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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. • There are significant differences between the calculated carbon emission factors for electricity generation and consumption in the provinces. • The western provinces with larger inflows of carbon emissions from electricity bear the pressure of carbon emissions from the eastern and southern provinces. • The provincial power carbon quota allocation scheme under the principle of comprehensive combines the advantages of the responsibility principle and the efficiency principle. [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]

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]

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]

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. • The model is applicable when data is few but detailed, early in projects. • The model has advantages of robustness in dealing with multi-index and noise. • If focusing on water quality, the synergies of the case study project will be poor. [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]

Abstract 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. Highlights • A short-term wind power forecasting method is proposed. • A case study in Northwestern China is performed. • The prediction accuracy of the proposed method approaches to 90%. • The proposed method has the smallest values of three evaluation indexes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [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]

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]

Abstract: Risk management project is an important aspect of general project risk element transmission theory. To solve the multi-objective time-cost trade-off problem considering the risk elements effectively, this paper establishes an analytical model for multi-objective risk-time-cost trade-off problem based on general project risk element transmission theory. We divide risk elements into discrete model and continuous model to be discussed separately, and the two models for multi-objective risk-time-cost trade-off problem are established by taking Markov dynamic PERT network into classical PERT network. Thus, we combine Radial Basis Function (RBF) neural network to solve the discrete model of the problem. Finally, a practical example illustrates the effectiveness of the algorithm. [Copyright &y& Elsevier]

The carbon emission from major sectors and key industries is the main source of regional carbon emission, which has caused severe climate anomalies. Decoupling analysis and prediction of carbon emissions are helpful to formulate effective carbon emission reduction targets and coordinate the relationship between carbon emission and economic development. Considering the differences between sectors, this paper selects 12 major industrial carbon emitting sectors in Henan Province as the research object, decomposes the carbon emission driving factors by Logarithmic Mean Divisia Index (LMDI) method, analyzes the decoupling relationship of carbon emission and economic development by Tapio decoupling model, constructs a decoupling effort model by combining the LMDI method and Tapio decoupling model to analyze the influence of each effect on the decoupling relationship, and sets up baseline scenario, low carbon scenario and enhanced low carbon scenario to forecast carbon emission and its decoupling state with economic development by the Long-range Energy Alternatives Planning system (LEAP) model. It is concluded that the restraining effects of energy structure, energy intensity and industrial structure are increasing. The decoupling index between carbon emission and economic development of 12 sectors shows a downward trend. Both energy structure effect and energy intensity effect have made weak decoupling efforts, and the decoupling efforts of industrial structure effect were relatively small, especially in Ferrous metal smelting and calendering industry and other five sectors. In 2035, four sectors will achieve carbon neutralization and the overall decoupling effect of carbon emissions will be obvious. Finally, this paper puts forward some recommendations to promote carbon emission reduction. • Decomposition, decoupling and forecasting analysis of carbon emission. • Combining decomposition and decoupling analysis to construct decoupling effort model. • Differences between sectors of industry are considered. • The decoupling efforts of industrial structure effect were relatively small. • Four industrial sectors will achieve carbon neutralization in 2035. [ABSTRACT FROM AUTHOR]

Electric power is the foundation of the development of national economy, it is necessary to make a comprehensive evaluation of its development. Firstly, 17 secondary electric power development evaluation indexes are established from four aspects: electricity supply, electricity consumption, electrification level and carbon emission. Secondly, based on the cloud model, the subjective evaluation and analysis of the primary evaluation index for the national electric power development are realized. Then, the entropy method is used to objectively evaluate and analyze the secondary evaluation index of national electric power development. And the objective evaluation value of the primary evaluation index for the national electric power development is obtained. Thirdly, the TOPSIS method is used to integrate the subjective evaluation value obtained by the cloud model and the objective evaluation value obtained by the entropy method to comprehensively evaluate the status of national electricity development. Next, by collecting the electric power development evaluation index data of 11 typical countries, the electric power development of 11 countries was evaluated and analyzed by using the CM-EM-TOPSIS method proposed in this paper. From the results of the comprehensive evaluation of China 's electric power development, it is found that compared with other countries, the sensitivity of the queueing indicator value to the subjective evaluation weight is lower in China. Finally, according to the results of comprehensive evaluation and analysis of electric power development, the managerial implication is explored in China. • A comprehensive evaluation index system for national electric power development is established. • Cloud model is used to convert the subjective evaluation value of the primary evaluation index. • The secondary evaluation index of national electric power development is evaluated and analyzed by entropy method. • A national electric power development comprehensive evaluation method based on CM-EM-TOPSIS is proposed. • The current status of electricity power development in 11 typical countries is evaluated. [ABSTRACT FROM AUTHOR]

