Cross regional cooperation on carbon emission reduction is an effective mechanism to achieve the goal of carbon neutrality. This paper， based on evolutionary game theory， constructs an evolutionary game model for collaborative carbon emission reduction in the Beijing-Tianjin-Hebei urban agglomeration， analyzing the evolution process of collaborative carbon emission reduction strategy selection between Beijing-Tianjin， Beijing-Hebei， and Tianjin-Hebei. The research results follow： ① The cost of collaborative carbon emission reduction is an important factor affecting the participation of Beijing， Tianjin， and Hebei in collaborative carbon emission reduction， and excessive cost can lead to the rupture of the collaborative carbon emission reduction relationship. ② Punishment has a certain moderating effect on maintaining a collaborative carbon emission reduction relationship. Increasing the opportunity cost of violating synergy helps both sides get out of the "prisoner's dilemma" and maintain the cooperative carbon emission reduction relationship， which is especially the case between Beijing and Tianjin. ③ The increase in the benefits of collaborative emission reduction is conducive to enhancing enthusiasm for collaborative carbon emission reduction in Beijing-Hebei and Tianjin-Hebei， ensuring the stability of the collaborative relationship. ④ The increase in emission reduction rate will be accompanied by the emergence of collaborative emission reduction fatigue， which should be given attention， especially between Tianjin and Hebei. Finally， targeted suggestions and measures are proposed to promote carbon emission reduction through collaborative cooperation between Beijing， Tianjin， and Hebei， providing a theoretical basis and decision-making support for collaborative governance between Beijing， Tianjin， and Hebei.

The decoupling of carbon emissions in the power industry and power generation is of great significance to carbon emission reduction. In this paper, the network effect of the decoupling state was analyzed by applying the modified Gravity model and Social Network Analysis (SNA) approach, and finally the influencing factors of the spatial correlation between decoupling of carbon emissions in the power industry and power generation were analyzed by the quadratic assignment procedure (QAP)method. The results show that: (1) the decoupling effect of national carbon emissions in the power industry and power generation is significant, the decoupling states of the Northwest Grid is relatively smooth, and the decoupling status of the rest of the five major power grids is getting better.(2) Network associations in the decoupling state between regions become closer and closer, and the number of transmission relationships within clustered panels gradually increases, but there is an imbalance in the development within the network, with the North China Power Grid and the East China Power Grid as the centers of the network. (3) Geographic proximity, differences in economic level and population can significantly strengthen network formation, while differences in industrial structure and urbanization inhibit spatial network formation.

The urban resilient distribution network is a crucial component of a resilient power network, facing various risks such as operational failures, climate impacts, and network attacks. These risks pose significant threats to the security of the power networks. However, the existing methods still have some problems, such as incomplete risk awareness indicators and inaccurate identification of power grid operational state. To ensure the secure and stable operation of an urban power grid, a risk situational awareness model for the resilient distribution network cyber-physical system (CPS) based on data density is developed, along with an enhanced Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS). Firstly, a more comprehensive risk situation index system is proposed to reflect the characteristics of power grid security situation and provide assessment basis for situation understanding and prediction. Second, the data density is used to determine the typical state of each node in order to distinguish between risk and normal states. Thirdly, a neural hierarchical interpolation algorithm with enhanced predictive capabilities is presented for forecasting future operational characteristics. Finally, a neural random network is used to classify the future states of nodes, and the part that does not belong to the typical state is identified as the risk state, so as to realize the risk situational awareness. The model is validated using actual data from a regional distribution network in East China. The results indicate that the proposed method achieves an identification rate exceeding 95% for all types of risks, and both the index system and the model can improve the situational awareness accuracy of each node by about 2%. Therefore, the proposed method can perceive the future risk posture of CPS nodes in the distribution network and have higher accuracy compared to traditional methods.

In order to quantify the systematic risks and vulnerabilities of grid disasters, a social network analysis approach was used to identify and analyze the key risk factors of grid disasters. This study constructed the grid disaster system by analyzing the core laws of the interaction between HILF events and the grid, and identified the core risk factors and key risk factor combinations of grid disasters based on the social network analysis framework, and examined the risk control effect by controlling the core risk factors and key risk factor combinations. The results show that 1) "downed poles and broken lines", "damaged distribution lines", and "line trips/outages" are the core risk factors of grid disasters. 2) "Heavy rainfall - traffic disruption", "Heavy rainfall - road collapse damage" are the key risk factor combinations. 3) Emergency decisions for risk control are proposed based on risk identification results.

