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The electric power sector is the primary contributor to carbon emissions in China. Considering the context of dual carbon goals, this paper examines carbon emissions within China's electricity sector. The research utilizes the LMDI approach for methodological rigor. The results show that the cumulative contribution of economies scale, power consumption factors and energy structure are 114.91%, 85.17% and 0.94%, which contribute to the increase of carbon emissions, the cumulative contribution of power generation efficiency and ratio of power dissipation to generation factor are -19.15% and -0.01%, which promotes the carbon reduction. The decomposition analysis highlights the significant influence of economic scale on carbon emissions in the electricity industry, among the seven factors investigated. Meanwhile, STIRPAT model, Logistic model and GM(1,1) model are used to predict carbon emissions, the average relative error between actual carbon emissions and the predicted values are 0.23%, 8.72% and 7.05%, which indicates that STIRPAT model is more suitable for medium- to long-term predictions. Based on these findings, the paper proposes practical suggestions to reduce carbon emissions and achieve the dual carbon goals of the power industry. [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]

China Power Grid is actively building a new energy-based ultra-high voltage grid system. Therefore, the researches on fire safety of power grid are of great importance. This paper firstly investigates the fire accident characteristics in the substation system. With the focuses on the transformer oil fires, the early detection and early warning, modification, fire monitoring and extinguishing systems of transformer oil are reviewed. It is found that the researches on flowing and jet fires should be reinforced while modified oil and pyrolysis gas detection can be applied for reducing the fire probability. Secondly, the fire risks and characteristics of cables are summarized for the transmission and distribution lines, and the methods of early warning, fire prevention and rescue are analyzed. For the wildfire threatening the transmission and distribution cables, the characteristics of wildfire and corresponding firefighting methods are reviewed. The water mist and liquid nitrogen are identified as the most effective ways of extinguishing cable fires and advanced fire detection and long-range drone fire extinguishing technologies should be improved for wildfires. Finally, the thermal runaway mechanism and fire characteristics of LIB in the energy storage system are summarized, and the difficulties in fire extinguishing are discussed. The application of the existing fire detection system in the field of LIB is proposed, and the early warning indicators for intelligent fire protection are introduced. In the future, the intelligent fire protection systems will improve the safety of energy storage systems, and efficient test platforms and reliable test standards will continue to be demanded to reduce the likelihood of thermal runaway and fire severity. [ABSTRACT FROM AUTHOR]

Electric charge service and management is an important part of electric power work. The effective recovery of the electric charge relates to the smooth development of daily work and continuous improvement of the operation and management of power supply enterprises. With the large-scale implementation of the card prepayment system, the problem of electricity customers defaulting on electricity charges has been solved to a large extent, but some large electricity users still fail to pay electricity charges on time. Therefore, under the current situation of power grid development, it is still necessary to strengthen the service and management of electricity charges to promote efficient recovery of electricity charges. Speech recognition technology has increasingly become the focus of research institutions at home and abroad. People are committed to enabling machines to understand human speech instructions and hope to control the machine through speech. The research and development of speech recognition will greatly facilitate people's lives shortly. The development of 5G technology and the proposal of 6G technology make the interconnection of all things not only a hope but also a reality. To realize the interconnection of all things, one of the key technical breakthroughs is the development of a new human–computer interaction sensing system. Under the guidance of relevant theories and methods, this paper systematically analyzes the user structure, electricity charge recovery management and service system, existing problems and causes in South China, and clarifies the necessity of design and application of electricity charge service system in South China power supply companies. The experimental data and empirical analysis results show that the optimized Bert fusion model can provide more digital support for the power supply companies in South China in terms of electricity charge recovery efficiency, management level system improvement, and electricity charge service. [ABSTRACT FROM AUTHOR]

As the technical demand-supply text information increases rapidly in electric power technology transfer, traditional matching methods could no longer meet the need. To solve this problem, this paper proposed a new technical demand-supply matching method based on electric power technical field feature. First, the improved TF-IDF algorithm was used to extract technical field feature, then scientific and technological achievements with higher relevancy were retrieved based on technical field feature to build a background library of electric power technological achievements, and the matching degree of electric power technological achievements based on word vector was used to verify the matching efficiency of this method. We adopted the China smart grid technical transaction service platform and the text message of electric power technical demand and technological achievements on Keyi net for application, and the results indicate this method has raised the matching efficiency by 23.4% compared to traditional methods. In this paper, a new idea of taking technical field feature as the demand-supply matching basis of electric power technology transfer platform was proposed, which opens a new perspective for the research of demand-supply matching efficiency on electric power technology transfer platform, helps to promote the matching efficiency of technical demand and technological achievements, and further raises the success rate of electric power technology transfer. [ABSTRACT FROM AUTHOR]

MTI programs in industry-specific universities have drawn little scholarly attention due to their short history and small scale, but they have irreplaceable advantages in training translators for specific industries because of their time-honored and well-established industry-specific disciplines and programs. For this reason, this paper focuses on MTI programs granted in 2014 in 16 industry-specific universities, which cover a variety of industries, including civil aviation, medicine, forestry, agriculture, post and telecommunications, architecture, ocean, petrol, electric power and water resources. All these MTI programs were subjected to the assessments by China National Committee for Translation and Interpreting Education in 2018. By inspecting their assessment data concerning teachers, students, training objectives, programs and characteristic courses, as well as horizontal communications with other disciplines of the respective universities and with language services companies, and by interviewing nine teachers from the universities concerned, the authors discuss the merits and demerits of these MTI programs. The paper concludes by offering some suggestions to translator training in China and beyond. [ABSTRACT FROM AUTHOR]

China has pledged that it will strive to achieve peak carbon emission by 2030 and realize carbon neutrality by 2060, which has spurred renewed interest in hydrogen for widespread decarbonization of the economy. Hydrogen energy is an important secondary clean energy with the advantage of high density, high calorific value, rich reserves, extensive sources and high conversion efficiency that can be widely used in power generation, transportation, fuel and other fields. In recent years, with the guidance of policies and the progress of technology, China's hydrogen energy industry has developed rapidly. About 42% of China's carbon emissions comes from the power system and Shenzhen has the largest urban power grid in China. Bringing the utilization of hydrogen energy into Shenzhen's power system is an important method to achieve industry transformation, achieve the "double carbon" goal and promote sustainable development. This paper outlines the domestic and international development status of hydrogen energy, introduces the characteristics of Shenzhen new power system, the industrial utilization of hydrogen energy and the challenges of further integrating hydrogen energy into Shenzhen new power system and, finally, suggests on the integration of hydrogen energy into Shenzhen new power system in different dimensions. [ABSTRACT FROM AUTHOR]

