典型文献
State Estimation Approach for Combined Heat and Electric Networks
文献摘要：
State estimation (SE) is essential for combined heat and electric networks (CHENs) to provide a global and self-consistent solution for multi-energy flow analysis.This paper proposes an SE approach for CHEN based on steady models of electric networks (ENs) and district heating networks (DHNs).A range of coupling components are considered.The performance of the proposed estimator is evaluated using Monte Carlo simulations and case studies.Results show that a relationship between the measurements from ENs and DHNs can improve the estimation accuracy for the entire network by using the combined SE model,especially when ENs and DHNs are strongly coupled.The coupling constraints could also provide extra redundancy to detect bad data in the boundary injection measurements of both networks.An analysis of computation time shows that the proposed method is suitable for online applications
文献关键词：
中图分类号：
作者姓名：
Tongtian Sheng;Guanxiong Yin;Bin Wang;Qinglai Guo;Jinni Dong;Hongbin Sun;Zhaoguang Pan
作者机构：
Department of Electrical Engineering,State Key Laboratory of Power Systems,Tsinghua University,Beijing 100084,China
文献出处：
引用格式：
[1]Tongtian Sheng;Guanxiong Yin;Bin Wang;Qinglai Guo;Jinni Dong;Hongbin Sun;Zhaoguang Pan-.State Estimation Approach for Combined Heat and Electric Networks)[J].中国电机工程学会电力与能源系统学报（英文版）,2022(01):225-237
A类：
CHENs,ENs,DHNs
B类：
State,Estimation,Approach,Combined,Heat,Electric,Networks,estimation,SE,essential,combined,electric,networks,provide,global,self,consistent,solution,multi,energy,flow,analysis,This,paper,proposes,approach,steady,models,district,heating,range,coupling,components,are,considered,performance,proposed,estimator,evaluated,using,Monte,Carlo,simulations,case,studies,Results,that,relationship,between,measurements,from,can,improve,accuracy,entire,by,especially,when,strongly,coupled,constraints,could,also,extra,redundancy,detect,bad,data,boundary,injection,both,An,computation,shows,method,suitable,online,applications
AB值：
0.581022
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