Online distributed tracking of generalized Nash equilibrium on physical networks

Autonomous Intelligent Systems - 2021
Yifan Su1, Feng Liu1, Zhaojian Wang2, Shengwei Mei1, Qiang Lu1
1The State Key Laboratory of Power System, Department of Electrical Engineering, Tsinghua University, Beijing, China
2The Key Laboratory of System Control, and Information Processing, Ministry of Education of China, Department of Automation, Shanghai Jiao Tong University, Shanghai, China

Tóm tắt

AbstractIn generalized Nash equilibrium (GNE) seeking problems over physical networks such as power grids, the enforcement of network constraints and time-varying environment may bring high computational costs. Developing online algorithms is recognized as a promising method to cope with this challenge, where the task of computing system states is replaced by directly using measured values from the physical network. In this paper, we propose an online distributed algorithm via measurement feedback to track the GNE in a time-varying networked resource sharing market. Regarding that some system states are not measurable and measurement noise always exists, a dynamic state estimator is incorporated based on a Kalman filter, rendering a closed-loop dynamics of measurement-feedback driven online algorithm. We prove that, with a fixed step size, this online algorithm converges to a neighborhood of the GNE in expectation. Numerical simulations validate the theoretical results.

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Autonomous Intelligent Systems

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