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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]

The renewable-dominant hybrid generation systems (HGSs) are increasingly important to the electric power system worldwide. However, influenced by uncertain meteorological factors, the operational robustness of HGSs must be evaluated to inform the associated decision-making. Additionally, the main factors affecting the HGS's robustness should be urgently identified under deep uncertainties, as this provides valuable guidance for HGS capacity configuration. In this paper, a multivariate stochastic simulation method is developed and used to generate uncertain resource scenarios of runoff, photovoltaic power, and wind power. Subsequently, a long-term stochastic optimization model of the HGS is employed to derive the optimal operating rules. Finally, these operating rules are used to simulate the long-term operation of an HGS, and the results are used to evaluate the HGS's robustness and identify its main sensitivities. A clean energy base located in the Upper Yellow River Basin, China, is selected as a case study. The results show that the HGS achieves greater operational robustness than an individual hydropower system, and the robustness becomes weaker as the total capacity of photovoltaic and wind power increases. Additionally, the operational robustness of the HGS is found to be more sensitive to the total capacity than to the capacity ratio between photovoltaic and wind power. [ABSTRACT FROM AUTHOR]

In order to achieve the ambitious goal of "carbon neutrality", countries around the world are striving to develop clean energy. Against this background, this paper takes China and Italy as representatives of developing and developed countries to summarize the energy structure composition and development overview of the two countries. The paper analyzes the serious challenges facing the future energy development of both countries and investigates the possibilities of energy cooperation between the two countries, taking into account their respective advantages in energy development. By comparing the policies issued by the two governments to encourage clean energy development, this paper analyzes the severe challenges faced by the two countries' energy development in the future and combines their respective energy development advantages to look forward to the possibility of energy cooperation between the two countries in the future. This lays the foundation for China and Italy to build an "Energy Road" after the "Silk Road". [ABSTRACT FROM AUTHOR]

As the global demand for renewable energy continues to rise, biogas production has emerged as a promising solution for sustainable energy generation. This review article presents the advantages of biogas technologies (mainly agricultural, based on waste of animal and plant origin) and extensively discusses the main principles of biogas production in the anaerobic digestion (AD). In this respect, the main parameters of the process, which require monitoring and decisive for its efficiency are described, therefore: temperature, pH value, retention time and organic loading rate (OLR). The principles of substrate selection are also discussed and the necessity and advantages of the use of organic waste according to the model of a circular economy and the concept of sustainable development, are indicated. It is emphasized that according to the new European regulations, the crops classified as food cannot be considered energy crops. The part on biogas production is summarised with an explanation of the necessity to treat and purify biogas. Biogas purification is important from the point of view of the efficiency of its conversion into electricity. A special place in this paper is devoted to the design, construction, functioning and operation of biogas plants, based on both scientific and practical aspects. In conclusion of this chapter, the economic aspects and profitability of operating biogas plants are discussed. Cost and benefit analyses are the major tool used for the systematic evaluation of the financial costs and potential benefits associated with the operation of biogas plants. The important fact is that the return on investment can be achieved within a few years, provided the activities are well-planned and executed. In addition to the fundamental issues of the operation of biogas plants, this article presents the global situation regarding the development of biogas plants, discussing in detail the specific needs and limitations on different continents. It is a interesting and extensive part of this article. The global agricultural biogas market is at very different levels of development. Most such installations are located in Asia and Europe. China has the highest number of biogas plants, with more than 100,000 biogas plants, followed by Germany with over 10,000 plants. In addition to the 100,000 biogas plants, China also has a large number of household biogas units, which gives a total of approx. 40 million operating units. The article concludes with a discussion of opportunities and barriers to the development of biogas plants, pointing to: financial issues, access to feedstock, political regulations, public awareness and the geopolitical situation. The most frequently cited reasons for investment failure include economic problems, lack of professional knowledge. [ABSTRACT FROM AUTHOR]

Sustainable energy sources, such as wind energy, are pivotal in driving our energy landscape towards a more environmentally conscious and responsible future. Wind power, as an exemplar of clean and renewable energy solutions, adeptly harnesses the kinetic energy of the wind to generate electricity. While wind energy significantly contributes to our sustainability objectives, the quality of the energy it produces is equally essential. A critical challenge in this context is harmonic distortion, which manifests as unwanted fluctuations in the frequency and amplitude of electrical waveforms. Effectively mitigating these distortions within wind energy systems is vital to maintaining the stability and reliability of power grids, guaranteeing that the electricity supplied adheres to high-quality standards. The objective of this study was to conduct a technological prospection focused on the contemporary scenario of genetic algorithm applications in addressing harmonic variations. This investigation unearthed a total of 634 relevant documents. The findings suggest that the utilization of genetic algorithms for enhancing energy quality is a relatively recent but promising field. The State Grid Corp of China emerged as the principal contributor, with ten noteworthy inventors identified. Remarkably, both China and the United States lead in patent filings. The insights gleaned from these documents underscore the potential for further exploration and the synergistic application of these techniques. These collaborative efforts have the potential to yield processes and devices that offer significant economic and environmental advantages for the energy industry, solidifying our commitment to a cleaner and more sustainable energy future. [ABSTRACT FROM AUTHOR]

As climate change impacts energy consumption, investments in clean energy are now associated with increased levels of risk and uncertainty. Consequently, the management of risk for clean energy investors has garnered significant academic attention. This study was designed to explore the risk transfers among clean energy markets, how they respond to market volatility, and how exceptional events impact the risk spillover. This was performed by examining the risk spillover of and asymmetric connectedness between clean energy markets, green bonds, and other financial markets in China, in line with the connectedness framework and minimum spanning tree technique. The findings revealed that clean energy markets exhibit heterogeneity in terms of the direction and magnitude of net risk spillover, the types of hedging assets involved, and their response to market volatility. Exceptional events, such as the Russian–Ukrainian conflict and COVID-19 pandemic, have an impact on the spillover relationships. During stable market conditions, green bonds experience fewer spillovers from clean energy markets, whereas, in times of volatility, gold markets are subjected to fewer spillovers. In the time domain, the overall long-term spillover is stronger compared to the short and medium terms. In the frequency domain, there is a significant risk of low-frequency transmission. These findings hold practical implications for energy investors in portfolio construction and for policymakers in pursuing sustainability objectives. [ABSTRACT FROM AUTHOR]

