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 midst...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 chain.The uncertainty nature of the problem intrigued the parameter estimation,which was conducted through discretizing the assumed probability distribution of the stochastic parameters.The modeling framework was adapted into a real-world scale of petrochemical enterprise and fed into optimization computations.Comparisons between the deterministic model and stochastic model were discussed,and the influences of the cost components on the overall profit were analyzed.The computational results demonstrated the rationality of using reasonable numbers of scenarios to approximate the stochastic optimization problem.展开更多
Scenario generation is a critical step in stochastic programming for energy systems applications,where accurate representation of uncertainty directly impacts the decision quality.Normalizing flows(NFs),a class of inv...Scenario generation is a critical step in stochastic programming for energy systems applications,where accurate representation of uncertainty directly impacts the decision quality.Normalizing flows(NFs),a class of invertible deep generative models,offer flexibility in learning complex distributions by maximizing the likelihood,but often suffer from limited accuracy in reproducing key statistical properties of real-world data.In this work we propose a moments-informed Normalizing Flows(MI-NF)framework,in which moment constraints are incorporated into the NF training process to improve the accuracy of scenario-based probabilistic forecasts.thermore,Fur-Gaussian Processes(GPs)are employed to adaptively determine the moment regularization weight.Case studies on the open-access dataset of the Global Energy Forecasting Competition 2014 demonstrate that scenarios generated by the MI-NF model achieve over 40%lower mean absolute error on the testing set.When applied within a stochastic programming framework for a local electricity-hydrogen market,the improved scenario accuracy leads to more cost-effective and robust operational decisions under uncertainty.展开更多
With the growing penetration of renewable energysources in power systems, it becomes increasingly important tocharacterize their inherent variability and uncertainty. Scenariogeneration is a key approach to provide a ...With the growing penetration of renewable energysources in power systems, it becomes increasingly important tocharacterize their inherent variability and uncertainty. Scenariogeneration is a key approach to provide a series of possible powerscenarios in the future for the system planner and operator tomake decisions. In this paper, a data-driven method is presentedfor renewable scenario generation using stable and controllablegenerative adversarial networks with transparent latent space(ctrl-GANs). The machine learning based algorithm can capturethe nonlinear and dynamic renewable patterns without the needfor modeling assumptions and complicated sampling techniques.The orthogonal regularization and spectral normalization areadopted to improve the training stabilization of the GAN model.To control the generation process, a relationship is built betweenfeatures of the generated scenarios and latent vectors on themanifold. Moreover, several new metrics for GANs are used toevaluate the quality of the scenarios. The proposed approachis applied to generate realistic time series data of wind andphotovoltaic power. The results demonstrate that our methodhas a better performance on numerical stabilization and is ableto control the generation process with latent space.展开更多
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 c...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.展开更多
The operation of integrated energy systems(IESs)is confronted with great challenges for increasing penetration rate of renewable energy and growing complexity of energy forms.Scenario generation is one of ordinary met...The operation of integrated energy systems(IESs)is confronted with great challenges for increasing penetration rate of renewable energy and growing complexity of energy forms.Scenario generation is one of ordinary methods to alleviate the system uncertainties by extracting several typical scenarios to represent the original high-dimensional data.This paper proposes a novel representative scenario generation method based on the feature extraction of panel data.The original high-dimensional data are represented by an aggregated indicator matrix using principal component analysis to preserve temporal variation.Then,the aggregated indicator matrix is clustered by an algorithm combining density canopy and K-medoids.Together 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 cost.Finally,case studies based on the data of Aachen,Germany in 2019 are performed.The 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 accuracy.The 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.展开更多
Scenario generations for renewable energy sources and loads play an important role in the stable operation and risk assessment of integrated energy systems.This paper proposes a deep generative network based method to...Scenario generations for renewable energy sources and loads play an important role in the stable operation and risk assessment of integrated energy systems.This 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 ones.After training the model,any number of new scenarios can be obtained by simply inputting Gaussian noises into the data generator of IMLEs.The 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 methods.The extensive experiments show that the IMLEs accurately capture the complex shapes,frequency-domain characteristics,probability distributions,and correlations of renewable energy sources and loads.Moreover,the proposed approach can be easily generalized to scenario generation tasks of various renewable energy sources and loads by fine-tuning parameters and structures.展开更多
