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Carbon emission flow is an effective tool to obtain carbon emission distribution in power systems, which can guide the active carbon reductions of consumers through proper incentive schemes. With the increasing penetration of renewable energy, the existing single-valued carbon emission flow model based on deterministic forecasted power outputs cannot describe the impacts of renewable energy uncertainties on carbon flows. Therefore, this paper mainly focuses on the assessment of the probabilistic carbon emission flow through the power systems considering the impacts of load and renewable energy uncertainties. In this paper, the probabilistic carbon emission flow (PCEF) is innovatively proposed. With the PCEF, consumers can make better energy use plans considering their own risk appetites for carbon emission payment. Firstly, the impact factors of the PCEF are modeled, including load and renewable energy uncertainties and integrated carbon intensity of thermal plants. The uncertainties of load, wind power and photovoltaics are modeled based on a p-box method while the uncertainty of hydropower is modeled based on the multi-state model. Besides, the integrated carbon intensity model of thermal plants are proposed considering the carbon intensity variations during the operating, start-up, and shut-down stages. Furthermore, the probabilistic carbon emission evaluation framework is proposed to assess the probabilistic carbon distribution. In this framework, novel probabilistic carbon indices are defined to precisely characterize the carbon emission situation with probabilistic representations. A solution method based on an adaptive interval point estimation (AIPEM) algorithm is proposed to efficiently solve the developed evaluation problem. Tests on an IEEE-30 node system, and a real provincial power system in China validate the effectiveness and application of the proposed model. The results showed that the PCEF can be a useful tool for obtaining the probabilistic representation of carbon emission flow and guiding the consumers' active carbon reduction in power systems. • Probabilistic carbon emission flow is proposed considering multiple uncertainties. • Integrated carbon intensity model for thermal plants is developed. • A p-box model is used to describe the uncertainties of load and renewable energy. • PCEF can help consumers balance the cost and carbon risk when making decisions. [ABSTRACT FROM AUTHOR]

Decarbonising the heating sector is one of the key elements to realizing the ambitious dual carbon goals of China, which is the largest carbon emitter and energy consumer globally. Currently, district heating (DH) systems have penetrated approximately 88% of the urban heating areas in Northern China. Nevertheless, around 90% of the heating demand in China still relies on fossil fuels. A larger scale integration of renewable energy and waste heat sources into the DH systems is critical for decarbonising the entire heating sector in China. However, a deeper level of comprehension is required to harness its full potential. This paper provides a thorough investigation of the status, potential, and national policy schemes of renewable energy and waste heat recovery in the DH systems of China. Combined with a critical review of recent literature on relevant areas published in both international and Chinese domestic sources, the trends, challenges, and future perspectives are discussed from scientific research and practical implementation aspects. This paper highlights the synergy of the integration of renewable energy and waste heat sources in DH, the energy efficiency improvements as well as the use of thermal storage technologies through the implementation of 4th generation district heating and smart energy systems that could offer a more economically viable pathway forward. • Focus on renewable energy and waste heat (REWH) in the district heating (DH) systems in China. • A systematic review of REWH in Chinese DH systems from status, policy, and literature aspects. • Identification of research gaps based on the review of both international and Chinese journals. • Policy recommendations proposed to promote REWH in Chinese DH systems. • The introduction of international experiences on REWH in DH for analysis in the Chinese context. [ABSTRACT FROM AUTHOR]

In the background of exhaustion of the traditional fossil energy sources and environmental deterioration, developing renewable energy has become a strategic choice for countries to achieve energy sustainable utilization and carbon neutrality target. 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. However, the commonly used weight calculation methods in the existing renewable energy evaluation have obvious shortcomings, such as the randomness of the subjective method is strong and the gap in the weight allocation of the objective method is too large, which affect the reliability and accuracy of the sorting result. In this paper, a novel weight calculation method based on non-dominated sorting genetic algorithm (NSGA-II) is proposed, and is applied to renewable energy evaluation. The study in this paper focuses on methodology. Firstly, for the linear normalized and Zeros normalized evaluation data, the differences between renewable energy plans in the two normalized evaluation data are measured, and the ranking deviations of the two normalized data are calculated. Then, the multi-objective weight solving model is constructed in terms of plans differences minimization and ranking vectors deviations minimization. The NSGA-II algorithm is applied to the optimization of multi-objective functions to find the Pareto non-dominated solution, so as to determine the weight values of each evaluation index. Finally, the TOPSIS method is used to determine the relative closeness value of each plan, and the rankings of renewable energy technical plans are achieved. Based on the actual renewable energy development data of 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 popular renewable energy evaluation methods. [Display omitted] • Propose a novel evaluation method for renewable energy technical plans. • Apply non-dominated sorting genetic algorithm II to determine the index weights. • Compared with the existing evaluation method, the proposed method performs better. [ABSTRACT FROM AUTHOR]

Whether renewable energy consumption is beneficial to reducing CO 2 emission intensity and carbon emissions in the context of the goal of "carbon peak and carbon neutrality" in China. To prove this conjecture, considering the spatial spillover effect of carbon dioxide, this paper adopts the spatial Dubin model for empirical analysis based on the panel data of China's 30 provinces over the period 2002–2019. This study explores the CO 2 intensity mitigation effect of China's renewable energy consumption and further investigates the mediation role of technological innovation, industrial structure upgrading and optimization, and energy efficiency in the nexus between renewable energy consumption and carbon intensity. The main findings present that: (1) China's carbon emission intensity is characterized by significant spatial correlation; (2) Renewable energy consumption can significantly reduce carbon intensity and per capita carbon emissions; (3) The mediation effect of renewable energy consumption in reducing carbon emission intensity is to promote technological innovation and industrial structure upgrading, as well as improve energy efficiency. Based on the conclusions, this paper offers several policy implications for governments to reduce CO 2 emission intensity. • The impact of renewable energy consumption on CO 2 emission intensity is explored. • Renewable energy consumption can reduce carbon emission intensity and per capita carbon emissions. • Renewable energy consumption reduces CO 2 intensity through the spatial mediation effect. [ABSTRACT FROM AUTHOR]

Wind power prediction technology is important for building novel power systems with a high proportion of renewable energy. The quality of Numerical weather prediction (NWP) has a significant impact on the accuracy of ultra-short-term wind power prediction (USTWPP). However, existing NWP do not reflect the adaptability of different weather processes, because of it' s forecasting errors. In view of this, this paper proposes an USTWPP method based on NWP wind speed correction and division of transitional weather process. The combined prediction method was first used to correct the NWP wind speed, and then we use the double clustering method to divide the transitional weather processes to establish a model for USTWPP based on different scenarios, the overall method was finally applied to a wind farm in west inner Mongolia, China. Compared to the pre-correction, the wind speed forecasted RMSE was reduced by 1.702 and the MAE by 1.366. Based on the wind power ultra-short-term prediction method proposed in this paper, the average reduction in RMSE is 5.93% and in MAE is 4.82% compared to the various comparison methods in the four seasons. The USTWPP method combining wind speed correction and double clustering division of transitional weather scenarios can significantly improve accuracy of USTWPP. • Propose a new NWP correction mechanism to improve the NWP forecasted errors. • Propose a multiple clustering method to improve the wind power prediction accuracy. • Propose weighted evaluation indicators of sequential and non-sequential clustering. • It provides new ideas for wind power prediction under different weather scenarios. • Proposed power prediction idea reduces the RMSE and MAE on average 5.93% and 4.82%. [ABSTRACT FROM AUTHOR]

