This paper proposes a hybrid multi-objective optimization and game-theoretic approach(HMOGTA)to achieve the optimal operation of integrated energy systems(IESs)consisting of electricity and natural gas(E&G)utility...This paper proposes a hybrid multi-objective optimization and game-theoretic approach(HMOGTA)to achieve the optimal operation of integrated energy systems(IESs)consisting of electricity and natural gas(E&G)utility networks,multiple distributed energy stations(DESs),and multiple energy users(EUs).The HMOGTA aims to solve the coordinated operation strategy of the electricity and natural gas networks considering the demand characteristics of DESs and EUs.In the HMOGTA,a hierarchical Stackelberg game model is developed for generating equilibrium strategies of DESs and EUs in each district energy network(DEN).Based on the game results,we obtain the coupling demand constraints of electricity and natural gas(CDCENs)which reflect the relationship between the amounts and prices of electricity and cooling(E&C)that DESs purchase from utility networks.Furthermore,the minimization of conflicting costs of E&G networks considering the CDCENs are solved by a multi-objective optimization method.A case study is conducted on a test IES composed of a 20-node natural gas network,a modified IEEE 30-bus system,and 3 DENs,which verifies the effectiveness of the proposed HMOGTA to realize fair treatment for all participants in the IES.展开更多
State estimation(SE)usually serves as the basic function of the energy management system(EMS).In this paper,the time-scale characteristics of the integrated heat and electricity networks are studied and an SE model is...State estimation(SE)usually serves as the basic function of the energy management system(EMS).In this paper,the time-scale characteristics of the integrated heat and electricity networks are studied and an SE model is established.Then,a two-stage iterative algorithm is proposed to estimate the time delay of heat power transportation in the pipeline.Meanwhile,to accommodate the measuring resolutions of the integrated network,a hybrid SE approach is developed based on the two-stage iterative algorithm.Results show that,in both steady and dynamic processes,the two-stage estimator has good accuracy and convergence.The hybrid estimator has good performance on tracking the variation of the states in the heating network,even when the available measurements are limited.展开更多
The eXtreme gradient boosting(XGBoost)algorithm is used to identify abnormal users.Firstly,the raw data were cleaned.Then user power characteristics were extracted from different aspects.Finally,the XGBoost classifier...The eXtreme gradient boosting(XGBoost)algorithm is used to identify abnormal users.Firstly,the raw data were cleaned.Then user power characteristics were extracted from different aspects.Finally,the XGBoost classifier was used to identify the abnormal users respectively in the balanced sample set and the unbalanced sample set.In contrast,under the same characteristics,the k-nearest neighbor(KNN)classifier,back-propagation(BP)neural network classifier and random forest classifier were used to identify the abnormal users in the two samples.The experimental results show that the XGBoost classifier has higher recognition rate and faster running speed.Especially in the imbalanced data sets,the performance improvement is obvious.展开更多
基金This work was supported by the State Key Program of National Natural Science Foundation of China(Grant No.51437006)the Natural Science Foundation of Guangdong Province,China(2018A030313799).
文摘This paper proposes a hybrid multi-objective optimization and game-theoretic approach(HMOGTA)to achieve the optimal operation of integrated energy systems(IESs)consisting of electricity and natural gas(E&G)utility networks,multiple distributed energy stations(DESs),and multiple energy users(EUs).The HMOGTA aims to solve the coordinated operation strategy of the electricity and natural gas networks considering the demand characteristics of DESs and EUs.In the HMOGTA,a hierarchical Stackelberg game model is developed for generating equilibrium strategies of DESs and EUs in each district energy network(DEN).Based on the game results,we obtain the coupling demand constraints of electricity and natural gas(CDCENs)which reflect the relationship between the amounts and prices of electricity and cooling(E&C)that DESs purchase from utility networks.Furthermore,the minimization of conflicting costs of E&G networks considering the CDCENs are solved by a multi-objective optimization method.A case study is conducted on a test IES composed of a 20-node natural gas network,a modified IEEE 30-bus system,and 3 DENs,which verifies the effectiveness of the proposed HMOGTA to realize fair treatment for all participants in the IES.
基金supported by the National Natural Science Foundation of China(NSFC)(No.51537006)the China Postdoctoral Science Foundation(No.2019M650675)
文摘State estimation(SE)usually serves as the basic function of the energy management system(EMS).In this paper,the time-scale characteristics of the integrated heat and electricity networks are studied and an SE model is established.Then,a two-stage iterative algorithm is proposed to estimate the time delay of heat power transportation in the pipeline.Meanwhile,to accommodate the measuring resolutions of the integrated network,a hybrid SE approach is developed based on the two-stage iterative algorithm.Results show that,in both steady and dynamic processes,the two-stage estimator has good accuracy and convergence.The hybrid estimator has good performance on tracking the variation of the states in the heating network,even when the available measurements are limited.
基金National Natural Science Foundation of China(No.61262044)
文摘The eXtreme gradient boosting(XGBoost)algorithm is used to identify abnormal users.Firstly,the raw data were cleaned.Then user power characteristics were extracted from different aspects.Finally,the XGBoost classifier was used to identify the abnormal users respectively in the balanced sample set and the unbalanced sample set.In contrast,under the same characteristics,the k-nearest neighbor(KNN)classifier,back-propagation(BP)neural network classifier and random forest classifier were used to identify the abnormal users in the two samples.The experimental results show that the XGBoost classifier has higher recognition rate and faster running speed.Especially in the imbalanced data sets,the performance improvement is obvious.
