Search

Technological innovation is the key to develop wind power and photovoltaic power industries. The feed-in tariff (FIT) policy, as a demand-pull policy, is important to support renewable energy technological innovation. Using the "difference-in-differences" method, this paper investigates the impact of FIT policy of wind power and the impact of the FIT policy designed according to differences in the distribution of resources on wind power technological innovation. The findings show that the FIT policy can drive patenting in wind power technologies during the implementation period, but may play a relatively weak promoting role in technological innovation in the latter term, and the FIT policy designed according to differences in the distribution of resources also stimulates more patent counts. Finally, based on the fixed effect negative binomial regression model, this paper finds that the higher feed-in tariffs can increase the patent counts in photovoltaic power technologies. [ABSTRACT FROM AUTHOR]

With the increasingly prominent issues of climate change and energy shortages, large-scale development of renewable and clean energy has become a common choice for countries around the world. Among them, onshore wind power plays an important role in the global energy transformation process due to its mature technology, safety, and efficiency advantages. However, because wind energy resources have the characteristics of low energy density, instability, and spatial dispersion, large-scale development of onshore wind power requires a large amount of land resources to support. To ensure the land resource demand for wind power development under the goal of 'carbon neutrality,' this paper conducted a quantitative study on the land occupation of onshore wind power in China. Firstly, based on the criterion of exclusivity, this paper reclassified the types of land occupation for wind farms from a spatio-temporal perspective. Secondly, based on the sample parameter method and the alpha shape algorithm, this paper calculated the land area of existing onshore wind farms in China categorically. Finally, considering the development trend of wind power technology, this paper systematically simulated and predicted the wind power land demand in different development scenarios under the 'carbon neutrality' goal. The results indicated that: (1) Based on the principle of exclusivity, the total land use area of wind farms could be divided into two parts in the spatial dimension: the direct impact area and the indirect impact area. The direct impact area could be divided into two parts in the temporal dimension: permanent land use and temporary land use. (2) In 2022, the land occupied by the wind turbine foundation, permanent land use, temporary land use, and direct impact area of China's onshore wind farms reached 48.28 km², 352.08 km², 1 234.86 km², and 1 638.81 km², respectively. (3) In 2022, the indirect impact area and total land use area of China's onshore wind farms reached 9.81x104 km ² and 9.99x104 km², respectively. The total land use area was almost equivalent to the total urban land use area of China. (4) Compared with the scenario of current technical level, under the scenario of technological progress and wind turbine upgradation, the various types of land use demand for wind power in China will have significantly decreased by 2060, and land use demand will have reached its peak by around 2050. The large-scale upgradation of wind turbines has great potential for saving land resources. Therefore, perfecting the land use standards of wind power construction, improving the land use efficiency of wind power development, and promoting the construction of offshore wind power are important paths for coordinating the sustainable use of wind energy resources and land resources. [ABSTRACT FROM AUTHOR]

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

The proposal of "double carbon" goal increases the pressure of power structure transformation. This paper sets up two scenarios according to the timing progress of realizing the "double carbon" goal and explores the transformation planning schemes of China's power structure. The conclusions are as follows: (1) Technological progress and policy support will greatly reduce the levelized cost of electricity (LCOE) of onshore wind power, offshore wind power, photovoltaic power, and photothermal power. The rapid rise in carbon price will lead to the LCOE of coal power in 2060 rising to 2 CNY/kWh. (2) The power consumption of the whole society in the baseline scenario can reach 17,000 TWh in 2060. In the acceleration scenario, this value may triple that in 2020 to 21,550 TWh. (3) The acceleration scenario will pay more newly added power costs and coal power stranded scale than the baseline scenario but can achieve carbon peak and negative emissions earlier. (4) More attention should be paid to the flexible level of power system, improve the allocation proportion and requirements of new energy storage on the power supply side, help the steady exit of coal power, and ensure the safety of low-carbon transformation of power structure. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The goals of carbon peak and carbon neutrality have put forward higher requirements for the low-carbon development of power supply. This paper discusses the impacts of multi-energy power generation on carbon emissions for 30 regions in China and proposes low-carbon development suggestions for the electric power industry. The research found that firstly there is a significant strong positive correlation between thermal power and carbon emissions in most regions of China, so the carbon emission reduction of power supply should still focus on thermal power. Secondly, wind power and photovoltaic power have positive effects or negative effects on carbon emissions in different regions. But combined with the analysis results in regions with the rapid development of wind power or photovoltaic power, it could be found that wind power and photovoltaic power contributed to reducing carbon emissions when they developed to a certain scale. It is proposed to speed up the construction of wind power and photovoltaic power in regions with rich wind resources or solar resources such as Inner Mongolia, Xinjiang, Liaoning, and Gansu. Thirdly, hydropower and nuclear power both have negative effects on carbon emissions. Considering the large demand for electricity in coastal regions where nuclear power is located, it is suggested that coastal regions should gradually promote the construction and application of nuclear power on the basis of safety. [ABSTRACT FROM AUTHOR]

As a new and cost-effective renewable energy power generation technology, offshore wind power is getting more and more attention. The development of offshore wind power industry is affected by policy-making, technology management, resources and environment, market supply and demand, and the relationship among the influencing factors is complex. This paper analyzes the factors that affect offshore wind power industry from a unique and comprehensive perspective. Fourteen factors are selected and interpretative structural model (ISM) is established to study the relationship between the influencing factors of offshore wind power industry. The results show that 14 influencing factors can be divided into five levels: the first level is the surface factors, including the economic incentive policy, operation mechanism, industrial chain, energy market mechanism, investment, and financing mechanism; the second and third levels are the intermediate factors, including generation cost, operation management, and offshore wind power technology; the fourth and fifth levels are deep-seated factors, including development planning and grid price, site selection, R&D investment, environmental protection policy, and offshore wind power supply. Deep-seated factors have a direct impact on the intermediate factors, the intermediate factors have an important impact on the surface factors, and the surface factors directly affect the development of offshore wind power industry. The influence of the 14 factors selected in this paper on offshore wind power industry is from bottom to top, from deep to shallow. [ABSTRACT FROM AUTHOR]

