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This study presents a bibliometric review focusing on the utilization of water electrolysis as a means of generating hydrogen as an energy carrier. The analysis includes research conducted over the past decade, covering from 2014 to 2023 (till August). Several key aspects are highlighted, including publication trends, the leading country in research output, journal rankings, and a citation analysis of papers related to water electrolysis. The findings reveal a notable increase in publication trends, with China emerging as the leading contributor in this research area. Furthermore, the International Journal of Hydrogen Energy is identified as the highest-ranked journal in terms of both publication number and citation impact. Additionally, the top ten most cited research articles and review papers are investigated to determine their influence within the field. To gain a deeper understanding of water electrolysis methods, the three primary approaches: alkaline water electrolysis (AWE), proton exchange membrane (PEM) electrolysis, and solid oxide electrolysis (SOE) is examined. The analysis emphasizes that PEM electrolysis is the most prevalent method for hydrogen generation, particularly when integrated with renewable energy sources such as solar and wind power due to its rapid response to electrical input fluctuation. Finally, the challenges and future directions of water electrolysis in hydrogen production are highlighted, including an exploration of economic and environmental considerations at large scale, offering insights into the path forward for advancing this technology sustainably. • A comprehensive bibliometric analysis of water electrolysis for hydrogen production. • Publication trends, journal rankings, and citation analysis are highlighted. • China is identified as the leading contributor in water electrolysis publications. • International Journal of Hydrogen Energy: the highest-ranked journal in this field. • Specific electrolysis methods, including AWE, PEM, and SOE are examined. [ABSTRACT FROM AUTHOR]

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

The renewable-dominant hybrid generation systems (HGSs) are increasingly important to the electric power system worldwide. However, influenced by uncertain meteorological factors, the operational robustness of HGSs must be evaluated to inform the associated decision-making. Additionally, the main factors affecting the HGS's robustness should be urgently identified under deep uncertainties, as this provides valuable guidance for HGS capacity configuration. In this paper, a multivariate stochastic simulation method is developed and used to generate uncertain resource scenarios of runoff, photovoltaic power, and wind power. Subsequently, a long-term stochastic optimization model of the HGS is employed to derive the optimal operating rules. Finally, these operating rules are used to simulate the long-term operation of an HGS, and the results are used to evaluate the HGS's robustness and identify its main sensitivities. A clean energy base located in the Upper Yellow River Basin, China, is selected as a case study. The results show that the HGS achieves greater operational robustness than an individual hydropower system, and the robustness becomes weaker as the total capacity of photovoltaic and wind power increases. Additionally, the operational robustness of the HGS is found to be more sensitive to the total capacity than to the capacity ratio between photovoltaic and wind power. [ABSTRACT FROM AUTHOR]

With the increasing application of wind energy, reliable wind speed prediction has become imperative. However, prior studies predominantly concentrated on single-model predictions, disregarding the inherent uncertainty in wind speed. This oversight resulted in inadequate deterministic and probabilistic forecasting outcomes across varying scenarios. To make up for these shortcomings, a novel forecasting system combining a data preprocessing technique, a sub-model selection method, and a modified multi-objective integrate optimization strategy is designed in this paper. According to the data obtained from China's wind farm, the forecasting efficiency of this system is verified from multiple perspectives. The findings show that the system takes advantage of each model to boost the precision and stability of point prediction successfully. Furthermore, it achieves higher interval coverage and narrower interval width under distinct confidence levels. These results highlight the system's potential as a reliable technical support for efficient dispatching of the entire power system. [ABSTRACT FROM AUTHOR]