Driven by China's long-term energy transition strategies, the construction of large-scale clean energy power stations, such as wind, solar, and hydropower, is advancing rapidly. Consequently, as a green, low-carbon, and flexible storage power source, the adoption of pumped storage power stations is also rising significantly. Operations management is a significant factor that influences the performance of pumped storage power stations in various domains, including environmental protection, economic benefits, and social benefits. While existing studies have highlighted the importance of stakeholder partnering in operations management, a systematic exploration of the causal relationships between partnering, operations management, and the performance of pumped storage power stations—especially from a multi-energy complementarity perspective—remains untouched. This paper strives to shed light on the vital role of stakeholder partnering in augmenting the operations management and overall performance of pumped storage power stations, thereby contributing to China's dual carbon goals. A comprehensive conceptual model was developed by reviewing the relevant literature to empirically examine the causal relationships among partnering, operations management, and power station performance, which was validated using data from the Liaoning Qingyuan Pumped Storage Power Station, which is the largest of its kind in Northeast China. The findings suggest: (1) Effective partnering among stakeholders, particularly with grid companies, significantly influences the operations management of pumped storage power stations, with deficiencies in partnering mainly attributed to the lack of effective communication channels and problem-solving mechanisms. (2) The level of operations management in China's pumped storage power stations is relatively high, averaging a central score around 4.00 (out of a full score of 5) on operations management indicators. However, there is a need to concentrate on enhancing multi-energy complementarity coordination, digital management system development, and profitability. (3) Path analysis further unveils that partnering not only improves operations management but also boosts the performance of pumped storage power stations. These findings suggest a wide range of practical strategies for operations managers at pumped storage power stations to forge partnerships with stakeholders and integrate complementary resources, aiming to achieve excellence in performance. [ABSTRACT FROM AUTHOR]

A Chinese solar greenhouse (CSG) is an agricultural facility type with Chinese characteristics. It can effectively utilize solar energy during low-temperature seasons in alpine regions. The low construction and operation costs make it a main facility for agricultural production in the northern regions of China. It plays an extremely important role in "Chinese vegetable basket projects". Energy is one of the important issues faced by CSGs. The better climate resources in the northern regions of China make it possible to apply solar energy as a green and sustainable energy source in the production of CSGs. Faced with the increasingly serious environmental problems of the new century, the Chinese government has made a decision and put it into practice to improve the rate and efficiency of solar energy utilization in agricultural facilities. In this paper, we summarize the research on the application of photovoltaic power generation and solar thermal technology in CSGs. The application of these advanced solar technologies has made great progress. With the further improvement of economic benefits and the establishment of relevant support policies and incentive mechanisms, the combination of CSG and solar energy technology will have certain application prospects and satisfy China's requirements for long-term sustainable development. [ABSTRACT FROM AUTHOR]

In light of the carbon neutrality goals set post-Paris Climate Conference (COP21), this study delves into the relationship between green technology innovations, energy consumption, and CO2 emissions in China, spanning the period of 1990 to 2021. The objective of this paper is to creatively present the idea of a low-carbon digital economy from the viewpoint of digital technology. Utilizing the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, we scrutinize this relationship, employing unit-root testing to verify the integrative attributes of the variables, inclusive of structural break data. Further analysis using the bootstrap Autoregressive Distributed Lag (ARDL) bound testing method corroborates the relationship between these key variables. The study reveals unidirectional co-integration over time among green technology innovations, renewable and non-renewable energy, per capita income, population, and CO2 emissions as per the Granger causality test. Interestingly, our findings suggest that while green technology innovation, per capita income, and renewable energy contribute to the reduction of CO2 emissions, non-renewable energy consumption and population growth exacerbate them. These insights offer valuable guidance for policymakers in formulating comprehensive strategies to enhance environmental quality through the promotion of renewable energy and green technology innovations, with a specific emphasis on the Chinese context. [ABSTRACT FROM AUTHOR]

As a clean energy resource, coalbed methane (CBM) is an important industry in China's dual-carbon strategic planning. Despite the immense potential of CBM resources in China, the current exploration level remains low due to outdated survey technology, impeding large-scale exploration and development. This study investigates the application of hyperspectral data in CBM enrichment areas, specifically focusing on the extraction of alteration minerals in the Hudi coal mine area of the Qinshui Basin using ZY-1 02D and Hyperion hyperspectral data. The hyperspectral alteration mineral identification methods are summarized and analyzed. A method that combines spectral feature matching and diagnostic characteristic parameters is proposed for mineral extraction based on the spectral characteristics of different minerals. The extraction results are verified through field samples using X-ray diffraction analysis. Results show that (1) both ZY-1 02D and Hyperion hyperspectral data yield favorable extraction results for clay and carbonate minerals; (2) the overall accuracy of clay and carbonate minerals extraction is higher using ZY-1 02D data compared with Hyperion data, with accuracies of 81.67% and 79.03%, respectively; (3) the proposed method effectively extracts alteration minerals in CBM enrichment areas using hyperspectral data, thereby providing valuable technical support for the application of hyperspectral data. [ABSTRACT FROM AUTHOR]