Scenario generations of cooling,heating,and power loads are of great significance for the economic operation and stability analysis of integrated energy systems.In this paper,a novel deep generative network is propose...Scenario generations of cooling,heating,and power loads are of great significance for the economic operation and stability analysis of integrated energy systems.In this paper,a novel deep generative network is proposed to model cooling,heating,and power load curves based on generative moment matching networks(GMMNs)where an auto-encoder transforms highdimensional load curves into low-dimensional latent variables and the maximum mean discrepancy represents the similarity metrics between the generated samples and the real samples.After training the model,the new scenarios are generated by feeding Gaussian noises to the scenario generator of the GMMN.Unlike the explicit density models,the proposed GMMN does not need to artificially assume the probability distribution of the load curves,which leads to stronger universality.The simulation results show that the GMMN not only fits the probability distribution of multiclass load curves very well,but also accurately captures the shape(e.g.,large peaks,fast ramps,and fluctuation),frequency-domain characteristics,and temporal-spatial correlations of cooling,heating,and power loads.Furthermore,the energy consumption of generated samples closely resembles that of real samples.展开更多
Stochastic optimization can be used to generate optimal bidding strategies for virtual bidders in which the uncertain electricity prices are represented by using scenarios.This paper proposes a hybrid scenario generat...Stochastic optimization can be used to generate optimal bidding strategies for virtual bidders in which the uncertain electricity prices are represented by using scenarios.This paper proposes a hybrid scenario generation method for electricity price using a seasonal autoregressive integrated moving average(SARIMA)model and historical data.The electricity price spikes are first identified by using an outlier detection method.Then,the historical data are decomposed into base and spike components.Next,the base and spike component scenarios are generated by using the SARIMA-and historical data-based methods,respectively.Finally,the electricity price scenarios are obtained by combining the base and spike component scenarios.Case studies are carried out for a virtual bidder in the PJM electricity market to validate the proposed method.The 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,respectively.Case 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.展开更多
Safety testing is a crucial phase in the development of autonomous vehicles,with scenarios serving as the fundamental medium for these tests.The construction of scenarios that accurately reflect the real-world traffic...Safety testing is a crucial phase in the development of autonomous vehicles,with scenarios serving as the fundamental medium for these tests.The construction of scenarios that accurately reflect the real-world traffic behavior is a key challenge for autonomous vehicle testing technology,given the need for authenticity and effectiveness in testing.Addressing this,this study introduces a method for generating test scenarios specifically for intersections.This method involves decoupling the scenario into interactive and non-interactive layers.For the interactive elements,namely the traffic participants,this study proposes a novel approach to construct a human-like decision-making model adapted to intersections,with the OF-T-GAIL algorithm at its core.This algorithm,through a novel perception-decision representation and an omnidirectional coordinate system transformation,effectively reduces the cumulative error in the trajectory generation.Furthermore,this model is extended to multiple agents,allowing the bulk generation of intersection test scenario segments.These scenarios and the traffic participant(TP)models are validated using the SinD dataset,demonstrating the applicability and effectiveness of the approach in creating testing environments for autonomous driving systems.展开更多
The frequent outbreaks of crop diseases pose a serious threat to global agricultural production and food security.Data-driven forecasting models have emerged as an effective approach to support early warning and manag...The frequent outbreaks of crop diseases pose a serious threat to global agricultural production and food security.Data-driven forecasting models have emerged as an effective approach to support early warning and management,yet the lack of user-friendly tools for model development remains a major bottleneck.This study presents the Multi-Scenario Crop Disease Forecasting Modeling System(MSDFS),an open-source platform that enables end-to-end model construction-from multi-source data ingestion and feature engineering to training,evaluation,and deployment-across four representative scenarios:static point-based,static grid-based,dynamic point-based,and dynamic grid-based.Unlike conventional frameworks,MSDFS emphasizes modeling flexibility,allowing users to build,compare,and interpret diverse forecasting approaches within a unified workflow.A notable feature of the system is the integration of a weather scenario generator,which facilitates comprehensive testing of model performance and adaptability under extreme climatic conditions.Case studies corresponding to the four scenarios were used to validate the system,with overall accuracy(OA)ranging from 73%to 93%.By lowering technical barriers,the system is designed to serve plant protection managers and agricultural producers without advanced programming expertise,providing a practical modeling tool that supports the construction of smart plant protection systems.展开更多