With the progress of society, the supply of electricity has become an inevitable choice for the development of all walks of life. The stability, safety, and economic dispatch of the electric energy system directly impact the entire country's economic development, from national defense, military, and even people's daily life, all closely associated with the smooth running of the network. In the aspect of power grid planning and operation, the economic security dispatching problem of power system is a very typical optimization problem. The problem is how to maximize the operating cost of the system under the premise of satisfying the system's load limit and safety and stability. Due to the rapid development of power technology, the research on power system security and economic planning is essential. As an important means to promote China's energy structure adjustment and promote the supply side structural reform, the regional spot electricity market is playing an increasingly important role in building a resource optimization allocation mechanism, improving China's energy resource allocation efficiency, and promoting social and economic development. However, the development of China's regional spot power market is still in its infancy, and the transaction price of the regional spot power market is quite different from that of other countries. It was given these problems, this article discusses the use of particle swarm algorithms for secure and economic scheduling of power systems in the context of securely reading blockchains and distributed data. The research results showed that: The introduction of environmental pollution penalty programs improved the priority dispatch of wind and photovoltaic power when considering environmental penalty programs and backup penalties for wind and photovoltaic power. The setting of the coefficient of the environmental penalty term depended on the designer's emphasis on the priority scheduling of wind power and photovoltaic (PV) power generation (PG), and the standby capacity penalty clause achieved reasonable dispatch and utilization of wind power and PV PG, which reduced the dramatic fluctuations and intermittent volume of the system, and facilitated safe and consistent operation of the network. The research of this paper shows the positive relationship between the blockchain and distributed data security reading algorithm and the clearing method of regional spot power market, and points out a new method for its development. [ABSTRACT FROM AUTHOR]

Currently, a standardized design and calculation specification for suction drum foundations has yet to exist in China. The engineering design currently depends mainly on the subjective understanding and engineering experience of the designers, which can be considered somewhat blind and subjective. In this paper, we utilize the offshore wind power project in Yangjiang City, Guangdong Province, as our case study. Building upon domestic and international research results, relevant investigations, design specifications, and engineering applications in related fields, we conduct a systematic study on the design calculation and construction control technology of the suction drum foundation. The document presents the design calculation and inspection of the suction drum foundation. Building on this foundation, we propose a sinking feasibility analysis method and a parameter value method for the suction drum foundation calculation. We also examine the suction drum foundation construction process, examining its control parameters, technology, and standards. Finally, based on the measured data from six four-barrel guided frame platform suction drum foundations that were successfully installed, the proposed design and control method are evaluated, and their effectiveness is verified. The results of this study can provide valuable references for the design and construction of similar suction drum foundation platforms. [ABSTRACT FROM AUTHOR]

As a kind of renewable energy enterprise, wind power enterprise plays an important role in the energy transformation. However, its financing problems are also widespread. The purpose of this paper is to study the financing mode selection in order to improve sustainable development and future benefits of wind power enterprises. First, an evaluation index system is constructed through literature research and expert interviews, and the weight of each index is determined comprehensively by G1 and information entropy methods. On this basis, a financing mode selection model of wind power enterprises is constructed by using the prospect theory and hesitant fuzzy set. Next, this framework is used for case study. Financing patterns are ranked according to the comprehensive prospect value. The results show that financial leasing is the most suitable financing mode. Finally, the applicability and optimization of the model are proved by sensitivity analysis and comparative analysis. [ABSTRACT FROM AUTHOR]

In the context of carbon neutrality target, renewable energy sources have been transforming from "supplementary energy" to "main energy", which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the "resource-environment-demand" comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry. • This study examines the impact of high-dimensional features variables on urban solar energy development. • Over 70% of solar PV installation capacity is concentrated in Northern and Northwest parts of China. • Water resources, solar radiation, population density have significant impact on solar PV distribution. • Solar PV distribution in China should shift from resource orientation to the comprehensive orientation. [ABSTRACT FROM AUTHOR]

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]

