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• A simple, large-scale fabrication of cooling paper via pulping and papermaking technology. • The cooling paper is facilely deconstructed and regenerated to architect the secondary cooler. • The cooling paper features a high solar reflectance of 94% and emissivity of 0.95. • The cooling paper shows the sub-ambient temperature drop of 6 ∼ 8.8 °C under directly solar radiation at daytime. Passive radiative cooling materials provide coldness without energy consumption for all-day thermal comfort by reflecting solar irradiation and emitting heat into the 3 K universe. However, the conventional radiative cooling materials involve complex multilayer structures and unsustainable polymers, showing the non-recyclable disadvantage, which induces resource waste and environmental issues. Herein, we reported a low-cost, scalable, and eco-friendly radiative cooling material (namely cooling paper), consisting of the delignified, fibrillated cellulose fibers and nano-sized hydroxyapatite (HA) via the mature pulping and papermaking process. The resulting cooled paper can be recycled, and it can be easily deconstructed to harvest cellulose fibers and HA, individually. The recovered source materials can be reused to prepare secondary products that show comparable performance to the original cooling paper, including a high solar reflectance of ∼94% and an infrared emissivity of ∼0.95 in the atmospheric transparency window. Such cooling paper enables superior cooling performance with the temperature drop of 6 ∼ 8.8 °C under directly solar radiation. Based on the EnergyPlus simulation, our cooling paper shows a average cooling energy saving of ∼29 % in buildings across China. The recyclable cooling paper with high cooling capability and scalability, as well as environmental friendliness, shows promising potential for sustainable energy-efficient buildings. [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]

Reducing greenhouse gas emissions is urgent for the global community with rising climates. Considering the importance of renewable energy in mitigating climate warming, forecasting renewable energy generation is vital for the Chinese government's future low-carbon and green development plan. This paper proposes a novel multivariable grey model based on historical data on China's renewable energy generation and three industries. A novel information accumulation mechanism with two adaptive factors is designed to improve the traditional multivariable grey modeling defect. Based on the proposed mechanism, this paper optimizes the initial and background values and nonlinear model structure with the whale optimization algorithm. The forecasting results show that the fitting MAPE is 1.13%, comprehensive MAPE is 2.60%, MSE is 50.86, and RMSE is 7.13, which significantly improve the forecasting accuracy of traditional GM(1,N) and are better than other compared models. The forecasting results show that China's renewable energy generation will gradually increase to 5834.02 TWh. The Chinese government should keep the previous Five-Year Plans rising trend of the three industries in the future Five-Year Plans to support renewable energy industries. In China's future energy system, it is necessary to promote incentive policies and capital investment for actively accelerated development to make renewable energy the leading force. [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]

The power industry in China is the foremost emitter of carbon, necessitating a clean transition in its power generation segment for successful decarbonization. Identifying and quantitative analysis the critical factors that shape the transition and their operational mechanisms will help achieve a secure low-carbon transformation of the energy system. Nonetheless, the body of extensive research in this particular field remains notably limited. To bridge this gap, this paper initiates by developing a comprehensive mechanism framework (REEP) for the transition towards cleaner power generation including Resource endowment (abbreviated as R), Electricity market (abbreviated as E), Economic development (abbreviated as E) and Policy implementation (abbreviated as P) four fields. This framework is based on monthly power generation data derived from four categories of power generation sources at the provincial level in China, spanning the years 2014–2019. It dynamically and quantitative elucidates the multifaceted impacts of various influencing factors on the evolution of the power generation structure. Based on the REEP framework, we observe the power generation structure evolution trends. The study's findings underscore the significance of resource endowment as a paramount internal factor influencing the clean evolution of the power generation structure, while economic development emerges as a pivotal external driving force propelling its transformation. Despite wind and solar power generation accounting for a relatively small proportion at the end of the 14th Five Year Plan period, it is imperative for China to continue promoting the extensive development of renewable energy. While we should coordinate national power transmission and distribution on the foundation of incentivizing technological innovation in emission reduction and the development of energy storage technology. This approach is essential to comprehensively advance the low-carbon and stable development of the energy system, ultimately facilitating the realization of the dual-carbon goal. [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]

As the integration of renewable energy sources (RES) progresses and energy technologies advance, integrated energy systems (IES) have emerged as a practical solution for leveraging RES and balancing local demand–supply dynamics. This paper investigates the planning and optimization of operations for an electricity-hydrogen integrated energy system (EH-IES), considering degradation and multi-timescale operations of storage devices to minimize the life cycle costs. To mitigate computational challenges, the planning model adopts a Representative Time Period (RTP) framework, and a novel intertemporal operation model is developed to coordinate the operations of battery and hydrogen storage. Furthermore, we propose a two-stage approach to derive representative weeks and hours from meteorological and load data using fewer data points, ensuring chronological coherence and preserving long-term patterns to validate storage device constraints and improve planning accuracy. Case studies in southeastern China's IES verify the effectiveness of our planning approach, which lowers equivalent annual costs by 4.5% and reduces battery capacity needs by 12.8%, thereby highlighting its significant economic benefits. Additionally, numerical analyses reveal that our two-stage method surpasses conventional RTP selection techniques, delivering at least 10% less average error with an equal number of operating points. • A novel planning and intertemporal model of EH-IES based on representative time periods is established. • The multi-timescale operations of the battery and hydrogen storage system is considered. • An efficient and accurate representative time-period selection method is developed. [ABSTRACT FROM AUTHOR]