As an effective carrier of integrated clean energy,the microgrid has attracted wide attention.The randomness of renewable energies such as wind and solar power output brings a significant cost and impact on the econom...As an effective carrier of integrated clean energy,the microgrid has attracted wide attention.The randomness of renewable energies such as wind and solar power output brings a significant cost and impact on the economics and reliability of microgrids.This paper proposes an optimization scheme based on the distributionally robust optimization(DRO)model for a microgrid considering solar-wind correlation.Firstly,scenarios of wind and solar power output scenarios are generated based on non-parametric kernel density estimation and the Frank-Copula function;then the generated scenario results are reduced by K-means clustering;finally,the probability confidence interval of scenario distribution is constrained by 1-norm and∞-norm.The model is solved by a column-and-constraint generation algorithm.Experimental studies are conducted on a microgrid system in Jiangsu,China and the obtained scheduling solution turned out to be superior under wind and solar power uncertainties,which verifies the effectiveness of the proposed DRO model.展开更多
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 m...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.展开更多
The generation of corner cases has become increasingly crucial for efficiently testing autonomous vehicles prior to road deployment.However,existing methods struggle to accommodate diverse testing requirements and oft...The generation of corner cases has become increasingly crucial for efficiently testing autonomous vehicles prior to road deployment.However,existing methods struggle to accommodate diverse testing requirements and often lack the ability to generalize to unseen situations,thereby reducing the convenience and usability of the generated scenarios.A method that facilitates easily controllable scenario generation for efficient autonomous vehicles(AV)testing with realistic and challenging situations is greatly needed.To address this,OmniTester is proposed as a multimodal Large Language Model(LLM)based framework that fully leverages the extensive world knowledge and reasoning capabilities of LLMs.OmniTester is designed to generate realistic and diverse scenarios within a simulation environment,offering a robust solution for testing and evaluating AVs.In addition to prompt engineering,OmniTester employs tools from Simulation of Urban Mobility to simplify the complexity of codes generated by LLMs.It further incorporates Retrieval-Augmented Generation and a self-improvement mechanism to enhance the LLM's understanding of scenarios,thereby increasing its ability to produce more realistic scenes.Experiment results demonstrated the controllability and realism of the proposed approaches in generating three types of challenging and complex scenarios.Additionally,OmniTester effectively reconstructs novel scenarios described in crash reports,driven by the generalization capability of LLMs.展开更多
To improve the efficiency of safety tests of driver-automation cooperation,a method for generating a scenario library is proposed that considers the probability of scenario occurrence and driver-handling challenges in...To improve the efficiency of safety tests of driver-automation cooperation,a method for generating a scenario library is proposed that considers the probability of scenario occurrence and driver-handling challenges in real driving situations.First,the original scenario data under cut-in conditions stored in a time series are extracted from the scenario data set.Then,a mathematical performance index is used to model the scenario and a significance function in terms of the occurrence frequency of the scenario,and the performance challenge between the driver and the vehicle is established.Next,the important scenario set is extracted from the original scenario set by constructing and optimizing a significance auxiliary function.Finally,the extracted important scenario sets are filtered by using the significance function values of the scenarios to generate a scenario library.Simulation results show that the proposed method for scenario library generation can effectively identify scenarios with potential adventure during driver-automation cooperation and thus accelerate safety tests compared with traditional methods.展开更多
Autonomous vehicles with self-evolution capabilities are expected to improve their performance through learning algorithms,to automatically adapt to the external environment.However,due to the infinity,complexity,and ...Autonomous vehicles with self-evolution capabilities are expected to improve their performance through learning algorithms,to automatically adapt to the external environment.However,due to the infinity,complexity,and variability of the actual traffic environment,it is necessary to develop quantitative representation indicators of scenario difficulty and generate targeted scenarios to ensure the evolution gradually,so as to quickly approach the performance limit of the algorithm.Therefore,this paper proposes a data-driven quantitative representation method of scenario difficulty.Specifically,the concept of environment agent is proposed,and a reinforcement learning method combined with mechanism knowledge is constructed for policy search to obtain an agent with an adversarial behavior.The model parameters of the environment agent at different stages in the training process are extracted to construct a policy group,and then agents with different adversarial intensities are obtained,which are used to realize data generation in different difficulty scenarios through the simulation environment.Finally,a data-driven scenario difficulty quantitative representation model is constructed,which is used to output the environment agent policy under different difficulties.Experimental results show the effectiveness of the proposed method.The result analysis shows that the proposed algorithm can generate reasonable and interpretable scenarios with high discrimination and can provide quantifiable difficulty representation without any expert logic rule design.Compared with the rule-based discrete scenario difficulty representation method,the proposed algorithm can achieve continuous difficulty representation.The video link is https://www.youtube.com/watch?v=GceGdqAm9Ys.展开更多
