewable energy system model optimization strategy is proposed based on satisfying customers' electricity demand response. Throughout the optimization process, the multistage energy storage system plays a vital role in the residual fluctuation absorption for renewable energy filtering, the dynamic adjustment process of the demand response, and the integrated renewable energy system optimization. Through extensive case studies and discussions, it is demonstrated that random forest (RF) and support vector machine (SVM) reach 80 % identification accuracy with a 20 % testing ratio. According to the optimization approach proposed in this paper, the minimum operation cost can be calculated to be 9.336*106 CNY. The methodology presented in this paper establishes a special relationship between electricity demand response identification and dynamic adjustments, and provides an optimization model for Fujian Province, China, which is a reliable reference for future work. [ABSTRACT FROM AUTHOR]

Electricity cross-subsidy policies have long been criticized for they involve subsidizing households by charging above-cost prices in industrial sectors. This paper examines the environmental and economic impacts of phasing out cross-subsidy in electricity prices in the context of China's Carbon Peaking and Neutrality goals by exploiting a large panel data from 2012 to 2020. We first adopt a price-gap approach to calculate the scale of negative cross-subsidy in China and different sectors. Next, we estimate the electricity demand price elasticity of high-, medium-, and low-emission industries to evaluate how the cross-subsidy affects electricity consumption, carbon emissions, and the output value. Finally, with the 2020 scenario as our baseline, we simulate three distinct policy change Schemes and evaluate the environmental and economic effects of phasing out electricity cross-subsidy in China. We find that if policy makers do not consider the emission levels of different industries when implementing cross-subsidy removal, great environmental harm will be caused. We also find that the potential burden on households of electricity cross-subsidy removal is likely to be relatively small at all implementation stages. This study provides timely policy implications for the ongoing electricity system reform in China. • This paper examines the environmental and economic impacts of phasing out cross-subsidy in electricity prices in the context of China's Carbon Peaking and Neutrality goals. • We find that if policymakers do not consider the emission levels of different industries in cross-subsidy removal, it will lead to large carbon emissions but small output in average. • If the cross-subsidy is fully borne by high-emission industries, the optimal Scheme is to reduce the cross-subsidy by approximately 50%–70% to obtain more economic output and less carbon emissions. • If high-emission bear twice as much cross-subsidy as the medium-emission industries, the carbon reduction and economic output will be maximised at 10%–30% of cross-subsidy removal. • We also find that the potential burden on households is relatively small at all implementation stages. [ABSTRACT FROM AUTHOR]

The impact of average wages on electricity consumption among urban residents in China has generated many fascinating debates for scholarly research, but only a few studies have considered the spatial spillover effect of average wages on residential electricity consumption. With the use of city-level panel data from 278 Chinese cities spanning 2005 to 2016, this preliminary study explores the impacts of the average wage on residential electricity consumption. Specifically, based on the spatial Durbin model with fixed effects, three different spatial weight matrices (the economic distance, the inverse distance, and the four nearest neighbours) are utilised to check the robustness of the results under different standards. The results show that the residential electricity consumption of each city increased during the observation period, presenting obvious spatial correlations. Secondly, the average wage of residents had a positive spatial spillover effect, which promoted the residential electricity consumption of both local and surrounding cities. Thirdly, the population density, electricity intensity, educational level of urban residents, and per capita household liquefied petroleum gas consumption in urban areas are key factors influencing residential electricity consumption. Therefore, improving the educational level of urban residents and reducing the electricity intensity can help reduce electricity consumption by residents in China. This paper also presents policy recommendations. [ABSTRACT FROM AUTHOR]

"Carbon peak and neutrality" are an important strategic decision to promote the transformation of China's energy economy and build a community with a shared future for mankind. China is a big energy consumer, and the whole society is facing huge challenges under the goal of carbon peak and neutrality. The realization of the goal of carbon peak and neutrality requires the guidance of correct theoretical methods and scientific deployment. This paper mainly studies the energy consumption scheduling, demand response management, and energy trading problems of microgrid. In view of the shortcomings of the existing energy optimization scheduling methods in the microgrid, a variety of energy resources such as electric energy, natural gas, heat energy, and cold energy are considered into the microgrid model. Based on the noncooperative game and Stackelberg game, a new energy optimal dispatch model is constructed with a variety of game methods such as two-layer game. Maximize the personal benefits of the microgrid while meeting the reliable operation of the system and the electricity demand of users. The three-stage noncooperative game problem is solved based on the reverse bootstrap method, and the closed expression of the optimal strategy in each stage is obtained. The power generation forecasting technology based on big data is studied, and a power forecasting method is proposed, which can effectively guide the energy consumption of the microgrid. The simulation results show the effectiveness of the proposed renewable energy management model based on big data, which verifies that the accurate wind power prediction results are conducive to better theoretical analysis of energy management. [ABSTRACT FROM AUTHOR]

