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Optimal Operation Strategy Analysis with scenario generation Method Based on Principal Component Analysis,Density Canopy,and K-medoids for Integrated Energy Systems

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 89-100页

作者： Bingtuan Gao Yunyu Zhu Yuanmei Li School of Electrical Engineering Southeast UniversityNanjingChina

The operation of integrated energy systems(IESs)is confronted with great challenges for increasing penetration rate of renewable energy and growing complexity of energy *** generation is one of ordinary methods to alleviate the system uncertainties by extracting several typical scenarios to represent the original high-dimensional *** paper proposes a novel representative scenario generation method based on the feature extraction of panel *** original high-dimensional data are represented by an aggregated indicator matrix using principal component analysis to preserve temporal ***,the aggregated indicator matrix is clustered by an algorithm combining density canopy and *** with the proposed scenario generation method,an optimal operation model of IES is established,where the objective is to minimize the annual operation costs considering carbon trading ***,case studies based on the data of Aachen,Germany in 2019 are *** results indicate that the adjusted rand index(ARI)and silhouette coefficient(SC)of the proposed method are 0.6153 and 0.6770,respectively,both higher than the traditional methods,namely K-medoids,K-means++,and density-based spatial clustering of applications with noise(DBSCAN),which means the proposed method has better *** error between optimal operation results of the IES obtained by the proposed method and all-year time series benchmark value is 0.1%,while the calculation time is reduced from 11029 s to 188 s,which verifies that the proposed method can be used to optimize operation strategy of IES with high efficiency without loss of accuracy.

关键词： scenario generation principal component analysis(PCA) density canopy K-medoids integrated energy system

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Forecasting scenario generation for Multiple Wind Farms Considering Time-series Characteristics and Spatial-temporal Correlation

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Journal of Modern Power Systems and Clean Energy 2021年第4期9卷 837-848页

作者： Qingyu Tu Shihong Miao Fuxing Yao Yaowang Li Haoran Yin Ji Han Di Zhang Weichen Yang State Key Laboratory of Advanced Electromagnetic Engineering and Technology Hubei Electric Power Security High Efficiency Key Laboratory School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhanChina Department of Electrical Engineering Tsinghua UniversityBeijingChina

scenario forecasting methods have been widely studied in recent years to cope with the wind power uncertainty problem. The main difficulty of this problem is to accurately and comprehensively reflect the time-series characteristics and spatial-temporal correlation of wind power generation. In this paper, the marginal distribution model and the dependence structure are combined to describe these complex characteristics. On this basis, a scenario generation method for multiple wind farms is proposed. For the marginal distribution model, the autoregressive integrated moving average-generalized autoregressive conditional heteroskedasticity-t (ARIMA-GARCH-t) model is proposed to capture the time-series characteristics of wind power generation. For the dependence structure, a time-varying regular vine mixed Copula (TRVMC) model is established to capture the spatial-temporal correlation of multiple wind farms. Based on the data from 8 wind farms in Northwest China, sufficient scenarios are generated. The effectiveness of the scenarios is evaluated in 3 aspects. The results show that the generated scenarios have similar fluctuation characteristics, autocorrelation, and crosscorrelation with the actual wind power sequences.

关键词： scenario generation wind farm regular vine Copula spatial-temporal correlation time-series characteristics

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Stochastic optimization based on a novel scenario generation method for midstream and downstream petrochemical supply chain

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Chinese Journal of Chemical Engineering 2020年第3期28卷 815-823页

作者： Peixian Zang Guoming Sun Yongming Zhao Yiqing Luo Xigang Yuan State Key Laboratory of Chemical Engineering Collaborative Innovation of Chemical Science and Engineering(Tianjin)School of Chemical Engineering and TechnologyTianjin UniversityTianjin 300072China

A two-stage mixed integer linear programming model(MILP)incorporating a novel method of stochastic scenario generation was proposed in order to optimize the economic performance of the synergistic combination of midstream and downstream petrochemical supply *** uncertainty nature of the problem intrigued the parameter estimation,which was conducted through discretizing the assumed probability distribution of the stochastic *** modeling framework was adapted into a real-world scale of petrochemical enterprise and fed into optimization *** between the deterministic model and stochastic model were discussed,and the influences of the cost components on the overall profit were *** computational results demonstrated the rationality of using reasonable numbers of scenarios to approximate the stochastic optimization problem.