The large-scale centralized development of wind and PV power resources is the key to China's dual carbon targets and clean energy transition. The vast desert–Gobi–wilderness areas in northern and western China will be the best choice for renewable energy development under multiple considerations of resources endowment, land use constraints, technical conditions, and economic level. It is urgent to carry out a quantitative wind and PV resource assessment study in desert–Gobi–wilderness areas. This paper proposed a multi-dimensional assessment method considering the influence of the power grid and transportation infrastructure distributions, which includes three research levels, namely, the technical installed capacity, the development potential, and the development cost. Nine main desert–Gobi–wilderness areas were assessed. The wind and PV technical installed capacities were 0.6 TW and 10.7 TW, and the total development potentials were over 0.12 TW and 1.2 TW, with the full load hours of 2513 and 1759 and the average development costs of 0.28 CNY/kWh and 0.20 CNY/kWh. Finally, this paper proposed the meteorological–electrical division distribution. A case study in the Kubuqi and Qaidam Deserts was carried out on wind–wind and wind–PV collaborative development across different meteorological–electrical divisions, which can reduce by 58% the long-term energy storage capacity and decrease the total system LCOE from 0.488 CNY/kWh to 0.445 CNY/kWh. [ABSTRACT FROM AUTHOR]

In order to achieve China's carbon peaking and carbon neutrality (double carbon) targets and to advance ecological conservation and high-quality development in the Yellow River Basin, it is essential that China reduces its energy intensity and increases its energy efficiency. This research developed an evaluation index system for energy efficiency and green development in the Yellow River Basin based on panel data collected from 64 of its prefecture-level cities and covering the period from 2011 to 2020. Each city's energy efficiency and green development level index was calculated, and was analyzed together with the characteristics of its spatial pattern progression. The STIRPAT model was then used to investigate the influence mechanism of energy efficiency on green development. The final step in the analysis was to assess the process by which technical innovation influences the rise in energy efficiency from a green development point of view. The findings of this study indicate that: (1) There was a marked improvement in energy efficiency and green development levels across the Yellow River Basin over the study period of 2011 to 2020, but there are notable disparities among prefecture-level cities, with higher levels found in capital cities and cities in the lower reaches of the basin. (2) The improvement in energy efficiency has had a positive impact on the transition to green development, with factors such as human capital, urbanization levels, and the upgrading of industrial structures contributing significantly, while the level of foreign direct investment has had a limited impact. (3) Technological innovation plays a partial role in mediating the relationship between energy efficiency and green development in the Yellow River Basin, and passes the single-threshold test. When technological innovation surpasses the threshold value, the effect of energy efficiency on green development is significantly strengthened. This study indicates that improving energy efficiency, stimulating emerging industries, and enhancing technological innovation capabilities can significantly promote transformative green and high quality development in the Yellow River Basin of China. [ABSTRACT FROM AUTHOR]

As the top emitter of carbon dioxide worldwide, China faces a considerable challenge in reducing carbon emissions to combat global warming. Carbon emissions from coal consumption is the primary source of carbon dioxide emissions in China. The decomposition of the driving factors and the quantification of regions and industries needs further research. Thus, this paper decomposed five driving factors affecting carbon emissions from coal consumption in China, namely, carbon emission intensity, energy structure, energy intensity, economic output, and population scale, by constructing a Kaya-Logarithmic Mean Divisia Index (Kaya-LMDI) decomposition model with data on coal consumption in China from 1997 to 2019. It was revealed that the economic output and energy intensity effects are major drivers and inhibitors of carbon emissions from coal consumption in China, respectively. The contribution and impact of these driving factors on carbon emissions from coal consumption were analyzed for different regions and industrial sectors. The results showed that carbon emissions from coal consumption increased by 3211.92 million tons from 1997 to 2019. From a regional perspective, Hebei Province has the most significant impact on carbon emissions from coal consumption due to the effect of economic output. Additionally, the industrial sector had the most pronounced influence on carbon emissions from coal consumption due to the economic output effect. Finally, a series of measures to reduce carbon emissions including controlling the total coal consumption, improving the utilization rate of clean energy, and optimizing the energy structure is proposed based on China's actual development. [ABSTRACT FROM AUTHOR]

As a kind of clean energy, the exploration and development of coalbed methane (CBM) are of great importance and significance. In this paper, the CBM reservoir parameters of a working area in Western Guizhou Province, China, were predicted by using 3D seismic exploration technology, and the sweet-spot area was evaluated based on the prediction results. In terms of data interpretation, while the traditional technology is extended to the study of CBM, new technologies and methods suitable for CBM were also actively explored, especially in the quantitative prediction methods of parameters such as coal seam thickness, CBM content, coal body structure, in situ stress, etc., and a three-highs quantitative prediction technology of CBM sweet spots characterized by high precision, high resolution, and high coincidence was explored, which is based on logging evaluation and petrophysics. In addition, in the comprehensive evaluation of multiparameter sweet spots, the multiparameter weighted step-by-step evaluation method based on inversion was innovatively proposed, which made the prediction of CBM sweet spots more focused, effective, and practical, and the distribution law of sweet spots was more consistent with the geological law, providing enough basis for subsequent well location deployment. Based on this method, the C409 coal seam was selected as the sweet spot. [ABSTRACT FROM AUTHOR]

Given the increase in population and energy demand worldwide, alternative methods have been adopted for the production of hydrogen as a clean energy source. This energy offers an alternative energy source due to its high energy content, and without emissions to the environment. In this bibliometric analysis of energy production using electrolysis and taking into account the different forms of energy production. In this analysis, it was possible to evaluate the research trends based on the literature in the Scopus database during the years 2011–2021. The results showed a growing interest in hydrogen production from electrolysis and other mechanisms, with China being the country with the highest number of publications and the United States TOP in citations. The trend shows that during the first four years of this study (2011–2014), the average number of publications was 74 articles per year, from 2015 to 2021 where the growth is an average of 209 articles, the journal that published the most on this topic is Applied Energy, followed by Energy, contributing with almost 33% in the research area. Lastly, the keyword analysis identified six important research points for future discussions, which we have termed clusters. The study concludes that new perspectives on clean hydrogen energy generation, environmental impacts, and social acceptance could contribute to the positive evolution of the hydrogen energy industry. [ABSTRACT FROM AUTHOR]