• The operational strategy and pricing mechanism applicable to microgrids and users. • Most microgrids tend to be connected to the grid and use real-time pricing mode. • The internal factors affecting the game: initial price and generation proportion. • The external factor affecting the game: tariff of the external grid. As an effective supplement to the power grid, microgrid systems can make full use of renewable energy and solve the problem of grid connection and absorption of renewable energy. However, microgrid faces the challenges of benefit fluctuation caused by fixed pricing mode with unstable renewable energy supply and changing user's demand. A well-designed pricing mode is necessary while yet to be fully discussed. To solve this problem, this paper introduces flexible operational strategies and pricing mechanism due to the regional differences in resource endowment, microgrid technology mix, user demand and electricity price of external grid. The evolutionary game method is used to explore the operational strategy and pricing mechanism applicable to microgrids with different characteristics and users with different demands, so as to provide valuable reference for pricing standardization of microgrids in China. Based on the theoretical model, we deduce the equilibrium strategy of microgrid facing stable users and flexible users, and discusses the operational strategy and pricing mechanism of microgrid facing different user demands by using numerical simulation method. The results indicate that: (1) Considering the technical characteristics and economic performance, most of the microgrids tend to be connected to the grid and use real-time pricing mode. (2) The initial price between the microgrid and the user, the proportion of independent power generation when the microgrid is connected to the grid, and the tariff of the external grid are the key factors affecting the equilibrium point of the game between the microgrid and the users. [ABSTRACT FROM AUTHOR]

China has been introducing reforms in the electricity market. Since 2016, electricity retailers are allowed to operate in the market once monopolized by traditional power companies. This regulatory environment demands new types of purchase contracts to remunerate the energy agents and deliver better prices for consumers fairly and sustainably. This paper proposes two new electricity purchase contracts with risk sharing mechanism and studies on wind power supplier's optimal capacity investment problem under supply and demand uncertainty. Firstly, a basic model for fixed demand contract is developed, and the optimal capacity is obtained. Secondly, the fixed commitment order contract and the forecast order contract with risk sharing mechanism are proposed to manage the fluctuation of wind power supply randomness. With backward induction, the optimal investment capacity of wind power supplier is solved, and then the fixed order quantity or forecast demand quantity of the electricity retailer can be obtained. Our results show that the fixed order quantity may have a positive influence on the wind power supplier's capacity investment decision. While, the forecast order quantity may have a negative influence on the wind power supplier's capacity investment decision. In addition, wind density has a strong influence on wind power capacity investment, and it has a positive impact on wind power supplier's profit, but it does not always have a positive impact on electricity retailer's profit. At last, based on numerical analysis, we examine how the wind density distribution coefficient change affect members' profit in the electricity supply chain. We also find that even with wind curtailment punishment, the forecast order contract can make a Pareto improvement on the whole electricity supply chain. [Display omitted] • Risk sharing mechanism is introduced to the fixed commitment order contract and the forecast order contract in the electricity supply chain. • Increasing the forecast order quantity may reduce the capacity investment. • The forecast order contract can improve the performance of the whole electricity supply chain. [ABSTRACT FROM AUTHOR]

The average electricity consumption per capita in China still has significant room for growth. Under the current rigid residential electricity pricing, it is very important to study how factors other than price can promote electricity conservation among residents. This paper uses micro-survey data of 2018 and 2020 to analyze energy efficiency and the rebound effect through a stochastic energy demand frontier model, as well as to examine the role of news propaganda in this context. The research conclusions are as follows: (1) News propaganda promoting electricity saving can weaken the rebound effect of electricity use, which can lead to better electricity savings. (2) The energy efficiency of residential electricity has improved significantly compared to previous years, and the rebound effect has decreased. (3) Electricity prices have a negative impact on electricity consumption, while income, household size, home area, and extreme temperatures have a positive impact. Based on these conclusions, we propose policy recommendations to address the current inflexibility in residential electricity pricing and to improve the efficiency of electricity use among residents. • News propaganda promoting electricity saving can weaken the rebound effect. • The energy efficiency of residential electricity has improved. • The rebound effect of residential electricity has decreased. • News propaganda can be a complement to the inflexibility of pricing policy. [ABSTRACT FROM AUTHOR]

