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.

电力现货市场实时交易可充分发挥市场调节作用,促进可再生能源消纳.基于数据实证分析可再生能源发电对实时电价的影响,对理解现货市场运行规律、开展市场成熟度评价等具有重要参考价值.选取德国电力现货市场开展数据实证,收集发电量、负荷量、预测误差、价格等多因素数据,基于时间序列特征表示方法,研究可再生能源发电对实时电价的影响.首先,使用特征表示方法将时间序列时域模型转化为特征向量.然后,采用贪婪向前特征选择算法提取关键特征,最大化因素间差异.接着,分别基于全部特征和关键特征讨论了多因素间的相关性,并构建了影响机理网络图.实证结果表明德国电力现货市场实时电价主要受到风力发电量预测误差影响,因素间相关性主要来自时间序列的傅里叶变换、小波变换、离散符号化等特征.最后,通过中德电力现货市场的定量对比,指出中国广东电力市场实时电价更易受新能源发电量而非预测误差的影响.

Water surface photovoltaic (PV) is in the early stage of development, facing the problem of weighing economic and environmental benefits. Due to the lack of long-term statistical data and complex environmental impact, it is difficult to carry out effective benefit assessment for water surface PV projects based on existing methods. This paper, focusing on ecological environment impact instead of carbon emission, proposes the economic and environmental synergies assessment model of water surface PV projects. The core of the model is to use the gain estimation of cross-spectrum analysis to evaluate the consistent fluctuation of economic benefit and environmental benefit. In order to expose the gain spectrum, the trend component of economic benefit is extracted by singular spectrum analysis, and the biological and chemical factors of environmental benefit are identified by factor analysis. An empirical study on a 10MWp “fishery and photovoltaics integration” demonstration project in Jiangsu Province of China is conducted. The assessment result shows that the economic and environmental synergies of the project are not optimal, and the economic benefit should be reduced to mitigate the ecological damage. The empirical study shows that the assessment model is insensitive to factor weight distribution, and is not affected by noise.

Abstract 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.

Photovoltaic (PV) Poverty Alleviation makes full use of the solar energy in poverty-stricken areas so as to achieve sTable incomes increase for the poor households for 25 years. It is an advanced mode integrating new energy development, emission reduction and accurate poverty alleviation. Post evaluation of PV poverty alleviation project is of great guiding significance for new energy development planning, poverty alleviation promoting and construction and operation of PV power stations. Under the guidance of the practical experience of PV poverty alleviation in Jiangxi province, China, this paper firstly builds a comprehensive evaluation index system with 7 second-level targets based on the post evaluation theory. Then, considering that projects with different sizes, construction and operation mode have different features in the evaluation, this paper uses pattern recognition method based on fuzzy C-means clustering algorithm and support vector machine to classify the projects. Then comparative analysis is carried out within each class to achieve comprehensive benefits evaluation. The method can reduce the information loss of multi-index weighted aggregation of traditional post evaluation methods. The features of PV poverty alleviation projects are highlighted to help to find the weak points of the projects. So the evaluation results are more scientific and reasonable.

能源互联网呈现物理信息深度融合的趋势,为电力系统管理研究定义了新的研究框架.作为一个先进的复杂系统,能源互联网信息物理系统(Energy Internet Cyber-Physical System,ECPS)在其发展过程中面临着一些新的挑战,其中一个就是耦合结构下的风险管理.本文结合复杂网络理论和风险传递理论,着重在拓扑层面分析了ECPS跨空间交互机理,并在此基础上定义了交互路径和交互系数;接着建立了ECPS跨空间风险传递模型,量化描述了风险的传递和演化过程,并进行了风险影响评估;最后,通过仿真实验分析了三种不同交互系数节点故障的风险传递过程和不同攻击模式下系统的崩溃过程.对仿真结果的讨论阐述了能源互联网风险跨空间传递的特点,为更深入地研究ECPS的风险管理提供了参考.

选取电力工业科研投入和行业发展相关的7个统计指标,搜集其年度统计数据.首先,提出基于教与学优化算法(TLBO)的专家赋权法,降低专家群体研究领域交叉导致的赋权结果偏差,对电力工业科研投入和行业发展进行综合评价.然后,基于综合评价结果,对电力工业科研投入与行业发展进行互谱分析.分析发现,从长期波动来看,我国电力工业科研投入超前行业发展6.10年,从短期波动来看,科研投入滞后行业发展2.89年.

