首页|An Embedded Consensus ADMM Distribution Algorithm Based on Outer Approximation for Improved Robust State Estimation of Networked Microgrids
An Embedded Consensus ADMM Distribution Algorithm Based on Outer Approximation for Improved Robust State Estimation of Networked Microgrids
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
维普
Networked microgrids(NMGs)are critical in the accommodation of distributed renewable energy.However,the existing centralized state estimation(SE)cannot meet the de-mands of NMGs in distributed energy management.The cur-rent estimator is also not robust against bad data.This study in-troduces the concepts of relative error to construct an improved robust SE(IRSE)optimization model with mixed-integer nonlin-ear programming(MINLP)that overcomes the disadvantage of inaccurate results derived from different measurements when the same tolerance range is considered in the robust SE(RSE).To improve the computation efficiency of the IRSE optimization model,the number of binary variables is reduced based on the projection statistics and normalized residual methods,which ef-fectively avoid the problem of slow convergence or divergence of the algorithm caused by too many integer variables.Finally,an embedded consensus alternating direction of multiplier meth-od(ADMM)distribution algorithm based on outer approxima-tion(OA)is proposed to solve the IRSE optimization model.This algorithm can accurately detect bad data and obtain SE re-sults that communicate only the boundary coupling information with neighbors.Numerical tests show that the proposed algo-rithm effectively detects bad data,obtains more accurate SE re-sults,and ensures the protection of private information in all microgrids.
Distributed optimizationalternating direction of multiplier methods(ADMM)robust state estimation(RSE)mixed-integer nonlinear programming(MINLP)networked mi-crogrid(NMG)
Zifeng Zhang、Yuntao Ju
展开 >
State Grid Information&Tele-communication Group Co.,Ltd.,Beijing Branch,Beijing,China
School of Electrical and Control Engineering,North China University of Technology,Beijing,China
万方数据
维普