The ability to accurately and reliably predict annual electricity demand is essential in modern society for effective planning, economic development, and to ensure the sustainability of the electricity supply. Considering the correlation between annual electricity demand and economic development, as well as annual electricity demand under low-carbon-economy targets, this paper proposes an improved quantum clustering algorithm (particle swarm optimization–weighted distance quantum clustering, PSO-WDQC) as a power demand forecasting model. This method can not only improve the accuracy of predictions but also accurately evaluate the economic development of a region. To demonstrate this ability, the paper applies the proposed method to low-dimensional Iris data as well as high-dimensional Wine data in order to verify the effectiveness of the method. Then, the method is combined with ridge regression to predict the demand for electricity under the low-carbon-economy target of China. The experimental results show that the method can accurately predict annual power demand with a relative error of 0.1674%. Moreover, the model accurately reflects that the Chinese economy has entered a new normal state since 2012, meaning that the economic growth rate has changed from high-speed to medium-high-speed. [ABSTRACT FROM AUTHOR]

In the context of the new normal, the global economy is entering a deep adjustment period, and the driving forces of development are also constantly changing. As a result, China's economy has also entered a "new normal" phase in which it is growing in a manageable and relatively balanced manner. In addition, the new normal characteristics of the power industry's development in China are also very significant, and they affect the adaptability of traditional power forecasting methods. By analyzing the new characteristics of China's economic development and the changing electricity demand in recent years, this paper quantitatively studied the effect of the national economy on the consumption of electricity. Meanwhile, a modified logistic model based on the change of the gross domestic product (GDP) growth rate is constructed to make a reasonable prediction of the future power consumption in China. Subsequently, by calculating the elasticity of electricity consumption in the future, it is found that the coefficient decreases each year, which indicates that electricity consumption in China is following a new trend. Based on the research results, this paper proposes rational suggestions for China's power development, and they are expected to provide references for the power planning and power industry layout in China. [ABSTRACT FROM AUTHOR]

Recently, more and more global attention has been paid to data centers' energy consumption and climate impact. Serving as the backbone of the digital world, the energy and carbon-intensive data centers have to find a green expansion way to fulfill their obligations in building a carbon-neutral society. Therefore, this paper proposes a net-zero carbon operation oriented data center expansion scheme for the central government to formulate incentive policies. The economic expansion model minimizes newly established data centers' investment and electricity consumption costs. Geographical location, on-site renewable utilization, and green certificate purchase are integrated and considered to achieve net-zero carbon operation by reducing total electricity consumption, increasing green power consumption, and offsetting inevitable carbon emissions. A case study in China is conducted, and results under different geographical location scenarios, power supply mix scenarios, and green certificates market scenarios are analyzed and compared. It is found that the zero-carbon operation of new data centers mainly relies on clean main-grid power, but a more decentralized layout and a provincial green certificate market can increase the contribution of on-site renewable energy. Lastly, four detailed policy implications for stimulating China's data center expansion are discussed. • A data center expansion scheme considering net-zero carbon operation is proposed. • Expansion model minimizes newly established data centers' expansion costs is built. • Three measures are integrated to achieve data centers' net-zero carbon operation. • Case study in China is conducted and core findings and key implications proposed. [ABSTRACT FROM AUTHOR]

In recent years, the issues related to carbon emissions and environment have attracted extensive attentions. Considering four scenarios (the energy conversion, energy capital savings and loans, energy exports and cement production carbon emissions), this paper adopts the energy consumption method and input-output method to analyze China's carbon emissions structure on the supply-side and demand-side of energy, and finally provides policy recommendations for China's structural emission reduction. The results show that, if the four influencing factors were not considered, the measurement of carbon emissions from the final demand was 44.91% higher than the baseline scenario, 12.36% lower than the baseline scenario from intermediate demand, and 10.23% lower than the baseline scenario from the total. For China's carbon emissions structure on the supply-side of energy, the carbon emissions from high-carbon energy, represented by raw coal, accounted for 66.805% of the total energy-related carbon emissions, while the carbon emissions from low-carbon energy, represented by natural gas, only accounted for 2.485%. For China's carbon emissions structure on the demand-side of energy, the carbon emissions from intermediate demand (enterprise production) accounted for more than 95% of total energy-related carbon emissions, while the carbon emissions from final demand (residents and government use) accounted for less than 5%. For each specific industry in intermediate demand for energy, the heavy industry, electric power, fossil energy, and chemical industry have high carbon emissions and low carbon emissions efficiency. However, the agriculture, construction, light industry, and service are the opposite. Finally, we provide policy recommendations for improving the accuracy of carbon emissions measurement and carbon emissions efficiency. [ABSTRACT FROM AUTHOR]