As more new energy power is integrated into the power grid in a large scale, the security problem of the new energy power system (NEPS) has been paid more and more attention in China. Therefore, in order to identify the risk elements of NEPS in China and analyze their internal influence relations, this paper proposes a risk identification and analysis model of NEPS based on D numbers theory and decision-making trial and evaluation laboratory (DEMATEL) method. First, a risk element set is established including new energy resource risk, technical risk, demand side risk, grid side risk and human factor, and Crombach α reliability analysis is introduced to verify the rationality of this set. Then, D numbers theory is employed to integrate the expert evaluation information and the direct relation matrix is obtained. Next, based on DEMATEL method, the cause-effect relations and significance degree of risk elements are analyzed. The results indicate that C10, C15 and C14 are in the top three according to prominence values which are 6.455, 6.186, 5.854 respectively. Meanwhile 18 criteria are divided into 8 causal factors and 10 effect factors which are also analyzed by θ in total relation matrix. Finally, according the risk analysis results, managerial and policy implications are explored. This study concluded that the proposed method provides a strong basis for future academic research.

In recent years, wind power industry has been developing rapidly as the wind resources are clean, cheap and inexhaustible. However, it is difficult to supply steady wind power generation due to the strong randomness, volatility and uncontrollability of wind energy. Therefore, it is significant to propose an efficient wind power prediction model. In this paper, a short-term wind power prediction model is proposed based on data mining technology and improved support vector machine method. In this model, data mining is employed to investigate the relationship between wind speed and wind power output and then modify the invalid original data. Then, based on wavelet transform method, the high frequency parts of the original signal can be eliminated. Next, cuckoo search algorithm is used to optimize kernel function and penalty factor of support vector machine in order to improve the accuracy of the forecast result. Finally, a wind farm located in the Northwest China is selected to perform the case study. The results indicate that the proposed model has the best performance according to the values of several error assessment indexes, including mean absolute error, mean squared error and mean absolute percentage error.

Renewable energy is the inevitable choice for the sustainable development of society and economy. How to select the most appropriate renewable energy for a region is a complex multi-criterion decision making (MCDM) problem. Taking Jilin Province as an example, this paper proposes a new MCDM method. In order to better express the hesitancy, inconsistency and uncertainty of decision makers’ preferences, linguistic hesitant fuzzy set (LHFS) is proposed. On the basis of cloud model, the rule of transforming LHFS to quantitative values is defined. Subsequently, the distance measure and support measure are established. In consideration of the interdependency of criteria, an LHFS aggregation operator based on improved Choquet integral is proposed. Finally, the ranking result of the aggregated LHFS corresponding to each renewable energy alternative is obtained according to the expectation values. The result shows that the preferred renewable energy for Jilin is biomass energy, followed by wind energy, hydro energy and solar energy. The validation analysis and comparison analysis are given to demonstrate the effectiveness of the proposed method.

能源互联网下电力信息融合风险管控将面临更多不确定性和复杂性,主要从复杂事故系统角度研究了电力CPS故障风险传递结构.首先,基于复杂事故系统概念构建了电力CPS事故系统,并对电力CPS事故致因因素进行提取梳理;在此基础上,建立DEMATEL-ISM解释结构模型对事故因素间传递关系进行分析,得到各风险致因因素间层次结构和复杂网络结构模型;最后,基于上述研究成果对电力信息融合下的风险传递发展趋势进行分析.通过研究发现,随着电力信息的深度融合,风险因素间扰动关系更加紧密复杂,关键节点失效更容易导致大规模系统崩溃.

Consumers equipped with standby electric source will become electricity providers at particular moments in the future smart grid. From the perspective of smart grid risk management, this paper concludes the supply risk factors that may lead to the dissatisfaction of consumer demands for security, q uality and benefit, which are further classified to supply risk elements. Then the mapping relations between supply risks and consumer demands is established. For the risk assessment problem involving consumer demands and power supply risk, a risk assessment model based on catastrophe theory and 2-tuple linguistic representation is proposed. Firstly, the experts’ linguistic variables are converted to 2-tuples linguistics, and catastrophe theory expands operations from real numbers to linguistic variables. Secondly, combining the expert subjective weights in the form of linguistic, a compromised weighting method based on similarity and deviation is proposed. Subsequently, the ranking results are obtained according to the total catastrophe 2-tuple linguistic value of each supply risk element. The ranking results are helpful for power supply companies to identify important risks and develop risk aversion strategies. At last, the feasibility and effectiveness of the proposed method is proved by contrastive analysis. Therefore, the study could provide reference for future smart grid risk management.