关键词： Petroleum Two-stage Optimization scenario generation Parameter estimation

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Hybrid scenario generation Method for Stochastic Virtual Bidding in Electricity Market

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CSEE Journal of Power and Energy Systems 2021年第6期7卷 1312-1321页

作者： Dongliang Xiao Wei Qiao the School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China the Power and Energy Systems Laboratory Department of Electrical and Computer EngineeringUniversity of Nebraska-LincolnLincolnNE 68588-0511 USA

Stochastic optimization can be used to generate optimal bidding strategies for virtual bidders in which the uncertain electricity prices are represented by using *** paper proposes a hybrid scenario generation method for electricity price using a seasonal autoregressive integrated moving average(SARIMA)model and historical *** electricity price spikes are first identified by using an outlier detection ***,the historical data are decomposed into base and spike ***,the base and spike component scenarios are generated by using the SARIMA-and historical data-based methods,***,the electricity price scenarios are obtained by combining the base and spike component *** studies are carried out for a virtual bidder in the PJM electricity market to validate the proposed *** optimal bidding strategies of the virtual bidder are generated by solving a stochastic optimization problem using the electricity price scenarios generated by the proposed method,the SARIMA method,and a historical data-based method,*** study results show that the proposed method is better than the SARIMA method in preserving statistical properties of the electricity price in the generated scenarios and is better than the historical data-based method in predicting the future trend of the electricity price and,therefore,can help the virtual bidder earn more profit in the electricity market.

关键词： Electricity market electricity price scenario generation stochastic optimization virtual bidding

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Optimal scenario generation Algorithm for Multi-objective Optimization Operation of Active Distribution Network

Optimal Scenario Generation Algorithm for Multi-objective Op...

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第36届中国控制会议

作者： Lizhen Wu Libo Jiang Xiaohong Hao College of Electrical and Information Engineering Lanzhou University of Technology

ISBN: (纸本)9781538629185

Considering the uncertain and stochastic of intermittent distributed generations（DGS） in active distribution network（ADN）, a scenario method using Wasserstein distance metric and K-means cluster scenes reduction technology to generate optimal scene is proposed in this paper. So the stochastic problem is transformed into a deterministic problem. The multi-scenario tree models of wind-photovoltaic-load are built. A multi-objective optimization mathematical model of active distribution network containing intermittent DGS is established, which includes objectives that are the annual profits, the total active power losses and the voltage deviations of the bus, and considering active management characteristic. The tabu search artificial bee colony algorithm is used to solve the optimization problems. The simulation results show that the optimal scenes based on the Wasserstein distance indicators and K-means cluster technique reflect the random feature of distributed generation active power output more accurately. Finally, the simulation analysis of IEEE33-bus distribution test system is carried out to verify the effectiveness and feasibility of the proposed method.

关键词： Active distribution network scenario generation Wasserstein distance K-means cluster Tabu search artificial bee colony algorithm

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scenario generations for Renewable Energy Sources and Loads Based on Implicit Maximum Likelihood Estimations

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1563-1575页

作者： Wenlong Liao Birgitte Bak-Jensen Jayakrishnan Radhakrishna Pillai Zhe Yang Yusen Wang Kuangpu Liu AAU Energy Aalborg UniversityAalborgDenmark School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyStockholmSweden

scenario generations for renewable energy sources and loads play an important role in the stable operation and risk assessment of integrated energy *** paper proposes a deep generative network based method to model time-series curves,e.g.,power generation curves and load curves,of renewable energy sources and loads based on implicit maximum likelihood estimations(IMLEs),which can generate realistic scenarios with similar patterns as real *** training the model,any number of new scenarios can be obtained by simply inputting Gaussian noises into the data generator of *** proposed approach does not require any model assumptions or prior knowledge of the form in the likelihood function being made during the training process,which leads to stronger applicability than explicit density model based *** extensive experiments show that the IMLEs accurately capture the complex shapes,frequency-domain characteristics,probability distributions,and correlations of renewable energy sources and ***,the proposed approach can be easily generalized to scenario generation tasks of various renewable energy sources and loads by fine-tuning parameters and structures.

关键词： Renewable energy source scenario generation implicit maximum likelihood estimation(IMLE) deep learning generative network

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Genetic Algorithm‑Based SOTIF scenario Construction for Complex Traffic Flow

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Automotive Innovation 2023年第4期6卷 531-546页

作者： Shulian Zhao Jianli Duan Siyu Wu Xinyu Gu Chuzhao Li Kai Yin Hong Wang The School of Vehicle and Mobility Tsinghua UniversityBeijingChina Yanshan University QinhuangdaoChina China Automotive Engineering Research Institute Co. LtdChongqingChina Beijing Jiaotong University BeijingChina

The Safety of The Intended Functionality(SOTIF)challenge represents the triggering condition by elements of a specific scenario and exposes the function limitation of an autonomous vehicle(AV),which leads to *** for operationcontent-related features,the scenario is similar to AVs’SOTIF research and ***,scenario generation is a significant topic for SOTIF verification and validation procedure,especially in the simulation testing of ***,in this paper,a well-designed scenario architecture is first defined,with comprehensive scenario elements,to present SOTIF trigger ***,considering complex traffic disturbance as trigger conditions,a novel SOTIF scenario generation method is *** indicator,also known as scenario Potential Risk,is defined as the combination of the safety control intensity and the prior collision *** indicator helps identify critical scenarios in the proposed *** addition,the corresponding vehicle motion models are established for general straight roads,curved roads,and safety assessment *** for the traffic participants’motion model,it is designed to construct the key dynamic *** efficiently search for critical scenarios with the trigger of complex traffic flow,this scenario is encoded as genes and it is regenerated through selection,mutation,and crossover iteration processes,known as the Genetic Algorithm(GA).Experimental results show that the GA-based method could efficiently construct diverse and critical traffic scenarios,contributing to the construction of the SOTIF scenario library.