Building a new type of power system is an important guarantee to support China's "dual carbon" goal. Due to the inseparable relationship between industrial and agricultural production and electric energy utilization, there must be interdisciplinary integration to achieve the goal of "dual carbon". The disciplines of horticulture and electric power are taken as examples in this paper to analyze the feasibility of carbon emission reduction through coordinating agricultural photovoltaic (PV) greenhouse and electric vehicle (EV) energy storage. Firstly, the mechanism of carbon emission difference caused by electric energy supplementing during EV charging is analyzed. Secondly, in the context of the contradiction between the reduction of battery life caused by discharging (increasing carbon emission) and the increase in PV output consumption by orderly charging and discharging (reducing carbon emission), an optimization model for the synergistic operation of EV clusters and greenhouse PVs (with the objective of minimizing carbon emissions) is proposed. Finally, the effectiveness of the proposed model is verified through simulation cases. The energy storage characteristics of EVs is capable of realizing the transfer of PV power generation in the time dimension, and the coordinated operation of greenhouse PVs and EVs' charging and discharging can effectively reduce carbon emission during the EV operation period. In a typical summer scenario of PV output, the carbon emission of EVs in V2G (vehicle to grid) mode was reduced by 69.13% compared to disorderly charging. It is shown that the adequacy of PV generation and the orderly dispatching of the charging and discharging of EVs are the key factors in reducing carbon emission throughout the life cycle of EVs. [ABSTRACT FROM AUTHOR]

In response to global climate change, China has committed to peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, commonly known as the "30–60 Dual Carbon". Under the background of "30–60 Dual Carbon", this article takes the electric power industry, which is the main industry contributing to China's carbon emission, as the research object, explores the time and peak value of the carbon peak of the electric power industry, and analyzes whether carbon neutrality can be realized under the peak method, so as to get the carbon neutrality path of the electric power industry and serve as the theoretical basis for the formulation of relevant policies. The Environmental Kuznets Curve inspection and the relationship analysis are carried out, then the system dynamics model is constructed, the carbon emissions from 2020 to 2040 are simulated, and the peak time is predicted. Three different scenarios are set to explore the path of electricity carbon neutralization under the premise of a fixed peak. It is shown that Gross Domestic Product per capita index factors have the largest positive contribution, and thermal power share index factors have the largest negative contribution to electricity carbon emissions. Based on the current efforts of the new policy, carbon emissions can achieve the peak carbon emissions' target before 2030, and it is expected to peak in 2029, with a peak range of about 4.95 billion tons. After the power industry peaks in 2029, i.e., Scenario 3, from coal 44%, gas 9% (2029) to coal 15%, gas 7% (2060), where the CCUS technology is widely used, this scenario can achieve carbon neutrality in electricity by 2060. Adjusting the power supply structure, strictly controlling the proportion of thermal power, optimizing the industrial structure, and popularization of carbon capture, utilization, and storage technology will all contribute to the "dual carbon" target of the power sector. [ABSTRACT FROM AUTHOR]

This paper searches and combs the achievements of grey system theory in the past nearly 40 years based on databases of China national knowledge internet (CNKI) and ISI Web of Science. More than 227 thousands literatures were found by inputting 10 phrases such as grey system‑grey number and sequence operator etc. in CNKI. After entering the 21st century, the number of grey system papers included in CNKI database is increasing rapidly. Since 2008, more than 10 thousands papers have been included per year, and more than 15 thousands papers have been included per year since 2014. The grey system method and model are widely used in natural science fields such as physics, chemistry, and biology, as well as engineering technology fields such as transportation, electric power and machinery, and a large number of valuable results have been achieved. From the large number of documents applying the grey system method and model, which are published by the project team undertaking important national science and technology plans and the double first‑class universities in China, it can be seen that the grey system theory plays an important role in promoting China’s scientific and technological progress, innovation and development, and high-level talent training. [ABSTRACT FROM AUTHOR]

Achieving China's carbon reduction targets for 2020 necessitates the consideration of integrated production chains (PCs) given that competition between sectors has been transformed into competition between PCs. This paper establishes an extended input-output (IO) model that calculates the cost multiplier transmission effect of emission reduction in low-carbon production chains (LCPCs). Empirical results show that in relation to PCs, the electricity power industry and urban residents are the key policy points in adjusting China's industrial structure. This paper suggests that policymakers consider the cost multiplier transmission effect imposed by upstream sectors to downstream industries. [ABSTRACT FROM AUTHOR]

The electric power sector is the largest contributor of CO2 emissions in China. With an increasing concern about environment problems, it is essential to identify key factors that affect CO2 emissions from China's electric power industry so as to help the fossil fuel-based country reduce carbon emissions. For this purpose, the two-phase Logarithmic Mean Division Index (LMDI) decomposition method is presented in this paper. Covering the whole industry chain including power generation, transmission, and consumption, the two-phase LMDI decomposition model is constructed. Then, the influencing aspects are decomposed into ten driving factors, namely, (1) fossil energy power generation structure, (2) fossil energy consumption coefficient, (3) thermal power proportion, (4) power generation and consumption ratio, (5) transmission and distribution loss, (6) industrial power consumption intensity, (7) industrial structure, (8) per capita gross domestic product (GDP), (9) total population, and (10) resident power consumption intensity. Based on data from China statistical yearbook, China energy statistics yearbook, and China power statistics yearbook (2005–2017 edition), the decomposition calculation results show that the power generation and consumption ratio, industrial structure, resident power consumption intensity, per capita GDP, population size, and transmission and distribution loss factors are positive driving factors with contributions of 1.2%, 2.47%, 1.5%, 94.29%, 5.43%, and 4.64%, respectively. However, the fossil energy power generation structure, fossil energy consumption coefficient, thermal power proportion, and industrial power consumption intensity are negative driving factors with contribution rates of −0.34%, −21.72%, −9.85%, and −8.44%, respectively. According to the main effect factors identified, some corresponding measures are proposed to reduce carbon emissions from China's power industry. [ABSTRACT FROM AUTHOR]