This paper estimates the short and long-run impact of foreign direct investment, electricity consumption, and real GDP on ecological footprints in the context of environmental Kuznets and Pollution haven hypothesis for China, India, and Pakistan over 1970–2016. Panel and time series models have been adopted in this research. Results of the cointegration test revealed the long-run association among the considered variables. Furthermore, Fully-Modified and Dynamic Ordinary Least Square validated the pollution haven and environmental Kuznets hypothesis for the study area. On the other hand, the linkages between income and ecological footprints have identified U-shaped EKC in the case of China and India. The empirical results of the Dumitrescu and Hurlin panel causality test indicated a unidirectional causality from income to ecological footprints. In contrast, bidirectional causality found between ecological footprints and foreign direct investment and between ecological footprints and electricity consumption. [ABSTRACT FROM AUTHOR]

The energy rebound effect is a potential threat to energy-saving targets based on energy efficiency improvements. This paper employs a stochastic energy demand frontier model to analyze the energy demand and rebound effect in China's urban residential sector. Using a panel data set of 30 Chinese provincial-level regions over the period 2001-2014, for the first time, we investigate the degrees and determinants of China's urban residential energy demand and energy rebound effect. The results show that residents' income level, energy price, temperature deviation, population scale, household size, and district heating system are significant influencing factors of residential energy consumption. Regarding the energy rebound effect, we find that energy price is negatively correlated with the rebound effect, and an inverted U-shaped relationship between residents' income level and rebound-effect size exists. The magnitude of the rebound effect varies across regions, with an average of 65.4%. The main policy implication generated by this study is that it should be in urgent need of energy pricing reform to mitigate the rebound effect in China. [ABSTRACT FROM AUTHOR]

Accurately forecasting China's total electricity consumption is of great significance for the government in formulating sustainable economic development policies, especially, China as the largest total electricity consumption country in the world. The calculation method of the background value of the GM(1, 1) model is an important factor of unstable model performance. In this paper, an extrapolation method with variable weights was used for calculating the background value to eliminate the influence of the extreme values on the performance of the GM(1, 1) model, and the novel extrapolation-based grey prediction model called NEGM(1, 1) was proposed and optimized. The NEGM(1, 1) model was then used to simulate the total electricity consumption in China and found to outperform other grey models. Finally, the total electricity consumption of China from 2018 to 2025 was forecasted. The results show that China's total electricity consumption will be expected to increase slightly, but the total is still very large. For this, some corresponding recommendations to ensure the effective supply of electricity in China are suggested. [ABSTRACT FROM AUTHOR]

At this critical junction of industrialization, China is facing an increasingly prominent imbalance between energy supply and demand. It is of great social significance to study the energy-saving of buildings that consume a huge amount of energy. For most heating systems, energy waste mainly arises from inefficient operations of boilers, low efficiency of pipe transmission, and poor quality of heat supply. This paper researches and evaluates the energy-saving reconstruction of intelligent community heating (ICH) system based on the Internet of things (IoT). Firstly, the energy-saving reconstruction of the ICH system was analyzed, and a hydraulic regime model was derived from the simplified model of the water supply and return pipe networks. Then, the system pipe network was investigated in three aspects, namely, node flow balance, pipeline pressure drop balance, and loop pressure drop balance. On this basis, the authors presented the balance equation and finalized the hydraulic regime model of the system. After that, the energy consumption of the system was evaluated before and after the reconstruction, and the energy-saving benefit was predicted. The effectiveness of the proposed method was proved through experiments. [ABSTRACT FROM AUTHOR]