Under the background of "carbon neutrality" and "carbon peak" concepts, China desires to develop a new power system based on renewable energy sources (RES), which will be the primary energy support in prospective China. Thus, it is imperative to improve the peak shaving capability of power system to address the problem of random fluctuation and intermittency of RES by developing various energy storage systems. An innovative approach to enhance the flexibility of the conventional thermal power unit (TPU) through the utilization of flywheel energy storage array (FESA) is presented, simulated, and evaluated in this paper. Using the relatively practical and specific model of TPU and FESA, modified frequency modulation and automatic generation control (AGC) instruction tracing operation scenario models are constructed. A comprehensive control strategy of FESA and a well-designed operation coordinated control method for TPU with FESA are proposed and applied. The simulation results are distinctively processed and evaluated by the methods in "two documents", aiming to assess the real-world operation performance of the model proposed in this paper. The comprehensive analysis of the indicators demonstrates that the FESA can improve the flexibility and economy of the units. This research may guide the usage of FESA to improve the operational flexibility of TPU. • Composition and Control Methods of the flywheel energy storage array are provided. • A coordinated control scheme for the thermal power unit with flywheel energy storage array is proposed. • Frequency modulation and AGC instruction tracking scenario models are constructed and simulated. • AGC regulation indicators are conducted and analyzed to evaluate the unit's performance. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy in the power system, it is necessary to develop large-scale and long-duration energy storage technologies. Deploying pump stations between adjacent cascade hydropower plants to form a cascade energy storage system (CESS) is a promising way to accommodate large-scale renewable energy sources, yet the mechanism how renewable curtailment is converted to hydroelectricity is still unclear. In this paper, we aim to clarify this mechanism by evaluating the CESS's long-term operational efficiency and changes compared to the cascade hydropower system. First, operational features and principle of the CESS was outlined. Then, long-term operations of the CESS and cascade hydropower system were, respectively, optimized using a simulation-based optimization framework. Finally, the operational efficiency of the CESS, defined as a ratio of reduced renewable energy curtailment to increased hydropower production, was calculated based on the above two optimized operation results. China's Longyangxia-Laxiwa CESS was selected as a case study. Results show that: (1) long-term operational efficiency of the CESS reached 81.6 %, i.e., 81.6 % of the curtailed renewable could be converted to hydroelectricity; (2) the increase in hydropower production was mainly attributed to the upper hydropower plant, accounting for about 96.5 % of the total increment; and (3) the increase in generation flow was the main factor to impact the increased hydropower production of the upper plant, although it could decrease the hydropower plant's hydraulic head. • Long-term operation of cascade energy storage system was modelled. • Operating rules of cascade energy storage system were derived. • 81.6 % renewable curtailment was converted to hydroelectricity. • Upper plant accounted for 96.5 % of hydroelectricity increment. • Water saving and renewable accommodation could be coordinated. [ABSTRACT FROM AUTHOR]

Renewable energy sources, such as wind and solar, can reduce environmental pollution and promote the transformation of the energy structure. However, the intermittency and uncertainty of new energy sources such as wind and photovoltaics pose challenges to large-scale grid integration. Effective utilization of intermittent energy sources requires large-scale energy storage facilities. Salt rock is recognized as an excellent medium for underground large-scale energy storage with a wide range of applications. This paper identifies the potential of salt caverns to be used for large-scale energy storage by analyzing the distribution of wind and solar energy resources in China, taking into account the grid-connected infrastructure of the Chinese power grid and the geographic location of salt mines. The overlapping areas are identified for utilization in compressed air energy storage and hydrogen storage. Through meticulous categorization and analysis of the factors influencing salt cavern underground storage site selection, the AHP-EWM method is introduced to establish an objective hierarchy model for a comprehensive evaluation system of salt cavern underground storage site selection. The comprehensive planning of China's 20 major salt mines is conducted, and the research findings hold significant reference value for facilitating the low-carbon energy transition in China. • The ways which salt mines can be used for renewable energy peaking are summarized. • Renewable energy religions combined with grid-connected infrastructure and salt mines in China were studied. • Objective hierarchical modeling of subsurface salt cavern siting was developed using a combinatorial assignment method. • Energy Storage Planning for 20 Major Salt Mines in China. [ABSTRACT FROM AUTHOR]

Chinese electricity market has undergone twice reforms since 2002. However, the current electricity market is still partially irrational in electricity pricing mechanism. Furthermore, the utilization of renewable energy is inefficient in western regions. From the perspective of market participants, electricity retailers are new entrants in deregulated electricity market. In this spot electricity market, they are faced with various challenges and opportunities. Thus, this paper introduces an inter-regional power transaction model based on bottom-up modeling. There are mainly three parts in this paper: ⑴ It examines the existing problems and achievements in the twice reforms, and introduces the participants of cooperative game in a spot market. ⑵It proposes an inter-regional electricity transaction model to illustrate the new pricing mechanism. ⑶ Based on cooperative game theory, it quantifies how costs, electricity prices and benefits affect the behavior of retailers in a spot market with some reasonable economic assumptions. What is novel about this research is that the proposed transaction model analyzes electricity retailers' behaviors in a spot market. The cooperative game model improves electricity retailer's competitiveness in a spot market, and is of great theoretical and practical significance for the reform and development of China's electricity market. [ABSTRACT FROM AUTHOR]

Microgrid is a promising way to integrate renewable distributed generation into the electric power sector, which has received widespread attention. In this paper, a uniform capacity-constrained optimization model under different power purchase and sale price contracts is developed to explore the optimal green investment strategy for grid-connected microgrid (GCM) with three configuration types. The impact of renewable energy source endowment and incentive policy is focused when macrogrid's generators emit more pollution than GCM's non-renewable generators, such as in China and India. The results show that: (1) GCM with renewable generation can only be invested in a site with relatively abundant renewable endowment; (2) carbon tax effectively promotes the deployment of GCM with renewable generation and achieves carbon emission abatement, but excessive carbon tax cannot incentive investment in the most desired GCM type; (3) a subsidy trap is found under feed-in tariff (FIT) subsidy, which implies that the fade-out or exit of FIT subsidy is beneficial for microgrid's healthy development; (4) and the comparisons under different price contracts emphasize the importance of macrogrid providing non-discriminatory access to feed-in and feed-out services to microgrid. • Investment strategy is explored for grid-connected microgrid (GCM) with three types. • Renewable source endowment and incentive policy determine the investment for GCM. • Excessive carbon tax cannot incentive investment in the most desired renewable GCM. • The fade-out or exit of FIT subsidy is beneficial for healthy GCM deployment due to the subsidy trap. • Emphasizing the importance of macrogrid providing non-discriminatory services to GCM. [ABSTRACT FROM AUTHOR]

Hydropower is the largest renewable energy power generation source with the largest construction scale and power generation capacity. A reasonable prediction of hydropower generation is conducive to achieving the carbon peak and neutrality goals. Hydropower generation is affected by seasons and precipitation, which have significant randomness and uncertainty. To realize a reasonable prediction of hydropower generation in China, this paper constructs a novel grey combination optimization model using different parameter combination optimizations based on the three-parameter discrete grey model TDGM(1,1). Further research shows that a better model performance can not necessarily achieved by a higher number of parameter combination optimizations, mainly due to the different effects and influence of various parameters on the model. Subsequently, the TDGM(1,1, r , ξ , Csz) model is applied to forecast China's hydropower generation. The results show that China's hydropower generation can reach 1687.738 hundred million kWh in 2025, an increase of 24.5% compared with 2020. Finally, the rationality of the prediction results is analyzed, and relevant countermeasures and suggestions are proposed. • The highlights of this paper are as follows: • A new grey model is proposed to forecast the hydropower generation in China. • This paper studies the optimization algorithm and implementation process of the new model. • It solves the problem of low accuracy of traditional model in China's hydropower prediction. • Finds can help government of China formulate reasonable power policies. [ABSTRACT FROM AUTHOR]