China is actively taking measures to guide the household energy‐saving consumption pattern because the total household indirect energy consumption and CO2 emissions have rapidly multiplied from 2002 to 2017 with an annual growth rate of 5.95%, and 5.56%, respectively. This paper calculates indirect energy consumption and CO2 emissions by four energy sources (raw coal, crude oil, natural gas, and electricity), analyzes the energy structure, and conducts a structural decomposition analysis in urban and rural areas in China from 2002 to 2017. The results reveal that the indirect energy consumption and CO2 emissions of urban households was 3.84 and 3.80 times, respectively, that of rural households in 2017 with most contributions from the 'Agriculture, Forestry, Animal Husbandry, and Fishery', 'Foods and Tobacco', and 'OTHERS' sectors. The energy consumption structure has changed. The percentage of coal consumption falls from 66.87% to 57.62%. Electricity consumption, ranked second, increased substantially from 10.77% to 22.90%. The key sectors of the energy mix have been found to help energy conservation. The high indirect energy consumption sectors with higher indirect energy intensity are the key sectors, especially the 'Processing of Petroleum, Coking and Processing of Nuclear Fuel' and 'Production and Supply of Electric Power and Heat Power'. The consumption structure effect is more obvious in urban areas than in rural areas. In the process of urbanization, its effect has little impact on urban indirect energy consumption. It implies that the focus is on optimizing consumption structure rather than reducing consumption scale. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

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Summary: As the installed capacity of wind power continues to increase, the problem of curtailed wind power is becoming serious in China, especially in the northern region during the winter heating season. To solve the problem of wind‐heat conflict during the heating period in the Three North area, an electric boiler with thermal storage (EBTS) is installed at the end of the grid where wind power is difficult to accommodate and using curtailed wind power to supply heat promotes local accommodation. In this paper, a multi‐objective optimization model of wind power accommodation based on the wind power–EBTS system for heating is established. The goals of maximizing wind power accommodation, minimizing the number of times EBTS must be adjusted, and minimizing operating costs are presented, and a bi‐level optimization scheme is designed. An improved multi‐objective particle swarm optimization algorithm is used to solve these functions, and an optimal compromise solution from the generated Pareto solution set is filtered using the fuzzy membership method. Based on actual data from a demonstration project in China's Jilin Province, the simulation results verify that this method can effectively reduce operating costs and improve wind power accommodation. [ABSTRACT FROM AUTHOR]

Existing studies have primarily focused on the resources and technologies related to renewable energy. However, it is essential to investigate the influence of economic, social, and governmental actions on the spatiotemporal changes in renewable energy. This paper utilizes spatial autocorrelation analysis, fixed effects modeling, and Geographical and Time Weighted Regression (GTWR) to analyze the phased characteristics and spatial differences of renewable energy electricity, based on time series and spatial layout data of renewable energy power by province and power source type in China. Additionally, it incorporates relevant economic, demographic, and investment data to identify the driving factors behind the spatiotemporal transformations of photovoltaic (PV) and wind power, as well as the main forces promoting sustainable growth. The study reveals the presence of spatial agglomeration in China's renewable energy power generation. Moreover, a positive correlation is observed between investments in environmental protection (EPI), state-owned electric power (SPI), scientific and technological advancements (STI), and renewable energy electricity. Technological progress has reduced the dependence of renewable energy on the natural environment, facilitating its promotion and widespread adoption. These findings provide empirical support and policy guidance for the sustainable and rapid growth of China's renewable energy industry. [ABSTRACT FROM AUTHOR]

Carbon emissions from the electricity industry (CEEI) account for a large proportion of China's total carbon emissions, and it is important to study the spatial correlation between CEEI and the influencing factors to promote cross-regional synergistic emission reduction and low-carbon development of the power system. In this paper, the quasi-input-output (QIO) model is applied to assess the transfer of carbon emissions generated by electricity trading based on the consideration of electricity carbon transfer, and the exploratory spatial data analysis (ESDA) method is applied to analyze the spatial correlation effect of carbon emissions from China's electric power sector from 2001 to 2020, analyzes its distribution pattern in both spatial and temporal dimensions, and applies the improved logarithmic mean Divisia index (LMDI) two-stage decomposition model to decompose the changes in CEEI into 11 influencing factors from the perspective of the whole industrial chain of power production, transmission, trade, and consumption. The research results show that (1) the spatial distribution of CEEI has obvious unevenness and aggregation characteristics, with high-high aggregation areas and hot spot aggregation areas generally concentrated in the North China Power Grid and the East China Power Grid, but the aggregation trend is gradually decreasing, while low-low aggregation areas and cold spot aggregation areas are concentrated in the Northwest China Power Grid and the Central China Power Grid, but the area is very limited. (2) The direction of carbon emission diffusion in China's electricity industry is gradually transitioning from southwest-northeast to northwest-southeast, and the east-west diffusion trend is stronger than the north-south diffusion trend and carbon emissions are gradually shifting to the northwest grid. (3) The total amount of electricity production is the most influential factor in the change of CEEI, driving the cumulative growth of CEEI by 4495.34 Mt, followed by GDP per capita and electricity consumption intensity. Coal consumption for power generation, the share of thermal power, and net electricity exports were the main factors inhibiting the increase in carbon emissions from the power sector, with cumulative contributions of −797.74 Mt, −619.99 Mt, and −47.76 Mt, respectively. [ABSTRACT FROM AUTHOR]

The KBQA (Knowledge-Based Question Answering) system is an essential part of the smart customer service system. KBQA is a type of QA (Question Answering) system based on KB (Knowledge Base). It aims to automatically answer natural language questions by retrieving structured data stored in the knowledge base. Generally, when a KBQA system receives the user's query, it first needs to recognize topic entities of the query, such as name, location, organization, etc. This process is the NER (Named Entity Recognition). In this paper, we use the Bidirectional Long Short-Term Memory-Conditional Random Field (Bi-LSTM-CRF) model and introduce the SoftLexicon method for a Chinese NER task. At the same time, according to the analysis of the characteristics of application scenario, we propose a fuzzy matching module based on the combination of multiple methods. This module can efficiently modify the error recognition results, which can further improve the performance of entity recognition. We combine the NER model and the fuzzy matching module into an NER system. To explore the availability of the system in some specific fields, such as a power grid field, we utilize the power grid-related original data collected by the Hebei Electric Power Company to improve our system according to the characteristics of data in the power grid field. We innovatively make the dataset and high-frequency word lexicon in the power grid field, which makes our proposed NER system perform better in recognizing entities in the field of power grid. We used the cross-validation method for validation. The experimental results show that the F1-score of the improved NER model on the power grid dataset reaches 92.43%. After processing the recognition results by using the fuzzy matching module, about 99% of the entities in the test set can be correctly recognized. It proves that the proposed NER system can achieve excellent performance in the application scenario of a power grid. The results of this work will also fill the gap in the research of intelligent customer-service-related technologies in the power grid field in China. [ABSTRACT FROM AUTHOR]

