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With the aggregation of renewable energy in the power system, the uncertainty caused by the renewable energy affects the planning and operation of power systems. Meanwhile, the existing planning models fail to consider renewable energy uncertainty methods, specifically concerning renewable energy confidence and future possible scenarios; thus, a confidence-based scenario cluster method is presented. A novel generator, network, load, and energy storage (GNLS) co-planning model is proposed in the paper. First, a confidence-based scenario cluster is built, which can reflect uncertainties by clustering and analyzing wind, solar, and load. Second, the proposed model focuses on load and energy storage co-planning, and in addition, relevant flexible indices are used to assess the model. Finally, the GNLS co-planning model is built as a bi-level stochastic model on continuous time scales. The model is solved using the Benders decomposition algorithm. The method in this paper is validated using an IEEE RTS 24-bus and a real test system in China to demonstrate the reduction in renewable energy curtailment and optimization of economic factors in power system planning. [ABSTRACT FROM AUTHOR]

This paper investigates the evolving landscape of blockchain technology in renewable energy. The study, based on a Scopus database search on 21 February 2024, reveals a growing trend in scholarly output, predominantly in engineering, energy, and computer science. The diverse range of source types and global contributions, led by China, reflects the interdisciplinary nature of this field. This comprehensive review delves into 33 research papers, examining the integration of blockchain in renewable energy systems, encompassing decentralized power dispatching, certificate trading, alternative energy selection, and management in applications like intelligent transportation systems and microgrids. The papers employ theoretical concepts such as decentralized power dispatching models and permissioned blockchains, utilizing methodologies involving advanced algorithms, consensus mechanisms, and smart contracts to enhance efficiency, security, and transparency. The findings suggest that blockchain integration can reduce costs, increase renewable source utilization, and optimize energy management. Despite these advantages, challenges including uncertainties, privacy concerns, scalability issues, and energy consumption are identified, alongside legal and regulatory compliance and market acceptance hurdles. Overcoming resistance to change and building trust in blockchain-based systems are crucial for successful adoption, emphasizing the need for collaborative efforts among industry stakeholders, regulators, and technology developers to unlock the full potential of blockchains in renewable energy integration. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy resources, their variable, intermittent and unpredictable characteristics bring new challenges to the power system. These challenges require micro-meteorological data and techniques to provide more support for the power systems, including planning, dispatching, operation, and so on. This paper aims to provide readers with insights into the effects of micro-meteorology on power systems, as well as the actual improvement brought by micro-meteorology in some power system scenarios. This paper provides a review including the relevant micro-meteorological techniques such as observation, assimilation and numerical techniques, as well as artificial intelligence, presenting a relatively complete overview of the most recent and relevant micro-meteorology-related literature associated with power systems. The impact of micro-meteorology on power systems is analyzed in six different forms of power generation and three typical scenarios of different stages in the power system, as well as integrated energy systems and disaster prevention and reduction. Finally, a case study in China is provided. This case takes wind power prediction as an example in a power system to compare the performance when applying micro-meteorological data or not. The experimental results demonstrated that using the micro-meteorological reanalysis dataset with high spatial--temporal resolution for wind power prediction performed better, verifying the improvement of micro-meteorology to the power system to some extent. [ABSTRACT FROM AUTHOR]

South Africa possesses extremely rich renewable energy resources, including solar energy and wind energy, in spite of this, South Africa traditionally relies on coal as its primary energy source. Recently, South Africa confronted with severe electricity shortage. With pressure of power shortage and the transition to low- carbon energy, South Africa has been actively promoting the development of renewable energy, aiming to diversify its energy structure. China, as the world's leading country in the development and utilization of renewable energy, has long been committed to helping South Africa achieve renewable energy development. This article reviews the cooperation progress between China and South Africa in renewable energy, and provides an in-depth analysis of the challenges currently faced by both sides. To address such challenges, it is suggested to strengthen the design of top-level cooperation mechanisms, create diverse collaboration platforms, prioritize the exchange and collaboration of scientific talents. This paper aims to provide a typical reference for deepening renewable energy cooperation between China and South Africa. [ABSTRACT FROM AUTHOR]

With a significant share of renewable power generation integrated into the power supply, it is crucial to timely adjust the regulating peak load for coal-fired power plants equipped with CO2 capture to ensure the stable operation of the multi-energy supply system. In this paper, the effects of varying boiler loads on the techno-economic performance of the retrofitted power plant were studied. Furthermore, the potential for reducing the cost of CO2 capture was investigated, and early opportunities for demonstration were discussed. Results showed that when the boiler load decreased from 100% turbine heat acceptance condition to 50% turbine heat acceptance condition, the cost of CO2 capture increased from 37.0 $/t CO2 to 57.0 $/t CO2, cost contribution of energy penalty and extra capital investment also increased from 20.6 $/t-CO2 to 25.7 $/t-CO2, and from 16.4 $/t-CO2 to 31.3 $/t-CO2, respectively. Furthermore, by improving separation efficiency from 0.15 to 0.5, a 25% to 30% reduction in CO2 capture cost can be achieved. The cost of CO2 capture could decrease by 42.2–50.5% when the cumulative capacity reaches 250 GW under the high investment learning rate scenario. According to the distribution of coal prices and renewable energy sources in China, the early demonstration projects of multi-energy supply systems should prioritize the northern region. The results of this work can provide informative references for making roadmaps and policies for CO2 emission reduction toward carbon neutrality. [ABSTRACT FROM AUTHOR]

Developing renewable energy has become a major strategy for China to accelerate the energy transition and combat climate change. Accordingly, a guarantee mechanism for renewable energy consumption with renewable portfolio standards (RPS) has been set in China. However, currently, the top-down allocation of regional renewable energy consumption targets often has issues of unfairness and inefficiency. It is necessary to investigate the issue of how to stimulate the renewable energy consumption potential on the demand side and reasonably formulate the consumption responsibility weights of various market entities. This paper aimed to develop a new methodology for the weight allocation of renewable energy consumption responsibilities. In doing so, an integrated model of an evolutionary game and stochastic optimization was constructed between market entities and governments. Then, the equilibrium strategies of market entities and governments were obtained through the evolutionary game. Furthermore, based on the equilibrium strategies, this paper optimized the renewable energy consumption weight of each market entity, which constitutes the optimal allocation scheme of renewable energy consumption responsibility weights. Finally, using the data of 7069 market entities in Hubei Province in 2021, this study simulated the model to verify its effectiveness and practicability. The results indicate that the willingness of market entities to assume more consumption responsibility is positively correlated with the government's incentives and the maturity of the green electricity trading market. This study provides important implications for optimizing government regulations and promoting renewable energy consumption. [ABSTRACT FROM AUTHOR]

A wind–hydrogen coupled power generation system can effectively reduce the power loss caused by wind power curtailment and further improve the ability of the energy system to accommodate renewable energy. However, the feasibility and economy of deploying such a power generation system have not been validated through large-scale practical applications, and the economic comparison between regions and recommendations on construction are still lacking. In order to solve the aforementioned problems, this paper establishes an economic analysis model for the wind–hydrogen coupled power generation system and proposes a linear optimisation-based priority analysis method focusing on the major net present value for regional energy system as well as a cost priority analysis method for hydrogen production within sample power plants. The case study proves the effectiveness of the proposed analysis methods, and the potential to develop wind–hydrogen coupled power generation systems in various provinces is compared based on the national wind power data in recent years. This provides recommendations for the future pilot construction and promotion of wind–hydrogen coupled power generation systems in China. [ABSTRACT FROM AUTHOR]