The medium- and long-term operation strategy of the hybrid power system needs to adapt to the continuous changes in the market. This paper presented a medium-term (monthly) operational simulation model and nested it into a long-term (yearly) optimization model. It was solved by the genetic algorithm (GA) to maximize yearly revenue. The planned delivered-electricity to power process constraints were present to improve the optimization efficiency. Using penalty price factor (α) and threshold of exempted punishment for the supply shortage δ to characterize the punishment for supply shortage risk. This framework was applied for a new hybrid power system so-called LCHES-WP (large-scale cascade hydropower energy storage system, wind power, PV power). It can participate in the electricity trading market with a bundled output pattern. The real cascade hydropower stations in Qinghai Province, China, are selected as the case study. In the baseline scenario, the current time of use (TOU) electricity price and punishment level (α = 0.05, δ = 5%) were considered. It reveals that (1) the revenue (baseline scenario, 6.15351 billion CNY) in the bundled output pattern is higher by 45.6% than in the separate output pattern. (2) the economic and technical indicators are sensitive to α , but not to δ. α increases make the punishment stricter, supply shortage risk was significantly reduced due to the probability of the shortage event, the maximum monthly supply shortage ratio and punishment electricity are reduced by up to 22.5 percentage points, 26.7 percentage points, and 1880.1 GWh, respectively. The yearly revenue is reduced by up to 4.7%. the reason is the yearly curtailment is increased and the penalty amount does not change dramatically with the changes in punishment. (3) The α = 0.6 and α = 1.8 can be regarded as demarcation point and upper boundary point for the case of hybrid power system, respectively. It is proposed that when planning a capacity scheme, stakeholders should consider that the demarcation point is higher than the current α. In general, this paper provides a reference for the medium- and long-term operation optimization of the LCHES-WP hybrid power system in the future. • Electricity settlement rule in medium and long term electricity trading is considered. • The monthly operational simulation model was nested in the yearly optimization model. • Bundled output pattern can rise by 45.6% of revenue than the separate output pattern. • The supply shortage risk is significantly reduced by increasing the punishment level. • The penalty amount does not change obviously with the changes in the punishment level. [ABSTRACT FROM AUTHOR]

As installed wind power capacity continues to rise, the cost of onshore wind power generation in China has fallen, far exceeding the world average. The purpose of this study is to explore the main factors affecting onshore wind power in China and to identify ways to reduce costs. So as to reduce the cost of wind power and promote the large-scale grid-connected use of wind power. Therefore, this paper summarizes various factors that affect the development of onshore wind power. We also provide a comprehensive and repeatable MFLC (multi-factor learning curve) method to evaluate the factors affecting the cost of onshore wind power. Subsequently, the learning rates of various factors are calculated using this method. The results show that empirical learning by increasing installed capacity/generation, improving technology and selecting sites which are rich in natural resources, and appropriately reducing capital investment and material prices can significantly reduce the LCOE (levelized cost of electricity) of onshore wind power in China. Compared with wind power giants of the United States and Germany, the reduction in the cost of onshore wind power generation in China is more dependent on inputs such as capital investment and raw materials, while experience plays a relatively minor role. The ability to make full use of natural resources and to convert wind energy into electricity more efficiently is crucial to reducing the cost of wind power. • This paper summarizes various factors that affect the development of onshore wind power. • A comprehensive and repeatable MFLC method is built in this paper. • The learning rates of various factors are calculated. [ABSTRACT FROM AUTHOR]