The prediction of oil demand is an important issue related to national energy security and economic development. With the COVID-19 outbreak, the international oil price fluctuates sharply, and oil consumption growth slows down. Therefore, accurate prediction of oil demand plays an important practical and theoretical role. In this paper, in accordance with the Chinese state policy stimulation of domestic demand for energy resources, we have selected 15 major factors and analyzed their influence on the domestic oil demand from the perspective of comprehensive tourism analysis. Based on the data analysis of oil consumption from 2000 to 2018, four neutral network methods are used to predict the influence of selected factors on oil consumption demand of China. The experimental results show that the best correlation is obtained between domestic tourism revenue and total tourism expenditure factors and oil demand, and the Layer Recurrent Neutral Network method has high prediction accuracy, stronger stability, and the best performance. [ABSTRACT FROM AUTHOR]

Despite the considerable contribution of hydropower for meeting electricity demand and meanwhile achieving free carbon emission, the rationality of hydropower exploitation needs to be reassessed in the context of hydropower curtailment being severe in some hydro-dominant areas. In this paper, a two-stage fuzzy-stochastic chance-constrained interval (TFCI) regional energy system optimization model under multiple complexities is developed for systematically assessing the rationality of existing hydropower exploitation plan, and reflecting the tradeoff between system cost and reliability for Sichuan territory where the exploitable hydropower resources rank first in China. Results showed that the degree of hydropower overcapacity would become far more severe under the real existing energy structure planning. Instead of continuously boosting hydropower exploitation, proper limitation on hydropower installed capacity expansion to make other renewable energy obtain deserved promotion can effectively reduce system cost and improve the utilization efficiency of all the renewable technologies. The highest utilization efficiency of hydropower, wind power and solar power could achieve [90%, 100%], [100%, 100%], and [100%, 100%], respectively. The obtained results also indicated that greater uncertainty about system cost may have to be faced if policymakers aim to lower risk and enhance system reliability. Suggestions are provided for eliminating the irrationality of hydropower in Sichuan and other similar hydro-dominant regions based on above mentioned conclusions. • Developing a TFCI model for the rationality of renewable energy development. • Evaluating Sichuan's hydropower exploitation from a comprehensive view. • Investigating far more severe hydropower curtailment under existing energy policy. • Suggesting proper limitation on hydropower exploitation. [ABSTRACT FROM AUTHOR]

In today's China, the reverse distribution of electricity resources and electricity demand makes large-scale and long-distance transmission of electricity inevitable, resulting in the problem of electricity allocation optimization. To address this problem in the region covered by the China Southern Grid (CSG), the paper establishes an optimized model for electricity generation and interprovincial trading strategies in southern China. Based on data from 2015, this paper simultaneously optimizes electricity generation and interprovincial trading strategies in each of the five provinces. The result shows that interprovincial trading in the CSG-served area can yield significant energy, economic, environmental and interconnection benefits. It can reduce the power supply cost and environmental pressure in Guangdong province, help make Yunnan province a hydropower base in China and a thermal power base in southern China, facilitate the completion of the "West-to-East Power Transmission" project, and partly resolve the issues raised in each province's 13th Five-Year Plan as well as the problem of optimizing electricity allocation in the region covered by the CSG. • We propose an annual total optimized model for electricity generation and interprovincial trading in Southern China. • The model optimizes the electricity trading and transmission routes across regions. • The model considers the generation cost and penalty cost of emitting CO 2 , SO 2 and NO x. • Corresponding policies and proposals are put forward for power grid and five regions in South China. [ABSTRACT FROM AUTHOR]

The credible sources of fossil energy efficiently are a vital cause of economic growth and considerable influence on adequate security. Whereas radiant energy positively enhances or ostensibly promotes socio-economic stability and the controlled environment. The fossil energy sources supply has become progressively stern in China and reconnoitering the beta decoupling relationships between CO2 emissions, GDP, energy consumption, electricity consumption, value-added industries, and population. The results will be favorable for illustrative the security of the valuable resources. This study adopts the extended stochastic model (STIRPAT) with Beta Decoupling Techniques (BDT). This modern technique merely employs the decoupling situation by the alpha and beta effects from 1989 to 2018 and calculates the % change in CO2 emissions by GDP growth and energy consumption. The estimated results represent negative and economic growth depends on coal and natural gas. First, CO2 emissions annually increasing cause of rapid growth, energy consumption, and electricity production, and the structural contradiction of energy remained static. Second, the Value-added industries estimated that CO2 emissions reduce by primary industries. Third, the decoupling states of CO2 emissions and population show an inverse relationship. This paper tentatively suggests China is sustainable, naturally strengthens energy output, transmutes the energy consumption structure, and advances development policies under environmental circumstances. [ABSTRACT FROM AUTHOR]