Renewable energy (RE), the key to realizing the low-carbon transformation of energy, is significant in reducing social carbon emissions (CEs). However, the regional differences in the impact of RE on CEs and the impact mechanism are still unclear. Clarifying the regional differences and impact mechanisms will encourage the government to formulate a development plan for RE in line with the actual situation. To explore the heterogeneous impacts and conduction paths of RE on CEs, this paper uses province-level data from 2000 to 2019 in China. Based on clarifying regional CE efficiency, panel quantile regression models are introduced to assess the differential impacts of RE at different CE efficiencies. And the conduction mechanism of the effect of RE on CE efficiency is explored through the parallel multiple mediation effect model. The results show that RE development, the accumulation of social affluence, and technological innovation can improve carbon efficiency while increasing population size and energy consumption inhibit carbon efficiency. Urbanization rate, industrial structure, and opening to the outside world affect travel alienation in regions with different levels of CE efficiency. RE development improves regional CE efficiency, which is more evident in regions with low and high efficiency. In addition, RE can increase CE efficiency indirectly through effects on population size, urbanization rate, scientific and technological innovation, and intensity of fossil energy consumption. However, it decreases CE efficiency indirectly through social enrichment. These findings will provide broader insights for improving the development of regional RE and formulating differentiated policies. [ABSTRACT FROM AUTHOR]

Hydrogen is receiving unprecedented momentum and is projected to meet a sizable share of the final energy demand in the future. The coupling between the hydrogen and power systems can help integrate volatile renewable energy, reduce curtailment, and realize long-term energy storage. Different coupling modes are being proposed, yet quantitative comparisons are absent. To fill this gap, this paper firstly summarizes the system composition of potential power–hydrogen coupling modes and analyzes their advantages and disadvantages. Then, a model for power–hydrogen coordinated planning optimization is proposed for quantitative analysis. Southern Xinjiang is chosen as a representative of the northwestern area in China, which has plentiful renewable resources and a relatively small local load at present, for a case study. Through result analysis, it is found that the mode of power–hydrogen coupling at the source side, either for in situ utilization or for long-distance transport via pipelines, shows better economic competitiveness. The discussion provides suggestions and a reference to policymakers for formulating infrastructure or industry plans in advance to better accommodate the rapidly developing renewable energy. [ABSTRACT FROM AUTHOR]

Additively manufactured nano-MEH systems are widely used to harvest energy from renewable and sustainable energy sources such as wind, ocean, sunlight, raindrops, and ambient vibrations. A comprehensive study focusing on in-depth technology evolution, applications, problems, and future trends of specifically 3D printed nano-MEH systems with an energy point of view is rarely conducted. Therefore, this paper looks into the state-of-the-art technologies, energy harvesting sources/methods, performance, implementations, emerging applications, potential challenges, and future perspectives of additively manufactured nano-mechanical energy harvesting (3DP-NMEH) systems. The prevailing challenges concerning renewable energy harvesting capacities, optimal energy scavenging, power management, material functionalization, sustainable prototyping strategies, new materials, commercialization, and hybridization are discussed. A novel solution is proposed for renewable energy generation and medicinal purposes based on the sustainable utilization of recyclable municipal and medical waste generated during the COVID-19 pandemic. Finally, recommendations for future research are presented concerning the cutting-edge issues hurdling the optimal exploitation of renewable energy resources through NMEHs. China and the USA are the most significant leading forces in enhancing 3DP-NMEH technology, with more than 75% contributions collectively. The reported output energy capacities of additively manufactured nano-MEH systems were 0.5–32 mW, 0.0002–45.6 mW, and 0.3–4.67 mW for electromagnetic, piezoelectric, and triboelectric nanogenerators, respectively. The optimal strategies and techniques to enhance these energy capacities are compiled in this paper. Highlights: The current technology evolution and advancements in 3DP-NMEHs were evaluated. Categorization and comparison of 3DP-NMEHs were performed in terms of inputs and energy conversion. The emerging applications of 3DP-NMEHs were discussed briefly. The potential challenges confronted by 3DP-NMEHs and respective recommendations were summarized. Sustainable use of recyclable waste generated during the COVID-19 pandemic was proposed. [ABSTRACT FROM AUTHOR]

Since the emergence of the COVID-19 pandemic, people all around the globe have seen its effects, including city closures, travel restrictions, and stringent security measures. However, the effects of the COVID-19 pandemic extend beyond people's everyday lives. It impacts the air, water, soil, and carbon emissions as well. This article examines the effect of energy and the COVID-19 pandemic on China's carbon dioxide emissions in light of the aforementioned context, using the daily data from 20 January 2020 and ending on 20 April 2022. Using the nonlinear autoregressive distributed lag model for empirical analysis, the findings indicate that COVID-19 pandemic confirmed cases and renewable energy advance environmental sustainability due to their negative effects on carbon dioxide emissions, whereas fossil fuel energy hinders environmental sustainability due to its positive effect on carbon dioxide emissions. Moreover, these results are also supported by the results of the frequency domain causality test and the Markow switching regression. In light of these results, there are several policy implications, such as vaccination, renewable energy utilization, and non-renewable energy alternative policies, which have been proposed in this paper. [ABSTRACT FROM AUTHOR]

LVDC buildings use a low-voltage direct current (LVDC) distribution system for energy transmission and integrate photovoltaic (PV), battery energy storage (BES) and the utility grid as building energy resources. This technology can reduce energy loss in conversion to a certain extent and increase renewable energy compared with traditional AC distribution systems. Under the national goal of carbon peaking and carbon neutrality, LVDC buildings have been proven to be a new approach to energy conservation and emission reductions, and have been applied in engineering in China. However, the construction methods and integrated technologies of those projects are not clear, and technical barriers and policy constraints for the engineering application of LVDC buildings are not systematically discussed yet. This paper presents the latest study of LVDC building engineering applications the advantages and drawbacks of LVDC development in China. First, relevant policies and standards which support the development of LVDC building industries are summarized. More than 60 practical LVDC projects are investigated, and the application characteristics and the technology status of building types, and their capacity and design methods, etc., are analyzed. The attitudes of stakeholders toward LVDC buildings are surveyed to determine the policy direction according to the demands of this technology, with reference to building practitioners who intend to engage in LVDC projects. [ABSTRACT FROM AUTHOR]

The current legal and policy system of renewable energy in China has set up a framework of policy instruments, which provides an important foundation for dealing with climate change and promoting the healthy development of the renewable energy industry with the goal of "carbon neutrality" and "emission peak". However, the current policy instruments cannot meet the needs for rapid development of the renewable energy industry. This paper investigates, analyzes, and concludes the status of renewable energy development along with related policy instruments in different renewable energy sources by literature and points out the problems of current policy instruments, and puts forward corresponding countermeasures. Moreover, from the perspective of energy consumption market transformation, the authors carried out an analysis of China's challenges of policy instruments, pointing out that China's renewable energy policy instruments have the potential to take one step further in the areas of the regulatory system, legal system and legal security system. [ABSTRACT FROM AUTHOR]