Methane (CH4) is an important greenhouse gas. There is increasing attention to CH4 abatement strategies because of its contribution to short-term warming and strong benefits of decreasing CH4 emissions. China greenhouse gas inventory methods are used to predict CH4 emissions from the energy industry and to assess the potentials of CH4 abatement policies and techniques by 2050. The NDC scenario results show using oil and gas as transitional clean energy sources instead of coal will increase CH4 emissions from oil and gas industries at least 70%, but CH4 emissions from the coal industry will decrease 45%, meaning total CH4 emissions from the energy industry will continually decrease at least 30% in 2030 compared with 2020. Energy-related CH4 emissions might peak around 2025, ahead of CO2 emission peaking. CH4 emissions will then decrease slightly and decrease markedly after 2030. Emissions in 2050 are expected to be 32% lower than emissions in 2020. In an extreme scenario, emissions may be 90% lower in 2050 than in 2020. It is suggested that the verification system for the energy industry's CH4 emission accounting at the national level be improved and CH4 control targets in line with national emission targets and the "14th Five-Year Plan" development stage be formulated. [ABSTRACT FROM AUTHOR]

Hydrogen energy leads us in an important direction in the development of clean energy, and the comprehensive utilization of hydrogen energy is crucial for the low-carbon transformation of the power sector. In this paper, the demand for hydrogen energy in various fields is predicted based on the support vector regression algorithm, which can be converted into an equivalent electrical load when it is all produced from water electrolysis. Then, the investment costs of power generators and hydrogen energy equipment are forecast considering uncertainty. Furthermore, a planning model is established with the forecast data, initial installed capacity and targets for carbon emission reduction as inputs, and the installed capacity as well as share of various power supply and annual carbon emissions as outputs. Taking Gansu Province of China as an example, the changes of power supply structure and carbon emissions under different scenarios are analysed. It can be found that hydrogen production through water electrolysis powered by renewable energy can reduce carbon emissions but will increase the demand for renewable energy generators. Appropriate planning of hydrogen storage can reduce the overall investment cost and promote a low carbon transition of the power system. [ABSTRACT FROM AUTHOR]

In 2017, the 19th CPC National Congress proposed to "establish a sound economic system of green, low-carbon and circular development", which indicates the direction of high-quality economic development in the new era of China. Clean power investment is a powerful way to promote high-quality economic development by adopting non-fossil-energy utilization and low-emission technologies, as well as creating new jobs. Meanwhile, large-scale investment and a long investment return period result in negative effects on local economies. To better understand the effect of clean power investment, this paper selects panel data of thirty provinces in China from 2010 to 2019 to establish a spatial Durbin model to explore the impact of clean power investment on regional high-quality economic development. The results show that inter-regional high-quality economic development shows significant spatial auto-correlation characteristics. Clean power investment has not only a positive direct effect on high-quality economic development but also generates positive spatial spillover effects. Human capital, degree of government intervention, and urbanization rate have positive effects on regional high-quality economic development, while they play a suppressed role on neighboring regions. [ABSTRACT FROM AUTHOR]

As a raw material for clean energy supply for the new generation, the soybean is conducive to the realization of global energy transition and sustainable development in the context of "carbon neutrality". However, global warming has been affecting soybean yields in recent years. How to clarify the correlation between meteorological factors and soybean yields, so as to ensure the security of soybean growth and development and the stability of renewable energy development, is a key concern of the government and academia. Based on the data of temperature, precipitation, sunshine duration and active accumulated temperature during the soybean growing season in Hulunbuir, Inner Mongolia Autonomous Region from 1951 to 2019, and soybean yield data of the city from 1985 to 2019, this paper adopted statistical methods such as the Trend Analysis Method, the Rescaled Range Analysis Method and so on to analyze the trends of yield changes, characteristics of abrupt changes and periodic patterns of climate factors and soybean yields in Hulunbuir. A Pearson Correlation Analysis and a Grey Relation Analysis were used to explore the correlation between climatic factors and soybean yields, followed by a comprehensive impact model of the combined effect of temperature and precipitation on soybean yields established by the Method of Integral Regression. The results showed that temperature and active accumulated temperature are the dominant factors affecting soybean yields in Hulunbuir, while the decrease in precipitation is unfavorable to the improvement of soybean yields. Meanwhile, temperature and precipitation have different effects on the growth and development of the soybean at different stages. The conclusion of this paper is of great practical significance for Hulunbuir to promote the sustainable development of clean energy. [ABSTRACT FROM AUTHOR]

With the proposal of China's green energy strategy, the research and development technologies of green energy such as wind energy and hydrogen energy are becoming more and more mature. However, the phenomenon of wind abandonment and anti-peak shaving characteristics of wind turbines have a great impact on the utilization of wind energy. Therefore, this study firstly builds a distributed wind-hydrogen hybrid energy system model, then proposes the power dispatching optimization technology of a wind-hydrogen integrated energy system. On this basis, a power allocation method based on the AUKF (adaptive unscented Kalman filter) algorithm is proposed. The experiment shows that the power allocation strategy based on the AUKF algorithm can effectively reduce the incidence of battery overcharge and overdischarge. Moreover, it can effectively deal with rapid changes in wind speed. The wind hydrogen integrated energy system proposed in this study is one of the important topics of renewable clean energy technology innovation. Its grid-connected power is stable, with good controllability, and the DC bus is more secure and stable. Compared with previous studies, the system developed in this study has effectively reduced the ratio of abandoned air and its performance is significantly better than the system with separate grid connected fans and single hydrogen energy storage. It is hoped that this research can provide some solutions for the research work on power dispatching optimization of energy systems. [ABSTRACT FROM AUTHOR]