Green total factor productivity is a vital measure of sustainable progress, holding immense implications for attaining both economic growth and environmental preservation in a mutually beneficial manner. Focusing on green total factor productivity, this paper examines its nexus with low-carbon energy technology using the Instrumental Variable Generalized Method of Moments (IV-GMM) model, based on a panel dataset in China during 2005–2018. This paper also examines the asymmetric impact of low-carbon energy technology on green total factor productivity. In addition, the moderating and mediating effects are checked. The main findings are that (1) low-carbon energy technology is a promoting factor for green total factor productivity. (2) Low-carbon energy technology exerts a more prominent marginal impact on green total factor productivity when green total factor productivity is at higher quantile levels. (3) Renewable energy generation not only drives green total factor productivity directly, but also further increases the promotion effect of low-carbon energy technology on green total factor productivity, showing a positive moderating role. (4) Energy structure transition and industrial structure transition are two internal impact mechanisms between low-carbon energy technology and green total factor productivity. These findings suggest that the government can promote green total factor productivity development by developing policies related to low-carbon energy technology and fully releasing the effects of technology on sustainable development. • This paper assesses the level of low-carbon energy technology (LCET) in China. • This paper examines the effect of LCET on green total factor productivity (GTFP). • LCET plays a significant role in enhancing GTFP. • Renewable energy generation positively moderates the nexus between LCET and GTFP. • Energy structure transition and industrial structure transition are two mechanisms. [ABSTRACT FROM AUTHOR]

Hydropower, an excellent power source for peak shaving, can respond quickly to load changes, which helps to overcome the anti-peak characteristics of wind and solar power. However, complementary operation with wind and solar power changes the operating boundaries of hydropower stations, making the original operating rules no longer applicable. Considering daily peak-shaving demands, this paper proposes a methodology to develop operating rules for a hydro–wind–solar hybrid system (HWSHS). First, a multi-objective and multi-timescale operation model is developed to ensure energy generation and peak-shaving performance simultaneously. Second, the feasible and reasonable region of available energy-based operating rules is analyzed, and three rule forms are employed to explore the optimal operating rules. Last, NSGA-II is utilized to optimize the rule parameters. A decoupling method is proposed to handle the shape constraints of operating rules, and the multi-timescale simulation method for a HWSHS is developed as the fitness function. In the simulation method, a load reconstruction-based load shedding (LRLS) method is introduced to determine the hourly output of each hydropower station. A forebay elevation check method is performed to obtain the hourly forebay elevation and correct the hourly output. The Beipan HWSHS in Guizhou Province, China, is selected as a case study, and the optimal operating rules are determined. The results also show that (1) the standard operating rules with hedging and packing increase peak-shaving performance by an average of 4.31% and 2.03% compared to the standard operating rules and linear operating rules, respectively; (2) the peak-shaving performance can be significantly increased (6.70%) with a slight decrease (0.37%) in the energy generation through changing the seasonal distribution of water and energy of the cascade hydropower stations; (3) the LRLS method, which makes the interday energy distribution more uniform, increases the peak-shaving performance by an average of 9.17% compared to the traditional load shedding method; (4) the energy generation and peak-shaving performance of the HWSHS are affected by the transmission capacity, and it is necessary to increase the transmission capacity by 25% to 35% of the installed capacity of wind and solar power stations. • A methodology is proposed to develop operating rules considering peak-shaving demands. • A multi-objective and multi-timescale operation model is developed. • The feasible and reasonable region of available energy-based operating rules is analyzed. • A multi-timescale simulation method is designed to simulate the operation of a HWSHS. • The methodology significantly improves the peak-shaving performance. [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]

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]

[Display omitted] • A wireless-energy green city concept integrating WPT, renewable energy, and infrastructure is proposed. • Security-electrification, digitization, and intelligence are bolstered by the incorporation of WPT into urban settings. • The SDGs are advanced by the wireless-energy green city's energy, environment, and social economy benefits. The significance of energy stability in urban development has increased as the use of intelligent devices continues to expand and extreme weather poses a threat to the integrity of electric networks. Wireless power transfer technology is well-known for its dependability, security, and adaptability, and has reached billions of market share in China alone in the cell phone and electric vehicle sectors over the past decade, with the electric vehicle wireless charging market expected to grow at a compound annual growth rate of over 30% by 2023. Combining WPT with renewable energy, electric vehicles, and recharge infrastructure is a promising new application for smart city development. This paper examines the history, characteristics, and six classifications of WPT technology through the lens of urban development, in addition to its successful implementations in seven contexts, such as portable devices and transportation, based on a survey of 161,425 papers. We propose a dependable and efficient research-to-application life cycle for high-power WPT operations, which is capable of achieving efficiencies of approximately 90 percent at approximately 50 percent of the original cost. In addition, the significance of incorporating WPT technology, renewable energy sources, and urban infrastructure into the design of sustainable cities is highlighted. This study determined that WPT is advantageous for promoting the construction of space and ground photovoltaic plants, as well as photovoltaic rooftops, and also serves to accelerate the promotion of electric vehicles, smart devices, and charging roads. The findings of this study cast light on three problem areas and six future prospects of WPT, as well as its multifaceted benefits in terms of energy, the environment, and the social economy of sustainable cities. [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]

In the context of China's dual‑carbon goals and electricity market reforms, the high penetration of renewable energy sources (RES) in a single microgrid (MG) imposes challenges to its flexible operation. This paper addresses the cooperation within a virtual power plant (VPP) aggregated by multiple MGs. The VPP, managed by the VPP operator, acts as an intermediate entity to facilitate MGs' economic and low-carbon operation. To ensure the interests of various stakeholders in the VPP, this work proposes a low carbon oriented tri-stage collaborative energy management framework. In the 1st stage, the day-ahead optimal scheduling of individual MGs and the bidding strategy of the VPP operator are modeled, where an internal pricing method is incorporated. The 2nd stage addresses the real-time redispatch of the VPP operator using a receding horizon-based model predictive control (MPC) solution. This aims to satisfy the real-time operational requirements of MGs and reduce adjustment costs. Furthermore, in the 3rd stage, a penalty allocation mechanism is established for MGs based on their forecasting errors, to protect the VPP operator's interests. The effectiveness of the proposed solution is evaluated through comparative simulations in the context of the Guangdong electricity market. • The virtual power plant (VPP) aggregated by multiple microgrids (MG) is studied. • The quantification of carbon emission is involved in the whole scheduling process. • An peer-to-peer (P2P) electricity trading pricing method is improved and adopted. • A penalty allocation mechanism is proposed to guarantee interests of each entity. • The Guangdong electricity market is considered as the market mechanism. [ABSTRACT FROM AUTHOR]

