Enhanced probabilistic contingency analysis considering fast ramping events of renewable generation

Abstract

One of the major challenges in transmission planning is accounting for the impacts of renewable generation. Contingency Analysis (CA) is a technique used by utilities to meet transmission planning criteria mandated by the North American Electric Reliability Corporation (NERC). In existing CA practices, renewable generation is treated like conventional generation and does not consider the intermittency and fluctuations inherent to renewables. As renewable generation continues to grow, considering the impacts of renewable intermittency is becoming increasingly important. In this paper, an innovative approach is proposed to model the renewable intermittency as generator outages by, for the first time, introducing the concept of intermittency-induced outages (IIOs) for a single renewable plant and common mode outages (CMOs) for multiple renewable plants. A probabilistic outage model features different outage modes and the associated parameters that can be derived based on historical renewable generation data. The IIOs and CMOs can be seamlessly integrated into the existing probabilistic contingency analysis (PCA) framework. This paper presents the details for modeling and parameterization of the renewable outage modes. Such probabilistic outage models have been implemented using a Python-based enhanced PCA (ePCA) tool that drives and enhances the capability of the built-in PCA module of the PSS/E software platform, a widely used tool by the industry. Using the Western Electricity Coordinating Council (WECC) system, a proof-of-concept case study is presented and discussed to illustrate the unique impacts of increased penetration levels of renewables on transmission planning studies.