Improving Operational Planning study models with historical generation and load data

Abstract

Variable generation dispatch and load distribution patterns due to renewables connected to transmission, distributed energy resources connected to the distribution system and the operation of electricity markets warrant that historical load and generation conditions be modeled in offline studies on a continuous basis instead of using the traditional peak load cases for Operations Planning studies. Modeling of these system conditions into Operations Planning study models is also essential for after-the-fact event analysis, improving study models and for providing better insight into outage reliability studies. This paper presents a method implemented at California ISO (CAISO) using Python and GE Positive Sequence Load Flow (PSLF) to integrate real-time system operating conditions with Operational Planning models to provide study models with real-time load and generation conditions.