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 ge...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 farms.Then,based on the typical scenarios,a robust TNEP problem is presented and formulated.The solution of the problem is robust against all the scenarios that represent the stochastic features of wind power.Three test systems are used to verify the HMM method and is compared against Taguchi’s Orthogonal Array(OA)method.The simulation results show that the HMM method has better performance than the OA method in terms of the trade-off between robustness and economy.Additionally,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.展开更多
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 hazards.A...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 hazards.As for operationcontent-related features,the scenario is similar to AVs’SOTIF research and development.Therefore,scenario generation is a significant topic for SOTIF verification and validation procedure,especially in the simulation testing of AVs.Thus,in this paper,a well-designed scenario architecture is first defined,with comprehensive scenario elements,to present SOTIF trigger conditions.Then,considering complex traffic disturbance as trigger conditions,a novel SOTIF scenario generation method is developed.An indicator,also known as Scenario Potential Risk,is defined as the combination of the safety control intensity and the prior collision probability.This indicator helps identify critical scenarios in the proposed method.In addition,the corresponding vehicle motion models are established for general straight roads,curved roads,and safety assessment areas.As for the traffic participants’motion model,it is designed to construct the key dynamic events.To 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.展开更多
With the increasing level of automation of autonomous vehicles,it is important to conduct comprehensive and extensive testing before releasing autonomous vehicles into the market.Traditional public road and closed-fie...With the increasing level of automation of autonomous vehicles,it is important to conduct comprehensive and extensive testing before releasing autonomous vehicles into the market.Traditional public road and closed-field testing failed to meet the requirements of high testing efficiency and scenario coverage.Therefore,scenario-based autonomous vehicle simulation testing has emerged.Many scenarios form the basis of simulation testing.Generating additional scenarios from an existing scenario library is a significant problem.Taking 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 highways.An 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 coverage.Experiments 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%,respectively.The 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.展开更多
The development of micro-grid renewable energy system in China has achieved rapid growth in recent years,and the micro-grid renewable energy system has been drawing more and more attention by its flexible operation.Du...The development of micro-grid renewable energy system in China has achieved rapid growth in recent years,and the micro-grid renewable energy system has been drawing more and more attention by its flexible operation.Due to the randomness,fluctuation,uncertainty of the wind and photovoltaic renewable generation,abundant flexibility is required to meet the needs of safe,reliable and independent operation of the micro-grid energy system.We need to connect large energy systems to accept outside assistance when the micro-grid renewable energy system is short of adjustment capability.Independent operation and network operation will affect the economic benefit of the micro-grid energy system,so it is practically meaningful to study on the economic benefit evaluation of the micro-grid renewable energy system.This paper proposes a micro-grid energy system operation simulation model about wind and photovoltaic generation,the uncertainty of which is tackled based on the scenario generation and extraction techniques.Based on the proposed indices,the economic benefit could be evaluated by simulating the micro-grid energy system operation.The proposed method is validated by a real micro-grid energy system.展开更多
基金the support from the National Natural Science Foundation of China(No.21676183)State Key Laboratory of Chemical Engineering,Collaborative Innovation of Chemical Science and Engineering(Tianjin)。
文摘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 chain.The uncertainty nature of the problem intrigued the parameter estimation,which was conducted through discretizing the assumed probability distribution of the stochastic parameters.The modeling framework was adapted into a real-world scale of petrochemical enterprise and fed into optimization computations.Comparisons between the deterministic model and stochastic model were discussed,and the influences of the cost components on the overall profit were analyzed.The computational results demonstrated the rationality of using reasonable numbers of scenarios to approximate the stochastic optimization problem.