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

The building integrated grid connected PV (BIGCP) is one of the most promising types of urban distributed renewable energy sources. Many researchers found high BIGCP potential in many cities. But this paper emphasized that two important facts are usually ignored. These facts will probably limit the willingness to invest (WTI) in BIGCPs. This problem is referred as an Available Investment Area for BIGCP Installation (AIABI) problem. This paper worked out a way to estimate the restriction brought by the AIABI problem in a certain urban district by applying high definition remote sensing images and signal processing methods. A cased study is carried out in Beijing city, the result shows that there exists a least-acceptable-SPBP range in which a large proportion of the total AIABIs will not be invested in the case. This result explained the low adoption rate of BIGCP in residential buildings in urban discovered by literature. It can be concluded that the AIABI problem in urban districts can restrict the grid connected BIPV potential even under most favorable financial incentives. A strategy to lower the AIABI Elimination Rate is introduced. [ABSTRACT FROM AUTHOR]

This paper construct a CCHP and renewable energy based hybrid energy system driven by distributed energy resources (DERs CCHP). Then, the paper constructs performance indexes from energy, economic and environment. Thirdly, the paper proposes a multi-objective optimization model for DERs CCHP system under four optimization of energy rate (ER), total operation cost (TOC), carbon dioxide emission reductions (CER) and joint optimization. Finally, Guangzhou Higher Education Mega Center (GHEMC) in China is taken as the object for comparatively analyzing the operation performance of DERs CCHP system and CCHP system driven by natural gas (NG CCHP system). Results show: Joint optimization mode could balance the results of different optimization modes. DERs CCHP system shows better operation performance. ER of ER optimization, CER optimization and joint optimization are higher than that of NG CCHP system. NPV of all optimization modes is positive, IRR are bigger than the expected yield rate. DERs CCHP system could reduce CO 2 emission by utilizing wind energy and solar energy to replace NG. The sensitivity analysis indicates the performance of the DERs CCHP system will become better with the increase of chiller COP, decrease of NG price and wind-photovoltaic equipment cost. [ABSTRACT FROM AUTHOR]

DSPV (Distributed solar PV) power, either located on rooftops or ground-mounted, is by far one of the most important and fast-growing renewable energy technologies. Since the second half of 2012, China has shifted from LSPV (large-scale solar PV) to DSPV and a series of policy to promote DSPV power deployment have been put in place. Unfortunately these policies were not well performed due to myriad constraints on DSPV power deployment across the country. Building mainly on non-academic sources including government documents and presentations, industry reports and presentations, media reports, and interviews, this paper firstly provides a comprehensive review of China's policy on DSPV passed between the second half of 2012 and the first half of 2014, then barriers associated with DSPV deployment are identified. This is followed by an account and discussions of recent policy changes since Sep 2014, and major local incentives. In addition, policy performance is briefly reviewed. At the end of the paper, conclusions and policy implications are provided. This paper provides an understanding of the recent DSPV policy progress in China and insights for policy makers in other economies which are experimenting DSPV power policies. [ABSTRACT FROM AUTHOR]

China has set more ambitious targets for controlling carbon emissions and poverty reduction at the end of 2020. In order to analyze China's experience in poverty reduction and CO 2 emission reduction and to provide guidance for fulfilling its emission reduction commitments and consolidating poverty reduction achievements simultaneously in the future, this paper introduces the Tapio decoupling index to study the relationship between electricity consumption and CO 2 emissions in more than 600 Chinese counties from 2009 to 2016. The results show that more counties decoupled their electricity consumption from CO 2 emissions in 2016 than in 2009. Then Logit model is proposed and the results show that poverty-stricken counties are more likely to decouple electricity consumption from CO 2 emissions than non-poverty-stricken counties, which suggests that China's poverty-stricken counties are not pursuing economic development at the cost of loosening carbon emission controls. This paper also finds that as the carbon intensity of the secondary industry has declined significantly in recent years, the development of secondary industry could help decouple electricity consumption from CO 2 emission, and the promotion of renewable energy can play the same role. • The relationship between electricity consumption and CO2 emissions is studied in county level. • More counties in China decoupled their electricity consumption from CO2 emissions. • Poverty-stricken counties are more likely to decouple electricity consumption from CO2 emissions. [ABSTRACT FROM AUTHOR]

Facing the mounting pressures of meeting energy and environment needs and reducing its dependence for fossil fuels, China needs to make more effort to develop renewable energy. This paper attempts to use portfolio theory to optimize China's overall energy system with considering the learning curve effect of renewable energy cost and the characteristic of fossil energy cost increasing over time. It also takes into account additional factors such as environmental costs of coal consumption and various growth rates of the cumulative R&D (research and development) capacity for solar power. This research has found that the development of renewable energy in China has tremendous potential but it will not replace fossil energy in the next decades. The sensitivity analysis of this paper indicates that development of solar power is driven not only by the cumulative installed capacity but also by the cumulative R&D capacity. [ABSTRACT FROM AUTHOR]

Rational planning for remote integrated energy systems is of great importance for demand guarantee and sustainable development of remote areas. A critical challenge is that comprehensive factors, e.g., technical, economic, environmental, and social, which are inherently intertwined, should be properly evaluated and balanced in terms of social benefits. In addition, the feasibility of each planning scheme can only be verified based on their local electrical conditions by employing multi-energy flow calculations. Thus, this paper develops a comprehensive methodology for the optimal planning of remote integrated energy systems taking into account the uncertainty of renewable energy, where a multi-criteria decision-making procedure is designed, including the candidate set construction, remote integrated energy systems modeling, multi-energy flow calculation of the candidates, index definition and selection, and the optimal scheme determination. The methodology is applied to a case study in Lankao County, China. The optimal scheme, selected by the proposed multi-criteria decision-making procedure from 432 candidates, shows the best comprehensive performance, which is verified by different ranking methods. The optimal scheme can rank in the top 13% of each aspect on average, with economic and environmental aspects ranking in the top 2%. The methodology can help decision-makers find reasonable solutions for energy system planning in remote areas. • A comprehensive methodology for planning remote integrated energy systems is proposed. • Refined multi-energy flow calculation fitting with remote area characteristics is presented. • Multi-criteria decision-making procedure realizes high social benefits. • A real case in Lankao is performed to verify the rationality of the proposed method. [ABSTRACT FROM AUTHOR]

This paper examines the asymmetric effects of oil price shocks on coal and renewable energy consumption in China over long and short periods. The novelty of this study is to investigate whether the consumption of coal and renewable energy are asymmetrically affected by the oil price in a time–frequency framework by performing a Fourier nonlinear ARDL model which is robust to overcome the multiple smooth structural changes in data. The bounds test of the FNARDL specification provide strong evidence of cointegration among variables, including coal energy consumption, renewable energy consumption, oil price, and economic growth. Results reveal the following: First, the oil price-coal energy consumption and oil price-renewable energy consumption relationships are nonlinear. Second, oil price increases have significant short-run effects on coal energy consumption. However, oil price increases induce an increase in renewable energy consumption in China in both the short and the long term. Finally, the relationship between coal energy consumption and economic growth is U-shaped. Hence, increasing oil prices boost China's prospects for coal in the short term and its usage of renewable energy in the long run. • Effect of oil price on coal and renewable energy consumptions in China is tested. • Strong evidence of cointegration among variables is shown. • Oil price increases have significant short-run effects on coal energy consumption. • Oil price increases induce an increase in renewable energy consumption in China. • Relationship between coal energy consumption and economic growth follows U-shaped. [ABSTRACT FROM AUTHOR]

