@Article{ee.2025.064206,
AUTHOR = {Hao Jiao, Xinyu Liu, Chen Wu, Chunlei Xu, Zhijun Zhou, Ye Chen, Guoqiang Sun},
TITLE = {Collaborative State Estimation for Coupled Transmission and Distribution Systems Based on Clustering Analysis and Equivalent Measurement Modeling},
JOURNAL = {Energy Engineering},
VOLUME = {122},
YEAR = {2025},
NUMBER = {7},
PAGES = {2977--2992},
URL = {http://www.techscience.com/energy/v122n7/62675},
ISSN = {1546-0118},
ABSTRACT = {With the continuous expansion of the power system scale and the increasing complexity of operational mode, the interaction between transmission and distribution systems is becoming more and more significant, placing higher requirements on the accuracy and efficiency of the power system state estimation to address the challenge of balancing computational efficiency and estimation accuracy in traditional coupled transmission and distribution state estimation methods, this paper proposes a collaborative state estimation method based on distribution systems state clustering and load model parameter identification. To resolve the scalability issue of coupled transmission and distribution power systems, clustering is first carried out based on the distribution system states. As the data and models of the transmission system and distribution systems are not shared. For the transmission system, equating the power transmitted from the transmission system to the distribution system is the same as equating the distribution system. Further, the power transmitted from the transmission system to different types of distribution systems is equivalent to different polynomial equivalent load models. Then, a parameter identification method is proposed to obtain the parameters of the equivalent load model. Finally, a transmission and distribution collaborative state estimation model is constructed based on the equivalent load model. The results of the numerical analysis show that compared with the traditional master-slave splitting method, the proposed method significantly enhances computational efficiency while maintaining high estimation accuracy.},
DOI = {10.32604/ee.2025.064206}
}