The supply chain of rare earth metals plays a crucial role in producing thin-film solar photovoltaics (PVs), which are vital for renewable energy generation. However, this supply chain is often characterized by opacity, inefficiencies, and security concerns especially since those rare earth metals come from mainly one supplier, China. Also, the solar PVs' certificates are purely technical and fail to consider the ethical sourcing and sustainable supply chain management conditions of mining. The working conditions of the miners are often neglected and are least prioritized. In this paper, a blockchain-based solution was proposed to leverage the intrinsic decentralized blockchain features including traceability, transparency, non-repudiation, and accountability in the supply chain of thin-film solar PVs, to safeguard not only the technical conditions of the mined products but also the ethical conditions of the workers during mining. Ethical mines must uphold good safety standards, pay their workers a fair wage, adhere to working hours, and legal working age. The solution paves the way to ethical mining where the certification of the PVs is not granted unless both technical and ethical conditions are met. The supply chain of thin-film PVs that goes from mining the rare earth metals in China, where more than 70% of the rare earth metals used are extracted from, to the disposal at the end-of-life (EOL) of the PVs was presented. Smart contracts to enable the on-chain traceability of the registration, manufacturing, assessment, delivery, and disposal of PVs were developed. The solution exploits the tamper-proof logs of the distributed ledger to ensure accountability and record transactions as part of the data provenance. The proposed solution includes a system design with sequence diagrams, smart contracts with algorithms, and a testing and analysis section. [ABSTRACT FROM AUTHOR]

The development of renewable energy is the key to tackling climate changes and ensuring energy security. Meanwhile, the process is usually associated with a huge investment and financing gap, especially for transitional countries like China. Hence, developing and innovating the traditional financial system is urgent. However, existing studies have mainly focused on traditional financial perspectives, and few papers have critically examined how digital financial inclusion affects the growth of renewable energy industries. To this end, using Chinese provincial data from 2011 to 2020 and applying Feasible Generalized Least Squares (FGLS) method, this study explores the effect of financial inclusion on renewable energy and its underlying mechanisms from the production and consumption perspectives. It is found that the impact of financial inclusion on the production side of the renewable energy industry is significantly better than on the consumption side, and there is heterogeneity effect in different energy resource varieties, different regional locations, and different financial regulation intensities. The financial inclusion on renewable energy has a significant pulling effect in photovoltaic and wind provinces, but not in large hydropower provinces. Then, its driving effect is particularly pronounced in the northern region. Nonetheless, it is not significant for renewable energy consumption in the southern region. Besides, this positive effect cannot be achieved without stronger financial regulation. The mechanism of action assessment finds that optimizes the consumption pattern, increase the competition of commercial banks, and improve technological innovation all contribute to the positive effect whereas, FDI shows the negative effect. This study provides new evidence on China's achievement of the energy transition, suggesting the need to further improve incentives for FDI firms and to strengthen consumption-side guidance. [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]

• Fast calculation methods for two types of overvoltage are developed. • The rising overvoltage during DC blocking period is revealed and studied. • An equivalent static load model of HVDC is proposed using singular perturbation method. • The relations between network structure and overvoltage is analyzed. • Voltage subsystem is proved to be a monotonic system holding order-preserving properties. In a multi-rectifier AC-DC system the blocking of a HVDC tie can cause severe transient overvoltage problems, which may trip off the renewable energy sources, endangering the safe operation of the system. This paper shows that the blocking of a HVDC tie can introduce two types of transient overvoltage. For Type I overvoltage, which is caused by the surplus reactive power injection immediately after the blocking, a simple estimation formula is proposed. Suppression strategies that can decrease the key matrix are examined using a matrix-theoretic approach. The formula is applicable for system integrated with renewable energy sources and it has reasonable accuracy for overvoltage level of interest. To study Type II overvoltage, a reduced-order mathematical model is first derived with singular perturbation method. Then it is demonstrated that this type of overvoltage is due to the increase of transient electromotive force of synchronous machines, and an analytical calculation procedure is suggested. In addition, it is found that the voltage subsystem of a multi-rectifier system is an input–output monotonic system with nice order-preserving properties. Increasing the proportional gain constant of excitation system or choosing generators with large open-circuit transient time constant can monotonically decrease this type of overvoltage. The simulations of an actual large-scale multi-rectifier system in China are described to validate the introduced results. [ABSTRACT FROM AUTHOR]

Among the regulatory policies, feed-in tariffs (FIT) and renewable portfolio standards (RPS) are the most popular to promote the development of renewable energy power industry. This paper uses system dynamics (SD) to establish models of long-term development of the renewable energy power industry under FIT and RPS schemes, and provides a case study of China's wind power industry by using scenario analysis method. The model, on the one hand, not only clearly shows the complex logical relationship between the factors but also reveals the process of coordination between the two policy tools in the development of the renewable energy power industry. On the other hand, it provides a reference for scholars to study similar problems in different countries, thereby facilitating an understanding of the renewable energy power's long-term sustainable development pattern under FIT and RPS schemes, and helping to provide references for policy-making institutions. The results show that the integrate implementation of FIT and RPS can promote long-term and rapid development of China's wind power industry given the constraints and actions of the mechanisms of RPS quota proportion, the tradable green certificates (TGC) valid period, and fines. In addition, in the period of the construction of the TGC market, RPS quota growth rate and fine have a no significant effect on industrial development, however, with TGC market game becoming more and more fierce, both of them have a significant positive impact on the long-term development of the industry. TGC valid period has little effect on industrial development in our study. At the end of the paper, policy implications are offered as references for the government. [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]

