An empirical approach to frequency droop characterization from utility‐scale photovoltaic plants operation in a power system

Abstract

PV plant power excursions can have adverse implications on grid frequency. This phenomenon is observable due to inherently uncertain cloud transients across a local PV plant. Hence, provision of decision-based controllers for centralized power inverters becomes imperative for supporting local grid operations. Such controllers can be improved to better counteract minutes-based PV power deviations from its stable equilibrium. Thus, grid frequency deviations require further investigation at PV plant point of interconnection to the grid. In this research, single and spatially distributed utility-scale PV plants operation is studied on a real-time power system simulator, under fast-changing meteorological conditions at different PV site loading levels (P PV −re f). Software-in-the-loop Monte Carlo simulation is conducted and an empirical approach is proposed for characterizing minutes-based variations in grid frequency originating from PV plant operation, that is, power fluctuations at different P PV −re f. The power−frequency curve obtained at the PV site can be incorporated in form of an empirical frequency droop function in characteristics curve of adjacent auxiliary power source(s). A prominent feature of this adaptive frequency droop is that it considers PV site loading levels during different hours, giving it leverage over common practice constant droop(s). A hardware-in-the-loop platform is presented allowing field derivation of adaptive frequency droop curves using hardware PMU time-series data analytics.

Detecting Large Frequency Excursions in the Power Grid With Bayesian Decision Theory

“…Extensive research has been conducted to characterize frequency variations in the network to provide better situational awareness to the power system operators as the penetration of renewable generation increases. A systematic empirical approach using real-time monitored time-series data was developed in [2] to improve frequency stability when considering photovoltaics (PV) plants operation and further developed in [3] using a supervised machine-learning-based approach to capture the non-linearity of PV plants adaptive frequency droop curves. Copula theory was successfully employed in [4] to model the stochastic dependence between loads and renewable energy sources, and [5] demonstrated further that ignoring the existing correlation among uncertainties leads to inaccurate predictions.…”