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Wind power operation capacity credit assessment considering energy storage

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Global Energy Interconnection 2022年第1期5卷 1-8页

作者： Wenhui Shi Jixian Qu weisheng wang China Electric Power Research Institute State Key Laboratory of Operation and Control of Renewable Energy&Storage SystemsBeijing 100192P.R.China

Research on wind power capacity credit at the operational level plays an important role in power system dispatching.With the popularity of energy storage devices,it is increasingly necessary to study the impact of energy storage devices on wind power operational capacity credit.The definition of wind power operational capacity credit is given.The available capacity model of different generators and the charging and discharging model of the energy storage are established.Based on the above model,the evaluation method of wind power operation credible capacity considering energy storage devices is proposed.The influence of energy storage on the wind power operation credible capacity is obtained by case study,which is of great help for the power system dispatching operation and wind power accommodation.

关键词： Wind power Operation capacity credit Energy storage Operation reliability

Large-Scale Renewable Energy Transmission by HVDC: Challenges and Proposals

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Engineering 2022年第12期19卷 252-267页

作者： weisheng wang Guanghui Li Jianbo Guo State Key Laboratory of the Operation and Control of Renewable Energy and Storage Systems China Electric Power Research InstituteBeijing 100192China

Renewable energy transmission by high-voltage direct current(HVDC)has attracted increasing attention for the development and utilization of large-scale renewable energy under the Carbon Peak and Carbon Neutrality Strategy in China.High-penetration power electronic systems(HPPESs)have gradually formed at the sending end of HVDC transmission.The operation of such systems has undergone profound changes compared with traditional power systems dominated by synchronous generators.New stability issues,such as broadband oscillation and transient over-voltage,have emerged,causing tripping accidents in large-scale renewable energy plants.The analysis methods and design principles of traditional power systems are no longer suitable for HPPESs.In this paper,the mechanisms of broadband oscillation and transient over-voltage are revealed,and analytical methods are proposed for HPPESs,including small-signal impedance analysis and electromagnetic transient simulation.Validation of the theoretical research has been accomplished through its application in several practical projects in north,northwest,and northeast region of China.Finally,suggestions for the construction and operation of the future renewable-energy-dominated power system are put forward.

关键词： Broadband oscillation Future power system HVDC transmission Renewable energy generation Transient over-voltage

Simultaneous Optimization of Renewable Energy and Energy Storage Capacity with the Hierarchical Control

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CSEE Journal of Power and Energy Systems 2022年第1期8卷 95-104页

作者： Zhaodi Shi weisheng wang Yuehui Huang Pai Li Ling Dong State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems China Electric Power Research InstituteBeijing 100192China State Grid Qinghai Electric Power Company Xining 810000China

To fully consider the complementary role of different energy sources and reduce the curtailment of renewable energy(RE)in high RE penetration systems,a hierarchical optimization algorithm is proposed to simultaneously optimize the capacity of RE generation and energy storage systems(ESS).Time sequence simulation(TSS)technology is adopted to fully consider the regional RE resource characteristics and make the model more reliable.An optimization model for evaluating ESS capacity is established at a lower level.To overcome the high dimensional complexity of time sequence data,this paper re-formulates this sub-model as a consensus problem,which can be solved by a distributed approach to minimize the system’s total investment costs.At the upper level,the model for assessing the proportion of wind and solar capacity is developed by maximizing the RE generation.The golden section Fibonacci tree optimization(GSFTO)algorithm is utilized to improve the efficiency and solution accuracy.The results show that the algorithm and model are feasible and applicable for the identified purposes,which can provide a useful guidance for the development of power generation and the energy storage capacity in high RE penetration systems.

关键词： Consensus problem energy storage planning renewable time sequence simulation(TSS)

Wind and Photovoltaic Power Time Series Data Aggregation Method Based on an Ensemble Clustering and Markov Chain

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CSEE Journal of Power and Energy Systems 2022年第3期8卷 757-768页

作者： Jingxin Jin Lin Ye Jiachen Li Yongning Zhao Peng Lu weisheng wang Xuebin wang College of Information and Electrical Engineering China Agricultural UniversityBeijing100083China School of Engineering Cardiff UniversityCF243AAUK China Electric Power Research Institute Beijing100192China State Grid Qinghai Electric Power Company Xining810000China