As the scale of distributed renewable energy represented by wind power and photovoltaic continues to expand and load demand gradually changes, the future evolution of the smart distribution network will be directly driven by both distributed generation and user demand. The smart distribution network contains a wide range of flexible resources, and its flexibility and uncertainty will bring great challenges to grid data acquisition and control feedback. To adapt to the precise control and feedback of smart distribution network access equipment under the high proportion of new energy access and to ensure the safe operation of the system, it is urgent to accelerate the study of the evolution of the future distribution grid based on the existing distribution grid. Hence, a multi-stage planning method for distribution networks based on source–load prediction is proposed in this paper. Firstly, a distribution network source–load prediction method based on the autoregressive integrated moving average model (ARIMA) and error gradient sampling is proposed, using ARIMA to predict the scale of source–load development and error gradient sampling based on the generation of source–load scenarios with error intervals. K-means is further used for scenario reduction, to explore multiple operating scenarios of China's distribution network source–load, and the unit's output forecast interval and load demand from 2021 to 2030 for typical regions are derived using rolling forecasts by combining the unit's output, end-demand and clean energy share over the years. Secondly, the planning model of distribution grid evolution in different stages is constructed to analyze the future evolution form of the distribution grid considering the distribution network's load cross-section, respectively, and to provide a development path reference for the future construction of distribution grid form in China. [ABSTRACT FROM AUTHOR]

With concerns about global warming and energy security, people are reducing fossil fuel use and turning to clean energy technologies. Mineral resources are used as materials for various energy technologies, and with the development of clean energy technologies, the demand for mineral resources will increase. China is a large country with various mineral resources, but its structural supply problem is severe. For China to reach the targets of carbon peaking before 2030 and carbon neutrality before 2060, they have set specific milestones for developing each clean energy industry; thus, the demand for mineral resources in clean energy will increase. We first summarise the mineral resources supply for China's development of clean energy technologies. We analyse the demand for various mineral resources in specific clean energy technology sectors under the stated policies scenario and sustainable development scenario through scenario setting. Finally, we combine current domestic mineral resource reserves and overseas import channels to analyse China's mineral resource supply and demand for developing the clean energy industry. Our results show that the surge in clean energy generation and electric vehicle ownership in China between 2020 and 2050 will lead to a significant increase in demand for mineral resources for these technologies and a shortage in the supply of some mineral resources. In particular, the supply of copper, nickel, cobalt, and lithium will be a severe constraint for clean energy development. We also find that secondary recycling of power battery materials in the electric vehicle sector could alleviate China's resource constraints. The findings of our study provide a better understanding of the kinds of mineral elements that are in short supply on the path of clean energy development in China under carbon peaking and carbon neutrality targets and the future channels that can be used to increase the supply of minerals. [ABSTRACT FROM AUTHOR]

With China's carbon peak and target proposed and the power system as an important source of carbon emissions, its carbon reduction issues are of great concern. However, the mismatch between power demand, resources, and generation facilities in different regions of China creates enormous carbon reduction pressures, especially in the region covered by China's Northeast Power Grid (NE Grid). Therefore, in this study, a two-stage power optimization model considering Carbon Capture, Utilization, and Storage (CCUS) was established to optimize the national power generation and transmission in China from 2020 to 2060 first and then to optimize the regional road map of energy structure adjustment for China's NE grid and its CO2 storage strategy. The first stage optimization results show that the national power interaction in China can make full use of the energy resource advantages of the NE grid, especially Jilin and Mengdong, delivering clean electricity to the North China grid as it reaches its "dual carbon" goal. Additionally, the second-stage optimization results show that the NE grid can make good use of CCUS for its thermal power transition. Annual CO2 storage will peak at 204 Mt in 2050 as thermal power withdraws in the NE grid region, which provides a reference for other regions to realize carbon neutrality of their power system by using CCUS. [ABSTRACT FROM AUTHOR]

Based on the panel data from 30 provinces in China from 2010 to 2020, this paper employs the panel vector autoregression (PVAR) model to investigate the dynamic relationship between a green economy and energy utilization level. The results show that: (1) the green economic development level and energy utilization level of 30 provinces in China have been continuously enhancing in recent years, the increase in green economic development level is higher than energy utilization level, and the geographical characteristics of both levels are significant, decreasing from East to West; (2) the causal relationship between the green economy and the energy utilization level has passed the Granger test; the two have formed a relatively high-level balanced relationship over a long period, showing a benign and orderly development trend; (3) the green economy generates a positive pulse response on energy utilization level in the initial period, but the impulse response weakens in the period of lag 1, entailing the relationship needs to be further strengthened in the long term; (4) in the process of continuous enhancement of energy utilization level, it is difficult to drive economic benefits due to high input costs in the initial stage, but it can form a two-way interaction relationship with a green economy as time goes by. [ABSTRACT FROM AUTHOR]

The annual electricity trade scheduling is the basis of long-term power generation scheduling. In recent decades, the ratio of new energy generation in China has increased annually, and the electricity market has operated under the "market electricity" and "planned electricity" double track mode in recent years. However, the existing annual electricity trade scheduling methods are extensive and cannot adapt to the new situation of "market electricity" and large-scale new energy generation. The annual scheduled energy of the power units is set as a decision variable, and a novel annual energy scheduling optimization model based on Gini coefficient of fairness is presented in this paper. In this model, "market electricity" capacity is conversed monthly, considering peaking reserve demand and monthly characteristics of new energy generation. The fairness constraint set based on Gini coefficient is introduced into the optimization model to solve various fairness problems. Simulation results show that the introduction of the Gini coefficient and the optimization model considering the monthly conversion of marketing electricity capacity can obtain more accurate and reasonable electricity distribution results, and the peaking demand can be considered more fairly and effectively. The proposed method provides a feasible solution to the annual electric energy scheduling for dual-track operation country such as China. [ABSTRACT FROM AUTHOR]

