Distributed Frequency Control with Operational Constraints, Part I: Per-Node Power Balance
This paper addresses the distributed optimal frequency control of multi-area power system with operational constraints, including the regulation capacity of individual control area and the power limits on tie-lines. Both generators and controllable loads are utilized to recover nominal frequencies while minimizing regulation cost. We study two control modes: 1) the per-node balance mode and 2) the network balance mode. In Part I of this paper, we only consider the per-node balance case, where we derive a completely decentralized strategy without the need for communication between control areas. It can adapt to unknown load disturbance. The tie-line powers are restored after load disturbance, while the regulation capacity constraints are satisfied both at equilibrium and during transient. We show that the closed-loop systems with the proposed control strategies carry out primal-dual updates for solving the associated centralized frequency optimization problems. We further prove the closed-loop systems are asymptotically stable and converge to the unique optimal solution of the centralized frequency optimization problems and their duals. Finally, we present simulation results to demonstrate the effectiveness of our design. In Part II of this paper, we address the network power balance case, where transmission congestions are managed continuously.
© 2017 IEEE. Manuscript received February 28, 2017; revised May 31, 2017; accepted July 20, 2017. Date of publication July 25, 2017; date of current version December 19, 2018. This work was supported in part by the National Natural Science Foundation of China under Grant 51677100, Grant 51377092, and Grant 51621065, in part by the Foundation of Chinese Scholarship Council under Grant 201506215034 and Grant 201606210173, in part by the U.S. National Science Foundation under Award EPCN 1619352, Award CCF 1637598, and Award CNS 1545096, in part by ARPA-E Award under Grant DE-AR0000699, and in part by the Skoltech through Collaboration Agreement under Grant 1075-MRA. Paper no. TSG-00296-2017.
Submitted - 1702.07965.pdf