With global climate change posing a major threat to human society, a growing number of countries have taken "carbon-neutral" as a national strategy and proposed a vision of carbon-free future. As an important supplement to traditional fossil energy, renewable energy is the main force to reduce the use of high-carbon energy and carbon dioxide emissions, which will become the trend of social development in the future. Finding the optimal renewable energy source is of particular significance for achieving the net zero emissions. However, the existing evaluation methods of renewable energy sources have obvious shortcomings. In terms of weight calculation methods, such as the randomness of the subjective method is strong and the index weights do not reflect the small changes of the evaluation matrix, which affect the reliability and accuracy of the evaluation result. The existing ranking methods can only achieve the complete ranking of the different objects, but cannot classify the renewable energy technical alternatives into different grades. Given this background, this paper proposes a novel evaluation method for renewable energy plans based on improved sparrow search algorithm and projection pursuit model. Firstly, this paper improves the traditional sparrow search algorithm from three aspects: population initialization, population update and population variation. Then, the projection pursuit model is constructed, and the improved sparrow search algorithm is applied to optimize the projection target to find the optimal projection direction, so as to determine the weight values of each evaluation index. Finally, the weighted rank-sum ratio method is used to select the best renewable energy technical plan, which can not only realize the complete ranking of different plans, but also classify the technical plans into different levels. Based on the actual renewable energy development data from a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some existing evaluation methods of renewable energy technical plans. [ABSTRACT FROM AUTHOR]

Power system planning is essential for ensuring development of a safe and stable power system. Articles on power system planning have been published in recent decades. In this paper, bibliometric analysis is applied to power system planning to identify its basic characteristics, and to summarize the research hotspots and trends. A total of 4562 publications were obtained from the Web of Science during 1971–2020. The 4562 publications cover 8136 keywords, 1526 journals, 107 countries/territories, and 2519 institutions. IEEE Transactions on Power Systems is the key journal in power system planning; USA, China, and England are the countries that dominate publication production. Research hotspots and trends were analyzed using author-keyword co-occurrence analysis and keyword burst detection. Four clusters were identified and topics from Cluster I (central nodes as power system stability and transient stability) and Cluster IV (central nodes as power system simulation and economics) are important for power system planning research. However, their relative popularities have declined over the past two decades. This indicates that Cluster II (central nodes as renewable energy, wind power, and energy storage) and Cluster III (central nodes as distributed generation and smart grids) will be the focus of future research. [ABSTRACT FROM AUTHOR]

A hybrid integrated energy system (HIES) optimal scheduling model is developed by considering energy storage (ES), solar thermal (ST), wind turbine (WT), demand response (DR), and carbon capture and storage (CCS) to minimize the economic cost and pollutant gas emission; A multi-population dung beetle optimizer based on Q-learning (MODBO-QL) is designed to solve the HIES optimal scheduling model. In MODBO-QL, three subpopulations are constructed first: Global search subpopulation (GSS), Local search subpopulation (LSS), and Balanced search subpopulation (BSS). Second, the search and evolution strategies corresponding to the population characteristics are given. The GSS and LSS perform global search and local search, respectively. At the same time, the BSS is responsible for randomly exploring or exploiting the solution to increase the diversity of the population. Finally, the number of populations is dynamically and adaptively adjusted using Q-learning to enhance information exchange between different sub-populations and improve solution performance. An optimal dispatch experiment was conducted using three typical seasonal load data and renewable energy forecast data in a region of China. The experimental results show that compared with the original system, the three typical quarters' average economic costs of the dispatch results obtained by the method in this paper are reduced by 8.7%, 20.99%, and 3.97%, and carbon emissions are reduced by 617.35t, 284t, and 19.5t, respectively; MODBO-QL performs better compared to MOPSO, MOWOA, and MODBO. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy sources (RES) significantly increases, their inherent uncertainty has brought about a paradigm shift in distribution system planning and operation. The existing planning methodologies usually consider the uncertainty during the investment and day-ahead stages while neglecting the interactive power fluctuations during the intraday stage, resulting in planning schemes with a risk of inadequate regional stability. In light of this, this paper proposes a novel adaptive robust distribution system planning approach that integrates local uncertainty management (LUM) to provide a comprehensive and refined consideration of uncertainty. First, we introduce the concept of LUM that can effectively address the deviations between predicted and actual power of RES and load. Second, we propose the adaptive robust planning model for the distribution system, in which the investment and day-ahead operation are considered in the first stage, while LUM is considered in the second stage to maintain the local stability of interactive power. The proposed model can actively manage uncertainties of RES and load demand, reaching the optimal balance between the investment cost, operational cost, and LUM cost. Finally, by employing the column-and-constraint generation (C&CG), the adaptive robust planning model is decoupled into a master min problem and a slaver max-min problem, after which the max-min problem is converted into a single mixed-integer linear programming (MILP) problem through the strong duality theory and big-M method. The effectiveness of the proposed model is demonstrated using a real-world distribution system located in Fujian Province, China. The simulation results indicate that the proposed model achieves a quantitative decrease of approximately 10% in total cost compared to deterministic planning. Sensitivity analysis is also conducted to reveal the trade-off between economic cost and robustness level. • Local uncertainty management (LUM) is incorporated into distribution system planning. • A three-stage framework is developed for the planning problem incorporating LUM. • An adaptive robust distribution system planning model incorporating LUM is proposed. [ABSTRACT FROM AUTHOR]

Even though China has mapped out different policies targeting the mitigation of its environmental degradation, the country still occupies the 1st position in the ranking of carbon emissions due to excessive utilization of non-renewable energy sources in its domestic economic activities. From a theoretical and empirical point of view, factors like economic growth, financial development, export diversification, technological innovation, and renewable energy can be considered to play a vital role in environmental quality. Therefore, this study exposes the environmental performance of China amidst export diversification, financial development, innovation, and clean energy use. Due to the non-availability of data, the annual data of China are converted to quarterly data from 1995Q1 to 2018Q4, and autoregressive distributed lag-(ARDL) and Granger causality approaches are adopted in this study for quantitative and insightful analysis. The findings from both approaches expose the environmental implications of the selected variables (renewable energy, financial development, technological innovation, and export diversification) to China's sustainable development. ARDL approach has confirmed the inverted U-shaped link between financial development and emissions of carbon for China, a negative link between renewables, technologies, and carbon emissions, and a direct association exists between the diversification of export, economic growth, and emissions of carbon. This pattern points toward mitigating environmental dilapidation with renewable energy, technology, and financial development. The Granger causality output lends support to the ARDL findings; hence, a policy initiative that will promote the renewable energy sector and technological innovation through financial programs is advised. [ABSTRACT FROM AUTHOR]