The global shift to renewable energy is conducive to carbon reduction, but its impact on the economy is still uncertain. Unlike existing research focusing on the effect of domestic renewable energy (RE), this paper considers the global RE connection among countries and examines the impact of global RE development on economic growth (EG) using China as a sample. In addition, we apply a novel wavelet-based quantile-on-quantile method, which can estimate the local influence of the α-quantile of global RE on the β-quantile of the EG in different periods. The finding shows that the upper quantiles of RE promote EG , and this effect is greater at the upper quantiles of EG in the medium- and long-term. This is mainly because China plays a leading role in RE development and has an advantage in exporting RE -related products with the development of global RE. The finding proves the growth effect, indicating that RE is a blessing for China's EG. However, in the short term, a higher level of global RE development impedes China's EG due to high upfront costs of RE production, fierce competition from other countries, and insufficient energy supply when significant investment shifts from non-renewable energy to RE. The empirical results provide important policy implications to balance RE 's short-term inhibitory effect and long-term promoting effect on EG in the process of the global energy transition. • The global RE connection and the impact of global RE development on economic EG. • Applying wavelet-based quantile-on-quantile method can estimate the local influence of global RE on EG in different periods. • Upper quantiles of RE promote EG is greater at the upper quantiles of EG in the medium- and long-term. • The growth effect indicating that RE is a blessing for China's EG. • Balance RE's short-term inhibitory effect and long-term promoting effect on EG in the process of energy transition. [ABSTRACT FROM AUTHOR]

Whether grid upgrading in China, centered on building trans -regional transmission lines, can promote a low-carbon transition in the power sector is controversial. Since there is no precedent to follow, various countries are interested in learning from China's experience. This paper adopts a two-way fixed effects model based on panel data for 30 Chinese provinces from 2007 to 2019 to reveal the nonlinear relationship between grid upgrading and renewable energy generation. The research finds that: (1) A U-shaped relationship exists between grid upgrading and renewable energy generation, which is robust to a series of robustness checks. (2) When the ratio of transmission lines over Extra High Voltage (EHV) exceeds 0.152, grid upgrading has a scale effect in promoting renewable energy generation to achieve a low-carbon transition in the power sector. (3) Building the EHV grid has made a significant contribution to renewable energy generation, particularly in resource-intensive areas. Conversely, building Ultra High Voltage (UHV) grids has not observed such a positive impact. Finally, targeted policy recommendations are put forward for the low-carbon transition of China's power sector. • A U-shaped relationship exists between grid upgrading and renewable energy generation. • Scale effect occurs when trans -regional transmission lines' ratio exceeds 0.152 • Extra High Voltage has made a significant contribution to renewable energy generation. • Ultra High Voltage has not shown a significant impact on renewable energy generation. • The contribution of grid upgrading increases with the installed capacity. [ABSTRACT FROM AUTHOR]

China has set a goal of carbon neutrality by 2060 and is determined to increase wind and solar power to 1200 GW by 2030, even though it already has the world's largest installed capacity of renewables. However, the lack of flexibility in the power system will limit the development of a higher proportion of renewable energy. This paper evaluates the applicability of different types of flexible power sources in China for the first time. Above all, it describes the current situation of China's power industry and the necessity of increasing flexible power sources, establishes an evaluation system from the perspectives of benefit, opportunity, cost & risk, then determines 19 evaluation criteria. The BOCR-ANP-TOPSIS method was used to evaluate the system, where the ANP method determines the weights and the TOPSIS method ranks the alternatives. The result shows that pumped storage hydropower has the highest overall ranking, followed by the flexibility transformation of coal-fired power. Coupled with sensitivity and comparative analysis, valuable findings that will help future energy policymakers are reported. • Evaluating the applicability of different flexible power sources in China for the first time. • The identification of criteria for the selection of flexible power sources was given. • Making recommendations for China's flexible power source development policy. • The BOCR-ANP-TOPSIS method adopted can be extended to select flexible power sources in different regions meticulously. [ABSTRACT FROM AUTHOR]

Faced with the growing renewable energy requirements, there is increased interest in cross-region of large-scale renewable energy market, which provides an alternative path for building sustainable power systems. Critically, the development of a renewable energy-dominated electricity market is an important way to achieve global climate goals and energy conversion. Despite improved achievement of electricity demand response (EDR) market, only limited development in the coordination capacity and development scale of EDR are dominated by renewable energy. Moreover, the normal operation and work efficiency of the system is greatly affected due to data transmission errors and other human factors. Thus, it is of great importance for the State Grid to accurately identify the data sources and realize the interactive development of cross-region power systems. This paper presents a new cross-region (province) electricity demand response (CR-EDR) model in China for large-scale renewable energy participating in EDR market. This model is applied to three provinces in China based on renewable energy, and fully consider how wind power is integrated with EDR in terms of operation, grid connection and optimization. Presently, China is in the initial stage of development, and the identification of data anomalies is inevitable for the CR-EDR. To solve this problem, a novel machine learning (ML)-based approach is proposed for effective identification of EDR. By calculating wind power output and customers' EDR results, reliable feature sequences can be obtained. Finally, all the feature sequences are uploaded to cross-region system operators (CR-SO) for classification and identification by ML. In the case studies and discussions, we integrated all feature sequences to CR-SO for identification and present a novel process approach to implement EDR, the random forest (RF) enables 100% accuracy for training set and 99.7685% for testing set with small training samples. Compared with RF, support vector machine only achieves 79.1667% for testing set with small training samples. With accurate RF identification results, the stable operation capability and management level of the system can be effectively improved. The proposed methodology creates a new provincial perspective of China and the establishment of CR-EDR, which provides a theoretical and methodological guidance for all countries and regions to develop CR-EDR. • A new cross-region (province) electricity demand response management is proposed. • Large-scale renewable energy participating in demand response is fully considered. • Machine learning-based approach is adopted for data integration and identification. [ABSTRACT FROM AUTHOR]

Integrated energy system is an important approach to promote large-scale utilization of renewable energy. Under the context of energy market reformation and technology advancement, the economic operation of integrated energy system confronts new challenges, in terms of multiple uncertainties, multi-timescale characteristics of heterogeneous energy, and coordinated operation of hybrid energy storage system. To this end, this paper investigates the multi-timescale rolling optimization of integrated energy system with hybrid energy storage system considering the above challenges. Firstly, a basic framework of an integrated energy system with hybrid energy storage system (consisting of battery and hydrogen storage) is proposed, and the typical devices are modeled in detail. Secondly, the parameters and variables are divided into fast/slow timescale according to dispatch needs, and the multi-timescale problem of heterogeneous energy and the coordinated operation of the hybrid energy storage system can be solved simultaneously through two-stage optimization. Thirdly, the dispatch model is incorporated into the framework of model predictive control, the uncertainty of price, renewable energy and load can be effectively handled. Finally, a case study is conducted using an industrial park in the southern coastal region of China, the comparison with the rule-based method shows 60.65% reduction in the operating cost of hybrid energy storage system, the comparison with robust optimization shows 7.01% reduction in the total cost of the system, thus the feasibility and effectiveness of the proposed method are verified. • The application of HESS in IES is considered and modeled in a more detailed way. • Based on the two-stage approach, the heterogeneous energy multi-timescale scheduling problem and the HESS coordinated operation problem of IES can be solved simultaneously. • The dispatch model is incorporated into MPC for rolling optimization, and multiple uncertainties are effectively handled. • Using an industrial park in the southern coastal region of China as case study, the feasibility and effectiveness of the proposed method are verified. [ABSTRACT FROM AUTHOR]