The serious problem of wind power curtailment in northern China has created a pressing need to enhance the peak-shaving ability of the power system. As the main source of power supply in northern China, combined heat and power (CHP) units have significant potential for peak-shaving. Currently, the Chinese government encourages CHP plants to increase their peak-shaving capacity by installing power-to-heat (P2H) equipment. In addition, the government has implemented auxiliary service market policies to encourage CHP plants to provide peak-shaving services. In order to maximize economic benefits for CHP plants, this paper proposes an optimal configuration method of P2H equipment with the static payback time (SPT) as the objective function. Cost and income models of installing the P2H equipment are constructed by taking into account the auxiliary service market policies. The peak-shaving income model of the CHP plant is derived emphatically as a key part of the proposed method. Finally, the district heating region in Jilin province is used as a case study example. The results show that adding the P2H equipment is significantly effective in improving the peak-shaving ability of CHP units, and investing in heat pumps is more cost-effective than electric boilers. The proposed method can be applied to other northern regions relying on CHP units for central heating, providing a valuable solution to the problem of wind power curtailment in these regions. [ABSTRACT FROM AUTHOR]

"Carbon peak and neutrality" are an important strategic decision to promote the transformation of China's energy economy and build a community with a shared future for mankind. China is a big energy consumer, and the whole society is facing huge challenges under the goal of carbon peak and neutrality. The realization of the goal of carbon peak and neutrality requires the guidance of correct theoretical methods and scientific deployment. This paper mainly studies the energy consumption scheduling, demand response management, and energy trading problems of microgrid. In view of the shortcomings of the existing energy optimization scheduling methods in the microgrid, a variety of energy resources such as electric energy, natural gas, heat energy, and cold energy are considered into the microgrid model. Based on the noncooperative game and Stackelberg game, a new energy optimal dispatch model is constructed with a variety of game methods such as two-layer game. Maximize the personal benefits of the microgrid while meeting the reliable operation of the system and the electricity demand of users. The three-stage noncooperative game problem is solved based on the reverse bootstrap method, and the closed expression of the optimal strategy in each stage is obtained. The power generation forecasting technology based on big data is studied, and a power forecasting method is proposed, which can effectively guide the energy consumption of the microgrid. The simulation results show the effectiveness of the proposed renewable energy management model based on big data, which verifies that the accurate wind power prediction results are conducive to better theoretical analysis of energy management. [ABSTRACT FROM AUTHOR]

Copyright of China Harbour Engineering is the property of Editorial Office of China Harbour Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Distributed energy resources aggregators (DERAs) are permitted to participate in regional wholesale markets in many counties. At present, new market players such as aggregators participate in China's power market transactions. However, studies related to market trading strategy have mostly focused on centralized wind power and PV generation units. Few studies have been conducted on the decision-making strategies for DERAs in China's power market. This paper proposes an auxiliary decision-making model for distributed energy systems to participate in the day-ahead market with more reasonable trading strategies. Firstly, the Gaussian mixture model (GMM) is used to deal with the uncertainties of wind power and photovoltaic (PV) output in the distributed energy system. Secondly, the information gap decision theory (IGDT) is used to deal with the uncertainty of price fluctuations in the spot electricity market. Thirdly, according to the different risk preferences of the DERAs facing market price fluctuation, the robust decision model and opportunity decision-making model in the day-ahead market are constructed, respectively. Finally, to deal with the irrational behavior of the DERAs' perception of "gain" and "loss" with market risks in China's two-tier market environment, the prospect theory and the marine predator's algorithm (MPA) are employed to obtain a day-ahead trading decision scheme for DERA. The analyses show that RDES with robust preference can withstand greater price volatility in the day-ahead market; they will reduce the bidding expectations and increase the system operating cost to improve the achievability of the expected revenue. However, DERAs under the opportunity strategy is more inclined to sell electricity to the market and offset system operating costs with revenue. The proposed model can provide strategic reference for DERAs with different risk preferences to bid in day-ahead market and can improve the level of aggregators' participation in electricity trading. [ABSTRACT FROM AUTHOR]

With the proposal of the carbon peaking and carbon neutrality goals, China's energy structure is gradually shifting from relying on fossil fuels to relying mainly on renewable and clean energy systems. However, clean energy represented by wind and photovoltaic power generation is difficult to operate and consume separately due to output fluctuations and uncertainties. Hydropower stations have good regulation capabilities and can operate in conjunction with wind farms and photovoltaic power stations to alleviate the instability of new energy generation. The southwestern region of China is rich in hydropower resources, and a large number of cascade hydropower stations with regulatory reservoirs have been built. The complementary operation of cascade hydropower with wind power and photovoltaic resources around the watershed, and the full utilization of the regulating capacity of cascade hydropower to bundle and deliver high-quality electricity, is an important means of achieving energy transformation. To quantify the carrying capacity of cascade hydropower stations on wind power and photovoltaic resources, this paper proposes a nested calculation model for medium to long-term optimal scheduling of hydropower to short-term optimal allocation of hydro-wind-photovoltaic power. The short-term scheduling calculation boundary is determined based on the mid-term and long-term optimization scheduling calculation results of hydropower, the scale of wind and photovoltaic power during short-term scheduling is adjusted, and scheduling with the goal of maximizing source load matching, maximizing power generation, and minimizing abandoned power are optimized. Decoupling is carried out by using a hierarchical nested strategy to calculate the scale of wind and photovoltaic resources that can be carried by cascade hydropower stations. Applying this model to the cascade hydropower station group in the Yalong River Basin, the scale of wind and photovoltaic resources carried by the "three reservoirs and seven cascade" cascade hydropower stations in the Yalong River Basin is calculated. The research results show that considering the 2025 planning level year and 5% curtailment rate constraint, the cascade hydropower in the Yalong River basin can carry 1.396 million kW of wind power resources and 10.74 million kW of photovoltaic power resources. The research results have a certain reference value for the optimization of water solar energy complementary power generation systems in the watershed. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Offshore wind energy resources far exceed those on land. However, the increase in water depth of the continental shelf in China's sea area slows down as the distance from the shore increases. In view of the characteristics of China's sea, how to optimize the design of mooring systems to adapt to the water depth conditions in China's waters and to resist the harsh sea conditions is one of the major problems encountered in the development of floating wind turbines. In this paper, the 5MW-OC4 semi-submersible floating wind turbine is taken as the research object, and the frequency domain and time domain calculation of the floating wind turbine are carried out by using SESAM software under the water depth of 40 m in the Bohai Sea. By comparing the motion response and tension of catenary and tension mooring floating wind turbine. The mooring system is optimized by combining the buoy and clump weight. The results show that the catenary mooring mode is more suitable for the floating wind turbine under shallow water conditions. Mooring accessories used in combination have the same effect on the overall response of the floating wind turbine as changing the same mooring parameters when used alone, but the effect of changing the mooring parameters on the performance optimization of the mooring system is more obvious when used in combination, and a reasonable combination of accessories can significantly change the overall characteristics of the floating system and affect the safety and cost of the system. [ABSTRACT FROM AUTHOR]

