  Distributed control/optimization is a promising approach for network systems
due to its advantages over centralized schemes, such as robustness,
cost-effectiveness, and improved privacy. However, distributed methods can have
drawbacks, such as slower convergence rates due to limited knowledge of the
overall network model. Additionally, ensuring privacy in the communication of
sensitive information can pose implementation challenges. To address this
issue, we propose a distributed model identification algorithm that enables
each agent to identify the sub-model that characterizes the relationship
between its local control and the overall system outputs. The proposed
algorithm maintains the privacy of local agents by only communicating through
dummy variables. We demonstrate the efficacy of our algorithm in the context of
power distribution systems by applying it to the voltage regulation of a
modified IEEE distribution system. The proposed algorithm is well-suited to the
needs of power distribution controls and offers an effective solution to the
challenges of distributed model identification in network systems.