Future energy system will feature in a high-share of renewable energies (REs), which poses huge challenges to obtain full utilization of renewable power generation. To solve the problem, this paper presents a joint-operation two-stage mixed integer linear programming model to coordinate the power system and train transportation system by optimizing the logistics of mobile batteries and real-time charge/discharge in cities. The full/empty batteries are transported through the train transportation system between the load side and the renewable energy station, which improves renewable energy penetration, economics, and mobilities. The first-stage model deals with the preliminary logistic optimization of batteries including routes, time and capacities of transportation, and the second-stage model optimizes real-time schedule of charge/discharge of batteries. The aim of the proposed model is to minimize the transportation cost, maximize the utilization rate of renewable energy in an energy system and smooth the daily load curve. The proposed model considers technical constraints such as railway transportation capacity, load demand satisfaction and renewable energy consumption in the power system. The optimal logistics plan and real-time charging and discharging plan can be obtained for both full and empty battery transportation. The validity of the method is verified with real data from Northeast China and Northern China, including railway routes, renewable energy output, and load profiles. The results show that the renewable energy penetration rate increases from 84.4% to 95.3% in Northeast China and from 42.7% to 78.2% in Northern China under the 2050 scenario with the proposed method. The peak-to-valley difference in the daily load curve decreases by 84% and 100% in Northeast China and Northern China, respectively. Meanwhile, the case study shows that the costs of battery transportation decrease from 0.398 CNY/kWh and 0.377 CNY/kWh to 0.252 CNY/kWh and 0.254 CNY/kWh when energy density of battery increases from 0.170 kWh/kg to 0.250 kWh/kg. This indicates that mobile energy storage has great economics. Moreover, renewable energy penetration rate is increased and peak-to-valley difference of daily load curve is decreased with no additional transmission lines. The model moves renewable energy from renewable energy power plants to end users. [Display omitted] • MESS aims to solve uneven spatio-temporal distribution of renewable energy and load. • A framework for joint operation of power system and transportation system is proposed. • The model is validated based on real data from Northeast and North China. [ABSTRACT FROM AUTHOR]

Rapid developments in environmental infrastructure have contributed to significant improvements in green total factor productivity, but further investigation is required to provide a detailed assessment to understand the policy mechanisms involved. This paper analyzes environmental progress in China through MMQR, CCEMG, and AMG as empirical strategies for 30 provinces in China. Our empirical results reveal that energy optimization through renewable energy is the most effective channel to improve green total factor productivity, though it is not the only available option. Since environmental regulations, infrastructure development, and green technology innovation also directly impact energy efficiency, adopting these within policy channels will positively impact environmental sustainability. Our empirical approach helps suggest novel environmental policy suggestions. In particular, policymakers must introduce structural changes within energy developments to foster renewable energy. Furthermore, China must increase environmental spending to upgrade its energy infrastructure further and solve ecological issues. These insights offer valuable policy guidance for decision-makers in China and globally, aiming to foster economic and environmental sustainability and achieve zero-carbon transition goals. • The current study analyses the role of green factor productivity in zero carbon transition. • We use 30 chinese provincial datasets from 2005 to 2020. • Energy transition is the most effective channel to improve green total factor productivity. [ABSTRACT FROM AUTHOR]

Large-scale renewable integration presents an effective way to decarbonize power grids, but carries increased risk of supply shortfalls owing to its volatility and uncertainty. Storage is a promising option to improve the generation adequacy of renewable. Thus, capacity credit assessment of renewable and storage is crucial in ensuring adequate generation capacity to meet loads. However, efficiently and accurately assessing capacity credit of these resources is challenging due to temporal dependencies in the operational time series (net loads and conventional generation units) and the need for extensive operation simulations. This paper develops a comprehensive multi-time-scale assessment framework integrating analytical and simulation methods to calculate the capacity credit of renewable and storage, thus capturing temporal features of these time series. Then, an interval-based strategy is proposed to simulate system operations, incorporating demand response in key scenarios. Furthermore, partitioning around medoids clustering and parallel computing techniques are employed to greatly accelerate the numerous operations for capacity credit assessment. The proposed method is validated using the RTS-79 system and a provincial real-world power grid in China. The results indicate that the developed framework can achieve efficient and refined capacity credit assessment and thus evaluate the impact of storage on the capacity credit. • Propose a novel method to quantify temporal features of operational time series for capacity credit (CC) assessment. • Develop a multi-time-scale CC assessment framework with an interval-based strategy to capture temporal features. • Utilize partitioning around medoids clustering and parallel computing to greatly enhance calculation efficiency. [ABSTRACT FROM AUTHOR]

As a renewable energy source, wind power has received growing attentions, but mostly been utilized in wind power farms or high-rise buildings where the wind with high speed is available. Few studies focus on the micro-wind utilization in low-rise buildings due to the bottleneck that the wind speed cannot meet the minimal speed requirement of small wind turbines. This paper aims to identify the optimal building design which could enable the harvesting of the maximal micro-wind power around low-rise residential buildings. Based on the comparison among different building shapes and the computational fluid dynamics (CFD) analysis via software Phoenics , this paper identified that the building shape of “composite prism” could enable the harvesting of the most micro-wind power. The identified building shape was then tested in a simulated environment of a residential community in Pingtan Island, China. The local wind conditions, long-term community planning, and the requirement of comfort level were all considered in the simulation model. The result shows that the potential of utilizing wind energy in low-rise residential buildings is huge by adopting the proposed building shape of “composite prism”. The finding has significant implications for renewable energy utilization in built environment. [ABSTRACT FROM AUTHOR]

Nuclear energy currently accounts for a declining share of global electricity, but it is possible that rising concerns about global climate change and China's ambitious nuclear program could reverse this trend. This review attempts to assess the global future of nuclear power, showing how the optimistic forecasts in the early days of nuclear power have been replaced by far more modest forecasts. The review first discusses the controversies surrounding nuclear power. It then briefly examines the prospects for three proposed reactors of the future: Small Modular Reactors; Generation IV breeder reactors; fusion reactors. It finally discusses the social and political context for nuclear power, both today and in the future. [ABSTRACT FROM AUTHOR]

In spite of ambitious investments by the Chinese government in renewable energy sources, the country's investment in coal power accelerated in recent years, raising concerns of massive overcapacity and undermining the central policy goal of promoting cleaner energy. In this paper, we ask why this happened, focusing on policies that incentivized excessive entry in the coal power sector and using a simple economic model to illustrate the policies' effects. Using coal-power project approval records from 2013 to 2016, we find the approval rate of coal power was about 3 times higher after approval authority was decentralized, with larger effects in regions producing more coal. We estimate that local coal production accounts for an additional 54GW of approved coal power in 2015 (other things equal), which is about 1/4 of total approved capacity in that year. [ABSTRACT FROM AUTHOR]

Renewable energy (RE) plays a crucial role in global energy transformation, and a thorough study of the potential impact of RE on regional carbon emissions is of great significance. This is particularly relevant to China, which needs to clarify its path to carbon reduction. Using the sample data of 30 provinces in China from 2000 to 2021, this paper uses the Granger causality test to verify the causal relationship between carbon emission intensity (CEI) and other factors. It builds a mediation effect model on this basis to explore the direct impact effect and indirect transmission path of renewable energy utilization (REU) on CEI. The results show that REU has a one-way causal relationship with CEI. REU can directly and indirectly reduce CEI by improving social wealth and changing the direction of energy investment. In addition, REU indirectly increases CEI through the transmission paths of investment in the energy industry - social affluence and industrial level-social affluence. The CEI is indirectly reduced through the conduction paths of (social affluence-Urbanization rate), (Investment in the energy industry-Urbanization rate), (Industrial level-Urbanization rate), and (Industrial level-Investment in the energy industry). These conclusions will assist policymakers in exploring targeted pathways for low-carbon power development, providing a reference for strategic and sustainable carbon reduction policies. [Display omitted] • Enrich the view of renewable energy's indirect impact on carbon emissions. • Renewable energy's carbon emission reductions go beyond replacing fossil fuels. • Constructed a mediating effect model between renewable energy and carbon emissions. • Renewable energy can promote or inhibit carbon emissions through various pathways. • Research helps develop more effective renewable energy sustainability strategies. [ABSTRACT FROM AUTHOR]