Currently, wind power prediction has so many problems in the ultra-short-term time scale (0–4h), which is difficult to improve the deterministic prediction and probability prediction accuracy of the wind farm cluster because it can not fully explore the spatio-temporal correlation between the physical change process and the wind farm. In this paper, a method of ultra-short-term deterministic and probability prediction of wind farm cluster power based on Graph Convolutional Network (GCN) considering fluctuation correlation (FC) is proposed. Firstly, the adjacency matrix is constructed based on the power sequence fluctuation information of each wind farm, and the GCN is designed. Secondly, the spatio-temporal features of power and Numerical Weather Prediction (NWP) wind speed are extracted and fused based on the network model of dual-channel and dual-adjacency matrix. Thirdly, in order to effectively improve the prediction accuracy, a trend switching mechanism is designed based on the effective trend of NWP. When the fluctuation information of NWP is not accurate, the graph structure is constructed by the adjacency matrix based on geographical location to achieve effective prediction. Finally, the method proposed in this paper is applied to wind farm clusters in three provinces of China, compared with some commonly used methods, the average RMSE is reduced by 1.34 %, 1.62 %, 2.07 %, respectively, and the average CWC is reduced by 6.12 %, 4.49 %, 6.62 %, which verifies the effectiveness of this method. • A method for calculating FCC is proposed. • Combined with FCC, the graph structure of wind farm cluster is constructed. • A two-module GCN is designed. • A trend-switching mechanism is designed. [ABSTRACT FROM AUTHOR]

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]

Compared with the point prediction of the wind power, the interval prediction provides more information to assist power dispatching and optimize quotation strategies in power trading. To improve the accuracy of the prediction intervals, this paper proposes a novel model based on the lower upper bound evaluation (LUBE) by the gate recurrent unit (GRU) network. First, the Pearson correlation coefficient is selected to screen out the variables related to wind power, which can simplify the input variables of the prediction model while preserving the feature information as much as possible. Afterwards, with the consideration of the prediction interval relative deviation (PIRD), the improved objective function is introduced. Based on the GRU network, the prediction intervals of the wind power can be obtained to minimize the new objective function. Finally, by choosing several mainstream neural networks, experiments are conducted towards a certain wind farm in China. The results show that the proposed model has a significant improvement in both prediction interval width and PIRD under the given prediction interval coverage probability (PICP). [ABSTRACT FROM AUTHOR]

China's coastal region is the major geographical unit for the future development of China's industrial sector. The transformation of basic structure to high-class development in China's coastal places is a significant tool for promoting the changes related to quality, power and efficiency in regional economic development. In the 21st century, environmental and energy issues have increased worldwide, and challenges related to environmental pollution, energy crises, and ecological imbalances have emerged. To climate change and energy utilization, the sustainable progress of clean energy is the new route of future energy development. Based on China's non-polluting energy growth process in the last ten years, this article explores China's clean/green energy policies and economic growth development plans. Clean energy utilization is crucial for sustainable development in the context of high-quality economic growth and climate change. However, the monetary evolution and carbon emission are not investigated whole from the clean energy aspects. Using Wind energy sources as the acceptable variable, this paper employs threshold regression and impulse functions to assess the energy consumption and economic growth on carbon emission in 30 Chinese provinces over the 2000 to 2020 period. The Deep Belief Network (DBN) model predicts wind energy utilization and efficiency. The results show that economic development and carbon emissions are connected. Further, growth influences promote the offset of carbon emissions. Green innovation alters the nexus of carbon emissions, and China's economy reduces carbon usage. It provides the decision-making policies for clean energy development. • China's Coastal region is the major geographical unit for the future development of China's industrial. • In high-quality economic growth and climate change, clean energy use is vital for sustainable development. • The utilization of wind energy and it's efficiency is predicted using Deep Belief Network (DBN) model. • Empirical results link economic growth with carbon emissions, showing growth promotes emissions offset. • Green innovation changes the economic growth-carbon link, aiding China's carbon reduction efforts. [ABSTRACT FROM AUTHOR]