With the deepening of China's low-carbon energy transformation, microgrids are an essential means to promote the consumption of renewable energy. Therefore, improving the operational efficiency of microgrids is the key to promote the development of renewable energy. This paper establishes a three-layer Multi-Agent system model considering the energy storage system and power-heat load demand response based on the actual situation of China to solve the problem of microgrids energy management. In order to verify the effect of the energy storage system and demand response in microgrids, this paper designs three simulation cases, namely the underlying case, energy storage case and demand response case. In order to prove the efficiency of the proposed method, this paper applies the proposed method to solve three cases and compare the result with other meta-heuristic algorithms. The comparison results show that: (1) Multi-Agent system model can realize the collaborative optimization of 'source, grid, load, and storage.' (2) The introduction of the energy storage system and demand response in microgrids can stabilize the output of renewable energy units, promote renewable energy consumption and reduce the overall operating cost of microgrids. (3) The proposed particle swarm optimization can effectively reduce the cost and the number of iterations. • This study realizes a real-time energy management system of MG based on MAS. • This paper proves the effect of ESS and DR in MG. • This paper points out how to apply ESS and DR to MG operators. • This paper sets the stopping conditions to a chaotic process for efficiency. • Adaptive weight is added in this paper to reduce the number of iterations. [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]

With the development of the integrated energy Internet, energy structure optimization and emission reduction have led to higher requirements for developing various energy sources to enable coordinated and sustainable development. However, data-mining methods are rarely used to study the coordination of multi-energy generation in published research results. In this study, from the perspective of power industry emissions, coordinated generation of various energy sources, and balance of power generation and consumption, a data-mining algorithm was used to analyze the development of thermal power, hydropower, wind power, waste heat, gas, and other power sources. The chi-square automatic interaction detection tree (CHAID), logistic regression, and two-step clustering methods were applied. The results show that: a) CO 2 and SO 2 emissions were mainly affected by thermal power generation, whereas NO x emissions were jointly affected by thermal power, garbage power, and gas-fired power, and the emissions of various pollutants increased with an increase in power consumption. The optimal power-generation scheme under minimum emission can be obtained. b) There was a strong correlation between thermal power generation and residential electricity consumption, and renewable energy (wind energy, photovoltaic, hydropower) exhibited the highest correlation with the electricity consumption of the tertiary industry, which indicates that renewable energy generation can be promoted by managing electricity consumption in the tertiary industry. c) When the electricity demand of all users was small, the proportion of renewable energy power generation increased; in contrast, the thermal power generation was larger. This indicates the importance of improving the sustainable and stable power supply of renewable energy. This study provides a data analysis model for the coordinated development of multiple energies, which will contribute to the decision-making basis for controlling power emissions, improving the utilization rate of renewable energy, and optimizing the energy structure. [Display omitted] • Rich data (8760 h): multi-energy power generation, consumption and emission. • Introduces a complete big data analysis framework to study multi-energy generation. • Three data mining methods are used to analyze power supply, demand and emission. • The conclusions of this paper have a positive effect on energy structure. [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]

Biomass energy has extensive application foreground in power generation because of its economic and social benefits. Aiming at selecting the best biomass fuel procurement and storage modes in Jilin province, this paper proposes an evaluation model with linguistic hesitant fuzzy sets (LHFSs), which is good at expressing the hesitancy, inconsistency and uncertainty of subjective evaluation information. Based on cloud model and three entropy principle, the transformation between linguistic hesitant fuzzy sets and comprehensive cloud model is realized. In order to eliminate interdependency among criterion, a new Choquet integral aggregation operator based on the Sigmoid function is proposed. According to the expectation values, the ranking result of aggregated LHFSs is obtained. At last, an illustrative example of modes evaluation in Jilin province is taken in the present study to make the proposed method comprehensible. The result shows that mode 3: farmer + middleman + third-party logistics + power plant, is the most reasonable mode for Jilin biomass energy development. From the perspective of supply chain, middlemen and third party logistics can reduce the procurement and transportation cost in the supply chain, which are two bigger influential factors of fuel cost. Middlemen are not only a bridge between plants and farmers, but also the operator in the supply chain. Through the analysis of the result, suggestions from the perspective of power plants and the government are given. • Linguistic hesitant fuzzy sets are used for evaluating biomass power generation fuel procurement and storage modes. • A multi-criteria decision-making method based on cloud model and Choquet integral is proposed. • A new linguistic hesitant fuzzy aggregation operator based on Choquet integral and the Sigmoid function is presented. • "farmer, middleman, third-party logistics and power plant" is the best mode for Jilin biomass power development. [ABSTRACT FROM AUTHOR]

Abstract In recent years, the wind curtailment has become a serious problem in China and the government are actively seeking solutions to deal with this energy loss. Therefore, the use of non-grid-connected wind power has received great attention since it can be supplied to local end users equipped with energy storage and then mitigate wind curtailment. Since the non-grid-connected wind power and local power load have to confront dramatic power fluctuations, a hybrid energy storage system (HESS) including batteries and supercapacitors is applied. This paper proposes a multi-objective optimization model of HESS configuration in non-grid-connected wind power/energy storage/local user system. In this model, two decision variables, numbers of batteries and supercapacitors, are determined based on the objective of annual profit maximization and wind curtailment rate minimization. To solve this model, a non-dominated sorting genetic algorithm II (NSGA-II) is employed to obtain Pareto front and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) technique is applied to select the optimal solution from Pareto solutions. Finally, a wind farm in Hebei province is studied and discussed. A scenario analysis, a sensitivity analysis and a comparative analysis are performed to show the advantages of the proposed model. Highlights • The local consumption of non-grid-connected wind is a solution to wind curtailment. • Hybrid energy storage increases the profit and reduces the wind curtailment rate. • NSGA-II-VIKOR is effective to determine the optimal energy storage capacity. • Energy storage cost and objective weight both influence the optimization result. [ABSTRACT FROM AUTHOR]