Reducing the input wind and photovoltaic power time series data can improve the efficiency of time sequential simulations.In this paper,a wind and photovoltaic power time series data aggregation method based on an ensemble clustering and Markov chain(ECMC)is proposed.The ECMC method can effectively reduce redundant information in the data.First,the wind and photovoltaic power time series data were divided into scenarios,and ensemble clustering was used to cluster the divided scenarios.At the same time,the Davies-Bouldin Index(DBI)is adopted to select the optimal number of clusters.Then,according to the temporal correlation between wind and photovoltaic scenarios,the wind and photovoltaic clustering results are merged and reduced to form a set of combined typical day scenarios that can reflect the characteristics of historical data within the calculation period.Finally,based on the Markov Chain,the state transition probability matrix of various combined typical day scenarios is constructed,and the aggregation state sequence of random length is generated,and then,the combined typical day scenarios of wind and photovoltaic were sampled in a sequential one-way sequence according to the state sequence and then are built into a representative wind and photovoltaic power time series aggregation sequence.A provincial power grid was chosen as an example to compare the multiple evaluation indexes of different aggregation methods.The results show that the ECMC aggregation method improves the accuracy and efficiency of time sequential simulations.

关键词： Aggregation method ensemble clustering markov chain time sequential simulations wind and photovoltaic power time series data

Regional wind power forecasting model with NWP grid dataoptimized

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Frontiers in Energy 2017年第2期11卷 175-183页

作者： Zhao wang weisheng wang Bo wang State Key Laboratory of Operation and Control of Renewable Energy &Storage Systems China Electric Power Research Institute Beijing100192 China Department of Electrical Engineering TsinghuaUniversity Beijing 100084 China State Key Laboratory of Operation and Control of Renewable Energy &Storage Systems China Electric Power Research Institute Beijing100192 China

Unlike the traditional fossil energy, wind, as the clean renewable energy, can reduce the emission of the greenhouse gas. To take full advantage of the environmental benefits of wind energy, wind power forecasting has to be studied to overcome the troubles brought by the variable nature of wind. Power forecasting for regional wind farm groups is the problem that many power system operators care about. The high-dimensional feature sets with redundant information are frequently encountered when dealing with this problem. In this paper, two kinds of feature set construction methods are proposed which can achieve the proper feature set either by selecting the subsets or by transforming the original variables with specific combinations. The former method selects the subset according to the criterion of minimal-redundancy-maximal-relevance (mRMR), while the latter does so based on the method of principal component analysis (PCA). A locally weighted learning method is also proposed to utilize the processed feature set to produce the power forecast results. The proposed model is simple and easy to use with parameters optimized automatically. Finally, a case study of 28 wind farms in East China is provided to verify the effectiveness of the proposed method.

关键词： regional wind power forecasting feature set minimal-redundancy-maximal-relevance (mRMR) principal component analysis (PCA) locally weighted learning model

Forecasted Scenarios of Regional Wind Farms Based on Regular Vine Copulas

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Journal of Modern Power Systems and Clean Energy 2020年第1期8卷 77-85页

作者： Zhao wang weisheng wang Chun Liu Bo wang State Key Laboratory of Operation and Control of Renewable Energy&Storage Systems China Electric Power Research InstituteBeijingChina Department of Electrical Engineering Tsinghua UniversityBeijingChina

Owing to the uncertainty and volatility of wind energy,forecasted wind power scenarios with proper spatio-temporal correlations are needed in various decision-making problems involving power systems.In this study,forecasted scenarios are generated from an estimated multi-variate distribution of multiple regional wind farms.According to the theory of copulas,marginal distributions and the dependence structure of multi-variate distribution are modeled through the proposed distance-weighted kernel density estimation method and the regular vine(R-vine)copula,respectively.Owing to the flexibility of decomposing correlations of high dimensions into different types of pair-copulas,the R-vine copula provides more accurate results in describing the complicated dependence of wind power.In the case of 26 wind farms located in East China,highquality forecasted scenarios as well as the corresponding probabilistic forecasting and point forecasting results are obtained using the proposed method,and the results are evaluated using a comprehensive verification framework.