Wind and solar power are expected to play important roles in many countries to achieve carbon neutrality; however, their inherent instabilities pose significant threats to existing power grids. Because hydropower has been recognized as a viable compensatory resource for solar and wind energy uncertainties, many studies have sought to determine optimal scheduling strategies for hydro-PV, hydro-wind, and hydro-PV-wind systems. However, few studies have simultaneously considered the sizing and scheduling of large-scale hydro-PV-wind hybrid systems connected to long-distance transmission lines. Therefore, this paper develops a mathematical metric to measure the wind and solar output complementarity and incorporates it into a multi-objective sizing and scheduling model for a hybrid hydro-PV-wind system, in which resource complementarity, profit generation, system reliability, and power curtailment trade-offs are concurrently considered. The ɛ -constraint method is employed and decision makers' attitude parameters are introduced to transform the proposed model into its equivalent single objective form. A case study in China reveals that the maximum wind and solar power output complementarity rate can be at least 0.19 for the studied hybrid hydro-PV-wind system. The incorporation of wind enables the hybrid hydro-PV-wind system to provide 4.11% more load than a hydro-PV system. In addition, if 2% of power curtailment is permitted, 1.53% more load can be served and 14.9% higher profits can be earned. To better explore the application of hybrid systems, management recommendations are provided for system operators, the power industry, and management administrations. • Multi-objective sizing and scheduling method for the hybrid renewable system is proposed. • Mathematical metrics to measure wind and solar power complementarity is provided. • A complementarity rate of 0.19 can be reached with 100% reliable service and no curtailment. • Integrating wind power into the hydro-PV system can reliably serve 4.11% more load. • An optimal rather than minimal level of the renewable power curtailment is more efficient. [ABSTRACT FROM AUTHOR]

[Display omitted] • A liner positive correlation is revealed between wind farm distance and ecological impacts. • The ecological obstacle points increased 350 % after construction of wind farm. • The proportions of forest around weak points raised from 38 % (50 m range) to 49 % (150 m range). • Protection, restoration, and compensation strategies are proposed according to recession degrees. The wind energy industry is developing rapidly in China with a maximum capacity worldwide. Meanwhile, the stability of the ecological network is challenged by extensive renewable energy expansion. There are still research deficiencies in wind farm ecological impacts at project scale from an ecological restoration perspective. It is of great necessity to monitor the ecological impacts with time going and explore ecological restoration strategies for wind power development. The research case of Zhongying Wind Farm is selected based on investigation and interviews regarding its ecological impact in the prior study. It employs a comparative methodology by comparing the ecological quality before and after the construction of Zhongying Wind Farm. Raw data from Landsat 8, the Digital Elevation Model (DEM), the Normalized Difference Vegetation Index (NDVI), and wind turbine statistics originating from open data platforms are analyzed and visually represented with ArcGIS. This paper establishes an ecological network based on morphological spatial pattern analysis (MSPA) and the Minimum Cumulative Resistance (MCR) model to quantify wind farms' impacts on ecological corridors. The circuit theory and Pajek software are used to identify and categorize the ecological nodes into weak and obstacle points. The results reveal that ecological impacts on ecological corridors decay with distance, reflecting changes in length, connection strength and cumulative values of corridors. While the impacts on ecological nodes depend on nodes' attributes: the number of weak points increased by 18.5 %, and obstacle points increased by 350 %, with their locations shifting from the edges of urban construction sites to the edges of wind farm. Differentiated strategies are proposed according to the impact magnitude of each ecological corridor and node: such as dynamic inspection mechanism, construction prohibition for protection; corridor structure and function recovering, stepping stones addition for restoration; buffer belts, warning radar, and monetary schemes for compensation. This research fills the gap of ecological impact assessment under the challenges of wind energy expansion. It helps to balance wind energy development and ecological restoration at the regional level. [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]

Wind energy assessment at multi-time scale is essential for the effective utilization of offshore wind resources. Conventionally, energy assessment factors are estimated based on parametric models under the assumption of wind speed stationarity. However, given the subjection of seasonal variability and long-term trends, the hypothesis is violated, especially in the context of climate change. In this paper, to advance the conventional approach, wind speeds were modelled using the decomposition-based method to consider the non-stationarity, and a novel framework for the estimation of energy factors at multi-time scale was designed. Using long-term ERA5 data, wind energy potential across the sea area along Chinese continental coastline was investigated. It is demonstrated that the established non-stationary models are superior to commonly used stationary models, allowing the estimation of energy assessment factors at arbitrary time scales after one-time parametrization of parametric distributions. Results confirmed that wind energy exhibits apparently temporal and spatial variability. The difference between annual averaged wind power density at different nodes exceeds 1000 W/m2. Moreover, significant long-term trends of wind energy characteristics were detected. The increase can be up to 16 W/m2/year in Taiwan Strait, emphasizing that an adequate consideration of the non-stationarity of wind speed in future research is required. [ABSTRACT FROM AUTHOR]