Timely, effective, and robust artificial intelligence (AI) technology is urgently needed to improve decision-making efficiency in the presence of renewable energy with high penetration by elevating the level of power grid intelligence. However, at this stage, AI technology lacks reliability and transparency, making it unable to play a full role in application areas with high-security requirements such as power systems. This paper presents a multi-hierarchical interpretable method for power system dispatch and operation based on the graph deep Q-network (GDQN) model to achieve active power corrective control. The multi-hierarchical interpretable method combined with an improved sample-balanced deep shapley additive explanation (SE-DSHAP) method and a subgraph explainer can promote a more intuitive and comprehensive explanation of decision-making for power systems with complex topology. Operators will obtain more noteworthy power grid areas through the proposed interpretable method as the basis of auxiliary decision-making to realize efficient and accurate control. Then, two cases studied in a modified 36-bus system in the IEEE 118-bus system and the 300-bus network of the China power grid validate the effectiveness of the proposed method. • A graph deep Q-network model achieve active power corrective in power systems. • The SE-DSHAP method improve the computational speed and accuracy. • A subgraph explainer bring a comprehensive understanding of the model predictions. • Two cases validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The utilization of renewable energy sources to produce electricity is capable of relieving the pressure of limited natural resources and achieving the sustainability of future energy. This paper addresses a multi-period multi-feedstock multi-technology biomass-based power generation supply chain planning problem with parameter uncertainty, balancing economic, environmental, and social objectives simultaneously. However, the main challenges in optimizing this problem are correlated with multiple conflicting objectives and uncertain parameters. This study proposes a novel globalized robust goal programming model with goal constraints to balance three conflicting objectives using priority levels and characterize the uncertainty of unit emissions and social scores by inner–outer uncertainty sets. After transforming globalized robust environmental and social goal constraints into their equivalent forms, the tractable counterpart of the proposed model is obtained, which is mixed-integer linear programming (MILP). Finally, the effectiveness of the proposed model is demonstrated through a case study about the design of a sustainable biomass-based power generation supply chain (SBPSC) network in Hubei Province, China. Computational results reveal that by adjusting several parameters, environmental and social aspired goals consistently can achieve, whereas the economic objective is vulnerable to being influenced. Comparative studies with nominal goal programming model and robust goal programming model indicate that the proposed model is uncertainty-immunized and less conservative. Under investigated circumstances, the realized economic profit by the proposed model is approximately 42.5% higher than that of the robust goal programming model on average. • A pair of uncertainty sets is presented to characterize the parameter uncertainty. • Globalized robust environmental and social goal constraints are established. • Established goal constraints are transformed into their computationally tractable forms. • A novel globalized robust goal optimization method is proposed to balance three conflicting objectives using priority structure. • A case study shows the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract To promote the renewable energy development and reduce the power grid safety risk, promoting the power local consumption generated by distributed photovoltaic energy has received increasing attentions. Moreover, with the power market development, integrating the power market trading with distributed photovoltaic energy is a trend in China. Aiming to these targets, the paper designs a business model integrating investment and consulting services into distributed photovoltaic energy system. First, to encourage the market trading and promote the power local consumption, the double layer power sale platform is designed consisting of both B to B and B to C platforms. And a discount power sale price for local consumption is carried out. In the power platform, power contracts with a punishment for power gap are signed before the power consumption. Second, the consulting services are cited to reduce the power gap caused by power market trading, consisting of power output, power load and annual new installed capacity prediction. Third, considering the local users' power demand increase brought by the discount power sale price, a dynamic investment is integrated in the business model. Moreover, the simulation model and a comparative analysis model of stakeholders' benefits of new business model is constructed. Finally, an empirical analysis is conducted. The results show that the new model not only can promote energy local consumption, improve environment quality, ensure power grid safety, but also increase distributed energy investors' benefits, and reduce users' electricity expenses and government subsidies. Graphical abstract Image 1 Highlights • The dynamic increment investment model of distributed generation is designed. • The simulation model of new distributed generation business model is set up. • Based on the consulting services, the investment mode and sale mode are combined. • The interactive consultation service module between supply and demand is designed. • The stakeholders' benefits under the new business model is calculated. [ABSTRACT FROM AUTHOR]

Highlights • Novel formulation of unit commitment model for large-scale power system simulation. • Validated with standard test system and applied to real regional systems in China. • Increase the computational speed by more than 20,000 times with ∼1% error. • Can be applied to planning issues, e.g. optimal sizing of wind, solar and hydro. Abstract Deploying high penetration of variable renewables represents a critical pathway for decarbonizing the power sector. Hydro power (including pumped-hydro), batteries, and fast responding thermal units are essential in providing system flexibility at elevated renewable penetration. How to quantify the merit of flexibility from these sources in accommodating variable renewables, and to evaluate the operational costs considering system flexibility constraints have been central challenges for future power system planning. This paper presents an improved linear formulation of the unit commitment model adopting unit grouping techniques to expedite evaluation of the curtailment of renewables and operational costs for large-scale power systems. All decision variables in this formulation are continuous, and all chronological constraints are formulated subsequently. Tested based on actual data from a regional power system in China, the computational speed of the model is more than 20,000 times faster than the rigorous unit commitment model, with less than 1% difference in results. Hourly simulation for an entire year takes less than 3 min. The results demonstrate strong potential to apply the proposed model to long term planning related issues, such as flexibility assessment, wind curtailment analysis, and operational cost evaluation, which could set a methodological foundation for evaluating the optimal combination of wind, solar and hydro investments. [ABSTRACT FROM AUTHOR]

Abstract Theses on China's low-carbon economy focus on the driving forces of carbon emissions, energy–carbon efficiency, and the carbon–economy nexus. Meanwhile, comprehensive frameworks that assess China's green economy and also analyze its driving forces are lacking. This paper constructs a green productivity index based on the Global Malmquist–Luenberger productivity index to evaluate the development of the low-carbon economy in China. A panel vector auto-regression model is adopted to further analyze the endogenous interactions and dynamic relationships among aspects of the green economy and its influencing factors. Empirical results show that China has seen a balanced development of green economic growth in 2000–2016, achieved in a series of clear stages. To maintain the sustainable growth of the low-carbon economy in the future, China must rely more on technological progress. This research also makes some important discoveries that have not been found in previous studies: the impact of China's industrial structure and energy consumption structure on the green economy are short term, having a duration of impact of approximately four years. Finally, we provide an assessment of provincial green economy development and describe the corresponding policy implications. Graphical abstract Image Highlights • A comprehensive analysis of China's green economy. • A slack-based Global Malmquist–Luenberger index is adopted to calculate the Green Productivity Index in China. • The stage characteristics of green economic development are identified. • A panel vector autoregressive model is used to analyze the interactions among aspects of the environmental economic system. • The findings that the impact duration of energy structure, industry structure, and urbanization on the green economy is about four years. [ABSTRACT FROM AUTHOR]