Governments actively encourage renewable energy use to deal with climate change and achieve carbon emission reduction targets. It is crucial to find out the driving factors that affect the utilization of renewable energy. Therefore, based on China's 2010–2016 input–output table, this paper uses the input–output model and structural decomposition analysis (SDA) to analyze the driving factors of renewable energy changes in the production end, household end, and the aggregate economy. The results show that the changes in the consumption structure (F) is the most crucial factor for renewable energy use, followed by technology progress (T) and final demand per capita (V). Sector SEHW (supply of electric power, heat power, and water) and MCRP (manufacture of coke and refined petroleum products) are the two vital sectors to achieve China's energy transition of the production level. However, as for households, the proportion of renewable energy has been declining. Hence, the government should promote renewable energy use and achieve the green transition in production and household levels. [ABSTRACT FROM AUTHOR]

Electricity has substantial effects on rescue and relief responses after strong earthquakes. Substations are essential components of electric power networks, and their seismic performance has recently attracted the attention of academia and industry, in particular, the recovery capacity from external events. This capacity is referred to as seismic resilience, which is defined as the ability of a system to resist, recover from, and adapt to an earthquake impact. It is quantified as the change in functionality over time. However, it is challenging to quantify the optimum recovery process dynamically due to interrelations between components. In this paper, a new probability-based method is proposed to assess the seismic resilience of complex systems, such as substations. A case study is performed on a typical 220 kV substation in China, and the seismic resilience of the system is investigated focussing on three important issues: (1) definition of system functionality; (2) a system model that considers the interrelations among the components; (3) recovery strategies to restore system functionality. The resilience index of the system with a functionality prioritisation recovery strategy is two to three times higher than that of a pre-determined one at the maximum considered earthquake. [ABSTRACT FROM AUTHOR]

Consumers’ transformers play an important role in power systems, and they are essential for operation reliability and commercial benefits. In the past, maintenance personnel had to spend plenty of time on examining consumers’ transformers one by one. Nowadays, with the wide deployment of power user electric energy data acquire system (PUEEDAS), informative metering data are becoming available, which can be utilized for further condition monitoring. Thus, this paper proposes a data-driven abnormal condition monitoring algorithm of data acquisition for consumers’ transformers, which could timely send abnormal condition alerts to operators and maintenance personnel. In the proposed algorithm, Spearman's rank correlation coefficient is utilized to show the degree of correlation among phase currents, and its t-Statistics is used to determine whether abnormal condition of data acquisition exists based on the hypothesis testing. Finally, actual acquisition data from Zhejiang power system in China are employed to validate the effectiveness of the proposed algorithm, and to analyze the characteristics of normal and abnormal conditions, respectively. Sensitive analyses on different significant levels and sampling rates are performed for considering its impact on monitoring results; the application in real power systems is also given to demonstrate the practicality of the proposed algorithm. [ABSTRACT FROM AUTHOR]

With the economic development, China has become the largest CO2 emissions country. China’s power industry CO2 emissions accounted for about 50% of total CO2 emissions. Therefore, exploring major drivers of CO2 emissions is critical to mitigating its CO2 emissions in power industry. Many studies considered the time series model to analyze the national influences factors of CO2 emissions. But this paper focuses on regional differences in CO2 emissions and adopts panel data models to explore the major impact factors of CO2 emissions in the power industry at the regional and provincial perspectives. The results indicate economic growth level plays a dominant role in reducing CO2 emissions. The power-consuming efficiency on the demand side has large potential to mitigate CO2 emissions, but its influences are different in three regions. The impacts of the electric power structure on CO2 emissions decline from the eastern region to the central and western regions. The influence of urbanization and industrialization also has significant regional differences. Therefore, the governments should consider the influencing factors and regional differences and formulate appropriate policies to decrease CO2 emissions in the power industry. [ABSTRACT FROM AUTHOR]

In order to explore the decoupling relationship and its influence factors between economic growth and carbon emissions in China, the decoupling elasticity decomposition quantitative model of carbon emissions based on extended Log-Mean Divisia Index and Tapio decoupling models is established in this paper. The carbon emissions induced by household (HOU) sector ranked fifth among the nine sectors; hence, we analyzed the HOU sector with the production (PRO) sector together. The results show that, first, the carbon emissions increased from 3.95 billion tons in 1996 to 10.49 billion tons in 2012, and the contributions of manufacturing sector and electric power, gas and water production and supply sector to carbon emissions account for approximately 81 %. Second, the economic output effect is the main contributor, and the energy intensity effect is the major inhibitor factor to the carbon emissions in both the PRO sector and HOU sector, respectively. Third, the decoupling state of the PRO sector mainly stayed at weak decoupling, while the decoupling state of HOU sector mainly stayed at strong decoupling. Fourth, the impact factors of the carbon emissions and the decoupling elasticity values are in complete agreement. At the end of this paper, we present some policy recommendations for China's government to realize the decoupling between CO emissions and economic growth in the near future. [ABSTRACT FROM AUTHOR]

The electric power industry is related to the national livelihood, and regional economic development is closely related to the pillar industry. At present, China's economy continues to develop steadily, and the promotion of domestic industrialization has also increased the demand for electricity. In recent years, the state power industry has made great progress, in order to further adapt to the needs of domestic and foreign market competition, the State Grid has also carried out a systematic reform of its own management system. In the reform process, strengthening the construction of performance appraisal system is a very important change. According to statistics, as of June 2020, the number of employees of State Grid has reached more than 900,000. As the most basic business unit of the state grid, the team is the cell of the state grid. It is the basis for the completion of various production management work and plays an important role in the safe production and stable output of electric power enterprises. Based on the relevant indicators of the work of the team, this paper analyzes the performance of the team through the production cost performance model, and provides suggestions for the optimization of the team for power grid enterprises [ABSTRACT FROM AUTHOR]

