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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. [ABSTRACT FROM AUTHOR]

Accidents in the booming renewable energy industries can negatively impact people, animals, and buildings in social systems and ecosystems. Many studies focus on the renewable energy incidents in a single wind-based, solar-based, or hydro-based system, and lack holistic views of incident risk management covering all the systems. This paper proposes a framework for evaluating the comprehensive and cross-system renewable energy incidents by ontology-based Bayesian networks extracted from incident reports using text mining methods. An ontology for renewable energy incidents involving 34 classes, 241 instances, and 352 properties is first created based on knowledge and a hand-annotated dataset with 2 237 event statements. Then the ontology is used to filter and map risk factors and their relations extracted from 11 504 renewable energy incident news reports into nodes and edges in a multi-graph. Finally, the multi-graph is utilized to learn Bayesian networks for modeling renewable energy incident risk propagation, which is evaluated and analyzed by Bayesian inference algorithms. The result shows that there are significant and widespread risk propagation effects in different renewable energy systems, especially the "Separation-Falling-Injury-Fatality" risk transmission chain in the wind-based systems. Fire, collision, deformation, wearing, and falling risks contribute the most to the consequences like damages, fatalities, and failure, and the combined effect of risk elements in the risk transmission chains is the deep-seated reason for generating those consequences for renewable energy incidents. The proposed framework provides new insights into risk analysis for the security planning and construction of renewable energy systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Currently, innovation is no longer limited to a single enterprise. The joint realization of collaborative innovation by multiple parties has become the main driving force for innovation and development. The Power Innovation Park is an emerging innovation and entrepreneurship model. It is guided by power grid companies, and the school, government, and enterprise are combined in the park. The Power Innovation Park urgently needs theoretical guidance in terms of the resource allocation mechanism, support incentive mode, and benefit-sharing mode, so this paper takes the park as an example to study the benefit-sharing mode of collaborative innovation. In this paper, the transformation methods of the Power Innovation Park are divided into two types: cooperation between members within the park and dominated by external enterprises in the park. This paper applies cooperative game theory and HJB equation to study the benefit-sharing model of each member in the Power Innovation Park under two different result transformation modes. Research has shown that an appropriate method of transformation of results can maximize the overall and individual interests of the park and that individual interests are affected by many factors. Finally, this paper puts forward policy recommendations to promote the collaborative innovation development of the innovation park through the research results. [ABSTRACT FROM AUTHOR]

Regional integrated energy system (RIES) provides a platform for coupling utilization of multi-energy and makes various energy demand from client possible. The suitable RIES composition scheme will upgrade energy structure and improve integrated energy utilization efficiency. Based on a RIES construction project in Jiangsu province, this paper proposes a new multi criteria decision-making (MCDM) method for the selection of RIES schemes. Because that subjective evaluation on RIES schemes benefit under criteria has uncertainty and hesitancy, intuitionistic trapezoidal fuzzy number (ITFN) which has the better capability to model ill-known quantities is presented. In consideration of risk attitude and interdependency of criteria, a new decision model with risk coefficients, Mahalanobis-Taguchi system and Choquet integral is proposed. Firstly, the decision matrices given by experts are normalized, and then are transformed to minimum expectation matrices according to different risk coefficients. Secondly, the weights of criteria from different experts are calculated by Mahalanobis-Taguchi system. Mobius transformation coefficients based on interaction degree are to calculate 2-order additive fuzzy measures, and then the comprehensive weights of criteria are obtained by fuzzy measures and Choquet integral. Thirdly, based on group decision consensus requirement, the weights of experts are obtained by the maximum entropy and grey correlation. Fourthly, the minimum expectation matrices are aggregated by the intuitionistic trapezoidal fuzzy Bonferroni mean operator. Thus, the ranking result according to the comparison rules using the minimum expectation and the maximum expectation is obtained. Finally, an illustrative example is taken in the present study to make the proposed method comprehensible. [ABSTRACT FROM AUTHOR]

This study investigates the evolution of provincial new energy policies and industries of China using a topic modeling approach. To this end, six out of 31 provinces in China are first selected as research samples, central and provincial new energy policies in the period of 2010 to 2019 are collected to establish a text corpus with 23, 674 documents. Then, the policy corpus is fed to two different topic models, one is the Latent Dirichlet Allocation for modeling static policy topics, another is the Dynamic Topic Model for extracting topics over time. Finally, the obtained topics are mapped into policy tools for comparisons. The dynamic policy topics are further analyzed with the panel data from provincial new energy industries. The results show that the provincial new energy policies moved to different tracks after about 2014 due to the regional conditions such as the economy and CO2 emission intensity. Underdeveloped provinces tend to use environment-oriented tools to regulate and control CO2 emissions, while developed regions employ the more balanced policy mix for improving new energy vehicles and other industries. Widespread hysteretic effects are revealed during the correlation analysis of the policy topics and new energy capacity. [ABSTRACT FROM AUTHOR]