To solve the problem of rising energy use and CO2 emissions, China issued the 14th Five-Year Plan in 2020, emphasizing the need to reduce its carbon intensity and achieve a carbon emission peak before 2030. In order to estimate the future path of carbon peak in China, a novel dataset was constructed to analyze 30 provinces in China, and found that the realization of carbon peaking in 2025 requires a reduction of 1.072 million tons of carbon emissions in 2025, at which point peak carbon emissions will be 11,008.4 million tons. Due to this energy gap caused by carbon emission reduction the total amount of clean electricity has reached 3600 billion kWh. In carbon emission allowance trading, provinces with large carbon emissions, like Jiangsu and Guangdong, prefer to buy carbon allowances, while those with small carbon emissions like Shanxi and Inner Mongolia prefer to sell carbon allowances. In the energy trading market, the overall situation meets the 14th Five-Year Plan of west-east and north-south power transmission, except for Shanghai, Hainan, Hubei, and other provinces selling power, due to excessive power generation from a particular energy source. [ABSTRACT FROM AUTHOR]

The uncertain output of variable renewables adds significant challenges to the generation of affordable, reliable, and sustainable power sources in a country or region. Therefore, we propose a new stochastic nonlinear multi-objective model to optimize the power generation structure in 31 provinces of China. Considering variable renewable integration, we use Monte Carlo simulation to describe the randomness and uncertainty of renewable power output. The learning curve in the exponential expression is used to describe the nonlinear relationship between generation cost and installed capacity. The optimized results show that China can substitute fossil power with clean power. Renewable power will account for more than 42% of total power in the optimal power generation structure in 2040. In particular, the annual average growth rate of non-hydro renewable generation is expected to be 12.06%, with solar photovoltaic (PV) power growing the most by 17.95%. The share of renewable power exceeds that of thermal power in 14 provinces, and PV power represents the highest proportion at 30.21%. Reducing transmission capacity can promote the development of advantageous power in each region, such as wind power in the Northwest region and PV power in the South region, with the share increasing by 36.33% and 132.59%, respectively. [ABSTRACT FROM AUTHOR]

In the ten years since committing to clean energy transition, China has formulated a large number of policies and programs to achieve some very ambitious targets. This paper argues that the dearth of empirical studies concerning the implementation of these new policies and programs has created a knowledge gap between official policy documents, which are vague and lacking in specifics, and official policy outcomes, which are unreliable. In particular, the merits and limitations of flexible implementation with regard to desirable outcomes need to be debated and clarified. This paper calls for more empirical investigation in four areas as a starting point: (1) the nature and extent of flexibility in the implementation; (2) implementation strategies and their impacts; (3) factors that shape the behavior of local officials responsible for implementation; and (4) the relationship between the central-local relation and policy implementation. [ABSTRACT FROM AUTHOR]

The coal power policy has been influencing the development of China's coal power industry from the aspects of quantity control, technological progress, and market-oriented reform. Meanwhile, the strategic coal power policy of the mid- and long-term future is yet to be well-designed under the carbon peak and neutrality targets. Therefore, it is necessary to have a comprehensive overview of the coal power policy during the past and give targeted prospects for policy-makers. This paper discusses the policy effect on the development and technological progress of the coal power industry by giving an overview of the policy and reform processes of the coal power industry in China in the past 20 years. The results indicate that China's coal power policy has promoted the progress of coal power generation technology, optimized the coal power structure, accelerated the diversified investment in the power industry, and alleviated the overcapacity problem of coal power generation. After discussion, we conclude that: (1) It is necessary to gradually reduce the average generation hours of coal-fired power units while retaining high-quality coal-fired power units. (2) The renewable energy enterprises that adopt the support services of coal power enterprises should provide reasonable capacity compensation costs to create favorable conditions and achieve sustainable development for China's coal power industry. (3) To deploy coal power units combined with large-scale renewable energy in the western and northern regions will achieve efficient energy utilization. (4) Different strategies should be adopted for different coal power units, and the peak regulation potential of coal power units with low-capacity parameters should be explored. [ABSTRACT FROM AUTHOR]

As one of the world's largest energy consumers, China is facing the challenge of growing energy demand. Under this background, China is actively implementing the concept of green development and sustainable development route. As inexhaustible green energy, solar energy, has been established as an independent energy type by the Renewable Energy Law and has a broad development prospect. At present, the industrialization level of photovoltaic manufacturing in China is constantly improving, and the efficiency of photovoltaic power generation is constantly improving. However, from the perspective of the system, China's photovoltaic industry supporting legal system is not perfect. There is a mismatch between the existing laws and industrial development needs, which restricts the future development of photovoltaic power generation in China. The legal environment is crucial to the development of a country's relevant industries. Only with a good supporting legal system can the development and utilization of solar energy be carried out reasonably and orderly. The PV industry legislation should be adjusted and responded to in a timely manner according to the development situation of the PV industry and the PV market, so as to speed up the introduction of core laws in the PV field, continuously improve the supporting legal system, and effectively play the role of institutional protection of the law. [ABSTRACT FROM AUTHOR]

