Distributed Automatic Load-Frequency Control with Optimality in Power Systems
This work addresses the challenge of balancing supply and demand in power systems with high renewable penetration, providing a distributed solution with optimality guarantees.
The paper proposes a fully distributed automatic load control algorithm for power systems that converges to an optimal operating point minimizing total user disutility, restoring nominal frequency and scheduled tie-line power flows, while respecting capacity and thermal constraints. The algorithm is proven to converge globally even with inaccurate system parameters.
With the increasing penetration of renewable energy resources, power systems face new challenges in balancing power supply and demand and maintaining the nominal frequency. This paper studies load control to handle these challenges. In particular, a fully distributed automatic load control (ALC) algorithm, which only needs local measurement and local communication, is proposed. We prove that the load control algorithm globally converges to an optimal operating point which minimizes the total disutility of users, restores the nominal frequency and the scheduled tie-line power flows, and respects the load capacity limits and the thermal constraints of transmission lines. It is further shown that the asymptotic convergence still holds even when inaccurate system parameters are used in the control algorithm. In addition, the global exponential convergence of the reduced ALC algorithm without considering the capacity limits is proved and leveraged to study the dynamical tracking performance and robustness of the algorithm. Lastly, the effectiveness, optimality, and robustness of the proposed algorithm are demonstrated via numerical simulations.