Wind farm inertia forecasting accounting for wake losses, turbine-level control strategies, and operational constraints

Abstract

Future inverter-based resources (IBRs) must provide grid-forming functionalities to compensate for the declining share of conventional synchronous machines (SMs) in the power generation mix. Specifically, decreasing power system inertia poses a significant challenge to grid frequency stability, as system inertia limits the rate of change of frequency (ROCOF). Conventional grid-following control decouples the physical inertia of wind turbines (WTs) from the grid frequency. Novel grid-forming control methods, such as virtual synchronous machine (VSM) control, provide (virtual) inertia to the system by extracting kinetic energy from WTs. Since the grid-forming capability of IBRs depends on volatile operating conditions, future market designs will remunerate inertia provision based on its availability. Thus, estimating grid-forming capabilities of WTs and forecasting inertia of wind farms (WFs) are of interest for both WF and system operators. In this paper, we propose a method for forecasting WF inertia that accounts for wake effects and WT characteristics. A wake model estimates individual inflow conditions for each WT in the WF based on forecasted site conditions. These inflow conditions enable the prediction of the grid-forming capabilities of each WT. Under varying inflow conditions and derating power setpoints, we simulate the WT inertial responses to a reference frequency event. Taking WT control strategies and operating limits into account, an optimization algorithm computes the maximum feasible inertia provision at the WT and WF levels. The proposed approach is demonstrated in a simulation environment, and the results also include a quantification of the uncertainties due to both wind forecasting and wake modeling errors.