What day-ahead reserves are needed in electric grids with high levels of wind power?

Abstract

Day-ahead load and wind power forecasts provide useful information for operational decision making, but they are imperfect and forecast errors must be offset with operational reserves and balancing of (real time) energy. Procurement of these reserves is of great operational and financial importance in integrating large-scale wind power. We present a probabilistic method to determine net load forecast uncertainty for day-ahead wind and load forecasts. Our analysis uses data from two different electric grids in the US with similar levels of installed wind capacity but with large differences in wind and load forecast accuracy, due to geographic characteristics. We demonstrate that the day-ahead capacity requirements can be computed based on forecasts of wind and load. For 95% day-ahead reliability, this required capacity ranges from 2100 to 5700 MW for ERCOT, and 1900 to 4500 MW for MISO (with 10 GW of installed wind capacity), depending on the wind and load forecast values. We also show that for each MW of additional wind power capacity for ERCOT, 0.16-0.30 MW of dispatchable capacity will be used to compensate for wind uncertainty based on day-ahead forecasts. For MISO (with its more accurate forecasts), the requirement is 0.07-0.13 MW of dispatchable capacity for each MW of additional wind capacity.

A review of large-scale wind integration studies

“…They found that the forecast uncertainty is greatest on days when the wind is forecast to be blowing strongly (see also [78]), thus there are some days when significant reserves are required, and other days when much smaller reserves are needed. When estimating the reserves that are needed to cover 95% of the dayahead forecast errors, [77] find reserve requirements substantially higher than those reported in many of the studies reviewed here. They suggest that a dynamic method should be used to schedule reserves, based on the day-ahead forecast values.…”

“…Several recent articles suggest, and apply, methods for quantifying costs associated with wind power variability. Mauch et al [77] estimated reserves requirements based on day-ahead load and wind forecast uncertainty. They found that the forecast uncertainty is greatest on days when the wind is forecast to be blowing strongly (see also [78]), thus there are some days when significant reserves are required, and other days when much smaller reserves are needed.…”

Evaluation of a proposal for reliable low-cost grid power with 100% wind, water, and solar

“…11 is a simplified representation of electric power system operations that does not capture requirements for frequency regulation to ensure operating reliability (additional details are in SI Appendix, section S3). Furthermore, the model is fully deterministic, implying perfect foresight about the electricity demand and the variability of wind and solar energy resources and neglecting the effect of forecast errors on reserve requirements (25). In a system where variable renewable resources make up over 95% of the US energy supply, renewable energy forecast errors would be a significant source of uncertainty in the daily operation of power systems.…”

Benefits of solar forecasting for energy imbalance markets

“…Reserve requirements also apply for EIMs and the costs for backup resources effect all market participants. There are various methods in practice to compute reserves [39,40,4]. The dominant approach is the n-sigma method, where reserves are calculated by assessing the standard deviation in generation and demand forecast.…”