Distributed economic model predictive control of networks in competitive environments

This paper addresses the control of large-scale networks of dynamical systems with a certain global objective that needs to be pursued through the actions of individual competing agents. In particular, we consider the stabilization of a specific network output at zero. This is challenging as each agent is interested only in its own objectives such as the maximization of its economic profit. We introduce a control scheme based on the economic Model Predictive Control (MPC) theory to optimize economic performance of the competing agents. The theory is then modified to take global, network-wide objectives into account as well. Additionally, it is shown that the control scheme can be distributed between the agents using the dual decomposition technique such that exchange of confidential information among these competitors is not required. Simulation results for a power system example illustrate the potential of the control strategy in terms of ensuring stable and economical operation of networks in competitive environments.