Wind energy is a type of renewable and clean energy which has attracted more and more attention all over the world. The Northwest China is a region with the most abundant wind energy not only in China, but also in the whole world. To achieve the goal of carbon neutralization, there is an urgent need to make full use of wind energy in Northwest China and to improve the efficiency of wind power generation systems in this region. As forecast accuracy of the near-surface wind is crucial to wind-generated electricity efficiency, improving the near-surface wind forecast is of great importance. This study conducted the first test to incorporate the subgrid surface drag into the near-surface wind forecast under the complex terrain conditions over Northwest China by using two TopoWind models added by newer versions of the Weather Research and Forecasting (WRF) model. Based on three groups (each group had 28 runs) of forecasts (i.e., Control run, Test 01 and Test 02) started at 12:00 UTC of each day (ran for 48 h) during the period of 1–28 October 2020, it was shown that, overall, both TopoWind models could improve the near-surface wind speed forecasts under the complex terrain conditions over Northwest China, particularly for reducing the errors associated with the forecast of the wind-speed's magnitude. In addition to wind forecast, the forecasts of sea level pressure and 2-m temperature were also improved. Different geographical features (wind-farm stations located south of the mountain tended to have more accurate forecast) and weather systems were found to be crucial to forecast accuracy. Good forecasts tended to appear when the simulation domain was mainly controlled by the high-pressure systems with the upper-level jet far from it. [ABSTRACT FROM AUTHOR]

The application of renewable energy has become increasingly widespread worldwide because of its advantages of resource abundance and environmental friendliness. However, the deployment of hybrid renewable energy systems (HRESs) varies greatly from city to city due to large differences in economic endurance, social acceptance and renewable energy endowment. Urban policymakers thus face great challenges in promoting local clean renewable energy utilization. To address these issues, this paper proposes a combined multi-objective optimization method, and the specific process of this method is described as follows. The Hybrid Optimization Model for electric energy was first used to examine five different scenarios of renewable energy systems. Then, the Technique for Order Preference by Similarity to an Ideal Solution was applied using eleven comprehensive indicators to determine the best option for the target area using three different weights. To verify the feasibility of this method, Xiongan New District (XND) was selected as an example to illustrate the process of selecting the optimal HRES. The empirical results of simulation tools and multi-objective decision-making show that the Photovoltaic-Diesel-Battery off-grid energy system (option III) and PV-Diesel-Hydrogen-Battery off-grid energy system (option V) are two highly feasible schemes for an HRES in XND. The cost of energy for these two options is 0.203 and 0.209 $/kWh, respectively, and the carbon dioxide emissions are 14,473 t/yr and 345 t/yr, respectively. Our results provide a reference for policymakers in deploying an HRES in the XND area. [ABSTRACT FROM AUTHOR]

According to the International Energy Agency (IEA)'s forecast, China will fully electrify its railway system by 2050. However, the development of electrified railways is limited in the weak areas of China's power grid. To surpass these limitations, we turn our attention to new railway energy sources, among which the most suitable is photovoltaic power generation. To evaluate the feasibility of integrating railway systems and photovoltaic power generation in China, this paper analyzes the geographical conditions and railway layout of China, gives a potential method for evaluating railway asset energy transformation measures and asset energy transformations that are suitable for China's national conditions, and proposes an application of railway system energy transformation on this basis. Then, this method is used to evaluate the potential of a substation section of the Lanxin high-speed railway in China for reference. The results show that the green energy potential and scheduling potential of China's railway assets are great and can effectively alleviate the energy anxiety of China's railway system. [ABSTRACT FROM AUTHOR]

The trade-off between economic growth and ecological improvement has always become an important and difficult issue for many countries, especially for developing countries. Due to a long-term extensive economic growth pattern, the regional resource allocation deviates from the optimal, especially the existence of energy misallocation, which hinders the maximization of economic output. Therefore, considering the characteristics and heterogeneity of resource endowments in different regions and increasing renewable energy consumption, that is, promoting energy transition, is it capable of sustainable development under China's actual conditions? The exploration of the issue is a core step in the research of the impact of renewable energy on industrial green transformation. Based on the panel data of 30 regions in China from 2009 to 2016, this paper constructs a threshold model from the perspective of regional energy misallocation and empirically tests the nonlinear mechanism of renewable energy consumption to promote industrial green transformation. The results show that China's energy allocation efficiency is low, there is a certain misallocation phenomenon, and the improvement effect in recent years is not satisfactory. Further, the relationship between renewable energy consumption and industrial green transformation is not a simple linear relationship, but a double threshold effect due to regional energy misallocation. In areas with severe energy misallocation, renewable energy consumption does not have a significant boost to industrial green transformation. Finally, this paper proposes the policy enlightenment of promoting industrial green transformation from the aspects of performance evaluation, market reform, and factor flow. [ABSTRACT FROM AUTHOR]

To study the impact of the promotion of electric vehicles on carbon emissions in China, the full life carbon emissions of electric vehicles are studied on the basis of considering such factors as vehicle weight and grid mix composition, and fuel vehicles are added for comparison. In this paper, we collect data for 34 domestic electric vehicles, and linear regression analysis is used to model the relationship between vehicle weight and energy consumption. Then, a Hybrid Life Cycle Assessment method is used to establish the life cycle carbon emission calculation model for electric vehicles and fuel vehicles. Finally, the life cycle carbon emissions of electric vehicles and fuel vehicles under different electrical energy structures are discussed using scenario analysis. The results show that under the current grid mix composition in China, the carbon emissions of electric vehicles of the same vehicle weight class are 24% to 31% higher than that of fuel vehicles. As the proportion of clean energy in the grid mix composition increases, the advantages of electric vehicles to reduce carbon emissions will gradually emerge. [ABSTRACT FROM AUTHOR]