Factors influencing the development of renewable energy have emerged as a hot topic of policy discussion in recent years. However, the direct impact of natural resource dependence on renewable energy has been overlooked, especially in emerging economies. Using Chinese province data from 2005 to 2019, this study employs a nonlinear panel model and a Quantile Regression with Nonadditive Fixed Effects for Panel Data (QRPD) model to examine the impact of natural resource dependence on renewable energy development and explores the great importance of government policy support. The study indicates that the influence of natural resource dependence on renewable energy development supports the "conditional resource curse" hypothesis, showing a U-shaped nonlinear relationship. The effect of natural resource dependence and government policy support on the development of renewable energy shows a dynamic change trend. In the early stage of renewable energy development, policy support is crucial, while during the growing and mature stages, natural resource dependence exerts a stronger driving force. The results of the mechanism test indicate that natural resource dependence can boost renewable energy development through green technology innovation and environmental regulation channels. This paper's conclusion provides a theoretical foundation for fostering the sustainable development of the renewable energy industry and is vital to China's efforts to reach peak carbon and carbon neutrality. • Nonlinear panel and QRPD models are employed. • Impact of natural resource dependence (NRD) on renewable energy development is revealed. • Dynamic effect of NRD and government policy support is discussed. • NRD has a negative and then positive impact on renewable energy development. • Pathways to improve renewable energy development through NRD are identified. [ABSTRACT FROM AUTHOR]

Transnational power cooperation contributes to promoting the large-scale use of renewable energies in Asia, but how to develop the structure of power transmission network deserves careful investigation. This paper thoroughly investigates the structure of power investment and transmission network in China and neighbouring countries, aiming to promote the large-scale use of renewable energies around Asia. Various power-generation and power-transmission technologies are considered in detail, and transnational power interconnection risk cost and carbon emission cost are embedded into a bottom-up optimization model. The future power-generation structures and networks installed under various policy scenarios are analysed in detail, and the corresponding economic, carbon emission reduction and renewable energy consumption benefits are evaluated. The results show that power transmission between China's internal regions and neighbouring countries can save $56.7 billion for the entire power system through the large-scale grid integration of renewable energy. The installed capacities of wind power, solar power, and hydropower renewable energy power generation could be increased by 141 GW, 163 GW, and 58 GW, respectively. The construction of transnational transmission lines from Central Asia to Northwest China, Mongolia to North China, the Russian Far East to Northeast China, and Northeast China to South Korea could effectively promote the utilization of renewable energy in the Russian Far East, Mongolia and Central Asia and further reduce carbon emissions in China, Japan and South Korea. • It examines how to construct a power investment and transmission network in Asia. • Various technologies, risks and costs are considered based on bottom-up model. • Huge benefits exist for power transmission between China and neighbouring countries. • Various power-generation structures and networks installed are analysed in detail. • Suggestions about power investment and transmission are given for Asian countries. [ABSTRACT FROM AUTHOR]

Renewable energy is an effective solution to alleviate the energy crisis and achieve sustainable development, and is gradually growing into an important energy source to support economic and social development. Different renewable energy technical plans have different characteristics under multiple criteria. Therefore, before the further exploitation of renewable energy sources, it is of great significance to evaluate the comprehensive performance of different plans and then determine the best renewable energy sources. Given this background, this paper proposes a novel evaluation method for renewable energy technical plans based on improved rough set theory and grey cloud model. Firstly, based on the normalized evaluation data, self-organizing map (SOM) clustering algorithm and system clustering analysis are adopted to discretize the evaluation attributes indexes and decision attributes indexes, respectively. Next, the improved rough set conditional information entropy method is used to calculate the index weights. Finally, the grey cloud model is employed to determine the grade classification of each evaluated plan. At the same time, each grade is quantified by assigning a corresponding score, and the comprehensive performances of renewable energy plans can be calculated, and then the ranking of different plans is achieved. Based on the actual renewable energy development data from a province in China, experiments were carried out to evaluate the performance of the proposed method. The results show that the proposed method performs better than some evaluation methods for renewable energy technical plans. [Display omitted] • Propose a novel evaluation method for renewable energy technical plans. • Use self-organizing map clustering algorithm to discretize the attributes indexes. • Apply improved rough set theory to determine the index weights. • Achieve the complete ranking of plans, and classify the plans into different levels. • Compared with the existing evaluation method, the proposed method performs better. [ABSTRACT FROM AUTHOR]

Multiple geographically dispersed data center buildings have been increasingly positioned in renewable-rich areas due to their high energy consumption. This paper proposes an optimal multi-energy portfolio for zero carbon data center buildings in the presence of proactive demand response programs. In the data center buildings, the complementarities of hydro-solar-wind hybrid renewable energy sources (RESs) are fully exploited via multi-energy conversion and storage devices, which are modeled as 100% renewable energy hubs for zero carbon multi-energy supplies. In order to exploit the hub-internal operational dispatchability and flexibility, a comprehensive energy consumption model of data center buildings is proposed to facilitate the demand response in terms of available serves, while the variable speed pumped storage (VSPS) is modeled to capture its dynamic operational characteristics. The energy‑carbon production, conversion, storage, and consumption are formulated as an energy‑carbon matrix, which are solved via a two-stage method to obtain the optimal portfolio. Case studies on southwest China data center buildings are implemented to demonstrate the effectiveness and superiority of the proposed methodology on cost-effective accommodation of RESs. Simulations results show that the system portfolio cost can be reduced by at most 17.8% with a higher operational flexibility. • A cost-effective data center building is proposed to accommodate zero carbon targets. • Hydro-solar-wind 100% renewables are formulated for multi-energy supplies. • VSPS and data center energy consumption are modeled to exploit inherent flexibility. • The energy-carbon production, conversion, storage, and consumption are optimized. [ABSTRACT FROM AUTHOR]

Background: Considering energy- and climate-related policies adopted, the European Union and the People's Republic of China are expected to be on the same trajectory of reducing pollution, aiming for carbon neutrality in 2050 and 2060, respectively. However, although they share a common goal of more sustainable development, their targets and means often collide. The main objective of the study is to identify the main similarities and differences in approaches to energy and climate policies in the European Union and the People's Republic of China, with special attention given to the scope, past, present, and future of Chinese investments in renewable energy projects in the countries of the Central and Eastern European region, and to reveal the prevailing factors of the (un)successful renewable energy projects in those countries eventually. The methods used are literature review and qualitative content analysis of the European Union's and the People's Republic of China's energy- and climate-related policies according to the prescribed indicators (from 2005 onwards) and in-depth exploratory desk research of cooperation in renewable energy projects between the People's Republic of China and 14 Central and Eastern European Countries (from 2014 onwards). Results: The study showed that despite the significant alignment of the European Union's and the People's Republic of China's energy- and climate-related policies on a normative level, renewable energy cooperation between the People's Republic of China and Central and Eastern European countries on a practical level is moderate. This state of play mainly results from political factors, such as rising levels of Sino-scepticism and the overall deterioration of the European Union–People's Republic of China relations. Conclusions: The study showed that political rather than economic or legal factors had a great impact on the Chinese presence in the domain of renewable energy in the countries of the Central and Eastern European region. However, the significant alignment of the European Union's and the People's Republic of China's energy- and climate-related policies and dedication to common energy transition targets offer room for improving renewable energy cooperation. Overcoming political and economic divergences imposes a condition for achieving better cooperation in the renewable energy domain. [ABSTRACT FROM AUTHOR]