China prioritizes clean energy for power generation in response to climate change and air pollution. By the end of 2021, the installed capacity of wind power has reached 328.48 GW, accounting for 13.8% of China's total installed capacity of 2376.92 GW. However, wind power is inherently unstable and its output depends on resources, which poses challenges for the national grid. Wind curtailment is often encountered when electricity production is plentiful. The main objective of this work is to develop a clean energy generation system for on-demand power supply to improve the utilization of wind resources. The LBE reactor is also a clean energy source with flexible power regulation that helps mitigate the impact of wind variability. Therefore, a hybrid wind-nuclear power system (HWNPS) is proposed. This paper demonstrates the technical feasibility of the LBE reactor and conducts a preliminary analysis of the economics of the HWNPS. The additional cost of HWNPS due to power regulation is about $ 9.04/MWh, which has potential for research and development. [ABSTRACT FROM AUTHOR]

As the installed capacity of renewable energy continues to increase, the proportion of China's renewable energy power generation has increased year by year, and carbon emissions have been effectively reduced. However, there is still a big gap between this and China's promised carbon emission reduction target. It is important to explore the coordination and optimization of power supply structure with the goal of carbon emission reduction. In the power market environment, there are conflicts of interest among wind power companies, thermal power companies, and energy storage equipment. Therefore, it is necessary to comprehensively consider their economic and environmental benefits to formulate subsidy policies. In order to solve the above problems, this paper studies the key issues of smart grid operation model, input-output efficiency, market game behavior, and policy effect simulation based on production simulation, game theory, statistics, and other theories. Finally, Matlab and Gridview were used to calculate the model results, and policy recommendations on wind power subsidies, thermal power carbon tax, and thermal power feed-in tariff were put forward. To achieve the goal of carbon emission reduction, renewable energy generation should be encouraged, and the proportion of installed capacity of wind and solar energy in the power structure should be increased. The most effective way is that the government should actively guide the subsidy mechanism into the electricity trading market. Based on the calculation results of the RTS-25 system in this article, the following policy recommendations can be put forward: within the government's financial capacity, at least 1 yuan per kilowatt-hour of wind power subsidy is provided, and the maximum of thermal power feed-in tariff is set at 0.7 yuan, and the higher the wind power subsidy and the lower the feed-in tariff of thermal power, the higher the carbon emission reduction. At the same time, a thermal power carbon tax of about 0.36 yuan per kilowatt-hour should be set. The results of this study can provide suggestions for consuming renewable energy and formulating competition rules in power market transactions. This article can be further studied. The scope of subsidy prices can be narrowed, and more precise subsidy prices that balance the benefits of various systems can be determined on the basis of this research. [ABSTRACT FROM AUTHOR]

China has been introducing reforms in the electricity market. Since 2016, electricity retailers are allowed to operate in the market once monopolized by traditional power companies. This regulatory environment demands new types of purchase contracts to remunerate the energy agents and deliver better prices for consumers fairly and sustainably. This paper proposes two new electricity purchase contracts with risk sharing mechanism and studies on wind power supplier's optimal capacity investment problem under supply and demand uncertainty. Firstly, a basic model for fixed demand contract is developed, and the optimal capacity is obtained. Secondly, the fixed commitment order contract and the forecast order contract with risk sharing mechanism are proposed to manage the fluctuation of wind power supply randomness. With backward induction, the optimal investment capacity of wind power supplier is solved, and then the fixed order quantity or forecast demand quantity of the electricity retailer can be obtained. Our results show that the fixed order quantity may have a positive influence on the wind power supplier's capacity investment decision. While, the forecast order quantity may have a negative influence on the wind power supplier's capacity investment decision. In addition, wind density has a strong influence on wind power capacity investment, and it has a positive impact on wind power supplier's profit, but it does not always have a positive impact on electricity retailer's profit. At last, based on numerical analysis, we examine how the wind density distribution coefficient change affect members' profit in the electricity supply chain. We also find that even with wind curtailment punishment, the forecast order contract can make a Pareto improvement on the whole electricity supply chain. [Display omitted] • Risk sharing mechanism is introduced to the fixed commitment order contract and the forecast order contract in the electricity supply chain. • Increasing the forecast order quantity may reduce the capacity investment. • The forecast order contract can improve the performance of the whole electricity supply chain. [ABSTRACT FROM AUTHOR]

