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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]

Accurately predicting wind and photovoltaic power is one of the keys to improving the economy of wind-solar complementary power generation system, reducing scheduling costs and no-load losses, and ensuring grid stability. However, the natural properties of energy result in complex fluctuations in their corresponding power sequences, making accurate predictions difficult. Therefore, this paper proposes an intelligent prediction system that combines decomposition algorithms and deep learning for ultra-short-term prediction of wind and photovoltaic power. First, an improved decomposition algorithm is proposed, based on fuzzy entropy's property that its value increases with the increase of sequence uncertainty, particle swarm optimization (PSO) is used to search for the optimal parameter combinations of variational modal decomposition (VMD), so that it can automatically adjust the parameters for energy data with different characteristics to reduce the human error. Then, a convolutional neural network (CNN) architecture that balances operational efficiency and prediction performance is constructed, and the hyperparameters of the CNN are optimized using the wild horse optimization algorithm (WHO) to improve the stability and accuracy of the prediction model. In this paper, real data from wind power plants and photovoltaic power plants in China are used as experimental objects, and experiments are carried out in three aspects, namely, benchmark model selection, decomposition algorithm comparison and combined model comparison. The results show that selecting CNN as the benchmark model is a good choice; the improved VMD has better decomposition performance than other state-of-the-art decomposition algorithms. The system proposed in this paper is highly generalizable and adaptive, and its prediction performance and accuracy greatly outperform that of other comparative models, with prediction accuracies improved by 72% and 79%, respectively, compared to a single CNN model. • The hyperparameters of VMD are determined by using PSO based on fuzzy entropy. • Optimize convolutional neural network using the wild horse optimization algorithm. • The intelligent prediction system can accurately predict wind power and PV power. • Experiments based on power data from actual wind farms and PV plants. • A deep learning prediction method applied to wind and solar complementary systems. [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]

Accurate and robust wind power forecasting (WPF) is great significance to ensure the safe and stable operation of the power system and promote the transformation of low-carbon energy. However, the high randomness and intermittency of wind power bring great challenges when designing reliable forecasting models. In this paper, a novel spatial-temporal attention graph convolutional network model is proposed. Firstly, the spatial attention mechanism is used to aggregate and extract the spatial correlations of the raw wind power data. Secondly, the temporal attention mechanism is applied to capture the temporal correlations. Then, the extracted spatial-temporal correlations were put into the temporal convolution network and the spatial convolution network to further obtain the temporal and spatial dependencies. Finally, the wind power forecasting results is output through the full connection layer. The proposed method is verified by using wind power data from real wind farm in China. The experimental results reveal that the proposed depth spatiotemporal prediction model has more significant advantages than other advanced models in terms of prediction accuracy and stability. • The spatiotemporal attention mechanism is used to learn the dynamic spatiotemporal correlations. • The spatiotemporal convolution mechanism is applied to enhance feature extraction of wind power. • The proposed model can improve the accuracy of wind power prediction by experiments. • Real datasets from wind farm in China is employed for the modeling study. [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]

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]

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]

Currently, most district heating networks in China are running in the heat-setting mode, which limits the adjustments of combined heat and power (CHP) units. By considering the electrical power system and district heating system together, the peak regulatory capabilities of CHP units can be improved, and renewable energy accommodation can be promoted. In this paper, a tractable integrated heat and electricity dispatch (IHED) model is described that addresses the thermal inertia characteristics of pipelines and buildings to increase flexibility. A water mass method (WMM) for pipeline thermal inertias is proposed to simplify the IHED model by removing the integer variables and differential equations. Benefiting from the WMM, the proposed IHED model is a single-level nonlinear model that is tractable. An efficient iterative algorithm based on the generalized Benders decomposition and a sequential approach combined with the iterative algorithm and Interior Point OPTimizer (IPOPT) are proposed to solve the IHED model. The WMM is tested in thermal inertia simulations compared to an existing node method (NM) and commercial simulation software. The proposed solution strategy is verified in a small-scale system and a practical system. The simulation results are discussed to demonstrate the feasibility and economy of the IHED model proposed here. • A water mass method (WMM) for pipeline thermal dynamics is proposed. • An integrated heat and electricity dispatch (IHED) model considering thermal dynamics is proposed. • A decomposition method and an efficient iterative algorithm are proposed to solve IHED. • A sequential approach combined with iterative algorithm and IPOPT is proposed to improve the optimality of the solution. • The WMM is tested in the simulations compared to an existing node method (NM) and a commercial simulation software. [ABSTRACT FROM AUTHOR]

COVID-19 brings new challenges to addressing climate change and achieving the goal of environmental sustainability. This paper examines how the COVID-19 pandemic affects transmission of returns spillovers among green finance, carbon market and renewable & non-renewable energy in China. We evaluate the dynamic and directional risk connectedness across time and at various frequency bands using Diebold and Yilmaz and Baruník and Křehlík approaches. First, the total spillover effects increase significantly when the COVID-19 outbreak. Second, the carbon price fluctuations are mainly influenced by non-renewable energy. Third, the spillover effect is mainly acted in the short term, meaning that it is strongly influenced by investor sentiment in the short term. Fourth, non-renewable energy and carbon markets are the main net recipients and can be used as hedge assets. Finally, the total spillover is time-varying that is affected by the unexpected crisis. The findings will help policymakers and environmental investors to develop policy measures and portfolios that are appropriate for different frequency domains. • Time-frequency connectedness among green finance, carbon and energy is tested. • Spillovers increase at the onset of the COVID-19 and mainly acted in the short-term. • The carbon price fluctuations are mainly influenced by non-renewable energy. • Non-renewable energy and carbon markets are the major net recipients. • The total spillover is time-varying affected by the unexpected crisis. [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]

