Load Frequency Control of Multiarea Interconnected Power System Based on Distributed Economic Model Predictive Control With Guaranteed Stability

• Published in: IEEE transactions on industrial informatics pp. 1 - 11
• Main Authors: Ma, Miaomiao, Cui, Jing, Chen, Hong, Lee, Kwang Y.
• Format: Journal Article
• Language: English
• Published: IEEE 2025
• Subjects: Cost function; Costs; Couplings; Distributed control; economic model predictive control; Frequency control; interconnected system; load frequency control (LFC); Load modeling; Power system dynamics; Power system stability; Stability criteria; Thermal stability; Wind power generation
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2025.3594079

This article develops a distributed economic model predictive control (DEMPC) method to address the load frequency control (LFC) issue in multi-area interconnected power systems, which are subject to control input constraints. The proposed method is designed to enhance economic performance, while ensuring the desired control performance in power systems. To overcome the challenges posed by disturbances and dynamic couplings in interconnected power systems, robustness constraints are incorporated into the framework of DEMPC. In addition, to optimize overall operational economics, the stage cost function encompasses costs associated with load frequency regulation, fuel consumption and wind power generation. Since the stage cost function is typically nonconvex and not positive definite, a positive definite function at the economically optimal equilibrium point is defined. Subsequently, the optimal value function of this function is utilized to establish an adjustable stability constraint for each optimization problem. The closed-loop stability is ensured through the combination of terminal constrained sets, terminal penalty functions, local controllers, and the appropriate sampling interval. Comprehensive analysis and simulation results, conducted on a multiarea interconnected power system, demonstrate possible improvements in computation performance, economic performance, and robustness, while respecting control input constraints.