Abstract By building a green economy development index to measure the level of green economy development, taking 150 renewable energy listed companies of China for example, this paper constructs a threshold effect model to investigate the non-linear relationship between renewable energy investment and the green economy development index from the perspective of green credit. Results mainly show that: firstly, the impact of renewable energy investment on the green economy development index includes dual threshold effects from green-credit, the effect is divided into three stages: promoting, restraining and promoting successively. Secondly, for large-sized companies, the impact of renewable energy investment on the green economy development index includes one threshold from green-credit, with the effect coefficients at each stage being 0.1034 and −0.0113, successively. However, for medium, small, and micro sized enterprises, the impact of renewable energy investment on the green economy development index includes dual threshold effects from green credit, with the effect coefficients at each stage being 0.1310, −0.0033, and 0.0244, successively, and the inhibition effect not being significant. Thirdly, our results also find that improving expenditure on environmental pollution control and adjusting industry structure are conducive to increasing the green economy development index. Highlights • Non-linear relationship between renewable energy investment and green economy development is explored here. • Dual threshold effects from green-credit on renewable energy investment and green economy development exists. • Medium, small, and micro sized enterprises are the dominant forces of green economy. [ABSTRACT FROM AUTHOR]

Abstract In the context of China's severe energy and environmental problems, as an alternative vehicle fuel bioethanol is a promising choice because its theoretically renewable and carbon neutral. To guarantee grain security, China is developing non-grain fuel ethanol. Based on a life cycle assessment (LCA), this paper assesses the uncertainty of energy efficiency and environmental performance in the development of cassava ethanol of 1.5-generation bioethanol whose raw materials are non-staple crops. The results show that cassava ethanol is a good alternative vehicle fuel from the view of energy efficiency. From an environment perspective, global warming potential and photochemical ozone formation potential of cassava ethanol are better than gasoline, but acidification potential and respiratory inorganics inferior. The Monte Carlo simulation reveals cassava ethanol is feasible compared with gasoline in the case of uncertain variables. Among these uncertain variables, cassava yield, nitrogen fertilizer use, and steam use are the most important variables for energy efficiency and environmental performance of cassava ethanol. Furthermore, this study calculates the potentials for energy savings and emissions reduction with improving key aspects, and gives the suggestions of enhancing the performance of key indicators to promote the development of bioethanol vehicle fuel. Highlights • Cassava ethanol is a good alternative vehicle fuel from the view of energy efficiency. • Cassava ethanol decreases GWP and POFP, but increases AP and RI compared with gasoline. • Cassava yield, nitrogen fertilizer usage and steam usage have the greatest impact on cassava ethanol. • The potentials of energy-saving and emission-reduction are calculated with improving key aspects. [ABSTRACT FROM AUTHOR]

Highlights • Renewable quotas incorporating equity and duty are allocated at regional level. • The entropy method coupled with Management by Objectives principles is adopted. • Quotas scheme is dominated by differences in resource endowment and infrastructure. • Nexus for renewable-rich and developed areas is vital to fulfill renewable quotas. • Renewable revolution needs a twin-track process of nearby and external consumption. Abstract Feed-in tariff and renewable portfolio standards are two major policies for renewable power generation and China is the first country to adopt both policies at the national level. The black box for regional allocation of renewable portfolio standards, however, may lead to the instability and inefficiency of this policy. This paper introduces the principles of Management by Objective to build a clear policy framework for renewable portfolio standards in China and employs the entropy method to equally and reasonably assign regional responsibility for renewable energy development. A comparison is made between our allocation and the goal-directed policy to determine whether historical responsibility and infrastructure construction should be further considered in crafting renewable portfolio standards. The trends for regional renewable quotas are influenced by the twin-track approach of nearby utilization and external transmission. Finally, based on our analysis, the policy implications behind the new incentive are presented to urge the establishments of supporting facilities. [ABSTRACT FROM AUTHOR]

In order to assess the viability of wind energy projects in China, this paper takes a real options approach by accounting for flexibility over deployment of the technology in response to a stochastic non-renewable energy cost. The difference between the latter and the R&D-enhanced cost of renewable energy operation may be thought of as a benefit from using wind. On the other hand, the Chinese government provides feed-in tariffs to encourage further adoption of wind, which should be included as a cost. This work, thus, shows that there is substantial options value to China's wind energy program, which is bolstered if the cost of carbon dioxide emissions is internalized. Moreover, by varying the rate of feed-in tariffs, the model may be used to show plausible ranges for the feed-in tariffs, which could be a useful policy insight. Furthermore, the study hypothesizes that with the likelihood of renewable energy to replace non-renewable energy, wind power in China can potentially reduce CO 2 emissions by approximately 6.4% by 2020 comparing to the 2005 level and highlights the significant role of wind power in China's emission targets and energy security. This is a relevant paper, especially given the importance of a sustainable energy transition for China. More generally, using the real options approach from a policy perspective could provide insights about the viability of renewable energy programs and robustness of feed-in tariffs rates. [ABSTRACT FROM AUTHOR]

With the goals of carbon peaking and carbon neutrality, China's renewable energy has entered a stage of rapid development. The government gives financial and power-rate subsidies to clean power while putting great support on its construction. However, there is currently a big gap in the government's subsidy on new energy power generation, which is challenging to meet the demand for renewable energy consumption and sustainable development. In this regard, participating in the market is inevitable for renewable energy development, which is significant for promoting renewable energy power consumption and alleviating the subsidy dilemma. Considering the market-based mechanism, this paper regards the electricity market, the tradable green certificate market, and the carbon emissions allowance trading market as an integrated energy platform system that couples carbon emissions with electricity, linking generators and retailers. We consider two scenarios, including green electricity–green certificate separation and unity, and introduce the intermittency of new energy generation to construct the optimal pricing decision for this platform supply chain. We explore the optimal price of tradable green certificate for renewable power and the equilibrium demand for green power and thermal power in two circumstances. The results show that: (1) there is an equilibrium solution in various conditions. Moreover, the optimal price of green certificate in condition two, namely green electricity–green certificate unity, is higher when the on-grid price of green electricity and thermal one and the substitution effect of these two powers satisfy a certain constraint, which can stimulate generator and retailer to generate and consume green electricity as much as possible. (2) The price of carbon emissions and the responsibility weight of renewable electricity consumption both have a positive influence on the optimal price of green certificate and the share of equilibrium demands for green power, whereas intermittency of new energy generation has a negative impact on them. Adjusting the carbon price and incentive responsibility weights and taking relevant measures to reduce intermittency in different situations help to reduce thermal power demand and promote green power consumption. (3) When the prices of green certificates in diverse circumstances are in a certain interval, the impact intensity of changes in green certificate price on electricity demand will arise. It is conducive to performing the facilitative role of green certificate price on the power market better and stimulates the generation and consumption of new energy power in the carbon-electricity coupling market. • Through the carbon-electricity coupling perspective, the optimal TGC price was explored. • Introducing substitution effects of green power and thermal power to analyze optimal TGC prices. • A higher incentive responsibility weight of renewable consumption in scenario two can promote the retailer to consume. • Impact intensity of changes in green certificate price on power demand will arise when the price is in a certain interval. [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]