The issue of large-scale retirements of renewable energy modules has attracted considerable attention from governments and the public worldwide. Using the Chinese wind and photovoltaic industries as examples, this paper, for the first time, employs a real options approach to explore the optimal time for energy firms to retire their power plants and to predict the future trend of renewable energy wastage from a bottom-up perspective. The results show that the optimal retirement time for the four subdivided categories ranges from 16 to 23 years. The later the investments are made, the earlier the decommissioning time comes and the lower the project returns. Furthermore, wind projects have been found to generate more revenue than PV projects for the same size, and the gap continues to widen. Offshore wind technology always has the longest optimal service life (18.62 years in 2022) and the greatest project returns (10.84 million CNY in 2022). Annual utilization hours, the renewable electricity price and the waste price can accelerate the retirement of plants and generate more revenue for energy companies, while the initial investment cost has a completely opposite effect. The first wave of PV retirements will occur in 2035 at 305.82 GW, while onshore wind will be in 2036 at 530.77 GW and offshore wind will not explode until 2040 at 65.84 GW. An assessment of potential policies suggests that for every 100 CNY increase in incentive strength, the retirement wave arrives 1 year earlier. In contrast, for every 100 CNY increase in the strength of penalties, the retirement wave is delayed by 1 year. In addition, we verify that the size of the plants does not affect the forecast of the retirement trend, indicating that the assumptions about installed capacity do not cause bias. [ABSTRACT FROM AUTHOR]

Estimating the total wind power output from the meteorological information at a province level (called Provincial Regional Wind Power Conversion Model, PRWPCM) plays vital and fundamental roles in energy modeling community and regional wind power forecasting. How to construct a reliable PRWPCM is a real challenge, since PRWPCM involves a large number of widely distributed wind turbines, massive meteorological data across the whole province, and complex nonlinear correlations. This paper proposes a lightweight PRWPCM by integrating Geographic Information System (GIS) analysis technology and Convolutional Neural Network (CNN). First, we conduct the land suitability analysis for wind turbine sites through the multi-criteria GIS layer overlay method to make the provincial wind turbine land suitability map (WTLSM) with scored divisions from the least suitable to the most suitable areas. On this basis, a new fusion mechanism for geographic and meteorological information is proposed, through which the raw meteorological data matrix can be reconstructed to filter and amplify the meteorological information that is more relevant to the total wind power output of the province, and avoid the time-consuming and labor-intensive data collection and processing, large-size model construction and validation. Second, a CNN-based regression architecture is designed to further capture the mapping relationship between the reconstructed meteorological data and total wind power output of the province; each type of meteorological factor is considered as an input channel and the attention modules are introduced to adaptively enhance useful channels and suppress less useful ones. Numerical experiments based on the wind power operation data of Yunnan Province, China, are conducted to validate the superiority of the proposed PRWPCM via benchmarking against 13 classical methods. • Mapping relationship between wind power output and meteorological information at a province level is recognized by integrating GIS and CNN. • A new fusion mechanism for geographic and meteorological information is proposed to achieve lightweight modeling. • Comparative experimental studies are conducted in Yunnan province, China. [ABSTRACT FROM AUTHOR]

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

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]