基金support from the Engineering&Physical Sciences Re-search Council(EPSRC),UK under the projects EP/T022930/1,EP/W003317/1 and EP/V051008/1,is gratefully acknowledged.
文摘Scenario generation is a critical step in stochastic programming for energy systems applications,where accurate representation of uncertainty directly impacts the decision quality.Normalizing flows(NFs),a class of invertible deep generative models,offer flexibility in learning complex distributions by maximizing the likelihood,but often suffer from limited accuracy in reproducing key statistical properties of real-world data.In this work we propose a moments-informed Normalizing Flows(MI-NF)framework,in which moment constraints are incorporated into the NF training process to improve the accuracy of scenario-based probabilistic forecasts.thermore,Fur-Gaussian Processes(GPs)are employed to adaptively determine the moment regularization weight.Case studies on the open-access dataset of the Global Energy Forecasting Competition 2014 demonstrate that scenarios generated by the MI-NF model achieve over 40%lower mean absolute error on the testing set.When applied within a stochastic programming framework for a local electricity-hydrogen market,the improved scenario accuracy leads to more cost-effective and robust operational decisions under uncertainty.
基金the National Key Research and Development Program of China under Grant 2018AAA0101505.
文摘With the growing penetration of renewable energysources in power systems, it becomes increasingly important tocharacterize their inherent variability and uncertainty. Scenariogeneration is a key approach to provide a series of possible powerscenarios in the future for the system planner and operator tomake decisions. In this paper, a data-driven method is presentedfor renewable scenario generation using stable and controllablegenerative adversarial networks with transparent latent space(ctrl-GANs). The machine learning based algorithm can capturethe nonlinear and dynamic renewable patterns without the needfor modeling assumptions and complicated sampling techniques.The orthogonal regularization and spectral normalization areadopted to improve the training stabilization of the GAN model.To control the generation process, a relationship is built betweenfeatures of the generated scenarios and latent vectors on themanifold. Moreover, several new metrics for GANs are used toevaluate the quality of the scenarios. The proposed approachis applied to generate realistic time series data of wind andphotovoltaic power. The results demonstrate that our methodhas a better performance on numerical stabilization and is ableto control the generation process with latent space.
基金This work was supported by the National Key Research and Development Program of China(No.2017YFB0902600).
文摘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.
基金supported by the State Grid Corporation of China“Research and Demonstration on Key Technologies of Distributed Energy Supply System with Complementary Renewable Energy”(No.5230HQ19000J).
文摘The operation of integrated energy systems(IESs)is confronted with great challenges for increasing penetration rate of renewable energy and growing complexity of energy forms.Scenario generation is one of ordinary methods to alleviate the system uncertainties by extracting several typical scenarios to represent the original high-dimensional data.This paper proposes a novel representative scenario generation method based on the feature extraction of panel data.The original high-dimensional data are represented by an aggregated indicator matrix using principal component analysis to preserve temporal variation.Then,the aggregated indicator matrix is clustered by an algorithm combining density canopy and K-medoids.Together 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 cost.Finally,case studies based on the data of Aachen,Germany in 2019 are performed.The 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 accuracy.The 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.
文摘Scenario generations for renewable energy sources and loads play an important role in the stable operation and risk assessment of integrated energy systems.This 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 ones.After training the model,any number of new scenarios can be obtained by simply inputting Gaussian noises into the data generator of IMLEs.The 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 methods.The extensive experiments show that the IMLEs accurately capture the complex shapes,frequency-domain characteristics,probability distributions,and correlations of renewable energy sources and loads.Moreover,the proposed approach can be easily generalized to scenario generation tasks of various renewable energy sources and loads by fine-tuning parameters and structures.
基金supported by the China Scholarship Council.The authors are very grateful for their help.