• The analysis framework of a digital business model of PV fishery is constructed. • Multiple value propositions in the digital business model of PV fishery to create value for stakeholders. • The digital business model of PV fishery improves the operational efficiency of PV power generation. • The digital PV fishery increases the diversification of revenue sources of PV agricultural company. A business model that combines solar photovoltaic and fishery plays a role to expand distributed renewable energy and improve rural economy development. However, the business model of solar photovoltaic fishery is encountering growing difficulties with revenues decreasing due to changes of policy such as withdrawal of photovoltaic subsidies and land control. The spreading of digitization in photovoltaic industry and rural areas provides new opportunities for innovation of traditional solar photovoltaic fishery. This paper focuses on multiple value propositions to discuss a digital business model based of solar photovoltaic fishery on a case with a local Chinese photovoltaic agricultural company. The study results show that the digital business model of solar photovoltaic fishery improves the operational efficiency of solar photovoltaic power generation, economic benefits of aquaculture, and the diversification of revenue sources of solar photovoltaic agricultural company and leasing company. The transformation of business model from traditional solar photovoltaic power generation and fishery business to digital service platforms can not only accelerate expansion of distributed renewable energy and be potential deployed globally but also assist photovoltaic agricultural company to effectively respond to policy change. Finally, this paper proposes that supportive policy may focus on advocating system coupling to expedite the development of distributed renewable energies based on digital technology, and digital photovoltaic combined with rural energy market and financial, which will advance development of renewable energy electrification and promote rural economic growth. [ABSTRACT FROM AUTHOR]

For the past three decades, the notion of sustainable cities has become central in planning and managing urban areas in the world and the large-scale city formation has been formed in big cities. As a result of economic growth and urban transformation, Shanghai, a major Chinese international metropolis, faces ongoing issues with resource waste and low energy utilization. Therefore, this paper aims to consider the availability of water for energy production from a consumption-based perspective and estimate the water footprint of the energy supply using a regional input-output analysis approach for Shanghai. As a result, we conclude that the water footprint of the energy supply of Shanghai is about 1.28 billion m 3 in 2007, with 46.1% of water used for electricity and heat supply. The total water withdrawal by the energy sector is about 2.35 billion m 3 and additional water withdrawal needed is about 35% of the total water withdrawals by the energy sector in Shanghai. Furthermore, an energy self-sufficiency scenario was developed, and the scenario analysis reveals that 55% more water would be required to ensure a self-sufficient energy sector. To guarantee future regional energy security, it is important to manage water resources effectively by allocating water resources among sectors. [ABSTRACT FROM AUTHOR]

The objective of this study is to trace and quantify the sources of rebound effect for different energy sources in China. This paper decomposes the economy-wide rebound effect into 135 production sector-level rebound effects and five final demand components. The sector-level rebound is further decomposed into output effect and substitution effect. A two-stage decomposition method and a static computable general equilibrium model are developed to achieve this. Five types of energy-specific efficiency improvements are introduced, respectively, in all the production sectors. Results show that improving efficiency of using coal leads to the smallest macro-level rebound. For rebound decomposition, four findings are generalized. First, final consumption shows larger impact on the rebound of secondary energy sources than on primary energy sources. Second, production sectors that are big user of the efficiency-exposed energy source are expected to be key rebound contributor. Third, energy-producing sectors make negative rebound contribution. Results also show that, substitution effect is the predominant mechanism that triggers sector-level rebound. Policy implications are also discussed. [ABSTRACT FROM AUTHOR]

In an effort to address climate change, in 2013 China launched the world’s largest government-driven carbon emission reduction programme, the National Low Carbon Industrial Parks Pilot Programme (LCIPPP). This paper analyses this newly developed pilot program. To deepen our understanding of the causes and the impact of industrial park CO 2 emissions, we use the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model and data from 20 pilot industrial parks involved in the LCIPPP for the period 2012–2016. This study quantitatively evaluates the effect of CO 2 emissions on output, energy structure, energy intensity, industrial structure, R&D intensity, and population change in different regions and nationally through an elasticity coefficient method. The results confirm that an increase in output and energy intensity is a dominant contributor to the growth of CO 2 emissions whereas an increase of the share of tertiary industry and R&D intensity has significant effects on reducing CO 2 emissions. The elasticity of energy intensity and renewable energy consumption on CO 2 emissions in the eastern region of China is the highest, indicating that using renewable energy to reduce CO 2 emissions for the industrial parks is more effective in the eastern region as compared to the central and western regions of the country. The elasticity of population is significantly negative in both the central and western areas while it is positive in eastern part of China, thereby illustrating that promoting labour intensive industries will be an effective way to reduce CO 2 emissions for the industrial parks in China’s central and western regions. Our study reveals that differentiated low carbon development pathways should be adopted. Concrete policy implications for reducing CO 2 emissions are also provided. [ABSTRACT FROM AUTHOR]