In China, a new-round marketization reform of electricity industry is in progress, and the electricity transmission and distribution tariff reform is the core and important task. Currently, the electricity transmission and distribution tariff regulation has gone to the second round in China, and the electric power grid enterprises are facing a closed-loop regulatory system and an increasingly strict regulatory environment. Therefore, it is urgent to evaluate the risk of electric power grid enterprise that is related to electricity transmission and distribution tariff regulation, which can aid the electricity regulators and electric power grid enterprise operators to manage risk and promote the sustainable development of electric power industry. In this paper, a hybrid novel multi-criteria decision making (MCDM) method combining the fuzzy Best-Worst method (FBWM) and improved fuzzy comprehensive evaluation method based on a vague set is proposed for the risk evaluation of electric power grid enterprise related to electricity transmission and distribution tariff regulation. The risk evaluation index system is built. Subsequently, the FBWM is utilized to determine the optimal weights of electric power grid enterprise risk criteria, and the improved fuzzy comprehensive evaluation method that is based on vague set is employed to rank the comprehensive risk grade of electric power grid enterprise related to electricity transmission and distribution tariff regulation. The risk of a province-level electric power grid enterprise that is located in Northern China is empirically evaluated using the proposed MCDM method, and the result indicates that the overall risk of this province-level electric power grid enterprise belongs to 'High' grade, but it is very close to 'Very High' grade. The results indicate that the proposed hybrid novel MCDM method in this paper is effective and practical. Meanwhile, it provides a new view for the risk evaluation of electric power grid enterprise that is related to electricity transmission and distribution tariff regulation. [ABSTRACT FROM AUTHOR]

With the expansion of the digital information industry, the size of global data centers is exploding. As part of a high-energy-consuming industry, data centers can support sustainable urban development through green transformation. This study uses a quasi-natural experiment of China's national green data center pilot policy implemented in 2015 to examine the impact of data center green transformation on green technology innovation in cities. Using a difference-in-difference (DID) analysis, this study finds that the national green data center pilot policy leads to higher levels of green technology innovation in cities, but the spillover effect of the policy on neighboring cities is insignificant. The mechanism test showed that the national green data center pilot policy could facilitate urban green technological innovation through capital deepening, market competition, and industrial agglomeration. In addition, the policy impact is more pronounced for cities with poorer environments, less civilization, high levels of network infrastructure, and more innovative endowments. The findings can provide new guidance for cleaner regional production from the standpoint of data center green development. [ABSTRACT FROM AUTHOR]

Socio-economic development is undergoing changes in China, such as the recently proposed carbon peak and carbon neutral targets, new infrastructure development strategy and the Coronavirus disease 2019 (COVID-19) pandemic. Meanwhile, the new-round marketization reform of the electricity industry has been ongoing in China since 2015. Therefore, it is urgent to evaluate the risk of electric power grid investment in China under new socio-economic development situation, which can help the investors manage risk and reduce risk loss. In this paper, a hybrid novel multi-criteria decision making (MCDM) method combining the latest group MCDM method, namely, Bayesian best–worst method (BBWM) and improved matter-element extension model (IMEEM) is proposed for risk evaluation of electric power grid investment in China under new socio-economic development situation. The BBWM is used for the weights' determination of electric power grid investment risk criteria, and the IMEEM is employed to rank risk grade of electric power grid investment. The risk evaluation index system of electric power grid investment is built, including economic, social, environmental, technical and marketable risks. The risk of electric power grid investment under new socio-economic development situation in Inner Mongolia Autonomous Region of China is empirically evaluated by using the proposed MCDM method, and the results indicate that it belongs to "Medium" grade, but closer to "High" grade. The main contributions of this paper include: (1) it proposes a hybrid novel MCDM method combining the BBWM and IMEEM for risk evaluation of electric power grid investment; and (2) it provides a new view for risk evaluation of electric power grid investment including economic, social, environmental, technical and marketable risks. The proposed hybrid novel MCDM method for the risk evaluation of electric power grid investment is effective and practical. [ABSTRACT FROM AUTHOR]

Node evaluation is an important topic in electric power communication network studies. In the view of the problem of single connection weights and single evaluation index for node importance evaluation, the bandwidth and the distance of the electric power communication network is used for the network's weight to compute three evaluating indicators: compactness, node degree and node spread. We use the "fast search and finding of density peaks" method to evaluate the importance of the nodes in power communication network to support the planning of grid communications, and to carry out unsupervised classification when the nodes are divided into four fragile levels. This can effectively improve the unsupervised classification method. Actual data from a province in China is tested to verify the usefulness of this method in electric power communication network. [ABSTRACT FROM AUTHOR]

The difference in electricity and power usage time leads to an unbalanced current among the three phases in the power grid. The three-phase unbalanced is closely related to power planning and load distribution. When the unbalance occurs, the safe operation of the electrical equipment will be seriously jeopardized. This paper proposes a Hierarchical Temporal Memory (HTM)-based three-phase unbalance prediction model consisted by the encoder for binary coding, the spatial pooler for frequency pattern learning, the temporal pooler for pattern sequence learning, and the sparse distributed representations classifier for unbalance prediction. Following the feasibility of spatialtemporal streaming data analysis, we adopted this brain-liked neural network to a real-time prediction for power load. We applied the model in five cities (Tangshan, Langfang, Qinhuangdao, Chengde, Zhangjiakou) of north China. We experimented with the proposed model and Long Short-term Memory (LSTM) model and analyzed the predict results and real currents. The results show that the predictions conform to the reality; compared to LSTM, the HTM-based prediction model shows enhanced accuracy and stability. The prediction model could serve for the overload warning and the load planning to provide high-quality power grid operation. [ABSTRACT FROM AUTHOR]