文摘Scenario generations of cooling,heating,and power loads are of great significance for the economic operation and stability analysis of integrated energy systems.In this paper,a novel deep generative network is proposed to model cooling,heating,and power load curves based on generative moment matching networks(GMMNs)where an auto-encoder transforms highdimensional load curves into low-dimensional latent variables and the maximum mean discrepancy represents the similarity metrics between the generated samples and the real samples.After training the model,the new scenarios are generated by feeding Gaussian noises to the scenario generator of the GMMN.Unlike the explicit density models,the proposed GMMN does not need to artificially assume the probability distribution of the load curves,which leads to stronger universality.The simulation results show that the GMMN not only fits the probability distribution of multiclass load curves very well,but also accurately captures the shape(e.g.,large peaks,fast ramps,and fluctuation),frequency-domain characteristics,and temporal-spatial correlations of cooling,heating,and power loads.Furthermore,the energy consumption of generated samples closely resembles that of real samples.
基金supported in part by the Nebraska Public Power District through the Nebraska Center for Energy Sciences Research。
文摘Stochastic optimization can be used to generate optimal bidding strategies for virtual bidders in which the uncertain electricity prices are represented by using scenarios.This paper proposes a hybrid scenario generation method for electricity price using a seasonal autoregressive integrated moving average(SARIMA)model and historical data.The electricity price spikes are first identified by using an outlier detection method.Then,the historical data are decomposed into base and spike components.Next,the base and spike component scenarios are generated by using the SARIMA-and historical data-based methods,respectively.Finally,the electricity price scenarios are obtained by combining the base and spike component scenarios.Case studies are carried out for a virtual bidder in the PJM electricity market to validate the proposed method.The 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,respectively.Case 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.
基金financial support of the National Science Foundation of China Project:52072215National Natural Science Foundation of China grant 52221005+2 种基金Beijing Natural Science Foundation(L243025)National key R&D Program of China:2022YFB2503003State Key Laboratory of Intelligent Green Vehicle and Mobility.
文摘Safety testing is a crucial phase in the development of autonomous vehicles,with scenarios serving as the fundamental medium for these tests.The construction of scenarios that accurately reflect the real-world traffic behavior is a key challenge for autonomous vehicle testing technology,given the need for authenticity and effectiveness in testing.Addressing this,this study introduces a method for generating test scenarios specifically for intersections.This method involves decoupling the scenario into interactive and non-interactive layers.For the interactive elements,namely the traffic participants,this study proposes a novel approach to construct a human-like decision-making model adapted to intersections,with the OF-T-GAIL algorithm at its core.This algorithm,through a novel perception-decision representation and an omnidirectional coordinate system transformation,effectively reduces the cumulative error in the trajectory generation.Furthermore,this model is extended to multiple agents,allowing the bulk generation of intersection test scenario segments.These scenarios and the traffic participant(TP)models are validated using the SinD dataset,demonstrating the applicability and effectiveness of the approach in creating testing environments for autonomous driving systems.
基金supported by Zhejiang Provincial Natural Science Foundation of China(Grant No.LR25D010003)The Zhejiang Provincial Key Research and Development Program(Grant No.2023C02018)National Natural Science Foundation of China(Grant No.42401400).
文摘The frequent outbreaks of crop diseases pose a serious threat to global agricultural production and food security.Data-driven forecasting models have emerged as an effective approach to support early warning and management,yet the lack of user-friendly tools for model development remains a major bottleneck.This study presents the Multi-Scenario Crop Disease Forecasting Modeling System(MSDFS),an open-source platform that enables end-to-end model construction-from multi-source data ingestion and feature engineering to training,evaluation,and deployment-across four representative scenarios:static point-based,static grid-based,dynamic point-based,and dynamic grid-based.Unlike conventional frameworks,MSDFS emphasizes modeling flexibility,allowing users to build,compare,and interpret diverse forecasting approaches within a unified workflow.A notable feature of the system is the integration of a weather scenario generator,which facilitates comprehensive testing of model performance and adaptability under extreme climatic conditions.Case studies corresponding to the four scenarios were used to validate the system,with overall accuracy(OA)ranging from 73%to 93%.By lowering technical barriers,the system is designed to serve plant protection managers and agricultural producers without advanced programming expertise,providing a practical modeling tool that supports the construction of smart plant protection systems.
基金supported in part by the National Natural Science Foundation of China(51977127)in part by the ShanghaiMunicipal Science and in part by the Technology Commission(19020500800)“Shuguang Program”(20SG52)Shanghai Education Development Foundation and Shanghai Municipal Education Commission.