The development of renewable energy is the key to deal with climate change and low-carbon economy development in China. Here, based on a balanced panel dataset of 30 provinces in China cover the period of 1996–2013, this paper explores the effect of economic growth, CO 2 emissions, foreign trade (exports or imports) and urbanization on renewable energy consumption at the national and regional levels by using a dynamic system-GMM panel model. The empirical results show that the influence of economic growth, CO 2 emissions, foreign trade and urbanization on the renewable energy consumption is heterogeneous across regions. First, economic development has an important positive impact on renewable energy consumption in the four panels we consider and the impact is the greatest in the eastern region, follows by the national, central and western regions. Second, CO 2 emissions have a significant positive impact on renewable energy consumption in the central and national regions. However, CO 2 emissions have a weak negative impact on the consumption of renewable energy in the eastern region, and have no impact in the western region; Third, per capita exports are found to have a positive effect on the renewable energy consumption, with the greatest impact in the central region, followed by the eastern and national regions and have a weak negative impact in the western regions; Fourth, per capita imports have a negative effect on renewable energy consumption in the eastern, western and national regions and have a weak positive effect in the central region; Finally, the changes in the urbanization level significantly affect the renewable energy consumption in the four panels we consider and the impact is the greatest in the eastern region with high level of urbanization, follow by the central and western regions. On the basis of this study, we put forward the corresponding countermeasures and suggestions to achieving sustainable development in China. [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]

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]

• Define and quantify hydropower flexibility in power regulation and frequency response. • Flexibilities are coordinated in day-ahead scheduling of hydro-wind hybrid power system. • Benefits and costs of hydropower providing flexibility. • Suggested schemes weighing benefits and costs under different capacity ratios. Uncertainty and instantaneous volatility of wind power make it crucial to schedule the hydropower scientifically to supply flexibility at multiple timescales in renewable energy hybrid power generation systems (RHPS). However, current day-ahead scheduling strategies do not consider the flexibility of time scales below the minimum scheduling time resolution, resulting in insufficient flexibility in intraday operation, power curtailment and load loss. Therefore, in this paper, day-ahead scheduling model coordinating power regulation flexibility (PRF) at 15 min timescale and frequency response flexibility (FRF) at seconds timescale is proposed for hydro-wind hybrid power generation system (HWHPS). Meanwhile, the ability, benefits, and costs of hydropower on supplying flexibility in HWHPS are studied in detail. First, the demand and supply for PRF and FRF are defined and quantified at different confidence levels considering the regulation characteristics of hydropower units. Then, a heuristic piecewise method is utilized to linearize the nonlinear FRF expression, and a day-ahead scheduling MILP model of the HWHPS is constructed. The MILP model is applied to a HWHPS composed of a hydropower station in southwest China and a virtual wind farm simulated based on the data representative for the hydropower station region. We found that the available hydropower flexibility has potential in reducing the maximum frequency deviation, wind power curtailment and load loss. Meanwhile, hydropower provides flexibility at the cost of hydro energy loss due to increased water consumption, especially for supplying FRF during periods of high wind penetration. Finally, since benefits and costs are affected by different confidence levels, a comprehensive index is proposed to evaluate different capacity ratios and scenarios to suggest confidence levels for day-ahead scheduling. Decision makers can also refer to the flexibility supply capacity of hydropower station to select the connected wind power capacity and system load to ensure comprehensive benefits. The methods, models and conclusions in this study can provide a valuable reference for the planning and dispatching of HWHPS, which contributes to the reliability and stability. [ABSTRACT FROM AUTHOR]

[Display omitted] • A method for spatiotemporal coordination of multienergy systems is proposed. • Virtual energy storage gain is calculated based on spectral analysis. • The hydropower regulation ability affects the virtual energy storage gain. • The optimal coordination range between multiple energy sources is obtained. Utilizing the spatial heterogeneity and climate periodicity of various available renewable energy sources can enhance the multienergy complementarity, which will further reduce the energy storage demand and contributes to the "virtual energy storage gain." In this paper, we propose a spatiotemporal coordination method based on spectral analysis for a wind-PV-hydropower system that targets the maximum virtual energy storage gain. The complementary effect of hydropower on wind and PV power can be seen as changes in the regulation ability resulting from the hydropower construction development as well as a decreased variance in the total system production output. This method is used to determine the optimal coordination distance of multiple energy sources that are matched over different periods representing typical climate variation. A case study in the Yalong River basin in China obtains the best range of hydropower bundling surrounding wind power and PV power stations under different hydropower construction planning scenarios, and it reveals that increased regulation capacity of hydropower will reduce the optimal coordination distance but can achieve more energy balance if both actual storage and virtual storage is accounted for. This approach can realize energy delivery with the optimal coordination distance to meet intensive and efficient development needs, which can provide guidance and support for the planning and construction of wind-PV-hydropower storage systems. [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]

Traditional olefins production is heavily dependent on oil. Due to the short supply of oil resources, there are four possible alternative feedstocks in China: (1) coal; (2) natural gas; (3) coke-oven gas; and (4) methanol, while the coal route is dominant among these processes from the perspective of Chinese fossil resources. Comparative study of the four processes is conducted in concerning of water consumption. Results show that the coal route and coke-oven gas route suffer from high water consumption: 44.2 and 55.0 t/t olefins. On the other side, the natural gas route and the oversea-imported methanol route are 27.2 and 7.3 t/t olefins, much lower than the coal route. The coal route is water intensive, but unfortunately, coal chemical plants are mainly located in the arid northern China. To address this predicament, this paper proposes three suggestions: (1) Adjusting water prices system to arouse the enthusiasm of saving water and energy; (2) Promoting olefins production from co-feeding of coal and gas in areas with rich coal and gas; (3) Encouraging to develop methanol-to-olefins plants in coastal areas. [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]