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The transfer of energy-intensive projects to areas abundant in clean energy resources holds promise for optimizing the energy structure. After obtaining specific information on transferred projects of listed energy-intensive firms through search or phone contact, this paper innovatively constructs a three-dimensional panel containing 1,859,220 observations to empirically examine the attraction of clean energy to energy-intensive projects under the carbon intensity constraint policy (CICP). Our findings reveal that: (1) the CICP promotes the transfer of gray energy-intensive projects, leading to the transfer structure trending towards graying. (2) Furthermore, we observe a positive correlation between the proportion of thermal power in transfer-out areas and the outflow of gray energy-intensive projects. Conversely, areas with higher wind power and photovoltaics attract a greater inflow of gray energy-intensive projects. (3) Notably, clean energy predominantly attracts the inflow of gray projects rather than green projects. Our findings provide micro evidence and valuable insights into the transfer of energy-intensive industries, informing the design and implementation of future environmental policies for policymakers and stakeholders. [ABSTRACT FROM AUTHOR]

Aiming at a large number of decentralized resources such as rural biomass, rooftop photovoltaic, and decentralized wind power, a novel rural virtual power plant (VPP) has been conceptualized. The VPP is integrated by biomass waste conversion system (BWC), carbon to energy (C2P), demand response aggregator (DRA), and distributed renewable energy (DRE), namely, BDCD-VPP. Then, this paper proposes a data-driven two-stage robust optimization dispatching model for BDCD-VPP considering carbon-green certificates equivalent conversion mechanism. This model aims to address the uncertainty of wind power plants (WPP) and photovoltaic (PV) power generation. Strong duality theorem (SDT) and C&CG algorithm are applied to construct this model. Thirdly, the Aumann-Shapley method (A-S) was enhanced by incorporating risk factor, cost factor, and carbon reduction factor. This refinement results in the development of a synergistic scheduling revenue allocation method for electricity‑carbon-green certificates. Finally, the Lankao rural energy revolution pilot program in China is selected as the case study, the results showed: (1) The BDCD-VPP aggregates distributed energy sources such as the rural WPP and the PV to realize electricity‑carbon-electricity cycle effect. The BDCD-VPP generates a green certificate revenue of 47.17¥/MWh and exhibited carbon emissions of 0.37 t/MWh. The proposed Carbon-Green Certificates Equivalent Conversion Mechanism increases BDCD-VPP benefit ratio by 3.52%. (2) The proposed two-stage robust optimization dispatching model enhances the ability of BDCD-VPP to adapt to uncertainty. Compared to the day-ahead stage, biomass power generation (BPG) and waste power generation (WPG) increase upward peaking power by 24.05% and 9.99% in intra-day stage. Flue gas treatment system (FG-TS), gas-power plant carbon capture (GPPCC) and power to gas (P2G) increase downward peaking power by 65.15%, 70.99% and 25.94%. (3) Utilizing the proposed benefit allocation methodology, BPG and WPG need to concede 38.25¥/MWh and 65.02¥/MWh for carbon emissions associated with electricity. WPP and PV need to concede 64.25¥/MWh and 33.71¥/MWh for power generation uncertainty. GPPCC, P2G, DRA, and small hydropower station (SHS) obtain revenues of 24.48 ¥/MWh, 55.07 ¥/MWh, 70.38 ¥/MWh, and 51.30 ¥/MWh. Compared to the A-S, the proposed benefit allocation methodology exhibits 6.92% improvement in satisfaction. Overall, the proposed data-driven two-stage robust optimization dispatching model and benefit allocation strategy facilitates the optimal aggregation and utilization of rural distributed energy resources, considering the interests of various stakeholders. This is conducive to achieving a clean and low-carbon transformation of the overall energy structure. • Proposed a novel rural virtual power plant considering biomass waste conversion and carbon to power. • Designed carbon-green certificate equivalence conversion mechanism. • Proposed a data-driven scenarios generation and reduction strategy for uncertainty factors. • Construct a two-stage robust optimization dispatching model and strong duality C&CG algorithm. • Construct an improved Aumann-Shapley-based benefit allocation strategy [ABSTRACT FROM AUTHOR]

The widespread implementation of feed-in tariff (FIT) policies has played a crucial role in fostering the development of wind power, with their positive effects firmly established in numerous studies. However, the impact of regionally differentiated FIT policies on the misallocation of wind power resources remains a topic of contention, with limited research dedicated to this area. This paper aims to address this gap by examining the implications of such policies on the intensive and extensive margins of wind power installed capacity in China, shedding light on the underlying mechanisms driving resource misallocation. Empirical findings indicate that, concerning the intensive margin, the policy amplifies the concentration of wind power investments in regions characterized by abundant wind resources but low electricity demand. These regions present favorable conditions for large-scale wind farms with cost advantages, consequently exacerbating the misallocation of wind power resources. However, on the extensive margin, the policy promotes the likelihood of locating small and medium-sized wind farms in regions with poor wind resources but higher tariff rates, thus partially mitigating resource misallocation. In summary, China's policy hampers wind power investments in regions characterized by high electricity demand but limited wind resources. This suggests that the negative impact on the intensive margin outweighs the positive impact on the extensive margin. The findings of this study bear significant implications for the development of renewable energy support policies, particularly in countries grappling with substantial regional disparities in renewable energy resources. • We cope with the controversial effect of FIT policies on the resource misallocation • We provide novel perspectives as the intensive and extensive margins • China's regionally differentiated FIT policy hampers wind power investments • We provide implications for countries implemented regionally differentiated FIT [ABSTRACT FROM AUTHOR]

• An advanced forecasting system is proposed for short-term wind speed prediction. • The data pre-processing technique can extract effective information from the raw sequences and eliminate the volatility and uncertainty of the data. • Choose the appropriate benchmark models in different situations based on optimal benchmark model selection strategy. • A multi-objective optimizer is applied to find the optimal weights and a theoretical proof indicates that the weights assigned by this optimizer are Pareto optimal solutions. • Our proposed forecasting system can quantify the uncertainty of the wind speed sequences. Facing the increasing depletion of traditional energy resources and the worsening environmental issues, wind energy sources have been widely considered. As an essential renewable energy resource, wind energy features abundant deposits, extensive distribution, non-pollution, etc. In recent years, wind power generation occupies a non-negligible position in the electric power industry. Stable and reliable power system operation demands accurate wind speed prediction (WSP), but the inherent randomness of wind speed sequences complicates their fluctuations and causes them to be uncontrollable. In this paper, an innovative WSP system is proposed, which combines data pre-processing technique, benchmark model selection, an advanced optimizer for point forecast and interval forecast. Furthermore, this paper theoretically demonstrates that the weights allocated by this optimizer are Pareto optimal solutions. Six interval data from two sites in China are utilized to validate the forecasting performance of our developed model. The experimental results indicate that the developed model can achieve superior accuracy compared to the tested models in all cases for point forecast, and also obtains the forecasting interval with high coverage and low width error, which is an extremely crucial instruction to guarantee the security and stability of the power system. [ABSTRACT FROM AUTHOR]

