Uncertainty in the Forecast of Net Load Ramp in CAISO Region

Abstract

In electricity systems, demand and supply must be in balance. The term net load refers to the portion of system demand that must be provided by non-renewable resources, equivalent to system demand minus the generation from variable energy resources such as solar and wind. The ramp rate of net load refers to its rate of change. The ramp rate of a power generator refers to the rate at which it can change its generation level. As more intermittent renewable resources are integrated into a system, the ramp rate of net load increases, and with that, the need for flexible generators with higher ramping capability (i.e. the ability to quickly ramp their power output up and down as needed). As more intermittent renewable resources are integrated into a system, the ramp rate of net load increases, and with that, the need for flexible generators with ramping capability. This Masters Project takes data on the forecast and realizations of load and renewable generation in the California Independent System Operator (CAISO) region from 05/01/2014 to 10/31/2014, and examines the statistical properties of the forecast errors of these quantities and the resulting ramp in net load. It focuses on addressing questions regarding the effects of increased penetration of renewables on market and system operations practices: 1) what is the pattern of forecast error of ramp in net load for different daily time periods? 2) Since net load is equal to system demand minus renewable generation, the forecast uncertainty of the two components contributes to the forecast uncertainty of ramp in net load. What is the element that has larger influence on the forecast error of ramp in net load? 3) A common assumptions about forecast error in system operation is that it follows a normal probability distribution. Is this assumption still valid under current renewable penetration levels? Does this assumption still hold when instead of looking at the forecast error during the day, the analysis is conducted independently for different daily time periods? 4) What are the implications of the findings of this study about the probability distribution of forecast error in net load to the procurement targets for reserves and ramp capability? Results show that a) the forecast error of ramp in the system’s net load is greatly affected by the forecast errors on generation from PV Solar, especially during twilight hours in the morning and evening, b) the data observed does not allow rejecting the hypothesis that forecast errors of ramp follow a normal probability distribution function. If the data used is representative of CAISO conditions, this suggests that at current penetrations of wind and solar energy, dispatching the system to provision ramping capability equal to 2 standard deviations above the mean of the forecast error of ramp in net load, would results in a system that is able to meet its ramping needs 95% of the time.