The extensive use of information and communication technologies (ICT) has facilitated people's lives and promoted the improvement of productivity. In the meantime, ICT has a profound effect on the efficiency of electricity utilization and the demand for electricity. The existing studies consider the direct effect of ICT on electricity consumption (EC) but neglect the spillover effect of ICT on EC and their action channels. Under the assumption of cross-section dependence, this paper introduces spatial modeling techniques to confirm the positive direct effect and negative spillover effect of ICT on EC. The positive direct effects and negative spillover effects of information technology on EC are similar to those of the communication technology, and the absolute value of the former is also greater than the latter. Additionally, the results of meditation effect modeling also confirm that there exists an incomplete mediating effect in the process of the ICT affecting EC through the channels of economic growth and the adjustment of the industrial structure. This study provides freshly empirical evidence for people to better understand the role of ICT in EC and opens fresh insights for policymakers to make corresponding policy adjustments. [ABSTRACT FROM AUTHOR]

• A user persona oriented toward the enterprise is constructed to characterize power consumption in the context of COVID-19. • Abstract labels are extracted from enterprise power profiles based on industrial, regional, periodic power profiles. • Enterprises with similar electricity consumption characteristics or the same label are grouped to allow recommendations for customized electricity products, policy, and services. The COVID-19 epidemic has led to devastating consequences worldwide due to strict social distancing measures, travel bans, city lockdowns, and other activity restrictions. Numerous lessons have been accumulated by various industries or sectors in the process of coping with COVID-19. In our paper, the actual electricity consumption of 1.145 million enterprises in a province of China is investigated in the normal period, the breakout period, and the recovery period. An artificial intelligence based enterprise profiling model is established to characterize power consumption profile geographically, temporally, and industrially. The recovery rate of enterprise power consumption is proposed as a key indicator representing the status of production resumption. Under lockdown measures, enterprise in different regions or sectors exhibits diversified responses in power consumption, the unsupervised learning model and the correlated coefficient extract the abstract characteristics and labels from various enterprise power profile. Ultimately, enterprises with similar electricity consumption characteristics are grouped and labeled to provide customized power services. Accurate enterprise profiling can effectively aid in facing with the challenges of insufficient orders, tight capital chains, and rising costs caused by the epidemic. [ABSTRACT FROM AUTHOR]

• Carbon emissions from energy consumption of 282 prefecture-level cities in China were calculated. • A new method for calculating carbon emission factors for electricity is proposed. • Extending the Atkinson Index to the application of Carbon Fairness. • Measured the inequality and fairness indicators of carbon emissions from energy consumption at the prefecture-level city level in China. This paper focuses on the conceptualization and quantification of carbon emission inequality and fairness from energy consumption. In order to more accurately measure the carbon emissions generated by electricity consumption in each city, we firstly propose a new method for calculating the carbon emission factor for electricity, and calculates the carbon emissions from energy consumption in 282 prefecture-level cities in China from 2003 to 2019. Through the application of the Theil-Kaya decomposition mechanism and the improved Atkinson index, we conducted an in-depth study of the carbon emission inequality and fairness indicators from energy consumption. The results show that (1) Although China's overall inequality has declined, it remains significant in the western region, with its inequality indicator fluctuating from 0.548 in 2003 to 0.683 in 2019.(2) The decomposition of the drivers of inequality indicators shows that the contribution of GDP per capita to inequality is increasingly being replaced by energy intensity and the energy carbon emission factor, with energy intensity emerging as the main driver, contributing more than 40% to inequality in 2019, both overall and in the three regions.(3) The results for the fairness indicator, on the other hand, show a certain degree of divergence. Further Dynamic Time Warping fitting analysis shows that regardless of how the public's sensitivity to carbon emissions varies, the change trends of the fairness and inequality indicators for carbon emissions in the eastern region show a high degree of convergence, even in the two most extreme cases. The fit is poorest in the western region, but improves significantly if public's sensitivity to carbon emissions increases. (4)To sum up, some policies that are consistent with the context of the times, such as the East-West regional counterparts and the construction of a new type of power system, are worthy of priority consideration by the Government. [ABSTRACT FROM AUTHOR]