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Multi-Lever EarlyWarning forWind and Photovoltaic Power Ramp Events Based on Neural Network and Fuzzy Logic
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作者 Huan Ma Linlin Ma +3 位作者 Zengwei Wang Zhendong Li Yuanzhen Zhu Yutian Liu 《Energy Engineering》 EI 2024年第11期3133-3160,共28页
With the increasing penetration of renewable energy in power system,renewable energy power ramp events(REPREs),dominated by wind power and photovoltaic power,pose significant threats to the secure and stable operation... With the increasing penetration of renewable energy in power system,renewable energy power ramp events(REPREs),dominated by wind power and photovoltaic power,pose significant threats to the secure and stable operation of power systems.This paper presents an early warning method for REPREs based on long short-term memory(LSTM)network and fuzzy logic.First,the warning levels of REPREs are defined by assessing the control costs of various power control measures.Then,the next 4-h power support capability of external grid is estimated by a tie line power predictionmodel,which is constructed based on the LSTMnetwork.Finally,considering the risk attitudes of dispatchers,fuzzy rules are employed to address the boundary value attribution of the early warning interval,improving the rationality of power ramp event early warning.Simulation results demonstrate that the proposed method can generate reasonable early warning levels for REPREs,guiding decision-making for control strategy. 展开更多
关键词 Early warning machine learning power system security renewable energy power ramp event smart grid
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Statistical scenarios forecasting method for wind power ramp events using modified neural networks 被引量:14
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作者 Mingjian CUI Deping KE +1 位作者 Di GAN Yuanzhang SUN 《Journal of Modern Power Systems and Clean Energy》 SCIE EI 2015年第3期371-380,共10页
Wind power ramp events increasingly affect the integration of wind power and cause more and more problems to the safety of power grid operation in recent years.Several forecasting techniques for wind power ramp events... Wind power ramp events increasingly affect the integration of wind power and cause more and more problems to the safety of power grid operation in recent years.Several forecasting techniques for wind power ramp events have been reported.In this paper,the statistical scenarios forecasting method is proposed for wind power ramp event probabilistic forecasting based on the probability generating model.Multi-objective fitness functions are established considering cumulative density functions and higher order moment autocorrelation functions with respect to the consistency of distribution and timing characteristics,respectively.Parameters of probability generating model are calculated by the iterative optimization using the modified genetic algorithm with multi-objective fitness functions.A number of statistical scenarios captured bands are generated accordingly.Eventually,ramp event probability characteristics are detected from scenarios captured bands to evaluate the ramp event forecasting method.A wind plant of Bonneville Power Administration with actual wind power data is selected for calculation and statistical analysis.It is shown that statistical results with multi-objective functions are more accurate than the results with single objective functions.Moreover,the statistical scenarios forecasting method can accurately estimate the characteristics of wind power ramp events.The results verify that the proposed method can guide the generation method of statistical scenarios and forecasting models for ramp events. 展开更多
关键词 Neural networks Genetic algorithm Probability generating model Statistical scenarios captured bands Statistical scenarios forecasting Wind power ramp events Wind power
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Smoothing ramp events in wind farm based on dynamic programming in energy internet 被引量:2
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作者 Jiang LI Guodong LIU Shuo ZHANG 《Frontiers in Energy》 SCIE CSCD 2018年第4期550-559,共10页
关键词 energy intemet wind power ramp events dynamic programming
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Bayesian Network Based Imprecise Probability Estimation Method for Wind Power Ramp Events 被引量:1
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作者 Yuanchun Zhao Wenli Zhu +1 位作者 Ming Yang Mengxia Wang 《Journal of Modern Power Systems and Clean Energy》 SCIE EI CSCD 2021年第6期1510-1519,共10页
Although wind power ramp events(WPREs)are relatively scarce,they can inevitably deteriorate the stability of power system operation and bring risks to the trading of electricity market.In this paper,an imprecise condi... Although wind power ramp events(WPREs)are relatively scarce,they can inevitably deteriorate the stability of power system operation and bring risks to the trading of electricity market.In this paper,an imprecise conditional probability estimation method for WPREs is proposed based on the Bayesian network(BN)theory.The method uses the maximum weight spanning tree(MWST)and greedy search(GS)to build a BN that has the highest fitting degree with the observed data.Meanwhile,an extended imprecise Dirichlet model(IDM)is developed to estimate the parameters of the BN,which quantificationally reflect the ambiguous dependencies among the random ramp event and various meteorological variables.The BN is then applied to predict the interval probability of each possible ramp state under the given meteorological conditions,which is expected to cover the target probability at a specified confidence level.The proposed method can quantify the uncertainty of the probabilistic ramp event estimation.Meanwhile,by using the extracted dependencies and Bayesian rules,the method can simplify the conditional probability estimation and perform reliable prediction even with scarce samples.Test results on a real wind farm with three-year operation data illustrate the effectiveness of the proposed method. 展开更多
关键词 Bayesian network(BN) conditional probability imprecise Dirichlet model(IDM) imprecise probability wind power ramp events
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