The developing countries rely heavily on imports of capital goods to spur economic growth. When the economy grows, energy consumption rises, adversely impacting climate change. The low levels of renewable energy share in total energy consumption, developing nations confront a difficult task in achieving the SDGs targets related to an increase in renewable energy share and access to affordable, reliable, and modern energy. Finding solutions to increase renewable energy usage is critical. International trade is an unavoidable part of development, prompting us to consider the impact of imports on renewable energy usage. This study explores the effects of imports of capital goods from China, EU and USA on renewable energy consumption in developing countries by using panel data from 20 countries spanning 2000–2018. It is found that capital goods imported from China in developing countries negatively impact renewable energy consumption while imports from EU have a positive impact on renewable energy consumption. However, in the case of US it is found negative but insignificant. The role of economic, social, and political globalization is explored, and it is found that three types of globalization are positively and significantly linked with renewable energy consumption. Thus, this study recommends that trade policies complement domestic efforts toward increasing renewable energy production and consumption in developing countries. [ABSTRACT FROM AUTHOR]

Reasonable regional integrated energy planning is an important prerequisite for the construction of a Near-Zero Carbon Emission Demonstration District (NCEDD). An integrated energy planning scheme that is based on a three-step planning method with the objective of achieving an NCEDD is proposed in this paper. First, the planning objectives should be determined. After that, the planning strategies should be established. Finally, the planning approaches should be proposed according to the previously determined objectives and strategies. A case study considering the integrated energy planning of the Meishan International Near-Zero Carbon Emission Demonstration District (MINCEDD) is investigated to explain the planning method. In addition, the planning results, which are indicated as indexes, are explained, analyzed, and compared to the ones of other districts. The indexes include a proportion of renewable energy to primary energy (73% by 2030 and 108% by 2050), a proportion of renewable power to total power consumption (98% by 2030 and 111% by 2050), and CO2 emission reduction rates (70% by 2030 and 100% by 2050) and are more advanced than other districts in China. This planning scheme and method can provide a reference for the integrated energy planning of NCEDDs in developed urban areas. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has significantly hampered energy transition. Globally, many are worried that energy transition is being neglected in favor of green economic recovery. This study first assesses the state of green economic development in China's urban areas. Therefore, we propose a low-carbon energy transition road plan for the post-pandemic age and provide a global analysis of the interplay between the energy transition and COVID-19. We examine the barriers to a smooth energy transition during the pandemic from the vantage points of governmental backing, carbon fuel divestment, renewable generation capacity, global distribution chain, and energy poverty. The pandemic created possibilities for a worldwide energy shift. Therefore, we also pinpoint possible windows of opportunity for energy transition afforded by the outbreak from the viewpoints of cost-effectiveness, policy execution efficacy, and renewable energy. Our findings have important implications for post-pandemic green recovery plans and provide new insights into a global low-carbon energy transition framework. [ABSTRACT FROM AUTHOR]

Renewable energy adoption is a complicated process that is influenced by a multitude of different factors. Previous studies seldom examine the social acceptance of renewable energy from the Chinese perspective in Shandong province. To bridge this gap, comprehensive research is crucial to find the public attitude towards renewable energy. We extended the theory of planned behavior by incorporating three additional factors, i.e., risk perception, environmental concern, and belief about renewable energy costs. A questionnaire survey was conducted in the four major cities of Shandong province. The Logit model was used to determine possible factors affecting public acceptance. Research findings reveal that residents significantly support renewable energy regarding its positive environmental impact. Individuals' education, personal income, awareness and belief of renewable energy consumption cost positively affect their intention to utilize renewable energy, as the willingness to pay increases with an increase in these factors while decreasing with individuals' age. Young people with higher education and income are willing to pay extra for green energy. Research results emphasized the importance of enhancing public awareness and highlighting renewable energy benefits to win public acceptance of renewable energy deployment. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) panels convert sunlight into electricity, and play a crucial role in energy decarbonization, and in promoting urban resources and environmental sustainability. The area of PV panels in China's coastal regions is rapidly increasing, due to the huge demand for renewable energy. However, a rapid, accurate, and robust PV panel mapping approach, and a practical PV panel classification strategy for large-scale applications have not been established. Here, we developed a new approach that uses spectral and textural features to identify and map the PV panels there were in coastal China in 2021 using multispectral instrument (MSI) and synthetic aperture radar (SAR) images, and the Google Earth Engine (GEE), to differentiate PV panels according to their underlying surface properties. Our 10-m-spatial-resolution PV panel map had an overall accuracy of 94.31% in 2021. There was 510.78 km2 of PV panels in coastal China in 2021, which included 254.47 km2 of planar photovoltaic (PPV) panels, 170.70 km2 of slope photovoltaic (SPV) panels, and 85.61 km2 of water photovoltaic (WPV) panels. Our resultant PV panel map provides a detailed dataset for renewable layouts, ecological assessments, and the energy-related Sustainable Development Goals (SDGs). [ABSTRACT FROM AUTHOR]

Developing energy from renewable sources and modernizing the energy system are critical components of China's efforts to combat climate change. Policymakers and authorities have made significant attempts to bring them. However, one of the major impediments to China's energy revolution is financial limitations, which are inextricably linked to the country's economic growth. The present research paper intends to investigate the relationship between economic growth and sustainable financial development on the use of energy from renewable sources in both the short and long run in the context of China. To achieve this, the researchers have utilized the panel data consisting of 10 years from 2011 to 2020. When compared to cross-sectional and time-series data samples, the panel data model offers many benefits. For starters, the panel data includes information on the passage of time and the cross-sectional area. Another benefit of using panel-data models with a larger degree of freedom is that they provide more stable and reliable estimates across short periods across cross-sections. In the case of the short run, there is a positive relationship between economic and financial development and the use of energy from renewable sources in the context of all of China. While in the case of long-term effects, the results indicate the adverse impact of financial development on the use of energy from renewable sources in the western regions of China. These results were deduced using the causality test Granger proposed to determine the path of the causal relationship and the direction of the relationship between the variables. These results indicated that the relationship between economic and financial development in east China was unidirectional, and the nature of the underlying relationship was causal. Meanwhile, in east and west China, economic development in China as a whole has been unidirectionally increasing energy from renewable sources. Our empirical findings suggest many strategies for promoting the growth of energy from renewable sources. [ABSTRACT FROM AUTHOR]

In the context of global warming and energy shortage, this paper discusses the techno-economic feasibility of a residential household based on 100% renewable energy in China. The energy storage life, equipment's residual value, system shortage capacity and atmospheric pollution emissions were considered comprehensively. A life cycle evaluation model based on the net present value (NPV) was built. Taking a real household as an example, the levelised cost of energy (LCOE) is 0.146 $/kW and the unmet load is only 0.86%, which has a big economic advantage when compared with diesel generators. If grid-connected, the system can bring $8079 in 25 years when the LCOE is −0.062 $/kW. The effects of the allowed shortage capacity, renewable energy resources, battery price and the allowed depth of discharge on the economy and energy structure were examined. For example, due to the features of the residential load, the influence of wind resource richness is more obvious than the irradiance. The maximum depth of discharge has less impact on the economy. This paper verifies the techno-economic rationality and feasibility of 100% renewable energy for a household. [ABSTRACT FROM AUTHOR]