[Display omitted] • It analyzes the feasibility of achieving net zero emission in the Chinese power sector by 2050 and the required policy measures. • It is essential to foster the concurrent development of renewable energy, energy storage, and demand response technologies. • The carbon pricing mechanism with exponential growth is more suitable. • Referring to the European offshore wind power investment model is beneficial for the development of offshore wind power in China. The low-carbon transition in China's power sector plays a crucial role in the mitigation of climate change worldwide, and increasing renewable energy penetration rate is widely recognized as the key approach to decarbonize this sector. This paper proposes a renewable energy penetration model to examine the feasibility for net zero emission by 2050 in China's power sector taking into account of economic and social development. It also evaluates the impacts of carbon pricing and financial incentives on renewable energy penetration and carbon emissions. The results indicate that along with economic development, China's electricity demand will continue to grow, but at a gradually slowing rate. It is possible for the power sector to achieve net zero emission by 2050 while meeting this growing electricity demand, with a renewable energy penetration rate of 75%. To ensure the achievement of the goal, we recommend setting the carbon price with the current market price as the baseline and increasing it annually by a relative increment index of 10%. Furthermore, given the high initial cost and late start of offshore wind power, we suggest incorporating associated grid connection cost into the transmission and distribution price to ensure the comprehensive and healthy development of renewable energy. [ABSTRACT FROM AUTHOR]

The increasing generation of renewable energy and its concurrent technological innovation are driving the transformations of the energy sector, but little effort has been made at the regional level to specify the heterogeneous impacts of different types of renewable energy development on multi-dimensional energy security. Therefore, this paper establishes a four-dimensional energy security assessment system based on the current energy market situation and economic context of China. Renewable energy generation and technological innovation were simultaneously used as the core proxies for renewable energy development. Then, the panel quantile regression method was used to analyze the heterogeneity of the impact of solar, hydro, and wind energy on energy security, covering 30 provinces in China from 2006 to 2019. The empirical results show that renewable energy power generation generally promotes energy security, but it cannot be effectively applicable to all levels of energy security. Renewable energy technology innovation has surprisingly hindered energy security. These results can serve as a policy and practical guide, suggesting that provincial governments in China should implement energy security and renewable energy policies in line with local characteristics. • The redefined provincial energy security presents obvious spatial heterogeneity. • Panel quantile regression was applied to balanced provincial panel data. • Hydropower contributes only to medium and low levels of energy security. • Wind and solar power contribute differently to all levels of energy security. • Renewable energy technological innovation impedes energy security. [ABSTRACT FROM AUTHOR]

As global energy demand continues to grow, the demand for renewable energy continues to rise. To meet this demand, many countries have begun to actively promote the development of offshore wind power projects. Since siting is the key to the success of offshore wind power projects, this paper aims to develop a mixed decision-making framework to address the siting of offshore wind power station (OWPS). Firstly, establish a relatively comprehensive OWPS evaluation attribute system. Secondly, the pythagorean hesitation fuzzy set (PHFS) was used to describe the evaluation information in the OWPS site selection process. It can ensure the hesitancy and fuzziness of evaluation information, and is not constrained by the sum of membership and non membership being less than 1. Thirdly, the improved SWARA method is used to calculate the weight of attributes. Finally, an extended MULTIMOORA (Multi-Objective Optimization on the basis of a Ratio Analysis plus the Full Multiplicative form) method is proposed to rank the siting alternatives for OWPS. The proposed method is applied to the study of OWPS site selection in Shandong Province, China. Through calculation, the comprehensive ranking values are G 1 = 14, G 2 = 7, G 3 = 4, G 4 = 8 and G 5 = 13. Therefore, A 3 is the best alternative. Through sensitivity analysis and comparative analysis, it was verified that the proposed model has good reliability and stability. • A new multi-attribute decision-making model is proposed. • The attribute system for site selection evaluation of OWPS is established. • Expression of evaluation information by pythagorean hesitation fuzzy set. • Pythagorean hesitation fuzzy set and SWARA are combined to calculate attribute weight. • An improved MULTIMOORA method is proposed. [ABSTRACT FROM AUTHOR]