A load model is one of the most important elements in power system simulation and control. Recently, the constant impedance, constant current, and constant power load in combination with the induction motor load have been widely used as the composite load model, whose parameters are all identified from the field measurements in measurement-based load modeling practices so far. However, there is virtually no research conducted on whether all these parameters could really be identified. This paper investigates the possibility on reducing the number of composite load model parameters to be identified from field measurements. This paper first shows that direct application of the IEEE load motor parameters in the composite load model may be inadequate on describing the load dynamics over different operating status. Then the perturbation method is used to derive the trajectory sensitivities of the equivalent motor parameters, based on which the reduction on the identified parameters of the composite load model is presented. Two cases of measurement-based load modeling in North China and Northeast China are studied to illustrate the validity of the reduction method. It is shown that the reduction does not lose the model's capability on describing the load dynamics. The reduction on the number of identified parameters not only provides a possible way to solve the multi-valued load model problem based on the current practices on measurement-based load modeling, but it also facilitates building of the load model with more components included in it. Meanwhile, reducing the identified parameters reduces the identification time; thus, the proposed strategy significantly enhances the efficiency of the load modeling work. [ABSTRACT FROM AUTHOR]

China’s electric utility industry is among the country’s main polluters. Increasing the efficiency of China’s power supply chain is therefore essential to promote more environmentally sustainable power generation. This paper assesses the performance of China’s regional power supply chains that include electric utilities and power grid companies. We rely on the notion of sustainable supply chains to propose a multi-stage model that accounts for negative externalities such as harmful emissions, power transmission line loss, and increased mortality caused by pollution. Different from the existing studies of power supply chains, our approach is based on the exponential directional distance function defined with respect to a multiplicative production technology that can help mitigate the impact of data outliers. We use a panel of 30 Chinese provinces covering the period 2008–2018 to measure the efficiency improvement potential within regional supply chains and assess the efficiency-enhancing role of digital innovation. Our results provide evidence of considerable regional disparities and suggest that China’s eastern provinces outperform other regions due to the relatively advanced state of their technology. Furthermore, we demonstrate that the efficiency improvement potential from better management or technological innovation remains significant across the majority of provinces. Finally, our results provide evidence of a significant role of digitalization in promoting the efficiency of power supply chains. Our findings offer important perspectives on the strategies policymakers can use to promote sustainable performance of electric utilities and power grid companies. [ABSTRACT FROM AUTHOR]

• 2018 CFETR design point recreated in PROCESS from the view of high-level objectives (different plasma scenario). • Given uncertainties on a number of key input parameters a number of output design points were created. • PROCESS found 124 feasible design points in the operational space near the CFETR design point. • Paper mentions that of particular is the magnet build to give the large fields required for the CFETR plasma scenario and the vary large Psep/R values. The Chinese Fusion Engineering Test Reactor (CFETR) bridges the gap between ITER and a fusion power plant (FPP). The primary objectives of CFETR are: ∼2 GW of fusion power, producing ∼700 MW of net electric power, demonstrate tritium self-sufficiency, operate in steady-state and have a duty cycle of 30–50%. CFETR is in the pre-conceptual design phase and is currently envisaged to be a four-phase machine (from phase I P fus ∼200 MW to phase IV P fus ∼2 GW). In 2016 the EU and China began a collaboration on topics relating to nuclear fusion research and one topic of the work is on CFETR and DEMO. This contribution documents the progress on the collaboration on systems codes studies of CFETR. Systems codes attempt to model all aspects of a fusion power plant using simplified models (0-D, 1-D) and capture the interactions between plant systems. This allows the user to explore many reactor designs at a high level and optimise for different figures-of-merit (e.g. minimise major radius, R 0 , or maximise fusion gain, Q). The EU systems code used for this work is PROCESS, which is the systems code used to create the EU-DEMO baseline designs. This paper details the work on analysing a 2018 CFETR design point in EU systems code PROCESS and the feasibility of the design with regards to meeting the performance objectives and operation of the machine. The work comments on the four-phased nature of the device and the systems code output focuses on phase IV. In combination with the systems code, an uncertainty quantification tool is used to investigate the sensitivity of a CFETR design point to changes in the input assumptions in the systems code. This paper details sensitivities of the CFETR design and shows that given the specified inputs and the uncertainties there are a reasonable number of feasible design points around the CFETR phase IV design point that still fulfil the high-level objectives of the machine. [ABSTRACT FROM AUTHOR]

This paper studies the impact of two decision makers' interaction with conflicts on the efficiencies of the system. We start with a general principal-agent framework where the principal and the agent make decisions independently and the principal has a contradictive objective to that of the agent. We develop data envelopment analysis (DEA) models in the principal's and the agent's perspectives respectively. Non-cooperation between the principal and the agent is discussed to illustrate how one decision maker affects the other and the corresponding efficiency and incentive contract of the system. In addition, cooperation of the two parties is also analyzed to better derive how the performance of the system is influenced by the parties and their interactions as well. Then, this study illustrates the proposed models and effective incentive contracts by applying them to the efficiency evaluations of 22 China listed electric power companies. [ABSTRACT FROM AUTHOR]

Since the development of large scale power grid interconnections and power markets, research on available transfer capability (ATC) has attracted great attention. The challenges for accurate assessment of ATC originate from the numerous uncertainties in electricity generation, transmission, distribution and utilization sectors. Power system uncertainties can be mainly described as two types: randomness and fuzziness. However, the traditional transmission reliability margin (TRM) approach only considers randomness. Based on credibility theory, this paper firstly built models of generators, transmission lines and loads according to their features of both randomness and fuzziness. Then a random fuzzy simulation is applied, along with a novel method proposed for ATC assessment, in which both randomness and fuzziness are considered. The bootstrap method and multi-core parallel computing technique are introduced to enhance the processing speed. By implementing simulation for the IEEE-30-bus system and a real-life system located in Northwest China, the viability of the models and the proposed method is verified. [ABSTRACT FROM AUTHOR]

With the development of the smart grid in China, new opportunities for responsive industrial loads to participate in the provision of ancillary services (AS) will become accessible. This paper summarizes AS in China and analyzes the necessary characteristics and advantages of industrial users to provide AS according to their response mechanism. Cement manufacturing and aluminum smelter processes are selected as two representatives of responsive industrial loads. An agent-based model that includes generation, industrial user, and grid agents is proposed. Using two case studies, we analyze the integrated power management of conventional units and industrial loads in day-ahead and real-time AS scheduling based on real device parameters, price mechanisms and production data. The simulation results indicate that the participation of responsive industrial loads in the provision of AS, in China, can improve the coal consumption rate and the system-wide load factor as well as reduce the total system cost for the provision of AS significantly. [ABSTRACT FROM AUTHOR]