Abstract: With the economic development, the demond for energy is growing in China, and the traditional non-renewable energy makes the energy transformation is imminent.In this paper, based on the fully understanding of our existing energy structure, and from the perspective of supply and demand, the author analysis the changes in the structure of future energy and gives the suggestion.The paper argues that China''s energy structure will gradually turn to be comprehensive and diverse, but the cost and market constraints will obstruct the promotion of renewable energy sources.Therefore, the traditional high-carbon energy is still the main form of domestic energy consumptionin a very long period of time.Only by improving technology and using of the macro-control can reduce the costs and regulate the import and export, which can achieve sustainable development of changing from a high-carbon energy to low carbon. [Copyright &y& Elsevier]

Improving the efficiency of renewable energy and electricity utilization is an urgent problem for China under the objectives of carbon peaking and carbon neutralization. This paper proposes an optimization scheduling method of electric vehicles (EV) combined with wind and photovoltaic power based on the Frank-Copula-GlueCVaR. First, a joint output model based on copula theory was built to describe the correlation between wind and photovoltaic power output. Second, the Frank-Copula-GlueCVaR index was introduced in a novel way. Operators can now predetermine the future wind-photovoltaic joint output range based on this index and according to their risk preferences. Third, an optimal scheduling model aimed at reducing the group charging cost of EVs was proposed, thereby encouraging EV owners to participate in the demand response. Fourth, this paper: proposes the application of a Variant Roth–Serve algorithm; regards the EV group as a multi-intelligent group; and finds the Pareto optimal strategy of the EV group through continuous learning. Finally, case study results are shown to effectively absorb more renewable energy, reduce the consumption cost of the EV group, and suppress the load fluctuation of the whole EV group, which has a practical significance and theoretical value. [ABSTRACT FROM AUTHOR]

The rapid growth of Chinese renewable energy innovation has shown great potential to improve total-factor carbon productivity (TFCP), but it still lacks sufficient literature understanding of the detailed impact and mechanisms. This paper empirically tests the influence mechanism, effect characteristics, and causal configurations based on a sample of 30 provinces from 2006 to 2017. We reveal optimizing the energy structure is the crucial intermediate channel for renewable energy technology innovation improving the TFCP, but it is not the unique mechanism. Since renewable energy technology innovation could directly improve the efficiency of the full industrial chain of electricity, the remaining channel can be summarized as the power operating efficiency improvement. From the heterogeneous analysis, the technological innovations in wind and solar energy display relatively larger contributions to the growth of total-factor carbon productivity. The threshold effect analysis further indicates the larger the coal consumption, the larger probability for renewable energy innovation to play a role in improving TFCP. Eventually, we observed four causal configurations of renewable energy technology innovation to determine the TFCP. It suggests renewable energy innovation cannot affect TFCP independently and often acts with other factors, eventually explaining the actual levels of regional TFCP. [Display omitted] • Renewable energy innovation can be a positive force in low-carbon transition by rising the total-factor carbon productivity. • Optimizing the energy mix is a channel through which renewable energy innovation contributes to the low-carbon transition. • The remaining channel can be summarized as the power operating efficiency improvement. • If there is a large coal consumption level gap, renewable energy innovation can profoundly impact on the low-carbon transition. • There are four causal configurations for renewable energy innovation to play a role. [ABSTRACT FROM AUTHOR]

• Proposing a new robust co-planning of AC/DC hybrid transmission network and energy storage. • Embedding BFM within the proposed co-planning model. • Using data-adaptive robust optimization to cope with uncertainties. • Validating the effectiveness of the proposed method in practical power system. This paper proposes a robust co-planning model of hybrid AC/DC transmission network and energy storage with the penetration of renewable energy to promote the accommodation of renewable energy and to avoid investment redundancy. The energy storage configured in the power grid can improve the power flow distribution and alleviate transmission congestion, postponing the investment of new devices. A deterministic co-planning model is firstly developed considering voltage fluctuation, reactive power flow and the flexibility of voltage source converter based high voltage direct current (VSC-HVDC). To address the solving complexity caused by the non-convexity of the model, second-order cone programming (SOCP) is applied to transform the proposed model into a convex problem. To cope with the uncertainty of renewable energy, the robust co-planning formulation is established, where the data-adaptive uncertainty set with the extreme scenario method is introduced to describe renewable generation uncertainty. The column-and-constraint generation (C&CG) algorithm is adopted to decompose the robust co-planning problem into a master problem and several slave problems, which reduces the calculation scale and accelerates the solving process. Two case studies on a modified Garver's 6-bus system and a practical Jiangxi province power system in China are carried out to verify the effectiveness and superiority of the proposed robust co-planning model. [ABSTRACT FROM AUTHOR]

Green bonds have increasingly been utilized around the world as a source of financing for renewable energy development, designed with compliance requirements and measurable economic returns to investors, while mitigating climate change. However, the efficacy of green bond arranged in the emerging economies for financing renewable energy assets and how the underlying risks are managed have remained to be explored. The paper aims to examine the evolving green financial system sponsored by both public and private institutions in managing such risks within China's emerging economy. A case study of green financing for a bundle of wind power assets led by a state-owned enterprise (SOE) reveals an alternative approach by structuring public–private collaboration while stipulating market-based financial incentives to institutional stakeholders under a political economy. This institutional consortium is composed of a state development bank, a commercial bank, credit rating agencies, institutional and private investors, regional power purchasers, and carbon trading entities. Financial stakeholders' risk in such emerging sustainable investment is moderated by these participating institutions and structured "upsides" from carbon trading aligned with the framework of green finance and standards for green bond development. The results reveal the potentials of scaling up the development of renewable energy by adequately managing and sharing key risks, while allocating substantial funding into renewable energy projects under such a green financial system that is to be complementary with a scalable post COVID-19 economic recovery. [ABSTRACT FROM AUTHOR]

Both air pollutant and carbon emissions are serious problems in China, particularly the economic and pollution intensive urban agglomerations. Therefore, this paper selected the most severely polluted Jing-Jin-Ji region (Beijing, Tianjin and Hebei urban clusters) to study the potential of mitigating both carbon and air pollutant emission. The Computable General Equilibrium (CGE) model was applied and six scenarios were constructed by considering policies of environmental tax and renewable energy. Business-as-usual (BaU) scenario assumed no tax and fixed renewable energy to 2002 level, whereas other scenarios assumed that intensified renewable energy (RE) and tax on sulfur dioxide (SO 2 ) (TAX1 and TAX2) were implemented either alone or combined. The results indicated that both RE and SO 2 tax could contribute to regional low-carbon development and air pollutant reduction, and the reduction is about 10–40% depending on scenarios. However, Renewable energy is always more effective in reducing CO 2 and air pollutant emissions than TAX1 but less effective than TAX2 for Hebei. From the sectoral perspective, power sector would exhibit both the largest emissions and reduction potentials in all three Cities, accounting for about 21%, 31% and 28% of total CO 2 emissions respectively for Beijing, Tianjin and Hebei, in 2020. Considering different policies, it is more effective for Hebei to apply renewable energy to reduce its power sector emissions in the long term, and tax policy is more effective for emission reduction from other sectors in Tianjin. Further analysis on the economic impact of mitigation policy revealed that Tianjin and Beijing would suffer relatively greater negative impacts than Hebei in terms of loss in total GDP, while developing renewable energy could help to reduce such negative impacts. It is suggested that the policymakers need to take into accounts the regional economic situation and natural resource potential when they formulate low-carbon policies. [ABSTRACT FROM AUTHOR]