To date, few studies have addressed the charging and discharging schedules of electric vehicle battery-swapping stations in China's isolated microgrids. Given that battery-swapping is expected to become increasingly widespread, this study innovatively considered distributed power sources, such as wind power and photovoltaic power, to analyze battery-swapping station operation and isolated microgrid operation schedules. Considering the needs of potential future communities that might depend on electric vehicle battery-swapping stations, two scenarios were analyzed, and the most effective integer planning method was adopted. The international algorithm software program YALMIP + CPLEX was used to address the problem, and simulation results proved that the proposed model and its solution method could effectively affect the safe operation and scheduling of islanded microgrid battery-swapping stations and reduce the cost of islanded microgrid operation, with significant advantages. [ABSTRACT FROM AUTHOR]

The reconstruction models of hourly 10‐m wind speeds were developed for each of 2384 stations over China using stepwise regression, random forest and XGBoost machine learning approaches based on hourly observed and ERA5 reanalysis data from 2005 to 2021. The reconstruction procedures applied observed hourly data to reduce the systematic biases of reanalysis data sets. Furthermore, the procedures employed the past dynamically consistent states of the atmosphere simulated by ERA5 reanalysis techniques to reduce/remove wind‐observed data impacts of long‐term non‐meteorological condition changes over time (e.g., urbanization around weather stations), which provide homogenous hourly wind speed data sets from 1959 to 2021. The systematic errors of the models' simulations derived from three approaches are similarly small, with almost two orders of magnitude smaller than ERA5 original data sets. The systematic errors of reconstructed data sets derived from stepwise regression are similar to its simulation; however, the biases from two machine learning methods are even much greater than ERA5 original data sets. This result implies that machine learning methods are not suitable for such typical time‐series predictions using the previous‐hour wind speed as a predictor to reconstruct wind speed data for the next hour. Therefore, stepwise regression was selected to reconstruct hourly wind speed data sets, which have much better quality than ERA5 reanalysis data with the median correctness increased by >50% and the median rRMSE decreased by 25%–50%. Consequently, the reconstructed wind speed data sets have great potential to be useful for more precisely assessing the characteristics/trends of wind energy resources in the past 60 years over China. Plain Language Summary: The reliable wind speed data with great spatial and temporal coverages are essential meteorological information for more accurately evaluating and forecasting wind power characteristics and variabilities. The observed historical wind speed data sets have various limitations: insufficient spatial/temporal coverages, non‐meteorological condition changes over time (e.g., urbanization around weather stations, measurement instruments, station locations). The reanalysis products cannot accurately reproduce surface wind fields since the systematic biases are usually caused by the reanalysis data assimilation processing/modeling. To overcome the limitations of both data sets, the reconstruction models of hourly wind speeds were developed based on hourly observed and reanalysis data from 2005 to 2021. The reconstruction procedures applied observed hourly data to reduce the systematic biases of reanalysis data sets and employed the past dynamically consistent states of the atmosphere simulated by reanalysis techniques to reduce/remove wind‐observed data impacts of non‐meteorological condition changes over time. Therefore, the reconstruction models are able to provide homogenous hourly wind speed data sets from 1959 to 2021 for available stations over China, which have much better quality than the reanalysis data. Consequently, the reconstructed wind speed data sets have great potential to be useful for more precisely assessing the characteristics/trends of wind energy resources in the past 60 years over China. Key Points: The reconstruction models of hourly wind speed data sets for available stations over China were developed using three different approachesThe hourly wind speed data from 1959 to 2021 for 2384 stations over China were reconstructed based on ERA5 reanalysis/observed data setsThe reconstructed historical homogeneous hourly wind speed data have advantages of being better quality than reanalysis data [ABSTRACT FROM AUTHOR]

In recent years, the development and utilization of China's wind energy resources have been greatly developed, but the large-scale wind power grid connection has brought threats to the safe and stable operation of the power grid. In order to ensure the stability of the power grid, it is necessary to reduce wind power output fluctuation and improve the tracking accuracy of dispatch instructions. Therefore, based on the distributed model predictive control of wind farm active power distribution strategy, an ultra-short-term wind power hybrid deep learning predictive model is proposed. The prediction results of a wind farm in North China show that the hybrid neural network model can achieve high ultra-short-term wind power prediction accuracy and is suitable for active power control prediction models. A two-layer distributed model is proposed to predict the active power control architecture of wind farms by implementing the clustering process with the Crow Search Algorithm. The distributed model predictive control strategy is proposed in the upper layer, and the centralized model predictive control algorithm is adopted in the lower control structure and optimized. The results show that the dual-layer distributed model predictive control strategy can better track the active power distribution instructions, reduce output fluctuation and scheduling value changes, and enhance the robustness of active power regulation, which is suitable for active power online control in wind farms. [ABSTRACT FROM AUTHOR]