The power industry is the industry with the highest carbon emissions in China, and the rational allocation of carbon quotas for the power industry is the key to the rational allocation of carbon quotas in China, while the transfer of carbon emissions generated by inter-provincial power trading makes it difficult to calculate the carbon emission quotas for power. The article calculates the comprehensive power carbon quota for each province based on the principles of equity(PEQ) and the principles of efficiency(PEF). The results of the study prove the following points. (1) There are significant differences in the carbon emission factors calculated for electricity generation and consumption in each province. (2) The western provinces with abundant electricity resources deliver electricity to the eastern and southern provinces to ensure their economic development, so the western provinces with higher inflows of electricity carbon emissions bear the carbon emission pressure of the eastern and southern provinces. (3) The provincial electricity carbon quota calculated by the principle of comprehensiveness(PC) takes into account social equity and economic efficiency, and the calculation results are more reasonable. This study achieves balanced regional development by reasonably allocating provincial electricity carbon quotas.

By re-examining, re-checking and effectively feeding back the whole process of science and technology projects, it helps to optimize the management level of the whole process of science and technology projects and promote the development of power science and technology innovation. The article focuses on the research focus of post-evaluation of power grid science and technology projects, and develops according to the research idea of "index system construction-evaluation model research-empirical analysis-countermeasure suggestion". First of all, the index system is constructed around the four aspects of technology level, advanced applicability and influence, comprehensive benefit and application promotion. Secondly, DEMATAL is used to assign the indexes, and then PROMETHEE-II method is combined to carry out post-evaluation and empirical analysis of the grid science and technology project to determine the final evaluation results.

电力大数据的应用主要集中在电力生产、运营、销售环节中,在工程建设环节应用较少.现有的配电网工程项目种类复杂、数量繁多,在建设过程中产生了海量数据,传统项目管控方法已经不能满足要求.利用数据挖掘技术对配电网工程建设项目大数据进行处理,既可以通过采集的关键管控指标大数据来分析管控指标之间的关联关系,又可以辅助管理人员进行重点管理,有针对性地关注容易导致偏差的关联规则.基于Hadoop平台改进后的Apriori关联算法从某省配电网工程精益化管控平台系统等数据源获取相关指标数据,可以挖掘得到配电网工程建设全过程关键管控指标关联关系,依据重点关联关系为配电网工程项目的管理与控制提供参考依据,强化数据统筹分析能力.

The pressing issue of climate change has led to a growing concern for low-carbon energy transformation. Urban energy internet (UEI) provides a means to facilitate renewable energy consumption by leveraging modern energy grid, smart energy services, and cyber-physical systems. However, the implementation of UEI entails complex and unknown risks arising from energy supplies, cyber-attacks, and defective equipment. To address this challenge, we propose an improved decision-making trial and evaluation laboratory based on knowledge graph and complex network (KG-CN-DEMATEL). Specifically, we employ a knowledge graph with Gaussian embedding (KG2E) to vectorize text information related to the risks. The vectors are combined with experts' scores, which evaluate the importance and cohesion of the complex network. We then analyze the significance and interrelations of the risks by computing their reason and central degree. Our results demonstrate that (1) the improved model offers higher accuracy and convenience than the conventional DEMATEL, (2) the 15 risks are ranked based on their importance, with R1 (Renewable energy's uncertainty), R6 (Generation technology), and R2 (Cyber-physical degree) being the most significant, with central degrees of 2.913, 2.635, and 2.566, respectively, and (3) the risks can be classified into six causal and nine effect elements, with their interrelations analyzed.

Carbon emissions from the electricity industry (CEEI) account for a large proportion of China’s total carbon emissions, and it is important to study the spatial correlation between CEEI and the influencing factors to promote cross-regional synergistic emission reduction and low-carbon development of the power system. In this paper, the quasi-input-output (QIO) model is applied to assess the transfer of carbon emissions generated by electricity trading based on the consideration of electricity carbon transfer, and the exploratory spatial data analysis (ESDA) method is applied to analyze the spatial correlation effect of carbon emissions from China’s electric power sector from 2001 to 2020, analyzes its distribution pattern in both spatial and temporal dimensions, and applies the improved logarithmic mean Divisia index (LMDI) two-stage decomposition model to decompose the changes in CEEI into 11 influencing factors from the perspective of the whole industrial chain of power production, transmission, trade, and consumption. The research results show that (1) the spatial distribution of CEEI has obvious unevenness and aggregation characteristics, with high-high aggregation areas and hot spot aggregation areas generally concentrated in the North China Power Grid and the East China Power Grid, but the aggregation trend is gradually decreasing, while low-low aggregation areas and cold spot aggregation areas are concentrated in the Northwest China Power Grid and the Central China Power Grid, but the area is very limited. (2) The direction of carbon emission diffusion in China’s electricity industry is gradually transitioning from southwest-northeast to northwest-southeast, and the east-west diffusion trend is stronger than the north-south diffusion trend and carbon emissions are gradually shifting to the northwest grid. (3) The total amount of electricity production is the most influential factor in the change of CEEI, driving the cumulative growth of CEEI by 4495.34 Mt, followed by GDP per capita and electricity consumption intensity. Coal consumption for power generation, the share of thermal power, and net electricity exports were the main factors inhibiting the increase in carbon emissions from the power sector, with cumulative contributions of −797.74 Mt, −619.99 Mt, and −47.76 Mt, respectively.