Building heating energy consumption accounts for about 19.12% of total energy consumption in China in 2014. With the development of building energy-saving technology, renewable energy heating systems, such as GSHP (ground source heat pump), solar energy, air source heat pump, show high energy efficiency and low emissions, which are gradually replacing traditional heating systems. However, due to the restriction of existing productivity and environment conditions, the capital investment of such renewable heating systems is often much higher than traditional ones. As a result, the advantage of renewable heating system is hard to be realized, since its application is highly limited by economic factors. In this paper, traditional thermal-economical evaluation method is modified and improved for feasibility analysis of renewable heating system. Moreover, an illustrative example, which combines solar heating and air-source heat pump together, in Sichuan is given to show the preliminary application of the proposed method. The results indicate that the modified thermal-economical evaluation method can assess the feasibility of renewable heating systems more comprehensively, since it takes the indirect economic effects caused by energy saving and environment protection into consideration, which can balance the huge capital investment of such systems. For the studied case, solar collector combined with air-source heat pump is more preferable than traditional systems. This work is important for feasibility analysis and design optimization of building heating system with renewable energy. [ABSTRACT FROM AUTHOR]

Wind curtailment is currently one of the major challenges in China’s renewable energy development. Technical solutions to wind curtailment could include more flexible power sources, storage and grid facilities. These, however, are long-term solutions due to physical, economic and institutional constraints. We argue that an effective short-term solution could be the full utilization of the technical potential of the existing power system, particularly by using China’s dominant coal-fired power capacity to provide peak regulation services for the priority dispatch of wind power. This paper assesses the economic and environmental effects of this approach. To this end, four scenarios are developed: Scenario I—full wind curtailment and average dispatch of coal-fired power, Scenario II—no wind curtailment and average dispatch of coal-fired power, Scenario III— no wind curtailment and optimal dispatch of coal-fired power, and Scenario IV —partial wind curtailment and optimal dispatch of coal-fired power. The economic and environmental effects under the four scenarios are then computed using the GAMS program based on the load curve on a typical spring day for a coal-fired power plant and a wind farm in China’s northeastern Harbin City. The results of our research show that whilst Scenario IV has the best economic effects, Scenarios III and IV have the best environmental effects. Scenario IV is considered the optimal solution as more economic benefits could be realized at the expense of smaller wind curtailment. Drawing on these results, it is recommended to amend the Renewable Energy Law to allow a small proportion of renewable energy curtailment; to enhance the flexibility of the existing coal-fired power units; to advance energy efficient power dispatch approach; and to incentivise coal-fired power to provide peak regulation services for renewable energy. [ABSTRACT FROM AUTHOR]

In this paper, an improved hybrid MADM method based on interval Decision Making Trial and Evaluation Laboratory (DEMATEL) and interval regret theory are put forward to evaluate five renewable energy alternatives for Fujian province, China. Firstly, the volatility of the evaluation information and the subjectivity of experts or stakeholders are taken into account. Second, political factors are further considered based on existing research. Third, the influence of the subjective initiative of decision makers on the decision result has been paid attention to. Furthermore, the result of the analysis shows that the extended regret theory method should set different parameters to reflect the preferences of decision makers. The ranking results of renewable energy are different with different parameter settings. For example, when α = 0.1 , δ = 0.1 , the priority of clean energy in Fujian Province is hydropower, followed by wind energy, solar energy, geothermal energy, and biomass energy. Sensitivity analysis indicates that the model possesses good serviceability. Based on considering different parameter settings, the Fujian government should invest, in order of priority, in these renewable energy sources to obtain benefits. And the projects related to these renewable energy sources should be evaluated. [ABSTRACT FROM AUTHOR]

• A novel fractional grey Chebyshev polynomial Bernoulli model is proposed. • The structure of the NGBM(1,1,Tn) model is variable. • Comparative study shows that the new model is superior to other five grey models. Accurate mid-to-long term China's renewable energy forecasting is becoming more and more important for integrating renewable energy systems with smart grid and energy strategic planning. For this purpose, this paper proposes a novel fractional structural adaptive grey Chebyshev polynomial Bernoulli model. The new model is based on NGBM(1,1) model to describe nonlinear phenomena. The proposed adjacent accumulation operator can balance old and new information accumulation with less fluctuation. The introduction of the Chebyshev polynomial enables the model to adaptively adjust the structure of the model, which reduces the difficulty of the modeler's operation from the point of view of the expert system. Meanwhile, an improved Grey Wolf optimization algorithm (IGWO) was used to optimize the parameters. Four real cases of renewable energy in China, including production and consumption are used for verification. In addition, Monte Carlo simulation and probability density analysis illustrate the robustness and accuracy of the proposed model. Finally, the development of the four cases in the next 5 years is predicted. [ABSTRACT FROM AUTHOR]

Currently, quality air and renewable energy are main concerns in protecting the environment. Comparing willingness to pay for quality air and renewable energy is rare in the existing literature. However, the public faces these issues simultaneously. In addition, population mobility under China's household registration system, i.e., urban living experience, may affect the willingness to pay for environmental protection. Consequently, the difference between people's willingness to pay for quality air and renewable energy in China is discussed. Binary logistic regression is adopted to analyze the correlation factors based on data from the China General Social Survey. The results show that willingness to pay is influenced by environmental attitudes, awareness of energy use, government responsibilities, age, household income level, and trust. The effects of urban living experience on willingness to pay weakens as age decreases. Thus, improving environmental awareness and specialized knowledge remain important means of promoting willingness to pay. Our findings can help marketers and policy designers develop balanced or targeted measures when taking joint actions. [ABSTRACT FROM AUTHOR]

In response to the rising importance of the climate agenda, many countries have restructured their electricity markets to facilitate the utilization of renewable energy. China is an interesting case because it has expanded its utilization of wind and solar energy with unmatched speed. This review starts with an analysis of the 2002 reforms that uncoupled electricity production from transmission. The investigation covers the period leading up to the 2022 proposal, which aimed to build a nationally integrated electricity market. The analysis suggests that a careful alignment of incentives for key market players to produce and consume renewable energy is vital during the process of energy transition. The introduction of feed-in tariffs in 2009 for wind energy, which were subsequently extended to solar energy, stimulated a high growth in installed capacity. However, a high electricity curtailment rate resulted. Since 2018, the Chinese government has resorted to curtailment caps and renewable portfolio standards to increase the utilization of renewable electricity. After the announcement of the "dual carbon goals" in 2020, the Chinese government launched a series of reforms that aimed to nurture growth in the green electricity market and the formation of a nationally integrated electricity market. The removal of interregional trade barriers is a key element of China's current electricity market reforms and will be crucial to determining whether China can achieve its climate goals. [ABSTRACT FROM AUTHOR]

Renewable energy consumption is one of the most important factors in meeting the goal of "30 and 60" in China. However, the maintenance of the transmission section affects the amount of generation transfer, further affecting the consumption of renewable energy. Hence, in this study, a time-series renewable energy production simulation (REPS) is proposed in order to accurately predict the power generation in a simulated situation. According to the results of the REPS, the sensitivity of the different sections' maintenance can be calculated and determined. The appropriate maintenance strategies can be selected for different situations by comparing the consumption rate; as an example, we conducted a case study. The results show that the quota in the transmission section has higher sensitivity; a larger quota indicates a greater sensitivity to the consumption rate. The results also show that a larger quota is more suitable for maintenance in February or November, since the consumption rate is higher regardless of if it is in a single-transmission-section maintenance strategy or in a two-section simultaneous maintenance strategy. [ABSTRACT FROM AUTHOR]