With the gradual transformation of global energy, photovoltaic power generation, wind power generation, and other renewable energy have attracted countries around the world. Western China has excellent wind and solar energy resources, which are highly vulnerable to external factors and have severe limitations. Considering the gradual maturity of storage and energy storage technology of abandoned mine reservoirs, the combination of storage and energy storage technology of abandoned coal mines and wind-solar power generation technology can realize the reasonable allocation of electric energy in the time dimension. This paper studies the regulation capability of the mine pumped-hydro energy storage system proposed by scholars and uses the wind-photoelectric field model to predict the output power of wind farms and solar power stations. Taking the Lingxin coal mine as an example, the optimization algorithm is used to optimize the dispatching ability of the entire PHS joint system. The results prove that the system proposed in this paper can significantly improve the instability of the generation power curve (In terms of variance, volatility decreases to 18.76%), and improve the overall system revenue to some extent (economic efficiency increased by 15.47%). This provides certain technical support for the better utilization of new energy in the future. • A energy dispatching system based on the underground reservoir of mine is presented. • The output power of the electric field is predicted based on proposed means. • GA-dog leg algorithm is used to optimize the system scheduling. • Discuss the robustness and rationality of the optimization method. [ABSTRACT FROM AUTHOR]

In China, with the increasing permeability of wind power, the power supply capacity is enough overall, but has shortage in partial-time. During peak hours, the capability of wind power consumption is poor and the power balance becomes more difficult. In order to maximize the utilization of wind power, the net loads are chosen as the response objectives, which contain significant uncertainties and have no probabilistic distribution characteristics. Under the traditional day-ahead power dispatch mode with fixed length time intervals and in the regions with insufficient hydroelectricity, the thermal generators take charge of the peak-load shaving. The frequent adjustments of thermal power output affect the system operation safety, economic benefits, and environmental benefits. Thus, a time-interval-varying optimal power dispatch strategy based on net load time-series characteristics is proposed in this paper. The net loads respond differently to intervals. The length of each time interval is determined based on the net load time-series characteristics analyzed by random matrix theory. The dispatch mode in each time interval is determined according to the characteristic quantification index calculated by the empirical modal decomposition and sample entropy. The proposed strategy and method can extend the continuous and stable operation time of the thermal generators, reduce the coal consumption caused by the ramping operation, and improve the safety, stability, and economy of the system. Furthermore, the proposed dispatch mode is environmentally friendly with reduced environmental cost and increased carbon credits. An actual provincial power grid in northeast China is taken as the example to verify the rationality and effectiveness of the proposed method and strategy. [ABSTRACT FROM AUTHOR]

With the gradual maturity of wind power technology, China's wind power generation has grown rapidly over the recent years. However, due to the on-site inconsumable electricity, the phenomenon of large-scale "wind curtailment" occurs in some areas. In this paper, a novel hybrid hydrogen/electricity refueling station is built near a wind farm, and a part of the surplus wind power is used to charge electric trucks, and the other part of the surplus power is used to produce "green hydrogen". According to real-time load changes, different amounts of liquid hydrogen and gas hydrogen can be properly coordinated to provide timely energy supply for hydrogen trucks. For a 400 MW wind farm in the western Inner Mongolia, China, the feasibility of the proposed system has been carried out based on the sensitivity and reliability analysis, the static and dynamic economic modeling, with an entire life cycle analysis. Compared to the conventional technology, the initial investment of the proposed scheme (700.07 M$) decreases by 13.97%, and the dynamic payback period (10.93 years) decreases by 25.87%. During the life cycle of the proposed system, the accumulative NPV reaches 184.63 M$, which increases by 3.14 times compared to the case by conventional wind technology. • A novel hybrid hydrogen/electricity refueling system to charge electric vehicles, and produce "green hydrogen". • A realistic multi-energy study based on a 400 MW wind farm in western Inner Mongolia, China. • Feasibility study with sensitivity and reliability analysis for the proposed system. • Static and dynamic economic modeling on a hybrid hydrogen/electricity refueling station. • The proposed system has technical and economic benefits, but much less carbon emissions. [ABSTRACT FROM AUTHOR]

In the process of building a new power system with new energy sources as the mainstay, wind power and photovoltaic energy enter the multiplication stage with randomness and uncertainty, and the foundation and support role of large-scale long-time energy storage is highlighted. Considering the advantages of hydrogen energy storage in large-scale, cross-seasonal and cross-regional aspects, the necessity, feasibility and economy of hydrogen energy participation in long-time energy storage under the new power system are discussed. Firstly, power supply and demand production simulations were carried out based on the characteristics of new energy generation in China. When the penetration of new energy sources in the new power system reaches 45%, long-term energy storage becomes an essential regulation tool. Secondly, by comparing the storage duration, storage scale and application scenarios of various energy storage technologies, it was determined that hydrogen storage is the most preferable choice to participate in large-scale and long-term energy storage. Three long-time hydrogen storage methods are screened out from numerous hydrogen storage technologies, including salt-cavern hydrogen storage, natural gas blending and solid-state hydrogen storage. Finally, by analyzing the development status and economy of the above three types of hydrogen storage technologies, and based on the geographical characteristics and resource endowment of China, it is pointed out that China will form a hydrogen storage system of "solid state hydrogen storage above ground and salt cavern storage underground" in the future. [ABSTRACT FROM AUTHOR]

Offshore wind power, with accelerated declining levelized costs, is emerging as a critical building-block to fully decarbonize the world's largest CO2 emitter, China. However, system integration barriers as well as system balancing costs have not been quantified yet. Here we develop a bottom-up model to test the grid accommodation capabilities and design the optimal investment plans for offshore wind power considering resource distributions, hourly power system simulations, and transmission/storage/hydrogen investments. Results indicate that grid integration barriers exist currently at the provincial level. For 2030, optimized offshore wind investment levels should be doubled compared with current government plans, and provincial allocations should be significantly improved considering both resource quality and grid conditions. For 2050, offshore wind capacity in China could reach as high as 1500 GW, prompting a paradigm shift in national transmission structure, favoring long-term storage in the energy portfolio, enabling green hydrogen production in coastal demand centers, resulting in the world's largest wind power market. Offshore wind power may play a key role in decarbonising energy supplies. Here the authors evaluates current grid integration capabilities for wind power in China and find that investment levels should be doubled for 2030, and that long-term storage and transmissions are key to accelerated developments of offshore wind in 2050. [ABSTRACT FROM AUTHOR]