文摘As an effective carrier of integrated clean energy,the microgrid has attracted wide attention.The randomness of renewable energies such as wind and solar power output brings a significant cost and impact on the economics and reliability of microgrids.This paper proposes an optimization scheme based on the distributionally robust optimization(DRO)model for a microgrid considering solar-wind correlation.Firstly,scenarios of wind and solar power output scenarios are generated based on non-parametric kernel density estimation and the Frank-Copula function;then the generated scenario results are reduced by K-means clustering;finally,the probability confidence interval of scenario distribution is constrained by 1-norm and∞-norm.The model is solved by a column-and-constraint generation algorithm.Experimental studies are conducted on a microgrid system in Jiangsu,China and the obtained scheduling solution turned out to be superior under wind and solar power uncertainties,which verifies the effectiveness of the proposed DRO model.
基金supported by National Natural Science Foundation of China(Grant No.51277034,51377027)
文摘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.
基金supported by National Key R&D Program of China(2023YFB2504400)Technology Research Program of MPS of China(2022JSZ16)+1 种基金Beijing Nova Program(20240484642,20230484259)Beijing Natural Science Foundation(4244092).
文摘The generation of corner cases has become increasingly crucial for efficiently testing autonomous vehicles prior to road deployment.However,existing methods struggle to accommodate diverse testing requirements and often lack the ability to generalize to unseen situations,thereby reducing the convenience and usability of the generated scenarios.A method that facilitates easily controllable scenario generation for efficient autonomous vehicles(AV)testing with realistic and challenging situations is greatly needed.To address this,OmniTester is proposed as a multimodal Large Language Model(LLM)based framework that fully leverages the extensive world knowledge and reasoning capabilities of LLMs.OmniTester is designed to generate realistic and diverse scenarios within a simulation environment,offering a robust solution for testing and evaluating AVs.In addition to prompt engineering,OmniTester employs tools from Simulation of Urban Mobility to simplify the complexity of codes generated by LLMs.It further incorporates Retrieval-Augmented Generation and a self-improvement mechanism to enhance the LLM's understanding of scenarios,thereby increasing its ability to produce more realistic scenes.Experiment results demonstrated the controllability and realism of the proposed approaches in generating three types of challenging and complex scenarios.Additionally,OmniTester effectively reconstructs novel scenarios described in crash reports,driven by the generalization capability of LLMs.
基金Major Project of Scientific and Technological Innovation 2030“New Generation Artificial Intelligence”(Grant No.2020AAA0108105)National Nature Science Foundation of China(Grants Nos.62103162 and U19A2069)+1 种基金Jilin Key Research and Development Program(Grant No.20200401088GX)the Jilin Major Science and Technology Projects(Grant No.20200501011GX).
文摘To improve the efficiency of safety tests of driver-automation cooperation,a method for generating a scenario library is proposed that considers the probability of scenario occurrence and driver-handling challenges in real driving situations.First,the original scenario data under cut-in conditions stored in a time series are extracted from the scenario data set.Then,a mathematical performance index is used to model the scenario and a significance function in terms of the occurrence frequency of the scenario,and the performance challenge between the driver and the vehicle is established.Next,the important scenario set is extracted from the original scenario set by constructing and optimizing a significance auxiliary function.Finally,the extracted important scenario sets are filtered by using the significance function values of the scenarios to generate a scenario library.Simulation results show that the proposed method for scenario library generation can effectively identify scenarios with potential adventure during driver-automation cooperation and thus accelerate safety tests compared with traditional methods.
基金the National Key R&D Program of China under Grant No.2022YFB2502900the National Natural Science Foundation of China(Grant Number:U23B2061)+1 种基金the Fundamental Research Funds for the Central Universities of Chinathe Xiaomi Young Talent Program,and we thank the reviewers for the valuable suggestions.