To help the government manage waste lead-acid batteries in a more targeted and sustainable way, accurately forecasting the number of waste lead-acid batteries and analyzing their recovery potential play a key role. In China, electric bicycles are one of the most common means of transportation. As of the end of 2017, the social holding quantity of electric bicycles in China was over 250 million and that of electric tricycles was over 50 million. The quantity is equal to the total number of electric bicycles manufactured between 2011 and 2017. Currently, 90% of electric bicycles adopt lead-acid batteries as their power batteries. However, there are a few studies on the lead-acid batteries used in electric bicycles as power batteries. In this paper, we have selected lead-acid batteries used in electric bicycles as the subject of research as such kind of batteries enjoys the widest user base, the most single-battery consumption volume, and the strongest mobility. Based on the output and sales of electric bicycles, we have obtained the quantity of power lead-acid batteries. We have then estimated the annual waste quantity of lead-acid batteries used in electric bicycles in 2000-2022 using the “market supply A model” and the “Stanford Model”, respectively, and based on the proportion of raw materials contained in lead-acid batteries and the proportion between reclaimed and discarded lead-acid batteries, we have estimated the recovery potential of discarded lead-acid batteries in 2000-2022. We estimate that the lead-acid batteries used in electric bicycles only have great recovery potential and there are abundant potential resources for recovery. The research data and results can help decision-makers make more effective and more accurate management measures and policies. [ABSTRACT FROM AUTHOR]

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Management and Economic department. North China University of Water Resource and Electric Power, Zhengzhou,450000, P.R. China Abstract This paper presents the cross evaluation grey incidence decision-making method under uncertain weights when decision makers have subjective preference. The model is applied in supplier selection decision. The results show that the subjective preferences of decision makers and the attribute values (objective preference) can be considered rationally and effectively in the model, and the model has high application level [ABSTRACT FROM AUTHOR]

Environmental protection pressures and green energy strategies have created major challenges for a cleaner production of China's coal-fired power generation. Although China's electric power dispatching department has tried to prioritize clean energy, the current dispatching models lack environmental indicators related to coal-fired power generation. The main purpose of this paper is to provide a comprehensive environmental indicator for the cleanliness evaluation of coal-fired power plants. In this paper, the (Emergy-based Life Cycle Assessment) Em-LCA method is used to measure and analyze environmental related resource consumption, socio-economic investment, and emissions in the whole life cycle of coal-fired power plants. At the same time, based on the above three environmental impacts in the whole life cycle, this paper constructs the (Em-LCA based Cleaner Production Comprehensive Evaluation) ECPCE index to guide a green dispatching plan. By comparing the calculation results of the index, this paper finds that there are differences in the environmental advantages of different generating units in green dispatching, which are closely related to the process management of coal-fired power plants in production and the environment. [ABSTRACT FROM AUTHOR]

Traditional energy planning methods are mostly related to newly-built area. However, in most already-built regions, the existing district energy systems (DESs) need to be ameliorated due to insufficient capacity and low efficiency. This paper investigates the methodology of expansion planning, which can offer planners a viable way to expand existing DESs. The term “expansion” aims to increase the supply capacity of the energy forms, e.g., steam, hot water and electricity, which cannot be accessed from national or urban energy supply network, and meanwhile increase energy utilization efficiency and cut CO 2 emission. Three parts of the methodology, including problem analysis in current DESs, demand side analysis and the existing DES expansion, are introduced in this paper. New concepts of load structure and evaluation system of the whole district are proposed in this methodology. A case analysis of Ningbo Hi-Tech District, China is conducted to shed light on the application of this method. [ABSTRACT FROM AUTHOR]

In order to solve the frequently occurring lightning flashover problems on ±500 kV dc transmission lines in China, the authors have conducted the research on the dc line arrester since 2011. In this paper, the structure and installation, technical parameters, and protection performances, as well as their deterministic processes of the arrester are presented and analyzed in detail. In 2013, the authors successfully developed the arrester products, and the type tests show that they have excellent performance parameters and can act effectively against the lightning impulse and cutoff the dc follow current. By now more than 500 dc line arresters have been installed and used on seven ±500 kV dc transmission lines in eight provinces of China. About four years of operating experience shows that the arresters work safely and reliably over a long period of time, and have been effectively reducing the lightning flashover rate of the ±500 kV dc lines. [ABSTRACT FROM PUBLISHER]

Summary: Sustainable energy production has been one of the contemporary concerns of society. In order to mitigate carbon dioxide emissions while generating the required demand for energy, various energy technologies, and production systems must be considered. In the consideration of different energy technologies, optimization is one of the important tools to optimally design energy systems. This study aims to analyze the progress of current research related to energy and sustainability that utilizes fuzzy optimization approach. Bibliometric methods based on the database of Web of Science core collection are used for the analysis. Of the 96 retrieved publications, 87% were journal articles. Analysis on the patterns for these articles were conducted such as highly cited articles, journals, subject categories, institutions, countries, and impact factor. The articles are evaluated into three categories such as technological, environmental, and economy and are comprehensively reviewed. The results showed a significant increase in publication related to fuzzy optimization in sustainability and energy technologies. The analysis showed China with 26 publications, which has the largest contribution in terms of the number of articles published. North China Electric Power University ranked as the number 1 institution with 10 publications. Based on the comprehensive analysis, fuzzy optimization showed significant results on technological, environmental, and economical factors. These findings help to identify hotspots in energy technology optimization for sustainability research. Similarly, the study gives useful inputs for selecting subtopics and strategy on publication related to fuzzy optimization on energy technologies. [ABSTRACT FROM AUTHOR]

Carbon trading is an important market instrument and policy tool to promote China's practice of low-carbon energy conservation and strive to achieve carbon peaking by 2030 and carbon neutrality by 2060. To improve market flow and respond to the national strategy to support the development of carbon trading in China, a smart carbon trading process optimization framework based on blockchain technology is developed in this article. First, the paper analyzes the current state of carbon trading in China's power sector and identifies the future development trend and barriers to carbon trading in China. Second, a framework for a smart carbon trading system based on the structure and traits of blockchain technology is created to optimize the carbon trading process. Third, the smart contract for the smart carbon trading system is built (including execution logic, transaction matching, contract fulfillment, and other elements). This mechanism measures the reputation of trading users. Finally, policy suggestions are provided to solve the current issues and challenges. According to the research conducted for this paper on "blockchain + carbon trading in China," carbon trading supported by blockchain technology can guarantee transaction security and efficiency, accurately preserve transaction records, and offer a high level of automated settlement. [ABSTRACT FROM AUTHOR]

