A novel framework to assess synthetic inertia & primary frequency response support from energy storage systems

Abstract

Grid integration of large scale uncertain Renewable Energy (RE) generation reduces the share of conventional generation, and thereby reduces the overall system inertia (SI) and Primary Frequency Response (PFR). This necessitates integration of additional sources of inertia and PFR for rapid generation-demand balancing during inevitable contingencies. Due to high-power density and fast response, Energy storage systems (ESS) are evolving as a potential solution for quick grid balancing. Technical viability of these sources to facilitate synthetic inertia and PFR is well established. However, to ensure inertial and PFR adequacy in operational time frame, the Frequency Response (FR) support from these sources must be integrated in Security Constrained Unit Commitment (SCUC) and dispatch decisions. In this context, the present work proposes a Stochastic SCUC framework to model synthetic inertia and PFR support from ESS. Synthetic inertia and PFR support from fast acting ESS are mapped with post-fault frequency dynamics to handle system frequency stability. Further, a machine learning-based Bayesian inference statistic using Markov Chain Monte Carlo algorithm is used to characterize and represent RE uncertainty in scheduling framework. Case studies are performed on IEEE 6 bus and New England 39 bus test systems under various RE integration scenarios to demonstrate the effectiveness of proposed framework. Simulation results highlight ESS potential to significantly enhance post-fault frequency stability in terms of rate of change of frequency, frequency nadir, nadir time, and steady-state frequency.