Requirement design for a reliable and efficient ramp capability product

With increasing intermittent renewable penetration, net load variations and uncertainties increase. When there is a sudden change in wind, real-time dispatches can be short of ramp capabilities from online generators, and offline generators may not respond fast enough. To manage the challenge of maintaining power balance, a ramp capability product is being developed by many ISOs with requirements set based on the Gaussian sigma rule, e.g., 2.5 sigma for the 99% confidence level. However, a simple Monte-Carlo simulation test shows that the realized confidence levels for different numbers of sigma's are generally different from what were prescribed. More importantly, there are significant rooms for cost savings while maintaining the required confidence. How do we design the ramp requirements so that the realized level truly satisfies the required confidence and the cost is optimized in a systematic way? Our idea is to use Monte-Carlo simulation to evaluate realized confidence levels and expected costs by mimicking real-time dispatches. Simulation-based optimization is then used to efficiently minimize the expected cost while maintaining the required confidence. Numerical results show that the designed ramp requirements can effectively manage net load variations and uncertainties at the specified confidence with significant cost savings.