Synthetic inertia and frequency support assessment from renewable plants in low carbon grids

Abstract

Increasing grid integration of uncertain Renewable Energy (RE) sources reduces system’s inherent Frequency Response (FR) and restricts System Operators’(SOs) ability to maintain frequency stability. This condition further aggravates as existing operational practices do not consider FR from RE based generation. Technical feasibility of RE generation to facilitate synthetic inertia and Primary Frequency Response (PFR) is well established. However, FR support from RE plants must be integrated in scheduling decisions to ensure inertial and PFR adequacy in operational time frame. Further, unlike conventional units, RE plants’ output is volatile due to inherent variability and uncertainty. Hence, the uncertainty of RE generation needs to be considered in scheduling horizon to accurately assess system-wide FR requirement and RE Plants’ potential in its’ contribution. In this context, the paper proposes a novel day-ahead stochastic security-constrained unit commitment framework to incorporate synthetic inertia and PFR support from wind and PV plants, and ensure frequency response adequacy. RE generation is forecasted using long-short-term memory, a deep learning approach. A machine learning-based Bayesian inference statistics is used to characterize RE power uncertainty. Simulations are carried out on New England test system for various RE integration levels. Numerical results demonstrate the effectiveness of frequency support from RE plants in terms of improvement in frequency security parameters. Operating cost and RE curtailment are reduced 11% and 68%, respectively with PFR support from RE plants.