This study intends to optimize the trading decision-making strategy of the new electricity market with virtual power plants and improve the transmission efficiency of electricity resources. The current problems in China's power market are analyzed from the perspective of virtual power plants, highlighting the necessity of reforming the power industry. The generation scheduling strategy is optimized in light of the market transaction decision based on the elemental power contract to enhance the effective transfer of power resources in virtual power plants. Ultimately, value distribution is balanced through virtual power plants to maximize the economic benefits. After 4 hours of simulation, the experimental data shows that 75 MWh of electricity is generated by the thermal power system, 100 MWh by the wind power system, and 200 MWh by the dispatchable load system. Comparatively, the new electricity market transaction model based on the virtual power plant has an actual generation capacity of 250MWh. In addition, the daily load power of the models of thermal power generation, wind power generation, and virtual power plant reported here are compared and analyzed. For a 4-hour simulation run, the thermal power generation system can provide 600 MW of load power, the wind power generation system can provide 730 MW of load power, and the virtual power plant-based power generation system can provide up to 1200 MW of load power. Therefore, the power generation performance of the model reported here is better than other power models. This study can potentially encourage a revised transaction model for the power industry market. [ABSTRACT FROM AUTHOR]

China is rich in wind- and solar-energy resources. In recent years, under the auspices of the "double carbon target," the government has significantly increased funding for the development of wind and solar resources. However, because wind and solar energy are intermittent and their spatial distribution is uneven, the profits obtained by the developers of wind- and solar-energy resources are unstable and relatively low. For this reason, we analyze in this article the spatiotemporal variations in wind and solar energy resources in China and the temporal complementarity of wind and solar energy by applying a Spearman correlation coefficient based on the Daily Value Dataset of China Surface Climate Data V3.0. Finally, we also strive to harmonize regions where wind and solar resources are less complementary by introducing hydro-energy resources. The results reveal that wind energy and solar energy resources in China undergo large interannual fluctuations and show significant spatial heterogeneity. At the same time, according to the complementarity of wind and solar resources, over half of China's regions are suitable for the complementary development of resources. Further research shows that the introduction of hydro-energy resources makes it feasible to coordinate and complement the development of wind- and solar-energy resources in areas where the complementarity advantage is not significant. This has a significant effect on increasing the profit generated by the complementary development of two or more renewable resources. [ABSTRACT FROM AUTHOR]

There are many promising renewable energy (RE) technologies that could help increase the contribution of RE in energy supply but which are not yet commercially available. The development rate of new RE technologies depends on many factors, such as Research and Development (R&D) efforts and policies. This study focuses on comparing China's efforts regarding the development of new RE technologies (e.g., wave and tidal, binary geothermal power, floating solar, micro hydro, osmotic energy, floating offshore wind and vertical axis wind turbines) with those of the European Union (EU). For this purpose, we collected data from publications and databases and analysed several indicators: e.g., the development of renewable electricity generation and capacity, demonstration projects, investments in R&D and patent applications. The results show that China has become a big player globally for mainstream renewable electricity (hydropower, wind and solar PV). This development is due to China's industrial policy and prioritization of effectiveness over cost efficiency. The main developments in China occurred in the 2010s, while the EU was a frontrunner in the 2000s. For the newer or less mainstream technologies, the application in China is still low, compared to the EU, except for floating solar, where China is a lead player. Regarding patent applications, China has shown a higher application amount compared to the EU since 2006. However, only a small share of China's patents are valid internationally. We conclude that China has emerged as a big player in mainstream renewable energy technologies over the last decade. In regard to new renewable energy technologies, China is predominantly involved in solar energy and, in comparison to the EU, less in other new technologies (e.g., binary geothermal systems and ocean energy). [ABSTRACT FROM AUTHOR]

• Integrating machine learning and vine-copula to catch the complex spatial correlations among the renewable resources. • Utilization of stochastic model predictive control to optimize the long-term hydro-wind-solar operation. • Potential assessment of hydropower to offset the effects of seasonal uncertainties and mismatch between renewable resources and requirements. It is popular to combine the hydropower plant with the wind and solar power plants to supply electricity, and the joint is termed as the hydro-wind-solar renewable energy supply system (RESS). The long-term optimal operation of the RESS is a challenging task, due to the nature of different spatial and temporal variabilities associated with renewable energy resources, and the significant operation uncertainties due to the changing natural environment. To address the task, the stochastic model predictive control (MPC), based on probabilistic forecasting and rolling stochastic optimization, is designed and implemented for the long-term operations of the RESSs in Yunnan province, China. The paper tests out the system efficiencies for different penetration levels of wind and solar power, and finds out that (1) the Long Short-Term Memory performs best among candidate point prediction algorithms; (2) the stochastic MPC considering the correlations among renewable resources helps the RESS operate with a higher efficiency; (3) the large-scale hydropower plant has great potential to offset the effects of seasonal uncertainties and demand-generation mismatch, but cannot completely avoid the electricity shortage enabled by unexpected scenarios of renewable resources. [ABSTRACT FROM AUTHOR]

• Two micro energy grid optimization models combined with regional energy characteristics are proposed. • An on-line adaptive optimization model and strategy for multiple MEG access to DN is proposed. • An effective measure of using distributed clean energy in mountainous area is provided. • The problem of various forms of energy supply in Southwest China has been solved. The rapid growth of distributed photovoltaic and wind power installed capacity in Southwest China has reduced the operation safety margin of the power grid. Power grid dispatchers face the dilemma of ensuring security of the power grid and the output curtailment of renewable energy. To achieve the full consumption of renewable energy, it is an effective way to make use of the space-time complementary characteristics of different energies by forming micro energy grids. By connecting to the distribution network, the energy among the micro energy grids can be transferred and distributed in the form of electricity. Therefore, this study builds collaborative operation optimisation models of distribution networks with multi-micro energy grids and proposes an adaptive dynamic real-time optimisation algorithm based on pre-training and online deep learning techniques for the joint system. A test system was constructed based on the actual equipment parameters and data from the Guizhou power grid. Through the calculation and simulation in the test system, the overload of the distribution network equipment and the network loss were effectively reduced while ensuring the full consumption of wind and photovoltaic power. In addition, the accuracy and effectiveness of the model and algorithm were verified. The results obtained in the case studies suggest that the method proposed in this paper can be applied to southwest China and similar regions. [ABSTRACT FROM AUTHOR]

