Quantifying the value of probabilistic forecasting for power system operation planning

Abstract

A recent key research area in renewable energy integration is the development of tools and methods to capture and accommodate the uncertainty associated with the forecast errors. While the research community has proposed numerous methods to improve the accuracy of probabilistic forecasts, their application to operational planning is still an open question. This work applies dynamic reserve determination methods to solar probabilistic forecasts and then feed them to a commercial production cost model simulator to assess the value of capturing the uncertainty endogenously in the reserve determination process. Testing is carried out on a calibrated real-size system representing the Southern Company for medium, and high solar penetration levels. Numerical results demonstrate the benefits that can be attained by explicitly modeling probabilistic uncertainty in terms of operating cost, and enhanced system reliability which is measured as the quantity of balancing and reserve violations. Additionally, these methods and results can pave the way for system operators to adopt probabilistic forecasting to draw the operating plans of the system, and this allowing the successful integration of variable renewable energy sources.