Renewable Portfolio Standards (RPS), as the strategic requirement effectively fits the top-level institution design of China's low-carbon energy transition, will help to promote China's energy structure optimization and power market reform. The success of its implementation depends on the scientific quasi-parameters design of the system. At present, China's tradeable green certificate (TGC) market is still in the voluntary trading stage. There are differences in natural attributes and transaction prices, resulting in the market segmentation and market distortion of the TGC market. Considering the non-neutral technology, this paper uses system dynamics to construct a long-term trading model under China's RPS, and analyzes the impact of relevant system quasi-parameters on the market trading process. The results show that:(1) The scientific quasi-parameters design of the RPS, such as technical conversion coefficient, TGC valid period, TGC benchmark price and RPS quota, will encourage and guide manufacturers to conduct TGC transactions, which will promote the development of renewable energy. (2) The introduction of the technical conversion coefficient can effectively promote TGC transactions, improve China's social welfare and optimize the power source structure. Therefore, the scientific design of the relevant quasi-parameters and strengthening of the supervision mechanism are conducive to implementing the RPS effectively in China. • The case of non-neutral technology is considered in this paper. • Use system dynamics to construct the model considering the non-neutral technology. • Analyze the impact of RPS quasi-parameters on the market trading process. • The scientific parameters can encourage and guide plants to conduct TGC transaction. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the relationship between the green economy and green jobs in China through the following question: Can the current GHG mitigation policies in China’s power generation sector bring more jobs to China? Using both analytical and input–output models this paper analyzes the direct and indirect employment impacts of two main mitigation policies in the power generation sector. This paper proves that the above-mentioned question is not simple. Mitigation policies in China’s power generation sector from 2006 to 2009 caused a total of 44 thousand net jobs losses. However, as the share of renewable energy that has an indirect employment impacts increased in 2010, the policies from 2006 to 2010 actually resulted in 472 thousand net job gains. This paper asserts that to ensure the co-existence of green economy and green jobs in China’s power generation sector, policy makers should further promote solar PV, biomass and wind technologies. In 2010, for every one percent increase in the share of solar PV generation there could be a 0.68% increase in total employment in China, larger than any other power generation technology. Finally, this paper argues that a matching educational system and personnel structure is also needed. [Copyright &y& Elsevier]

Abstract Compared with wind and solar power, biomass power has grown relatively slowly in China. With abundant biomass resources, the development of biomass electric power in China has potential advantages. This paper analyzed the environmental impact of biomass power in the construction and operation stages in comparison with wind and solar power. The results showed that biomass power produced relatively less emissions in the system construction stage at around 1700 ton CO 2 -e/MW. In the operation stage, biomass power projects achieved an average of 131462 ton CO 2 -e per year, which is greater than wind and solar power of equal installed capacity. Biomass power plants could achieve net emission reductions in a shorter time (0.39 year) after operation. The life cycle GHG emissions of biomass power projects are between 42 and 85 g CO 2 -e/kWh. The evidence pointed out that biomass power is worth supporting in China, from the perspective of environmental performance. Local governments should promote the sustainable supply of biomass materials and the development of renewable energy industry should depend on local conditions. Highlights • The unit emissions of biomass power from construction is around 1700 ton CO2-e/MW. • Biomass power projects achieved net emission reductions less than half year. • The life cycle GHG emission of biomass power projects are between 42 and 85 g CO2-e/kWh. [ABSTRACT FROM AUTHOR]

Abstract: This paper applies portfolio theory to evaluate China''s 2020-medium-term plans for generating technologies and its generating portfolio. With reference to the risk of relevant generating-cost streams, the paper discusses China''s future development of efficient (Pareto optimal) generating portfolios that enhance energy security in different scenarios, including CO2-emission-constrained scenarios. This research has found that the future adjustment of China''s planned 2020 generating portfolio can reduce the portfolio''s cost risk through appropriate diversification of generating technologies, but a price will be paid in the form of increased generating cost. In the CO2-emission-constrained scenarios, the generating-cost risk of China''s planned 2020 portfolio is even greater than that of the 2005 portfolio, but increasing the proportion of nuclear power in the generating portfolio can reduce the cost risk effectively. For renewable-power generation, because of relatively high generating costs, it will be necessary to obtain stronger policy support to promote renewable-power development. [Copyright &y& Elsevier]

Abstract: The fast-growing Chinese economy will inevitably need more and more energy. This not only makes China''s energy shortage situation severer, but also causes more and more worries from the countries all over the world, especially those developed countries. In this paper, China''s energy state is analyzed and the influences of promoting new socialist countryside construction on China''s energy source problem and its energy strategy making are discussed. It is stressed in this paper that in drawing the national energy development plan, it is of vital importance to take the impacts of new socialist countryside construction on energy production, supply and consumption into consideration. Traditional mineral energy supply that has been used for towns and cities should not be simply copied to solve the energy problem in countryside construction. The best choice for combating the huge energy increase resulted from speeding up the countryside construction is promoting utilization of biomass and solar energy and various advanced energy technologies. To make the utilization of biomass and solar energy and various advanced energy technologies in countryside, the government should give necessary technical and financial supports. [Copyright &y& Elsevier]

Intermittent renewable energy sources have been transformed from supplementary energy to dominant incremental alternative energy sources in the context of carbon neutrality target worldwide. For coastal countries, it is urgent to integrate abundant offshore energy into regional onshore renewable power sources economically and reliably considering the scarcity of land resources and negative environmental impacts. However, limited knowledge on the complex spatiotemporally synergy relationships between energy potential (onshore and offshore) is a barrier in making reasonable policies to promote collaborative development of onshore and offshore energies. This study presents a spatiotemporally explicit modelling framework for assessing the synergy potential characteristics of onshore and offshore renewable energy sources. The framework firstly estimates the technical potential of solar PV and wind energy across the country by using 40 years of hourly meteorological reanalysis data (1980–2020), and then investigates the long-term changes trend, stability, and complementarity of solar-wind energy potential spatiotemporally at both onshore and offshore sub-regions. We find that a considerable amount of solar PV and wind energy sources could be harnessed in China, and onshore potential across the country (178 PWh/yr) is approximate 20 times higher than offshore energy potential (8.9 PWh/yr). Overall, onshore solar-wind energy potentials exhibit statistically insignificant but slightly increasing trend over the past 40 years, while offshore wind potentials observe an opposite change trend for solar PV and wind energy production. Complementarity assessment showed the northeast and offshore parts of China present high complementarity effects for hourly and daily power production of solar-wind systems. Furthermore, inter-regional combination between offshore and onshore northwest regions also exhibits promising synergy effect for renewable energy power output. The results on resources dynamic and complementarity highlight the advantage of interregional resources cooperative configuration and electric power grid interconnections. The analysis performed in this paper could provide a global picture for the integration planning and collaborative development strategies formulation of on-/offshore renewable energy across the country. • Synergy potential of onshore and offshore renewable energy was evaluated. • Trend, stability, and complementarity of solar and wind potential were examined. • Onshore energy potential in China is 20 times higher than offshore potential. • Synergy development strategies of onshore and offshore energy were proposed. [ABSTRACT FROM AUTHOR]

Under the carbon peak and neutrality targets, the power generation industry in China is facing an urgent demand for a low carbon transition and participation in the electricity-carbon market. This paper novelty proposes a near-zero carbon emission power plant (NZCEP) integrating gas turbines, wind turbines, power-to-gas and the carbon capture, utilization and storage system. And a two-stage Data-driven Set based robust optimization (DSRO) model, including a day-ahead dispatching phase and a real-time adjustment phase, is conducted to ensure the consumption of renewable energy resources and to develop the optimal operating strategy for NZCEP participation under the electricity-carbon market. The results demonstrated the following outcomes: (1) NZCEP shows better environmental benefits by reducing carbon emissions and consuming renewable energy resources. (2) the DSRO model can resist the interference of wind power output uncertainties and address the issue of traditional robust models being overly conservative. (3) under the electricity-carbon market, NZCEP shows better economic benefits, which can generate additional revenue from selling surplus carbon emission allowances (CEA). Moreover, when the annual average CEA price reaches 355 CNY/t, the NZCEP will achieve full capital cost recovery. • Optimal operation of near-zero carbon emission power plant (NZCEP) is studied. • A Data-driven robust optimization model is proposed for NZCEP operating. • Carbon emissions reduction and wind power accommodation of NZCEP are verified. • The economy of NZCEP participation in the electricity-carbon market is analyzed. [ABSTRACT FROM AUTHOR]