The problem of wind curtailment is serious in China. It not only restricts the development of renewable energy, but also brings economic losses to the enterprises and the society. Unfortunately, the problem of wind power curtailment is difficult to eliminate under existing facilities and technical conditions. Therefore, it is a practicable way to control the wind power curtailment rate as far as possible on the premise of guaranteeing the profit of the power generation enterprise and meeting the demand for electricity users. In the context of the coordinated development of the Beijing-Tianjin-Hebei region, this article presents a deep analysis of other energy generation, power grid planning, social benefits, electricity prices, and other factors associated with the wind power generation, then we constructed a complete "cost of wind curtailment" model. Finally, an uncertainty analysis is added to show the impact of some uncertainties on the model, which makes the model more complete, and after calculating the cost and benefit of "reducing the rate of wind curtailment," some pertinent suggestions are given, which will provide policy basis for the development of renewable energy. [ABSTRACT FROM AUTHOR]

Air pollution in northern China is relatively serious during the winter heating period, which attracts the attention of the state and government, especially in Beijing–Tianjin–Hebei Region. To further explore the issue of air pollutant emission and energy utilization in Beijing–Tianjin–Hebei during the heating season, this paper establishes a panel data model which describes the long-term relationship between air pollutant emission, heating capacity, coal, and power consumption with the data from 2004 to 2017. Based on this, we draw the following conclusions: there is a positive relationship between winter heating capacity and air pollutant emissions, which indicates that the energy consumed by heating will produce atmospheric pollutants. However, the increase of electricity consumption does not necessarily reduce pollution, which means that replacing coal with electricity is not the best way to solve air pollution in heating season, but requires the coordination of multiple energy sources. In addition, there are obvious differences in the analysis results in Beijing, Tianjin, and Hebei. For example, the impact of coal and electricity consumption on pollutant emissions in Beijing is quite different from that in Tianjin and Hebei. The local economic development and energy conditions should be fully taken into account when formulating policies. [ABSTRACT FROM AUTHOR]

Advanced grid technology represented by smart grid and energy internet is the core feature of the next-generation power grid. The next-generation power grid will be a large-scale cyber-physical system (CPS), which will have a higher level of risk management due to its flexibility in sensing and control. This paper explains the methods and results of a study on grid CPS's behavior after risk. Firstly, a behavior model based on hybrid automata is built to simulate grid CPS's risk decisions. Then, a GCPS risk transfer model based on cooperative game theory is built. The model allows decisions to ignore complex network structures. On this basis, a modified applicant-proposing algorithm to achieve risk optimum is proposed. The risk management model proposed in this paper can provide references for power generation and transmission decision after risk as well as risk aversion, an empirical study in north China verifies its validity. [ABSTRACT FROM AUTHOR]

The growing share of renewable energy has an increasingly significant impact on the electricity market. Locational marginal price (LMP), as a sensitive price signal, has a potential but considerable correlation with renewable energy generation. In this context, this paper focuses on the joint fluctuation laws between LMP and renewables consumed to generate electricity. At first, LMP and renewables consumption data from Independent System Operator New England (ISO‐NE) are collected and preprocessed. Then, through highly comparative time‐series analysis (hctsa), it is found that discrete symbolization features have effective description ability. Therefore, a coarse‐grained method is conducted to convert the real matrix data into a symbol vector based on numerical distribution analysis. Next, joint fluctuation complex networks are constructed according to the symbol vector. From a global perspective, the joint fluctuation network follows an exponential distribution and exhibits hierarchical modularity. From a local perspective, the joint fluctuation network follows a power‐law distribution. The research results of this paper provide a new perspective to understand the evolution of the electricity market with large‐scale renewable energy and have reference value for identifying the valuable joint fluctuation patterns and improving the LMP forecast results. [ABSTRACT FROM AUTHOR]

The main difference between flexible high-voltage direct current (HVDC) system and the traditional HVDC system is that there are two control degrees of freedom in the flexible HVDC system that is the amplitude and phase angle of the output voltage. While, there is only one control degrees of freedom in the traditional HVDC system which is the amplitude of the output voltage. Therefore, compared with the traditional HVDC system, there will not be the problem of commutation failure in the flexible HVDC system, and the control systems of the rectifier side and the inverter side being operated independently make the reliability of the system greatly improved. This article analyses and compares the main methods of reliability assessment. Analysing and comparing the reliability parameters of the two systems by using the state enumeration method, and using the economic characterisation to make the comparison more intuitive. [ABSTRACT FROM AUTHOR]

The selection of electrical equipment condition-based maintenance alternatives is a multi-attribute decision-making problem. Choosing the proper maintenance scheme can not only accurately grasp the operation state of power equipment, but also weaken the blindness of maintenance work and improve economic benefits. Therefore, it is particularly important to choose a scientific decision-making method. In this paper, a multi-attribute decision-making method based on cloud model and grey D-S evidence theory is proposed. Firstly, cloud model is applied to deal with qualitative criteria, which reduces the fuzziness and randomness of qualitative language and remains linguistic information as much as possible in the transformation process. Secondly, on the basis of the concept of grey correlation degree, a new method to calculate basic probability assignment (BPA) or mass function in D-S evidence theory is presented which diminishes the grey character in decision-making process. Finally, the example analysis and sensitivity analysis verify the effectiveness and practicability of the proposed model. [ABSTRACT FROM AUTHOR]