Wind power has become the main power generation method in China's clean energy power generation because of its clean and high efficiency, as well as its high power utilization rate. The research on its pricing mechanism has also become the main research focus of the wind power industry. However, wind power pricing is still at the stage of price benchmarking and no market mechanism has been introduced in China. There are still much research on the pricing mechanism of wind power for us to study. In this paper, the Kernel method is used to distribute wind power income. On the basis of the distribution result, considering the contract execution risk of wind power, cooperative game theory and the Shapley value method are used to redistribute the revenue of wind power connected to power grid. Based on the characteristics of alliance members, ANP (Analytic Network Process) was used to modify the apportioned benefits to obtain the benefit distribution method that was more in line with the interest demands of members, and an example was analyzed. The wind power pricing model based on the cooperative game established in this paper can guarantee the smooth operation of the alliance, reach the pareto optimum, and improve the activity of the wind power market. It will effectively shorten the negotiation time, and reduce the transaction cost and the uncertainty of the wind power transaction. [ABSTRACT FROM AUTHOR]

To solve the problem of the low utilization ratio of clean renewable energy in public institutions, the basic information of energy utilization in public institutions was investigated. The suitability of passive energy use in public institutions was studied. According to the basic information and evaluation index of passive energy utilization in public institutions, the suitability of different types of passive energy (solar and geothermal energy) was studied by combining the resource conditions in different climate zones and the characteristics of energy utilization in typical public institutions, and the suitability distribution map was formed. In terms of research methods, the CRITIC (Criteria Importance Though Intercrieria Correlation) method based on the characteristics of objective data, and the natural breakpoint method based on the structure of objective data, were selected. Based on the climatic zoning of the buildings, this study conducted a suitability zoning. Each climatic region of buildings was divided into three sub-regions, which were the passive energy suitability regions of public institutions in the climatic region of the buildings. Finally, all of the regions with the same suitability were partitioned in order to obtain the final results. The distribution map of suitability for the different types of public institution buildings in the different regions is creatively established, which provides the basis for the selection of passive energy technology application schemes for public institution buildings in different regions, and provides macro guidance for energy planners and scheme designers. [ABSTRACT FROM AUTHOR]

Residents' willingness to use green energy products is a major concern for different stakeholders and policymakers due to the reformed Chinese electricity market since 2015. This study focused on the Chinese consumer's willingness to opt and pay for environmentally-friendly electricity sources in Shanghai's residential sector. A questionnaire survey was used to find out the influence of gender, age, education, awareness and income level on consumers' attitudes to alter their energy sources to green ones. The results indicated that income level and awareness are significant barriers in the usage of green energy products. Increasing the respondent's awareness about the issues of non-green energy products convinced 97% of them to change their electricity sources completely or partly, in line with their monthly income. This clearly shows that clarifying the benefits of green energy products is a key tool to achieve a green environment in China. [ABSTRACT FROM AUTHOR]

In recent years, growing peak pressure is posing a huge challenge for the operators of electrical power systems. As the most important clean renewable energy, hydropower is often advised as a response to the peak loads in China. Thus, a novel hybrid sine cosine algorithm (HSCA) is proposed to deal with the complex peak operation problem of cascade hydropower reservoirs. In HSCA, the elite-guide evolution strategy is embedded into the standard sine cosine algorithm to improve the convergence rate of the swarm. The Gaussian local search strategy is used to increase the diversity of the population. The random mutation operator is adopted to enhance the search capability of the individuals in the evolutionary process. The proposed method is applied to solve the complex peak operation problem of two hydropower systems. The simulations indicate that in different cases, HSCA can generate the scheduling results with higher quality than several benchmark methods. Hence, this paper provides a feasible method for the complex peak operation problem of cascade hydropower reservoirs. [ABSTRACT FROM AUTHOR]

As a clean and efficient energy, natural gas is the main driving force for China's energy transformation and the realization of green energy development. Distributed natural gas combined cooling, heating and power (DNG-CCHP) is the main utilization method of distributed natural gas with many advantages. However, the economics of China's DNG-CCHP system are poor, hindering the healthy and sustainable development of the system in China. Thus, this paper systematically analyzes the factors affecting the economics of DNG-CCHP system in China. Firstly, through the analysis of the development status and obstacles of China's DNG-CCHP system, the economic impact factor set of DNG-CCHP systems involving 34 factors is constructed. Then, the integrated DEMTTEL-ISM method is used to construct the multi-level hierarchical structure of influencing factors, the influence mechanism of the factors on the economics of DNG-CCHP system is systematically and comprehensively analyzed, and the path of improving system economics is proposed accordingly. Finally, based on the research findings and the development of China's DNG-CCHP, the coordinated development of DNG-CCHP and distributed renewable energy was discussed, and several policy suggestions are put forward to contribute to the integration of distributed natural gas and distributed renewable energy. [ABSTRACT FROM AUTHOR]

Along with the high growth rate of economy and fast increasing air pollution, clean energy, such as the natural gas, has played an important role in preventing the environment from discharge of greenhouse gases and harmful substances in China. It is very important to accurately forecast the demand of natural gas in China is for the government to formulate energy policies. This paper firstly proposes a combined forecasting model, name GM-S-SIGM-GA model, to forecast the demand of natural gas in China from 2011 to 2017, by constructing the grey model (GM(1,1)) and the self-adapting intelligent grey model (SIGM), respectively; then, it employs a genetic algorithm to determine the combined weight coefficients between these two models. Finally, using the tendency index (the annual changes of the share of natural gas consumption from the total energy consumption), which completely reveal the annual natural gas consumption share among the market, to successfully adjust the fluctuated changes for each data period. The natural gas demand data from 2002 to 2010 in China are used to model the proposed GM-S-SIGM-GA model, and the data from 2011 to 2017 are used to evaluate the forecasting accuracy. The experimental results demonstrate that the proposed GM-S-SIGM-GA model is superior to other single forecasting models in terms of the mean absolute percentage error (MAPE; 4.48%), the root mean square error (RMSE; 11.59), and the mean absolute error (MAE; 8.41), respectively, and the forecasting performances also receive the statistical significance under 97.5% and 95% confident levels, respectively. [ABSTRACT FROM AUTHOR]