Accurate prediction of renewable energy generation can provide a reference for policymakers to formulate energy development strategies. However, it is difficult to predict the renewable energy generation due to its nonlinear feature. Given this situation, this paper presents a novel flexible nonlinear multivariable discrete grey prediction model with power exponential terms and adjustable time power item. To improve the generalization ability and avoid the over-fitting problem, the hold-out cross validation method and Grey Wolf Optimizer are designed to solve hyper-parameters. For validation purpose, the novel model is implemented to predict the hydropower and total renewable energy generation in China. Results show that the new model has higher prediction accuracy than other competing models, and the prediction accuracy of the model in two cases is at least 57.5995% and 42.3322% higher than similar grey models. The findings also indicate that the hydropower and total renewable energy generation will reach 1469346.28 GWh and 2711622.04 GWh in 2023 with an average growth rate of 3.0232% and 8.6909%, respectively, and the share of hydropower generation is projected to fall to 54.1870% in 2023. Based on the superior modeling performance, the prediction model can be extended for renewable energy generation prediction in other countries. • A flexible nonlinear multivariable discrete grey prediction model is proposed. • Adopt hold-out cross validation method and GWO to solve optimal hyper-parameters. • Comparative study shows that novel model is superior to other competing models. • The hydropower and total renewable energy generation in China have been predicted. [ABSTRACT FROM AUTHOR]

The comprehensive promotion of electric vehicles is critical for green and low-carbon energy transformation and dual-carbon goals in China. Battery swapping station is a hub to meet the battery-swapping demands of EVs and consumes renewable energy power (REP). Moreover, the potential of battery swapping station cluster (BSSC) must be explored urgently. Therefore, the day-ahead dispatching model of BSSC is proposed for the efficient consumption of REP and battery-swapping demands of electric taxis (ETs) in this paper. Firstly, the battery-swapping demands of ETs in dynamic spatiotemporal distribution are aggregated by the KNN algorithm. Secondly, a dual-objective model of the day-ahead dispatching model with economy and efficient consumption of REP is constructed, and the NSGA-II algorithm obtains the optimal strategy. Finally, taking a city of China as an example, the charging-discharging strategy of BSSC is provided. The results show that the day-ahead dispatching model is of practical significance to get an optimal charging-discharging strategy of cluster loads for REP consumption and economic battery-swapping service. • Aggregate the battery-swapping demands of ETs by the KNN algorithm. • Construct a day-ahead scheduling model for balancing CDC of BSSC and REP consumption. • Apply the NSGA-II algorithm to solve the bi-objective model. • Select a city of China for the study to verify the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Carbon neutrality goal puts forward new requirements for the energy structure of the power industry in China. The impact of electricity marketization reform on energy transition is one key issue that we need to consider. This paper constructs a system framework for the internal connection of the four primary energy sources and two system dynamics (SD) models to analyze the impact of the marketed on-grid price on the energy transition in the power industry. The results show that: (1) Compared with the benchmark on-grid price, the marketed on-grid price can promote the realization of the energy transition goal in the power industry to a certain extent. In 2060, the marketed on-grid price will be stable at about 0.1 yuan/kWh. (2) The energy transition goal can be achieved if the degree of market openness exceeds 60%. However, if coal-based power fully enters the marketization stage, the proportion of renewable energy can even reach more than 90% in 2060. (3) It is difficult to achieve the carbon neutrality goal only by increasing the proportion of renewable energy. Finally, we put forward corresponding policy recommendations. • The electricity marketization reform has an impact on the energy transition. • Driving mechanism of the marketed on-grid price is investigated in the power industry. • Two system dynamics models are built. • The energy transition goal can be achieved if the market openness exceeds 60%. [ABSTRACT FROM AUTHOR]

Under the global implementation of low-carbon economy, the development of renewable energy becomes an important way of energy saving and emission reduction. Multi-criteria decision-making (MCDM) techniques are gaining popularity in renewable power sources (RPS) evaluation since this process involves many conflicting criteria. Classical MCDM techniques assume that decisions are conducted in a deterministic environment and decision-makers (DMs) are completely rational while facing with investment risks. However, these hypotheses are not supported in the RPS selection. Fortunately, fuzzy set theory enables to cope with vagueness of evaluations in decision-making process, and cumulative prospect theory can reflect the risk preference of DMs and describe the actual behavior of them. Therefore, in this paper, a fuzzy MCDM technique based on cumulative prospect theory is proposed for selecting the most appropriate RPS in China. A case study in China is carried out to illustrate the rationality and feasibility of the proposed method. The results show that the solar PV is determined to be the best one in China, but the optimal alternative is sensitive to the prospect parameters. This research provides insightful information for the public investors with different risk preferences to evaluate the RPS and select the most appropriate one under uncertain environment. [ABSTRACT FROM AUTHOR]

Rapid economic growth in China has lead to an increasing energy demand in the country. In combination with China's emission control and clean air initiatives, it has resulted in large-scale expansion of the leading renewable energy technologies, wind and solar power. Their intermittent nature and uneven geographic distribution, however, raises the question of how to best exploit them in a future sustainable electricity system, where their combined production may very well exceed that of all other technologies. It is well known that interconnecting distant regions provides more favorable production patterns from wind and solar. On the other hand, long-distance connections challenge traditional local energy autonomy. In this paper, the advantage of interconnecting the contiguous provinces of China is quantified. To this end, two different methodologies are introduced. The first aims at gradually increasing heterogeneity, that is non-local wind and solar power production, to minimize production costs without regard to the match between production and demand. The second method optimizes the trade-off between low cost production and high utility value of the energy. In both cases, the study of a 100% renewable Chinese electricity network is based on 8 years of high-resolution hourly time series of wind and solar power generation and electricity demand for each of the provinces. From the study we conclude that compared to a baseline design of homogeneously distributed renewable capacities, a heterogeneous network not only lowers capital investments but also reduces backup dispatches from thermal units. Installing more capacity in provinces like Inner Mongolia, Jiangsu, Hainan and north-western regions, heterogeneous layouts may lower the levelized cost of electricity (LCOE) by up to 27%, and reduce backup needs by up to 64%. [ABSTRACT FROM AUTHOR]

Based on the extended environmental input-output model and complex network theory, this paper reviews and evaluates the global energy transition based on the principle of shared responsibility, and identifies the key countries affecting the energy transition from the perspective of embodied energy. The results show that although the proportion of clean energy and renewable energy use remains low (only 34.6% and 8.8%), the global energy transition process has progressed rapidly in the past 20 years. From 2000 to 2016, global energy consumption decreased global warming potential (GWP) by 18.7%, acidification potential (AP) decreased by 33.37%, but eutrophication potential (EP) increased by 28.42%. The United States is the largest contributor to the global energy transition, with a total reduction of 1.52 × 10^5 Mt CO 2 -eq, 148.32 Mt SO 2 -eq and 12.4 Mt P O 4 3 − -eq through the energy transition. The energy-consumption emissions from China and other developing countries show an increasing trend (From 2000 to 2016, China increased its emissions by 1.49 × 10^5 Mt CO 2 -eq, 65.09 Mt SO 2 -eq and 76.64 Mt P O 4 3 − -eq). With the slowdown of economic growth and the promotion of energy transition, the growth rate of energy-consumption emissions gradually decreases. Considering embodied energy plays an important role in the scientific evaluation of global energy transition. Under the principle of shared responsibility, ignoring embodied energy is expected to overestimate China's GWP by 4.5%, AP by 3.7%, and EP by 7.4%, and underestimate America's GWP by 10.5%, AP by 7.48%, and EP by 12.5%. We, therefore, focus on the transfer of pollution due to embodied energy and find that China, Russia, India, and South Africa are important agents for the embodied pollution transfer between communities. • Consider the impact of embodied energy on energy transition assessments. • Identifying key countries affecting energy transition from embodied energy. • Calculated the energy transition process of each country. • Measure the energy transition process from multiple perspectives. [ABSTRACT FROM AUTHOR]

