Data-driven approaches for power system operation and planning under high renewable energy penetration

High renewable energy penetration fundamentally changes power system operation modes and security mechanisms. Such changes pose two challenges to power system operation and planning with traditional analytic methods: the diverse operation modes make it difficult to identify power system patterns, and the complex security mechanism of converter-based devices jeopardizes power system security and stability. Data-driven approaches provide a promising way to handle these challenges in power system operation and planning, especially for power system security and stability problems. This article summarizes a data-driven framework to extract power system security rules and embed the rules into power system optimization under high renewable energy penetration. First, we propose a data-driven method to identify power system operation patterns and explore how high penetrated renewable energy impacts power system security and stability. Then, we review various data-driven methods for modeling complex security and stability rules. Finally, we show how the data-driven rules are transformed into optimization constraints that can be embedded into the power system operation model. The framework is validated through case studies of real-world power systems.