关键词： SOTIF triggers conditions scenario generation Genetic algorithm Complex traffic disturbance

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SceGAN:A method for generating autonomous vehicle cut-in scenarios on highways based on deep learning

Journal of Intelligent and Connected Vehicles

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Journal of Intelligent and Connected Vehicles 2023年第4期6卷 264-274页

作者： Lan Yang Jiaqi Yuan Xiangmo Zhao Shan Fang Zeyu He Jiahao Zhan Zhiqiang Hu Xia Li School of Information Engineering Chang’an UniversityXi’an 710064China

With the increasing level of automation of autonomous vehicles,it is important to conduct comprehensive and extensive testing before releasing autonomous vehicles into the *** public road and closed-field testing failed to meet the requirements of high testing efficiency and scenario ***,scenario-based autonomous vehicle simulation testing has *** scenarios form the basis of simulation *** additional scenarios from an existing scenario library is a significant *** the scenarios of a proceeding vehicle cutting into an adjacent lane on highways as an example,based on an autoencoder and a generative adversarial network(GAN),a method that combines Transformer to capture the features of a long-time series,called SceGAN,is proposed to model and generate scenarios of autonomous vehicles on *** evaluation system is established to analyze the reliability of SceGAN using discriminative and predictive scores and further evaluate the effect of scenario generation in terms of similarity and *** showed that compared with TimeGAN and AEGAN,SceGAN is superior in data fidelity and availability,and their similarity increased by 27.22%and 21.39%,*** coverage increased from 79.84%to 93.98%as generated scenarios increased from 2,547 to 50,000,indicating that the proposed method has a strong generalization capability for generating multiple trajectories,providing a basis for generating test scenarios and promoting autonomous vehicle testing.

关键词： scenario generation autonomous vehicles testing cut-in transformer generative adversarial network(GAN)

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A scenario-Based Robust Transmission Network Expansion Planning Method for Consideration of Wind Power Uncertainties

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CSEE Journal of Power and Energy Systems 2016年第1期2卷 11-18页

作者： Jinghua Li Liu Ye Yan Zeng Hua Wei School of Electrical Engineering Guangxi UniversityNanning 530004China

This paper uses a novel scenario generation method for tackling the uncertainties of wind power in the transmission network expansion planning(TNEP)problem.A heuristic moment matching(HMM)method is first applied to generate the typical scenarios for capturing the stochastic features of wind power,including expectation,standard deviation,skewness,kurtosis,and correlation of multiple wind ***,based on the typical scenarios,a robust TNEP problem is presented and *** solution of the problem is robust against all the scenarios that represent the stochastic features of wind *** test systems are used to verify the HMM method and is compared against Taguchi’s Orthogonal Array(OA)*** simulation results show that the HMM method has better performance than the OA method in terms of the trade-off between robustness and ***,the main factors influencing the planning scheme are studied,including the number of scenarios,wind farm capacity,and penalty factors,which provide a reference for system operators choosing parameters.

关键词： Heuristic moment matching method robust optimization scenario generation transmission network expansion planning uncertainty wind power

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Copula-Based Monte Carlo scenarios generation Method for STOPF Problem

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Electricity 2014年第2期25卷 41-50页

作者： Li Jinghua Lan Fei College of Electric Engineering Guangxi University

This paper introduces a Monte Carlo scenario generation method based on copula theory for the stochastic optimal power flow （STOPF） problem with wind power. By using copula theory, the scenarios are simulated from multivariable joint distribution but only from their dependency matrix. Hence, the scenarios generated by proposed method can contain flail statistical information of multivariate. Here, the details of simu- lating scenarios for multi-wind-farm are explained with four steps： determine margin of one wind farm, fit the copulas, choose optimal copulas and simulate scenarios by Mote Carlo. Moreover, the producing process of scenarios is demonstrated by two adjacent actual wind farms in China. With the scenarios, the STOPF is con- verted into the same amount deterministic sub OPF models which can be solved by available technology per- fectly. Results using copula theory are compared against results from history samples based on two designs： IEEE 30-bus and IEEE 118-bus systems. The comparison results prove the accuracy of the proposed methodology.

关键词： multi-wind-farm correlation relationship copula function scenario generation stochastic programming

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