A safe and dependable supply of energy and power is directly correlated with the quality of distribution network engineering.The assessment and diagnosis of the design quality and economic viability of a distribution ...A safe and dependable supply of energy and power is directly correlated with the quality of distribution network engineering.The assessment and diagnosis of the design quality and economic viability of a distribution network engineering process are essential for guaranteeing the steady functioning of the corresponding power system.In this paper,an intelligent assisted assessment technique for distribution network engineering is proposed to address the issues of inefficiency,high manual dependence,and low utilization of vital information in the text during the eval-uation of projects related to distribution network engi-neering.To improve the model’s contextual learning ability,the robustly optimized bidirectional encoder representa-tions from transformers pretraining approach and whole-word masking are adopted to extract useful features from the distribution network engineering project review text.Principal component analysis is then used to downscale the high-dimensional features,thereby greatly increasing the efficiency of downstream classification.The light gradient boosting machine performs classification on the downscaled text features,and the Bayesian optimiza-tion approach is utilized to identify the best hyperparame-ter combinations.This significantly lessens the impacts of random parameters on the model performance.Tenfold cross-validation results demonstrate that the model can quickly and accurately identify common problems in dis-tribution network projects’technical and economic di-mensions.展开更多
To enhance the deployment capability and low-carbon degree of virtual power plants(VPPs),a novel optimized scheduling model is proposed in this paper for a multi-energy VPP.To explore the distribution potential of the...To enhance the deployment capability and low-carbon degree of virtual power plants(VPPs),a novel optimized scheduling model is proposed in this paper for a multi-energy VPP.To explore the distribution potential of the VPP and bolster its multi-energy complementarity,an architecture integrated with electric vehicle(EV)charging stations is introduced,and a battery health degradation mechanism is constructed.To address the uncertainty exhibited by EV behaviors,a feature extraction method based on deep Q-network and maximum rele-vance-minimum redundancy(mRMR)is then proposed.This method optimizes the applicability of mRMR in large datasets,thereby improving the accuracy of charge be-havior prediction.Next,to achieve a complex optimization dispatch,a twin delayed deep deterministic policy gradient algorithm is employed.The twin Q-value truncation mechanism and smooth regularization effectively suppress the issue of policy overestimation biases.Furthermore,to validate the performance of the proposed model and algorithm,four different cases are designed,and the scheduling effects achieved for EVs are compared with those of the traditional battery energy storage system framework.The simulation results show that the pro-posed model significantly reduces both the operational cost and carbon emission level while slowing the battery health degradation process.展开更多
基金supported by the Project of the National Social Science Fund of China(No.19BGL003).
文摘A safe and dependable supply of energy and power is directly correlated with the quality of distribution network engineering.The assessment and diagnosis of the design quality and economic viability of a distribution network engineering process are essential for guaranteeing the steady functioning of the corresponding power system.In this paper,an intelligent assisted assessment technique for distribution network engineering is proposed to address the issues of inefficiency,high manual dependence,and low utilization of vital information in the text during the eval-uation of projects related to distribution network engi-neering.To improve the model’s contextual learning ability,the robustly optimized bidirectional encoder representa-tions from transformers pretraining approach and whole-word masking are adopted to extract useful features from the distribution network engineering project review text.Principal component analysis is then used to downscale the high-dimensional features,thereby greatly increasing the efficiency of downstream classification.The light gradient boosting machine performs classification on the downscaled text features,and the Bayesian optimiza-tion approach is utilized to identify the best hyperparame-ter combinations.This significantly lessens the impacts of random parameters on the model performance.Tenfold cross-validation results demonstrate that the model can quickly and accurately identify common problems in dis-tribution network projects’technical and economic di-mensions.
基金supported by the National Nature Sci-ence Foundation of China(No.72401182).
文摘To enhance the deployment capability and low-carbon degree of virtual power plants(VPPs),a novel optimized scheduling model is proposed in this paper for a multi-energy VPP.To explore the distribution potential of the VPP and bolster its multi-energy complementarity,an architecture integrated with electric vehicle(EV)charging stations is introduced,and a battery health degradation mechanism is constructed.To address the uncertainty exhibited by EV behaviors,a feature extraction method based on deep Q-network and maximum rele-vance-minimum redundancy(mRMR)is then proposed.This method optimizes the applicability of mRMR in large datasets,thereby improving the accuracy of charge be-havior prediction.Next,to achieve a complex optimization dispatch,a twin delayed deep deterministic policy gradient algorithm is employed.The twin Q-value truncation mechanism and smooth regularization effectively suppress the issue of policy overestimation biases.Furthermore,to validate the performance of the proposed model and algorithm,four different cases are designed,and the scheduling effects achieved for EVs are compared with those of the traditional battery energy storage system framework.The simulation results show that the pro-posed model significantly reduces both the operational cost and carbon emission level while slowing the battery health degradation process.
