Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational s...Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational scenarios,considering the large amount of historical operational snapshot data.Specifically,DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios.A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional spaces.This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models.The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots.Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods.In addition,experiments with different new energy access ratios were conducted to verify the robustness of the proposed method.DTSAs enable dispatchers to master the operation experience of the power system in advance,and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.展开更多
With the increasing complexity of power system structures and the increasing penetration of renewable energy,the number of possible power system operation modes increases dramatically.It is difficult to make manual po...With the increasing complexity of power system structures and the increasing penetration of renewable energy,the number of possible power system operation modes increases dramatically.It is difficult to make manual power flow adjustments to establish an initial convergent power flow that is suitable for operation mode analysis.At present,problems of low efficiency and long time consumption are encountered in the formulation of operation modes,resulting in a very limited number of generated operation modes.In this paper,we propose an intelligent power flow adjustment and generation model based on a deep network and reinforcement learning.First,a discriminator is trained to judge the power flow convergence,and the output of this discriminator is used to construct a value function.Then,the reinforcement learning method is adopted to learn a strategy for power flow convergence adjustment.Finally,a large number of convergent power flow samples are generated using the learned adjustment strategy.Compared with the traditional flow adjustment method,the proposed method has significant advantages that the learning of the power flow adjustment strategy does not depend on the parameters of the power system model.Therefore,this strategy can be automatically learned without manual intervention,which allows a large number of different operation modes to be efficiently formulated.The verification results of a case study show that the proposed method can independently learn a power flow adjustment strategy and generate various convergent power flows.展开更多
基金The Key R&D Project of Jilin Province,Grant/Award Number:20230201067GX。
文摘Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational scenarios,considering the large amount of historical operational snapshot data.Specifically,DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios.A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional spaces.This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models.The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots.Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods.In addition,experiments with different new energy access ratios were conducted to verify the robustness of the proposed method.DTSAs enable dispatchers to master the operation experience of the power system in advance,and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.
基金supported by the Science and Technology Project of the State Grid Corporation of China(No.5400-201935258A-0-0-00)the National Natural Science Foundation of China(No.51777104)
文摘With the increasing complexity of power system structures and the increasing penetration of renewable energy,the number of possible power system operation modes increases dramatically.It is difficult to make manual power flow adjustments to establish an initial convergent power flow that is suitable for operation mode analysis.At present,problems of low efficiency and long time consumption are encountered in the formulation of operation modes,resulting in a very limited number of generated operation modes.In this paper,we propose an intelligent power flow adjustment and generation model based on a deep network and reinforcement learning.First,a discriminator is trained to judge the power flow convergence,and the output of this discriminator is used to construct a value function.Then,the reinforcement learning method is adopted to learn a strategy for power flow convergence adjustment.Finally,a large number of convergent power flow samples are generated using the learned adjustment strategy.Compared with the traditional flow adjustment method,the proposed method has significant advantages that the learning of the power flow adjustment strategy does not depend on the parameters of the power system model.Therefore,this strategy can be automatically learned without manual intervention,which allows a large number of different operation modes to be efficiently formulated.The verification results of a case study show that the proposed method can independently learn a power flow adjustment strategy and generate various convergent power flows.