The long-term stability and sustainability of offshore wind energy resources are very important for wind energy exploration. In this study, the Cyclostationary Empirical Orthogonal Function (CSEOF) method, which can determine the time varying spatial distributions and long-term fluctuations in the cyclostationary geophysical process, was adopted to investigate the geographical and temporal variability of offshore wind resources in China Seas. The CSEOF analysis was performed on wind speeds at 70 m height above the sea surface from a validated combined Quick Scatterometer (QuikSCAT) and Advanced Scatterometer (ASCAT) wind product (2000–2016) with high spatial resolution of 12.5 km, and Climate Forecast System Reanalysis (CFSR) wind data (1979–2016) with a grid size of 0.5° × 0.5°. The decomposition results of the two datasets indicate that the first CSEOF mode represents the variability of wind annual cycle signal and contributes 77.7% and 76.5% to the wind energy variability, respectively. The principal component time series (PCTS) shows an interannual variability of annual wind cycle with a period of 3–4 years. The second mode accounts for 4.3% and 4.7% of total wind speed variability, respectively, and captures the spatiotemporal contribution of El Niño Southern Oscillation (ENSO) on regional wind energy variability. The correlations between the mode-2 PCTS of scatterometer or CFSR winds and the Southern Oscillation Index (SOI) are greater than 0.7, illustrating that ENSO has a significant impact on China's offshore wind resources. Moreover, the mode-1 or mode-2 spatial pattern of CFSR winds is basically consistent with that of scatterometer data, but CFSR underestimates the temporal variability of annual wind speed cycle and the spatial changes of wind speed related to ENSO. Compared with reanalysis data, scatterometer winds always demonstrate a finer structure of wind energy variability due to their higher spatial resolution. For ENSO events with different intensities, the impact of ENSO on regional wind resources varies with time and space. In general, El Niño has reduced wind energy in most regions of China Seas except for the Bohai Sea and Beibu Bay, while La Niña has strengthened the winds in most areas except for the Bohai Sea and southern South China Sea. [ABSTRACT FROM AUTHOR]

A complex and varied terrain has a great impact on the distribution of wind energy resources, resulting in uncertainty in accurately assessing wind energy resources. In this study, three wind speed distributions of kernel, Weibull, and Rayleigh type for estimating average wind power density were first compared by using meteorological tower data from 2018 to 2020 under varied desert steppe terrain contexts in northern China. Then three key parameters of scale factor (c) and shape factor (k) from the Weibull model and surface roughness (z0) were investigated for estimating wind energy resource. The results show that the Weibull distribution is the most suitable wind speed distribution over that terrain. The scale factor (c) in the Weibull distribution model increases with an increase in height, exhibiting an obvious form of power function, while there were two different forms for the relationship between the shape factor (k) and height: i.e., the reciprocal of the quadratic function and the logarithmic function, respectively. The estimated roughness length (z0) varied with the withering period, the growing period, and the lush period, which can be represented by the estimated median value in each period. The maximum and minimum values of surface roughness length over the whole period are 0.15 and 0.12 m, respectively. The power-law model and the logarithmic model are used to estimate the average power density values at six specific heights, which show greater differences in autumn and winter, and smaller differences in spring and summer. The gradient of the increase in average power density values with height is largest in autumn and winter, and smallest in spring and summer. Our findings suggest that dynamic changes in three key parameters (c , k , and z0) should be accurately considered for estimating wind energy resources under varied desert steppe terrain contexts. [ABSTRACT FROM AUTHOR]

A profound transformation of China's energy system is required to achieve carbon neutrality. Here, we couple Monte Carlo analysis with a bottom-up energy-environment-economy model to generate 3,000 cases with different carbon peak times, technological evolution pathways and cumulative carbon budgets. The results show that if emissions peak in 2025, the carbon neutrality goal calls for a 45–62% electrification rate, 47–78% renewable energy in primary energy supply, 5.2–7.9 TW of solar and wind power, 1.5–2.7 PWh of energy storage usage and 64–1,649 MtCO2 of negative emissions, and synergistically reducing approximately 80% of local air pollutants compared to the present level in 2050. The emission peak time and cumulative carbon budget have significant impacts on the decarbonization pathways, technology choices, and transition costs. Early peaking reduces welfare losses and prevents overreliance on carbon removal technologies. Technology breakthroughs, production and consumption pattern changes, and policy enhancement are urgently required to achieve carbon neutrality. China's transition path toward carbon neutrality remains uncertain. Here the authors combine Monte Carlo analysis with an energy-environment-economy model to present a probabilistic view of China's energy transition across 3,000 cases. [ABSTRACT FROM AUTHOR]

Structures with energy supply capacity are an important part of lifeline engineering. Post-disaster assessment is therefore vital to assess the economic losses caused by the collapse of such structures and is an integral part of the overall post-disaster assessment. The identification of structures in synthetic aperture radar (SAR) imagery is mostly influenced by buildings, exposed rocks and riverbeds, because of the similar scattering intensity effects. In this letter, a new spatial reasoning method is proposed based on spatial and morphological feature parameters to identify isolated structures with an ordered distribution in SAR imagery. The wind power generation base in the north-western part of the city of Yumen, Gansu province, China, is taken as the research area. Numerous wind power generation facilities, which are important power sources in north-western China, are found in this area. The recognition accuracy of the proposed method for structures in this area is 98.31%. Compared with the traditional method, the recognition rate for structures is improved by 35.59%. The 'distance deviation risk' evaluation index is also proposed to evaluate the deviation between the recognition results and the actual ground objects. Overall, the proposed method can more accurately identify the structures in SAR imagery. [ABSTRACT FROM AUTHOR]