目前我国服装行业面临着多重压力,对服装行业供应链运行效率进行研究,找到合理控制供应链效率的方法显得尤为迫切.运用DEA的方法构建了服装行业供应链运行效率评价模型,使用2020至2021年中的20个月的数据进行了投入产出效率测算,对服装行业供应链效率的有效性进行了分析,并针对分析结果提出了改进建议.

The surge of renewable energy systems can lead to increasing incidents that negatively impact economics and society, rendering incident detection paramount to understand the mechanism and range of those impacts. In this paper, a deep learning framework is proposed to detect renewable energy incidents from news articles containing accidents in various renewable energy systems. The pre-trained language models like Bidirectional Encoder Representations from Transformers (BERT) and word2vec are utilized to represent textual inputs, which are trained by the Text Convolutional Neural Networks (TCNNs) and Text Recurrent Neural Networks. Two types of classifiers for incident detection are trained and tested in this paper, one is a binary classifier for detecting the existence of an incident, the other is a multi-label classifier for identifying different incident attributes such as causal-effects and consequences, etc. The proposed incident detection framework is implemented on a hand-annotated dataset with 5 190 records. The results show that the proposed framework performs well on both the incident existence detection task (F1-score 91.4%) and the incident attributes identification task (micro F1-score 81.7%). It is also shown that the BERT-based TCNNs are effective and robust in detecting renewable energy incidents from large-scale textual materials.

China faces enormous pressure to reduce carbon emissions. Since the agglomeration and driving effect of urban agglomerations have continued to increase, relying on the network relationship within urban agglomerations to coordinate emission reduction becomes an effective way. This paper combines the modified Gravity model and Social Network Analysis method to measure the structure characteristics of carbon emission spatial correlation network of the seven urban agglomerations as a whole and each urban agglomeration in China, analyzes the interaction mechanism between cities and between urban agglomerations, and finally explores the influencing factors of carbon emission spatial correlation through the QAP analysis method. The results are as follows: (1) As for the overall network, overall scale was increasing, but the hierarchical structure had a certain firmness. YRD and PRD urban agglomerations were at the center of the network and received the spillover relationship of MRYR, CC, CP, and HC urban agglomerations. (2) As for the networks of urban agglomerations, the allocation of low-carbon resource elements still needed to be optimized, especially BTH urban agglomeration. Beijing, Shanghai, Nanjing, Wuxi, etc. were at the center of the network. The influencing factors and degree of carbon emission spatial correlation in each urban agglomeration were different.

燃煤发电机组运行过程中面临各种风险,一旦发生故障将造成不小的经济损失和社会影响.为了保障机组的安全生产和稳定运行,建立了燃煤发电运行风险实时评估模型,从而及时制定故障检修计划.基于大数据关联规则分析了燃煤发电运行风险与影响因子的关联关系.在此基础上,基于熵权法对影响因子赋权,并结合灰色关联理论、证据理论和Dempster合成规则实现基本信度分配函数的确定和融合,从而得到燃煤发电机组运行风险值和风险等级.最后,以发电厂A的燃煤发电机组进行算例分析,其风险评估结果与实际运行情况具有相关一致性,证明了模型的现实意义.

Energy internet (EI) is the framework foundation for tackling climate change and environmental issues and achieving "carbon peak and carbon neutral". In this paper, considering the important function of pumped-storage power station (PPS) in promoting the "source-grid-load-storage" synergy and complement in the construction of EI, a novel evaluation index system and evaluation model for the site selection of PPS is proposed to provide decision support for the orderly construction of EI. Firstly, compatible with traditional engineering construction factors and multi-energy complementary needs, a systematic evaluation index system of PPS site selection is established from hydrological conditions, topographical and geological conditions, construction conditions, power grid development planning and economic and environmental benefits. Secondly, considering the inconsistency of conclusions of a single evaluation method, the cycle elimination mechanism based on Kendall's concordance coefficient (KCC) is introduced into the combination evaluation model of PPS site selection, and the validity measurement method of the model is defined. Finally, a case study on the site selection of PPS in Hebei province of China is carried out to verify the effectiveness of the combination evaluation model, and the sensitivity analysis is carried out. The research shows that it is highly feasible to use the index and model constructed in this paper for the site selection of PPS. The combination evaluation model based on cycle elimination solves the inconsistency of the evaluation conclusions and greatly improves the effectiveness of the combination evaluation model.