文摘Autonomous vehicles with self-evolution capabilities are expected to improve their performance through learning algorithms,to automatically adapt to the external environment.However,due to the infinity,complexity,and variability of the actual traffic environment,it is necessary to develop quantitative representation indicators of scenario difficulty and generate targeted scenarios to ensure the evolution gradually,so as to quickly approach the performance limit of the algorithm.Therefore,this paper proposes a data-driven quantitative representation method of scenario difficulty.Specifically,the concept of environment agent is proposed,and a reinforcement learning method combined with mechanism knowledge is constructed for policy search to obtain an agent with an adversarial behavior.The model parameters of the environment agent at different stages in the training process are extracted to construct a policy group,and then agents with different adversarial intensities are obtained,which are used to realize data generation in different difficulty scenarios through the simulation environment.Finally,a data-driven scenario difficulty quantitative representation model is constructed,which is used to output the environment agent policy under different difficulties.Experimental results show the effectiveness of the proposed method.The result analysis shows that the proposed algorithm can generate reasonable and interpretable scenarios with high discrimination and can provide quantifiable difficulty representation without any expert logic rule design.Compared with the rule-based discrete scenario difficulty representation method,the proposed algorithm can achieve continuous difficulty representation.The video link is https://www.youtube.com/watch?v=GceGdqAm9Ys.
基金supported in part by the National Natural Science Foundation of China under Grant No.51377027The National Basic Research Program of China under Grant No.2013CB228205by Innovation Project of Guangxi Graduate Education under Grant No.YCSZ2015053.
文摘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 farms.Then,based on the typical scenarios,a robust TNEP problem is presented and formulated.The solution of the problem is robust against all the scenarios that represent the stochastic features of wind power.Three test systems are used to verify the HMM method and is compared against Taguchi’s Orthogonal Array(OA)method.The simulation results show that the HMM method has better performance than the OA method in terms of the trade-off between robustness and economy.Additionally,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.
基金the financial support of the National Science Foundation of China Project:U1964203 and 52072215National key R&D Program of China:2020YFB1600303.
文摘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 hazards.As for operationcontent-related features,the scenario is similar to AVs’SOTIF research and development.Therefore,scenario generation is a significant topic for SOTIF verification and validation procedure,especially in the simulation testing of AVs.Thus,in this paper,a well-designed scenario architecture is first defined,with comprehensive scenario elements,to present SOTIF trigger conditions.Then,considering complex traffic disturbance as trigger conditions,a novel SOTIF scenario generation method is developed.An indicator,also known as Scenario Potential Risk,is defined as the combination of the safety control intensity and the prior collision probability.This indicator helps identify critical scenarios in the proposed method.In addition,the corresponding vehicle motion models are established for general straight roads,curved roads,and safety assessment areas.As for the traffic participants’motion model,it is designed to construct the key dynamic events.To 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.
基金supported by the National Key R&D Program of China(2021YFB2501200)the National Natural Science Foundation of China(52131204)the Shaanxi Province Key Research and Development Program(2022GY-300).
文摘With the increasing level of automation of autonomous vehicles,it is important to conduct comprehensive and extensive testing before releasing autonomous vehicles into the market.Traditional public road and closed-field testing failed to meet the requirements of high testing efficiency and scenario coverage.Therefore,scenario-based autonomous vehicle simulation testing has emerged.Many scenarios form the basis of simulation testing.Generating additional scenarios from an existing scenario library is a significant problem.Taking 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 highways.An 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 coverage.Experiments 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%,respectively.The 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.
基金the Ministry of Education in China(MOE)Project of Humanities and Social Sciences(Grant No.16YJC630141)the Social Science Foundation of Shaanxi Province(Grant No.2015R016)+1 种基金the Soft Science Research Program of Shaanxi Province(Grant No.2017KRM034)the Social Science Foundation of Xi’an City(Grant No.18J141).
文摘The development of micro-grid renewable energy system in China has achieved rapid growth in recent years,and the micro-grid renewable energy system has been drawing more and more attention by its flexible operation.Due to the randomness,fluctuation,uncertainty of the wind and photovoltaic renewable generation,abundant flexibility is required to meet the needs of safe,reliable and independent operation of the micro-grid energy system.We need to connect large energy systems to accept outside assistance when the micro-grid renewable energy system is short of adjustment capability.Independent operation and network operation will affect the economic benefit of the micro-grid energy system,so it is practically meaningful to study on the economic benefit evaluation of the micro-grid renewable energy system.This paper proposes a micro-grid energy system operation simulation model about wind and photovoltaic generation,the uncertainty of which is tackled based on the scenario generation and extraction techniques.Based on the proposed indices,the economic benefit could be evaluated by simulating the micro-grid energy system operation.The proposed method is validated by a real micro-grid energy system.