关键词： Forecasted scenarios wind power distanceweighted kernel density estimation(KDE) regular vine(R-vine)copula spatio-temporal correlation

A fewest-turn-and-shortest path algorithm based on breadth-first search

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Geo-Spatial Information Science 2014年第4期17卷 201-207页

作者： Yan ZHOU weisheng wang Di HE Zhe wang School of Resources and Environment University of Electronic Science and Technology of ChinaChengduP.R.China

Many cognitive studies have indicated that the path simplicity may be as important as its distance travelled.However,the optimality of paths for current navigation system is often judged purely on the distance travelled or time cost,and not the path simplicity.To balance these factors,this paper presented an algorithm to compute a path that not only possesses fewest turns but also is as short as possible by utilizing the breadth-first-search strategy.The proposed algorithm started searching from a starting point,and expanded layer by layer through searching zero-level reachable points until the endpoint is found,and then deleted unnecessary points in the reverse direction.The forward searching and backward cleaning strategies were presented to build a hierarchical graph of zero-level reachable points,and form a fewestturn-path graph(G^(*)).After that,a classic Dijkstra shortest path algorithm was executed on the G^(*) to obtain a fewestturn-and-shortest path.Comparing with the shortest path in Baidu map,the algorithm in this work has less than half of the turns but the nearly same length.The proposed fewest-turn-and-shortest path algorithm is proved to be more suitable for human beings according to human cognition research.

关键词： fewest-turn-and-shortest path breadth-first search hierarchical graph

Extreme scenario extraction of a grid with large scale wind power integration by combined entropy-weighted clustering method

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Global Energy Interconnection 2020年第2期3卷 140-148页

作者： Kui Luo Wenhui Shi weisheng wang State Key Laboratory of Operation and Control of Renewable Energy&Storage Systems China Electric Power Research InstituteHaidian DistrictBeijing 100192P.R.China

Large-scale integration of wind power into a power system introduces uncertainties to its operation and planning,making the power system operation scenario highly diversified and variable.In conventional power system planning,some key operation modes and most critical scenarios are typically analyzed to identify the weak and high-risk points in grid operation.While these scenarios may not follow traditional empirical patterns due to the introduction of large-scale wind power.In this paper,we propose a weighted clustering method to quickly identify a system’s extreme operation scenarios by considering the temporal variations and correlations between wind power and load to evaluate the stability and security for system planning.Specifically,based on an annual time-series data of wind power and load,a combined weighted clustering method is used to pick the typical scenarios of power grid operation,and the edge operation points far from the clustering center are extracted as the extreme scenarios.The contribution of fluctuations and capacities of different wind farms and loads to extreme scenarios are considered in the clustering process,to further improve the efficiency and rationality of the extreme-scenario extraction.A set of case studies was used to verify the performance of the method,providing an intuitive understanding of the extreme scenario variety under wind power integration.

关键词： Wind power Load Weighted clustering Entropy weight Extreme scenario extraction

Integration of optical and SAR remote sensing images for crop-type mapping based on a novel object-oriented feature selection method

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International Journal of Agricultural and Biological Engineering 2020年第1期13卷 178-190页

作者： Jintian Cui Xin Zhang weisheng wang Lei wang State Key Laboratory of Remote Sensing Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Xinjiang Institute of Ecology and Geography Chinese Academy of SciencesUrumqi 830011China

Remote sensing is an important technical means to investigate land resources.Optical imagery has been widely used in crop classification and can show changes in moisture and chlorophyll content in crop leaves,whereas synthetic aperture radar(SAR)imagery is sensitive to changes in growth states and morphological structures.Crop-type mapping with a single type of imagery sometimes has unsatisfactory precision,so providing precise spatiotemporal information on crop type at a local scale for agricultural applications is difficult.To explore the abilities of combining optical and SAR images and to solve the problem of inaccurate spatial information for land parcels,a new method is proposed in this paper to improve crop-type identification accuracy.Multifeatures were derived from the full polarimetric SAR data(GaoFen-3)and a high-resolution optical image(GaoFen-2),and the farmland parcels used as the basic for object-oriented classification were obtained from the GaoFen-2 image using optimal scale segmentation.A novel feature subset selection method based on within-class aggregation and between-class scatter(WA-BS)is proposed to extract the optimal feature subset.Finally,crop-type mapping was produced by a support vector machine(SVM)classifier.The results showed that the proposed method achieved good classification results with an overall accuracy of 89.50%,which is better than the crop classification results derived from SAR-based segmentation.Compared with the ReliefF,mRMR and LeastC feature selection algorithms,the WA-BS algorithm can effectively remove redundant features that are strongly correlated and obtain a high classification accuracy via the obtained optimal feature subset.This study shows that the accuracy of crop-type mapping in an area with multiple cropping patterns can be improved by the combination of optical and SAR remote sensing images.

关键词： crop-type mapping synthetic aperture radar(SAR) high-resolution remote sensing image segmentation feature subset selection object-oriented classification