In recent years, wind curtailment in China has seriously affected the utilization rate of wind power resources and hindered the process of China’s energy structure transformation. To effectively improve wind power performance and speed up the progress of energy revolution, this paper conducts differentiated research on regional wind power development of various provinces in China. The DEA-TOPSIS model is used to measure wind power performance, and the result is compared with that of the super efficiency DEA model. It is verified that the proposed model is more suitable for wind power performance evaluation. The space panel model is introduced to analyze the influencing factors in wind power performance, and the differentiation strategy is formulated to promote the development of wind power. The results show that the pure technical efficiency of wind power in various provinces is lower than the scale efficiency as a whole, and the geographical proximity has a positive impact on China’s wind power development. At present, the main factors affecting the performance of wind power in China are the level of regional economic development and wind curtailment. The research provides a decision-making reference for the optimization of wind power operation and management in various provinces.

生物质掺烧可减少煤电机组的碳排放,促进"双碳"目标实现,但易引发烟风系统相关设备的运行风险.为此,借助数据密度提出了基于Transformer与信息融合的风险态势感知模型.首先,基于数据密度,识别海量历史数据的典型状态;其次,借助Transfomer模型机制,预测未来时刻的运行特征;再次,融合近邻点信息,判别并预警风险态势;最后,运用实际数据进行算例分析.结果表明:掺烧机组烟风系统可识别为低负荷和高负荷2类典型运行状态;所提Transformer模型在掺烧机组烟风系统的未来特征预测中优于其他模型;近邻信息融合可以有效判别掺烧机组烟风系统的风险状态.因此,该模型可有效感知掺烧机组烟风系统的风险态势,确保其运行可靠性.

Science and technology projects are an essential starting point for the digital transformation of electric companies. Irrelevant projects’ reviewers will cause unrealistic research results. The companies will also waste research funding. We use knowledge graphs and collaborative filtering to recommend experts for projects in the electric power field to solve this problem. First, we constructed an electric project knowledge graph through the project abstract and CNKI database. Then, we use semantic and collaborative similarity to find the experts most relevant to the project. Finally, we discussed the outperformance and conditions of the proposed model and compared the recommendation results with state-of-the-art methods. Research indicates: (1) The knowledge graph can effectively solve the cold-start problem of collaborative filtering. (2) The KG2E-CF model can improve the relevance between the results of the recommendation. (3) The proposed model should be combined with the theme words extraction algorithm to increase the relevance of the recommendation results. Therefore, the expert recommendation in the electric power field can adopt the model proposed in this paper.

通过三阶段的数据包络分析方法(DEA),基于我国30个省份四年面板数据,剔除环境变量的基础上对各省众创空间运行效率进行测算.结论表明众创空间运行中存在最主要的问题是规模效率低下.时间角度对比四年运行效率分析得出众创空间运行整体较好,空间角度对30个省份进行地域划分,分析东中西三部分地区发展差异和原因.通过tobit回归模型,探讨影响众创空间发展的因素,结论表明当地科技企业孵化器数量是影响众创空间运行效率的首要因素.其次是人员教育素养,随后是政府补贴.结合实证研究结果和相关双创政策,为众创空间发展提出相关政策建议.

Energy structure reform is the common choice of all countries to deal with climate change and environmental problems. Pumped-storage power station (PPS) will play an important role in the green and low-carbon energy era of "source-grid-load-storage" synergy and multi-energy complementary optimization. In this context, this paper puts forward a PPS selection evaluation index system and combination evaluation model for energy internet. First of all, on the basis of the construction conditions of traditional PPS, considering the multi-energy complementary demand of energy internet, a three-dimensional evaluation index system of "hydrology & geological conditions", "construction conditions" and "energy structure demand" has been established. Secondly, concepts such as degree of center cohesion, similarity threshold and core method set are introduced to construct a cycle elimination mechanism and eliminate the edge single evaluation method (SEM) to improve the scientificity of combination evaluation. Site selection combination evaluation of PPS based on cycle elimination is constructed, and effectiveness measure test of site selection combination evaluation method is defined. Finally, PPS in North China is taken as an example to verify the effectiveness of the model and the sensitivity analysis is carried out. The research shows that the effectiveness of the combination evaluation method is not only determined by the mechanism of the method itself, but also affected by the consistency of the SEM set, and the cycle elimination mechanism can improve the effectiveness of the combination evaluation method. The evaluation index system, site selection model based on cycle elimination and effectiveness measure are constructed in this paper has high feasibility. The classification characteristics of PPS sites are proposed for the first time, and PPS sites are divided into seven types according to three index dimensions: it provides the basis for differentiated decision making of PPS site selection.