With the limiting supply of fossil fuel and the beneficial impact of technological innovation on renewable energy costs, PV power generation is increasingly considered a promising way to generate renewable power. Under the support of the national emerging industry, China's PV industry has experienced a dramatic development over recent years, catapulting into a vital position in the world PV market. The newly installed PV capacity has led to cost reductions. This paper chooses the methodology of techno-economic evaluation to analyze current market application of residential PV power generation, including grid-connected and off-grid systems. One of the main innovations is choosing five Chinese cities in different areas of solar radiation as research objects, which enables regional differentiation in calculating levelized cost of energy (LCOE). The results show that grid-connected PV systems with 3 kW PV modules can meet the electric demand of a 60–90 m 2 residential building. The capacity of off-grid systems are 5–10 kW, which is determined by local solar radiation. By incorporating a learning curve, we forecast that off-grid PV systems for each of the five cities will reach grid parity over the next several decades. The estimation is used to offer policy recommendations for PV market diffusion in China. [ABSTRACT FROM AUTHOR]

Energy planning is crucial to regional sustainable development, it contributes to dealing with electricity demand and supply effectively and tackling air-pollution control in a long-term view. However, the planning is complicated with various factor interrelationships and uncertainties. In this paper, an inexact Bi-level optimization method based on provincial scale hybrid renewable and non-renewable energy planning is developed. This method incorporates Analytic Hierarchy Process based on induced ordered weighted averaging operator and demand side management policies (IOWA-AHP-DSM), interval linear programming (ILP), and bi-level programming method (BLP) into electric power system (EPS) to optimize energy planning and air pollution control. A case study with both environmental and economic objects in Shanxi Province, China, are involved to demonstrate the availability of this method. Seven renewable energy proportion scenarios (0%, 5%, 10%, 15%, 20%, 25% and 30%) are set in this study. Results show that as the proportion increases, the amount of power generation and capacity expansion from natural gas and renewable energy resources increases, while the amount of power from coal and oil, the pollutants emissions and the trading volume of SO 2 decreases. According to the satisfaction degrees of these solutions, results show that it meets both goals when the proportion is 20%. [ABSTRACT FROM AUTHOR]

Renewable energy plays significant role in achieving energy savings and emissions reduction. As a sustainable and environmental friendly renewable energy source, concentrated solar power (CSP) is of interest for research and development. This is because CSP plants can be equipped with thermal storage systems, thereby producing electricity when sunlight is not available. The installation of CSP plants also leads to substantial ‘peak shaving effects’. However, the cost of CSP generation is an obstacle hampering the commercialization of this emerging industry. This paper constructs a mathematical model of the levelized cost of energy (LCOE) to calculate the power generation cost of CSP projects on the basis of lifetime cost structure analysis. A sensitivity analysis is conducted to examine the impact of different variables on the LCOE of CSP projects. The variables considered in this study are investment cost over the construction period, annual operation and maintenance cost, annual electricity production and the discount rate. Finally, the influence of incentive policies such as preferential loans, tax support and zero land cost for power stations is analyzed. This research offers a new method for power generation cost calculation of CSP projects and provides support for governments to formulate incentive policies for the industry. [ABSTRACT FROM AUTHOR]

China's power industry contributes around 40% CO 2 emissions to the national total. The substitution of wind power for coal-fired power is crucial to the realization of CO 2 emission reduction target in China. By deploying the Life Cycle Assessment (LCA) method, this paper identifies that the carbon footprints of a representative coal fired plant and a wind farm in China are 810.35 g/kWh and 12.51 g/kWh respectively. Built on these results, the substitution capacities of wind power for coal-fired power to realize the CO 2 emission reduction target in 2020 and 2030 are discussed. It is concluded that China's wind power needs to develop faster than the goal set in the 12th Five Year Renewable Energy Development Plan for achieving the target of 45% emission reduction in 2020. Moreover, the feasibility of the substitution of wind power for coal-fired power is analyzed. It is argued that whilst China has great potential to realize such substitution given its rich wind resources, the full realization of the substitution requires some radical reforms in the power sector in the country. [ABSTRACT FROM AUTHOR]

With a high proportion of renewable energy generation connected to the power grid, the combined heat and power (CHP) units need to have flexible operation and control capabilities over a wide range of variable load conditions. In China, the proportion of supercritical combined heat and power (S-CHP) units gradually increases due to their high thermal efficiency. In this paper, we propose a data-driven environment modeling method and an offline reinforcement learning-based electricity–heat coordinated control approach for the wide and flexible load adjustment capabilities of the S-CHP unit. First of all, a modeling method based on the multiple multilayer perceptron (MLP) ensemble is proposed to address the possible over-fitting produced by a single MLP. Then, the state and reward are set considering the dynamic characteristics of the S-CHP unit. Moreover, policy training is implemented through a soft actor-critic algorithm with a maximum entropy model to ensure more robust search capabilities under variable load conditions and targets. The simulation results show that the generalization ability of the environment in the multiple MLP ensemble mode is more substantial than that in the single MLP mode. In addition, when the electric load command is between 267 MW and 325 MW, the offline reinforcement learning can significantly reduce the integral of absolute error matrices of the output parameters, demonstrating that the proposed strategy can achieve electricity–heat coordinated control under variable load conditions. • Offline RL ensures the electricity–heat coordinated control in variable loads. • Ensemble MLP structure implements the model with better generalization ability. • The state is selected considering the dynamic response of the S-CHP unit. • The reward is designed in power-dominated and heat-dominated modes. • The IAE metrics of system outputs are improved by the proposed policy. [ABSTRACT FROM AUTHOR]

Promoting electrical transition in vehicle industry has become one of the main objectives for transportation sectors nowadays. However, no consensus has been reached as to whether the penetration of electric vehicles (EVs) have superiority in energy conservation and carbon reduction with life-cycle thinking. Consequently, this paper applies network evolutionary game theory to investigate the evolution trend of industry energy conservation and carbon reduction with promoting electrification in China. To fit the real decision-making environment faced by manufacturers, the greenness competition interactions, competitive strategy invasion, and market uncertainty are further considered under the impact of dual credit policy. Results show that implementing dual credit policy makes electrical transition an irreversible trend for manufacturers, but it does not mean that internal combustion engine vehicle will disappear, rather, it coexists with EVs in a certain proportion, which is negatively related to consumer's acceptance toward EVs. Meanwhile, to address energy conservation and carbon reduction, manufacturers should be encouraged to choose a fuel reduction strategy to decelerate the industry electrification trend, thus acquiring better energy conservation performances. And before increasing consumer's acceptance toward EVs, increasing the proportion of renewable source in primary energy warrants greater urgency and effectiveness to achieve life-cycle emission reduction advantage of EVs. • It is an irreversible trend for all manufacturers to choose electrical transition. • Internal combustion engine vehicles will coexist with electric vehicles eventually. • High invasion ratio of fuel reduction strategy initially is suggested. • Promoting the clean energy supply is of more urgency and effectiveness. • Optimal new energy credit price exists for fuel and carbon reduction objectives. [ABSTRACT FROM AUTHOR]