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]

With the support of incentive policies, the diffusion of wind power has achieved tremendous achievements in China. However, behind this rapid expansion, it is raised a question: what are the driving forces behind the diffusion of wind power? And which policies are more efficient to improve the diffusion rate of wind power? To scientifically answer this question, this paper combines system dynamics and the two-factor learning curve to investigate the dynamic diffusion process of wind power. The results show that: (1) The demand-pull policy through feed-in tariff (FIT) policy can promote the diffusion of wind power; (2) The technology-push policy through R&D policy also promotes the diffusion of wind power; (3) A higher level of FIT is more efficient to expand the installed capacity of wind power, in addition, a higher level of R&D intensity is more efficient for cost reduction; (4) The coefficient of learning by doing of wind power is 0.0274 and the coefficient of learning by research of wind power is 0.2540, which indicates China's wind power industry is transitioning from price subsidies to technology innovation policies. The research results can enrich the research on the diffusion of wind power and provide a reference for other regions to study the diffusion of renewable energy. • Both demand-pull and technology-push can promote the diffusion of wind power. • The coefficient of learning-by-doing and learning-by-researching is 0.0274, 0.2540 • The reduction in wind power costs is driven more by innovation policies. • A higher level of FIT policy is more efficient to expand the installed capacity. • A higher level of R&D intensity is more efficient for cost reduction. [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]

Abstract: Large district heating (DH) system accounts for 70% of urban building heating in China. In North China, this proportion is even higher (The North China is the north of the Huai River and Qinling Mountains). Many cities in North China can exploit various kinds of low-grade renewable energy. This paper presents a new heating method to realize renewable energy recovery by absorption heat pumps associated with municipal network. In the DH substations, absorption heat pumps are driven by the exergy-difference originated from the larger temperature difference of heat exchange between primary and secondary heat network. There are two configurations—type I and type II substations based on the temperature of renewable energy. A reasonable parameter setting of system is suggested. The equipment operational performance was optimized based on a practical example. The low-grade renewable energy can be recovered effectively in this method. As a result, both heating capacity and energy efficiency of the DH system can be improved. Furthermore, operating costs may be reduced remarkably, due to the reduction in both the coal consumption of heat production unit and the power consumption of delivery pump. Therefore, the system is superior in energy conservation and has a promising application prospect. [Copyright &y& Elsevier]

Abstract: This paper first provides an overview of the context of renewable energy development in China, including the country''s recent renewable energy legislation. Further, it summarizes the current status of renewable energy development and the role it plays in the national energy supply. Next it introduces the national indicative targets for renewable energies in 2010 and 2020, and conducts a long term scenario of the role of renewable energies in China''s energy system transition till 2050. It discusses the main risks involved in China''s renewable energy development, and proposes some policy measures for risk management. [Copyright &y& Elsevier]

This paper presents a transition approach for markets with high-share renewables to connect the delivery of physical medium and long-term market contracts with daily system operation while ensuring safe and reliable grid operation given the under-developed electricity market environment in China. The proposed approach decomposes the market contracts into daily energy schedules with rolling updates of subsequent daily schedules to ensure fairness in contract delivery, then employs security-constrained economic dispatch to allocate the decomposed energy to each power plant to determine the day-ahead schedules. In the security check, a multi-section coordinating strategy is incorporated to maximize the utilization of transmission capacity and determine the optimal power flow. Results of the Yunnan case study demonstrate the feasibility and effectiveness of the proposed approach in modeling the day-ahead schedules and handling the multiple objectives and numerous constraints, and therefore provide a reference for similar hydro-dominated systems in transitioning to electricity spot markets. Finally, discussions and suggestions are proposed to promote electricity markets further. • An approach connecting physical contracts with daily generation schedules is proposed. • The approach is verified to be feasible and effective in the Yunnan electricity market. • The Yunnan case study provides a reference for similar systems transitioning to spot markets. • Discussions and suggestions are proposed to promote electricity markets. [ABSTRACT FROM AUTHOR]

Hydropower can be used to smooth out the output fluctuations from power systems including wind and solar sources. This paper proposes a double-layer model for coordinating the operations of cascaded hydropower and neighboring wind and photovoltaic (PV) facilities. In a local complementary model, hydropower with less regulating ability (local complementary hydropower, LCH) is dispatched to alleviate short-term fluctuations (i.e., hourly variations), whereas in the global one, hydropower with greater regulating capacity (global complementary hydropower, GCH) is operated to alleviate long-term fluctuations (i.e., intraday peak-valley differences). The results of a case study of a hydro-wind-PV cluster project in southwestern China show that most (90%) short-term fluctuations can be smoothed by LCH with a minimum impact on the capacity factor (−0.02). In contrast, alleviation of long-term fluctuations can be achieved with GCH, but this causes a large capacity factor reduction (−0.25). The discharge fluctuation of GCH is notably lower (70–75%) than that of LCH. In this system (which includes 5200 MW wind and 1400 MW PV), the output fluctuations can be eliminated if hydropower capacity reaches 2200 MW. These findings demonstrate that clustering of hydropower cascades with wind and solar generation facilities is a promising avenue for the decarbonization of electricity systems. • A power system with hydro, wind, and PV is partitioned into local/global clusters. • Clusters are formed according to the regulating ability and proximity. • A multi-objective function is employed to maximize power and minimize fluctuation. • Local and global complementary hydropower sequentially smooth output fluctuation. • The discharge fluctuation is lower for global complementary hydropower than for local. [ABSTRACT FROM AUTHOR]