This study aims to propose; a distributed problem encountered in power grids with the ultimate optimization algorithm to solve the economic dispatch objective of minimizing the total generation cost. The proposed approach is based on the gradient descent method and the consensus protocol. No central unit was required to broadcast the global information to each bust and only local information was exchanged between the neighboring buses to balance power supply and demand. Theoretical analysis revealed that the proposed algorithm can converge to the optimal solution of the primal problem by selecting the appropriate step size and initial values. Simulation studies on the IEEE 9-bus system were conducted to show the validity of the proposed algorithm.
Scientia Sinica Informationis
gradient descent method
Kai MA received his B.E. degree in Automation and Ph.D. degree in control science and engineering from Yanshan University, China, in 2005 and 2011, respectively. In 2011, he joined Yanshan University. From 2013 to 2014, he was a postdoctoral research fellow at the Nanyang Technological University, Singapore. He is currently an associate professor with the Department of Automation, School of Electrical Engineering, Yanshan University, China. His research interests include demand responses in smart grids and resource allocation in communication networks.;Yangqing YU received her B.Eng. degree in automation from the University of Science and Technolog,y Liaoning in 2015. She is currently pursuing her master＇s degree in control theory and control engineering at the Yanshan University. Her research interests include demand response and economic dispatch in smart grids.;通信作者．E—mail：shyzhu@sjtu．edu．cn.Shanying ZHU received his B.S. degree in information and computing science from the North China University of Water Resources and Electric Power, Zhengzhou, China, in 2006. In 2008, he received his M.S. degree in applied mathematics from the Huazhong University of Science and Technology, Wuhan, China; and the Ph.D. degree in control theory and control engineering from the Shanghai Jiao Tong University, Shanghai, China, in 2013. From 2013 to 2015, he served as a research fellow at the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore and the Berkeley Education Alliance for Research, Singapore. He is currently an associate professor in the Department of Automation, Shanghai Jiao Tong University, China. His research interests include on multi-agent systems and industrial control systems, particularly the coordination control of mobile robots and distributed estimation and optimization in sensor networks.;通信作者．E-mail：jyangysu@ysu．edu．cn.Jie YANG received her B.Eng. degree in electrical engineering and automation and her master＇s degree in control