The electric power network in rural China is distributed over a substantial land area and has a very large number of transformer substations. It is difficult and expensive to monitor and control each substation manually. Therefore, it is important to be able to monitor the network remotely and this is best achieved using a real-time, web-based system. However, there is currently no good way to display the status of so many substations on a single web page and without time-lapse or to manage each of the substations according to actual rural transformer substation topology. This paper offers a working solution to the above. First, we choose a new Scalable Vector Graphics (SVG) criterion using eXtensible Marked Language (XML) to communicate between client and server. This avoids bandwidth restrictions leading to time-lapsed graphics, but not a traditional vector display method on web in geographical information system, such as JPG. Second, we create a web-based framework based on Model-View-Control (MVC). Lastly, we develop and deploy a transformer configuration system for a Peking suburb to monitor and with it control about 140 substations. The feedback we obtained from this trial indicates the advantages of our no-time-lapse graphics and show that it can support the functions of power network enlargement, zooming, and roaming. Our system enabled the monitoring and controlling of power substations in real-time. [ABSTRACT FROM AUTHOR]

Regional energy development and approaches are significant for China's overall economic and social development. The GDP in Zhejiang province surpassed 6 trillion yuan in 2019, and its energy consumption reached 200 million tons of standard coal (tce), both of which are at the forefront of China. In order to explore the main factors of the increase in energy consumption in Zhejiang and provide essential references for energy saving and other provinces, this paper analyzes the total energy consumption and industrial sectors on the basis of the logarithmic mean divisia index (LMDI) model. Study results show that the economy's scale is the most crucial factor affecting Zhejiang's energy consumption, with a significant growth effect. In 2015, the scale effect increased energy consumption to the highest value of 14 million tce and then reduced it to 13 million tce in 2019. The impact of the population on energy consumption increased by 10 million tce from 2010 to 2019. Energy intensity reduces energy consumption by between 0.05 and 0.15 billion tce per year, which is the main factor in reducing energy consumption. The energy structure generally plays a weak positive role due to the different energy types. The decomposition of the energy consumption per unit of value added in the industrial sector showed that the intensity and structural effect primarily reduce energy consumption, for example, the metal smelting and rolling, textile printing and paper, electric power, heating, and other industries. According to the results, enterprises should enhance the intelligence and efficiency of dispatch management and emergency responses. Zhejiang should also accelerate an international oil and gas trading center and resource allocation base to reach its carbon-neutrality goal. [ABSTRACT FROM AUTHOR]

In 2013, “haze-fog” became an annual buzzword in China; this “haze-fog” is primarily attributed to coal combustion. If coal continues to be used as it has been previously, China will suffer as a result of air pollution. Different from coal, electricity is a highly efficient energy source with no pollution emissions. Electric power substitution (EPS) for coal can reduce air pollution and transmit electricity to a load intensive area by an ultra-high voltage transmission project that can promote renewable energy accommodations in remote areas. However, EPS was proposed in 2013 and is still in the initial stage. The present paper studies the relationship between coal and haze-fog, proposes an EPS action mechanism, and summarizes the EPS market and policies. A SWOT model has proved to be an efficient tool to analyze the internal and external competitiveness of a system. Therefore, to fully study the current development status of electric power substitution in China, this paper adopts a SWOT model to analyze the strengths, weaknesses, opportunities and threats of EPS. The conclusion is drawn that EPS is a good substitution for coal, but increased interest and use of EPS in China still needs support, including energy price marketization, electricity price subsidy, UHV optimization, clean coal technology innovation, and so on. This research can promote optimization in the energy structure and provides policy makers with good references and a clear direction in EPS development. [ABSTRACT FROM AUTHOR]

Demand Response (DR) has been extensively developed and implemented in the US and Europe. However, DR hardly exists in many developing countries for similar problems such as rigid power market and state monopoly. With the increasing imbalance between supply and demand in China's power industry, the government has issued new policies on DR and approved the first batch of pilot cities. China is setting a good example of how to encourage DR under monopolistic electric market and open up the market to aggregators and DR suppliers. This paper summarizes the current DR status, feasible DR market schemes and DR pilot projects in China. First, electric power system reform, renewable energy policies and power industry development are reviewed, highlighting the problems associated with the current dispatch mechanisms of DR policies and markets. New DR programs and DR-related policies are also introduced. On this basis, the driving forces and challenges associated with DR in China are analyzed. The major challenge is the lack of a suitable market mechanism for the current Chinese power industry. Hence, this paper presents six feasible strategies that fully utilize the existing policies. Additionally, the latest DR applications in different pilot cities are summarized and analyzed. [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]

With the comprehensive progression of China's electric power reform, the participation of hydropower in the spot market has become inevitable. For hydropower-dominated power grids, the tight hydraulic and electric coupling relationship between cascade hydropower plants and the policy requirement of clean energy consumption greatly increase the challenge of optimal scheduling. Based on the market characteristics and actual demand of the hydropower-dominated power grids, this paper discusses the day-ahead spot market trading mechanism to promote the guaranteed consumption of renewable energy. On this basis, a day-ahead optimal scheduling model is established which aims to minimize the comprehensive power purchase cost of the power system. The nonlinear factors in the scheduling model, such as the relationship between the water level and storage capacity of hydropower plants, the relationship between the water level and water consumption rate, and the constraints on the start-up and shutdown durations of thermal power units, are linearized through multiple linearization techniques. The original nonlinear model is transformed into a mixed integer linear programming (MILP) model and is then solved using a CPLEX solver. The developed model is verified by taking 10 thermal power plants and 22 hydropower plants in Yunnan Power Grid as a case study. The calculation time is 183.6s, the comprehensive power purchase cost of the system is 155.35·million CNY, and no abandoned water is generated in the three basins. The simulation results show that the proposed model has high solution accuracy and efficiency, and maximizes the hydropower consumption under the premise of meeting the minimum comprehensive power purchase cost. The findings of this study can provide a scientific basis for the development of the day-ahead spot market of hydropower-dominated power grids. [Display omitted] • A day-ahead spot market trading mechanism to promote consumption of renewable energy. • Day-ahead optimal scheduling of hydropower-dominated power grids under a spot market environment. • A novel MILP approach is employed to solve the model. • Proposed model has high solution accuracy and efficiency. [ABSTRACT FROM AUTHOR]