Uncertainty decrease is an important attribute during the risk elements transmission process, and the occurrence of risk elements may not be clear until all factors tend to be steady. To study the interrelation among risk elements and their occurrence mechanism, this paper makes an analogy between risk elements transmission effect and molecular motion of thermodynamics and defines the entropy system based on entropy theory to integrate the macroscopic transmission with the microcosmic entropy change. A hypothesis about the state of risk elements during the entropy reduction process is proposed. And a risk elements neural network is built to simulate the transmission effect with simulated annealing algorithm controlling the entropy reduction. With a practical example, the hypothesis is proved and the coming risk elements are forecasted. This shows the model with a fast convergence rate effectively combines risk elements and entropy, which can be widely applied in the risk early-warning management. [ABSTRACT FROM AUTHOR]

In consideration of the interaction among attributes and the influence of decision makers’ risk attitude, this paper proposes an intuitionistic trapezoidal fuzzy aggregation operator based on Choquet integral and prospect theory. With respect to a multiattribute group decision-making problem, the prospect value functions of intuitionistic trapezoidal fuzzy numbers are aggregated by the proposed operator; then a grey relation-projection pursuit dynamic cluster method is developed to obtain the ranking of alternatives; the firefly algorithm is used to optimize the objective function of projection for obtaining the best projection direction of grey correlation projection values, and the grey correlation projection values are evaluated, which are applied to classify, rank, and prefer the alternatives. Finally, an illustrative example is taken in the present study to make the proposed method comprehensible. [ABSTRACT FROM AUTHOR]

Annual power load forecasting is essential for the planning, operation and maintenance of an electric power system, which can also mirror the economic development of a country to some extent. Accurate annual power load forecasting can provide valuable references for electric power system operators and economic managers. With the development of Energy Internet and further reformation of electric power market, power load forecasting has become a more difficult and challenging task. In this paper, a new hybrid annual power load forecasting model based on LSSVM (least squares support vector machine) and MFO (Moth-Flame Optimization algorithm) is proposed, which the parameters of LSSVM model are optimally determined by the latest nature-inspired metaheuristic algorithm MFO. Meanwhile, the rolling mechanism is also employed. The forecasting results of China's annual electricity consumption indicate the proposed MFO-LSSVM model shows much better forecasting performance than single LSSVM, FOA-LSSVM (LSSVM optimized by fruit fly optimization), and PSO-LSSVM (LSSVM optimized by particle swarm optimization). MFO, as a new intelligence optimization algorithm, is attractive and promising. The LSSVM model optimized by MFO can significantly improve annual power load forecasting accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we give a risky decision-making approach based on prospect theory and cloud theory to solve the wind power investment problem. In this problem, the criteria value of alternative is linguistic assessment information and the criteria's weights are partially known. Firstly, we use the cloud theory to transfer the linguistic variables into one-dimensional normal cloud model. On the basis of defining the normal cloud model comparison rule and viewing all other alternatives as the reference point, a cloud prospect value function can be defined. Then, the cloud prospect decision-making matrix can be attained. Secondly, to get the optimal criteria weights, an optimization programming model which satisfies the algorithm of maximizing deviation and the decision makers' subjective information is enacted. After that, we aggregate the clouds of each alternative as a comprehensive cloud. Then the order of alternatives can be listed by comparing comprehensive cloud of each alternative. Finally, an illustrative example about wind power investment is given and we verify the effectiveness and feasibility of this approach which can be valuable in the wind power investment decision-making. [ABSTRACT FROM AUTHOR]

With rapid speed on electricity using and increasing in renewable energy, more and more research pay attention on distribution grid planning. For the drawbacks of existing research, this paper proposes a new risky group decision-making method for distribution grid planning. Firstly, a mixing index system with qualitative and quantitative indices is built. On the basis of considering the fuzziness of language evaluation, choose cloud model to realize 'quantitative to qualitative' transformation and construct interval numbers decision matrices according to the '3En' principle. An m-dimensional interval numbers decision vector is regarded as super cuboids in m-dimensional attributes space, using two-level orthogonal experiment to arrange points uniformly and dispersedly. The numbers of points are assured by testing numbers of two-level orthogonal arrays and these points compose of distribution points set to stand for decision-making project. In order to eliminate the influence of correlation among indices, Mahalanobis distance is used to calculate the distance from each solutions to others which means that dynamic solutions are viewed as the reference. Secondly, due to the decision-maker's attitude can affect the results, this paper defines the prospect value function based on SNR which is from Mahalanobis-Taguchi system and attains the comprehensive prospect value of each program as well as the order. At last, the validity and reliability of this method is illustrated by examples which prove the method is more valuable and superiority than the other. [ABSTRACT FROM AUTHOR]