Under China's current Renewable Portfolio Standards (RPS) policy system, the provincial power grid is the assessment subject, and then the assessment is conducted on the user side. However, in the centralized clearing power market, renewable energy (RE) consumption on the user side cannot be accurately identified, so accurate value transmission cannot be formed to guide users to optimize their electricity consumption behavior. To improve this problem, this paper identifies the irreplaceable role of users in RE consumption through time series simulation. We construct a three-layer RPS assessment value transmission model of "power grid company - retailers - users" based on the identified unique contributions of each user. The equilibrium state of multi-market participants is obtained based on the decentralized training framework of reinforcement learning. The following conclusions are drawn through simulation analysis:(1) the user-side RE contribution identification method can effectively identify the user's contribution to RE consumption, which can reduce about 8% or increase about 7.48% of RPS cost allocation according to different contribution degrees; (2) the RPS value allocation mode can optimize users' power consumption behavior, achieve peak shaving and valley filling, promote RE consumption, and reduce about 0.6% of the abandoned wind power; (3) Under the RPS system, the effect of improving the response-ability of the user-side is better than increasing the RPS target and the peak shaving capacity. • Accurately identify the RE consumption of users under the unified clearing mode. • Construct a transmission mode of RPS assessment among grid, retailers, and users. • Construct a distributed training framework of multi-agent. • The research is helpful in promoting the consumption of new energy. [ABSTRACT FROM AUTHOR]

As the world's largest developing country, China has set the target of "reaching carbon peak by 2030 and carbon neutrality by 2060". China's power sector faces many challenges in reducing carbon emissions. This article forecasts electricity consumption from 2021 to 2060 by selecting five influential elements on power demand. Furthermore, the system dynamics model simulates the carbon emission system on the supply and demand side of the power industry. This paper innovatively incorporates the technology factor of carbon capture, utilization, and storage (CCUS) into the supply-side model. In this study, the process of carbon neutrality is separated into three stages, including carbon peak stage (2020–2030), deep low-carbon stage (2030–2050) and zero-carbon stage (2050–2060). The factors of energy storage technology and CCUS technology make significant contributions in the latter two stages. Based on the different development levels of the variables, three scenarios are set as business-as-usual scenario (BAU), low carbon scenario(LC) and ultra-low carbon scenario (ULC). Then scenario analysis is employed to simulate the carbon emission in the future. The scheme under the ULC scenario is the optimal solution to achieve carbon neutrality. Improving flexible transition of thermal power units and promoting the growth of renewable energy are China's primary strategies of achieving carbon peak and carbon neutrality. • System dynamics model is applied to simulate scenarios on carbon emission. • The carbon emission models of the power supply and demand sides are constructed. • Three scenarios for achieving the dual-carbon target are proposed. • The electric energy substitution and CCUS are investigated in the model. [ABSTRACT FROM AUTHOR]

Under China's current Renewable Portfolio Standards (RPS) policy system, the provincial power grid is the assessment subject, and then the assessment is conducted on the user side. However, in the centralized clearing power market, renewable energy (RE) consumption on the user side cannot be accurately identified, so accurate value transmission cannot be formed to guide users to optimize their electricity consumption behavior. To improve this problem, this paper identifies the irreplaceable role of users in RE consumption through time series simulation. We construct a three-layer RPS assessment value transmission model of "power grid company - retailers - users" based on the identified unique contributions of each user. The equilibrium state of multi-market participants is obtained based on the decentralized training framework of reinforcement learning. The following conclusions are drawn through simulation analysis:(1) the user-side RE contribution identification method can effectively identify the user's contribution to RE consumption, which can reduce about 8% or increase about 7.48% of RPS cost allocation according to different contribution degrees; (2) the RPS value allocation mode can optimize users' power consumption behavior, achieve peak shaving and valley filling, promote RE consumption, and reduce about 0.6% of the abandoned wind power; (3) Under the RPS system, the effect of improving the response-ability of the user-side is better than increasing the RPS target and the peak shaving capacity. • Accurately identify the RE consumption of users under the unified clearing mode. • Construct a transmission mode of RPS assessment among grid, retailers, and users. • Construct a distributed training framework of multi-agent. • The research is helpful in promoting the consumption of new energy. [ABSTRACT FROM AUTHOR]

As the world's largest developing country, China has set the target of "reaching carbon peak by 2030 and carbon neutrality by 2060". China's power sector faces many challenges in reducing carbon emissions. This article forecasts electricity consumption from 2021 to 2060 by selecting five influential elements on power demand. Furthermore, the system dynamics model simulates the carbon emission system on the supply and demand side of the power industry. This paper innovatively incorporates the technology factor of carbon capture, utilization, and storage (CCUS) into the supply-side model. In this study, the process of carbon neutrality is separated into three stages, including carbon peak stage (2020–2030), deep low-carbon stage (2030–2050) and zero-carbon stage (2050–2060). The factors of energy storage technology and CCUS technology make significant contributions in the latter two stages. Based on the different development levels of the variables, three scenarios are set as business-as-usual scenario (BAU), low carbon scenario(LC) and ultra-low carbon scenario (ULC). Then scenario analysis is employed to simulate the carbon emission in the future. The scheme under the ULC scenario is the optimal solution to achieve carbon neutrality. Improving flexible transition of thermal power units and promoting the growth of renewable energy are China's primary strategies of achieving carbon peak and carbon neutrality. • System dynamics model is applied to simulate scenarios on carbon emission. • The carbon emission models of the power supply and demand sides are constructed. • Three scenarios for achieving the dual-carbon target are proposed. • The electric energy substitution and CCUS are investigated in the model. [ABSTRACT FROM AUTHOR]

In renewable dominated power systems, the primary challenge is how to incentivize the utilization of subsistent renewable energy instead of how to incentivize the investment to expand renewable energy. This paper proposes an incentive policy named incremental green certificate (IGC) to cope with the challenge. The proposed IGC directly transfers voluntary consumers' payment to cover the premium of flexible resources to utilize more renewable energy. To make the IGC cooperative with power markets, a two-stage day-ahead market clearing model is proposed. In the model, the first stage follows on-going power market rules to clear the locational price, and the second stage reschedules the flexible resources to clear the IGC. Also, the incentive compatibility of the proposed policy is demonstrated, and the system dynamic model is built to quantify the long-term influence. Conclusions are drawn from the case studies on modified PJM 5-bus and a real-life provincial power system in China. The IGC would boost the investment of flexible resources, reduce the curtailment of renewable energy, and benefit renewable energy companies in the long term. • Proposed an IGC policy to reduce the curtailment of highly penetrated green energy. • A two-stage market clearance model is proposed to maximize green energy penetration. • Proved the incentive compatibility of the IGC policy. • Quantified the long-term effect of IGC on flexible resources using system dynamics. [ABSTRACT FROM AUTHOR]