The proposal of carbon peak and carbon neutrality goals will inevitably promote the large-scale development of renewable energy, and the market mechanism is also being pushed forward to absorb more renewable energy in China. Considering electricity-carbon integrated market mechanism, this paper constructs a hybrid game model to study the evolutionary process of renewable energy generation companies (GENCOs) in market trading by setting up three renewable energy bidding modes. Among them, the leader-follow relationship between GENCOs and the day-ahead electricity market is reflected by the Stackelberg game model, and the bidding behavior of different GENCOs is described by cooperative and non-cooperative game models. The role of different stages for the carbon emissions trading (CET) market on the electricity market is discussed based on the sensitivity analysis. The simulation results show that the bundled sales between wind, photovoltaic and thermal power GENCOs could improve the expected profits of each GENCO and reduce the total carbon emissions. As the proportion of renewable energy in the electricity market increases, the clearing prices will decrease. Meanwhile, the peak-shaving times of thermal power units will raise, and thermal power GENCOs are more willing to participate in the auxiliary service market. Besides, the complete marketization of renewable energy GENCOs will affect the recovery of the initial investment. Moreover, it can be found that lower free carbon quota, higher CET and Chinese certified emission reduction (CCER) prices can effectively guide the electricity market to be cleaner. • Propose effective bidding modes for renewable energy. • Consider the impact of electricity-carbon integration mechanism on renewable energy. • Determine the optimal alliance of different types GENCOs for bundling electricity. • Set dynamic CET and CCER prices to better reflect market supply and demand. [ABSTRACT FROM AUTHOR]

Power demand in China has increased rapidly in the past decade, and it is projected to grow even further. To gain insights into how this demand growth could optimally be met, addressing regional differences such as resource distribution and power demand, is crucial. For long-term planning purposes, using a multi-regional mathematical model that divides China's power sector into regions overcomes the limitations of viewing as a single network. Reflecting how inter-regional power transmission could best be utilized is critical in understanding how flows between regions could be optimised. In this paper, a multi-regional model that reflects actual grid infrastructure with an objective function to maximize accumulated total profits gained by the power generation sector from 2013 to 2050 is proposed. A case study is provided to illustrate how inter-regional power transmission could influence the regional deployment of technologies under different policy scenarios. The results show that energy subsidies and national targets will remain important to the deployment of renewable energy in both the short and long term. In addition, potential downside implications of lower demand growth on the long-term prospects of long-distance inter-regional power transmission and options, such as more flexible electricity pricing mechanisms, to limit the effects are discussed. [ABSTRACT FROM AUTHOR]

Among the numerous types of renewable energy technologies, wave energy technologies have been considered by many experts as one of the most promising for Taiwan. However, the amount of energy presently provided by wave energy technologies constitutes a minor percentage of Taiwan's total energy production. This paper presents experimental results using a laboratory water chamber, wherein the fundamental parameters of an oscillating water column (OWC) system's geometrical design are individually investigated and optimized for maximum amplification factor of wave energy to mechanically useful energy. The effects of back plate angle, fence plate and open wide parameters of chamber in a view of wave energy catching capability are analyzed. Three experimental models are evaluated to include a discussion of the back plate, fence plate, and open wide. The OWC's operation qualitatively differs from that predicted by linear theory, identify to critical flow characteristic; front wall down-wash in the water column. The surveys were then expanded to offer general arrangement or a planning material for the geometry optimization of the chamber that could potentially achieve the largest amplification factor of an oscillating water column (OWC) system. The amplification factor of the change in back angle of the OWC is significant. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to assess the status and progress of rural household energy sustainable development in China. A new composite indicator, rural energy sustainable development index (RESDI), is developed based on the improved grouped principal component analysis method (GPCA) which is the combination utilization of principal component analysis (PCA), factor analysis (FA), and entropy weight method. The improved grouped principal component analysis method keeps the advantages of principal component analysis and factor analysis. To capture the characters of rural energy sustainable development, ten indicators selected based on the criteria presented by OECD are designed to construct the RESDI. The main results are as follows. The RESDI increased from 0.185 in 1991 to 3.189 in 2012. However, the curve of RESDI can be divided into three phases: a slow increase stage between 1991 and 1996, a rapid decrease stage from 1997 to 1998, and a rapid increase stage between 1999 and 2012. [ABSTRACT FROM AUTHOR]

Renewable energy generation is a crucial route to achieve carbon neutrality. In this paper, a methodology integrating richness, stability, and complementarity to assess accommodable potential of solar and wind power generation was proposed and implemented in Ordos, a resource-based city in northwestern China. Results showed wind and especially solar resources were abundant, with maximum of 6.74 kWh/m2/day in summer and 6.9 m/s in spring based on hourly data from 1981 to 2020. Availability of solar (>60%) and wind resources (>80%) was best in the abovementioned seasons. A comprehensive evaluation of stability showed these resources were both stable in spring. Continuity of wind resources was stronger than that of solar (94.5% > 91.3%), and it's variability was also higher (1.9 > 1.4). Then, using correlation coefficients, complementarity degree of these resources was qualitatively identified as moderate. Aiming for stability, the optimal complementary ratio of solar and wind power generation was quantified at 1:0.27, with instability reduced by 10.4%–44.4%, and their power generation could be maximally accommodated by the grid. Scenario analysis predicted a maximum of 2.46% of Ordos land could meet the demand for renewable energy development in 2025. This study provides methodology and model research on orderly replacement of fossils by renewable energy in northwestern China as well as in similar regions globally. • An improved methodology was proposed to assess renewable energy generation potential. • Power stability can be greatly enhanced by this system's evaluation and selection. • Optimal complementary ratio of solar and wind power generation was 1: 0.27 in Ordos. • Resources of regional differences can get various optimal ratios by this methodology. [ABSTRACT FROM AUTHOR]

Annual power load forecasting is essential for the planning, operation and maintenance of electric power system, which can also mirror the economic development of a country or region to some extent. Accurate annual power load forecasting can provide valuable reference for electric power system operators and economic managers. With the development of smart grid and renewable energy power, power load forecasting has become a more difficult and challenging task. In this paper, a hybrid optimized grey model (namely Grey Modelling (1, 1) optimized by Ant Lion Optimizer with Rolling mechanism, abbreviated as Rolling-ALO-GM (1, 1)) was proposed. The parameters of Grey Modelling (1, 1) were optimally determined by employing Ant Lion Optimizer, which is a new nature-inspired metaheuristic algorithm. Meanwhile, the rolling mechanism was incorporated to improve the forecasting accuracy. Two cases of annual electricity consumption in China and Shanghai city were selected to verify the effectiveness and feasibility of the proposed Rolling-ALO-GM (1, 1) for annual power load forecasting. The empirical results indicate the proposed Rolling-ALO-GM (1, 1) model shows much better forecasting performance than Grey Modelling (1, 1), Grey Modelling (1, 1) optimized by Particle Swarm Optimization, Grey Modelling (1, 1) optimized by Ant Lion Optimizer, Generalized Regression Neural Network, Grey Modelling (1, 1) with Rolling mechanism, and Grey Modelling (1, 1) optimized by Particle Swarm Optimization with Rolling mechanism. Ant Lion Optimizer, as a new intelligence optimization algorithm, is attractive and promising. The Grey Modelling (1, 1) optimized by Ant Lion Optimizer with Rolling mechanism can significantly improve annual power load forecasting accuracy. [ABSTRACT FROM AUTHOR]

Distributed PV (DPV) has become the policy priority of renewable energy development in China since 2012 and ambitious planning for it has been formulated and updated. In 2013 a national unified subsidy policy was put into implementation. However, the actual progress is far below expectation. In this paper, the analytical framework of levelized cost of electricity (LCOE) is employed to estimate the generation cost of DPV in China. We find that under existing tariff and subsidy policies, with ideal conditions of most favorable financial arrangement and maximum annual operation hours, DPV projects located in regions with best solar resource or high commercial/industrial retail electricity prices are able to break even; but with practical conditions DPV projects are economically unfeasible at most cases. We further find that, difficulty in obtaining roof space, complicated grid integration procedure, barriers in obtaining bank loan, and unattractive and rigid subsidy policy, lead to high investment risks, which largely explains why DPV can't take off in China. A series of policy packages are proposed to address these issues. [ABSTRACT FROM AUTHOR]