Scalable and Data Privacy Conserving Controller Tuning for Large-Scale Power Networks

TL;DR

This paper introduces a hierarchical decentralized controller tuning method for large-scale power networks that preserves data privacy, reduces computational complexity, and adapts to time-varying dynamics caused by increased renewable energy integration.

Contribution

It presents a novel scalable and privacy-preserving approach for controller tuning in large power systems using reduced models and hierarchical coordination.

Findings

The method achieves similar performance to centralized tuning.

It effectively handles time-varying dynamics due to renewable energy.

Demonstrated scalability on systems with over 2500 states.

Abstract

The increasing share of renewable generation leads to new challenges in reliable power system operation, such as the rising volatility of power generation, which leads to time-varying dynamics and behavior of the system. To counteract the changing dynamics, we propose to adapt the parameters of existing controllers to the changing conditions. Doing so, however, is challenging, as large power systems often involve multiple subsystem operators, which, for safety and privacy reasons, do not want to exchange detailed information about their subsystems. Furthermore, centralized tuning of structured controllers for large-scale systems, such as power networks, is often computationally very challenging. For this reason, we present a hierarchical decentralized approach for controller tuning, which increases data security and scalability. The proposed method is based on the exchange of structured reduced models of subsystems, which conserves data privacy and reduces computational complexity. For this purpose, suitable methods for model reduction and model matching are introduced. Furthermore, we demonstrate how increased renewable penetration leads to time-varying dynamics on the IEEE 68 bus power system, which underlines the importance of the problem. Then, we apply the proposed approach on simulation studies to show its effectiveness. As shown, a similar system performance as with a centralized method can be obtained. Finally, we show the scalability of the approach on a large power system with more than 2500 states and about 1500 controller parameters.