A predictive tool for power system operators to ensure frequency stability for power grids with renewable energy integration

Abstract

The increased penetration levels of renewable energy resources (RESs) transform the power generation schema into one that is more susceptible to changes due to weather conditions, complicated load profiles, and reduced inertia levels. One of the main challenges that power grids can encounter under varying and low system inertia (SI) is poor frequency response (FR). Therefore, this paper proposes a novel online tool for assessing, predicting, and enhancing the frequency stability (FS) of power systems with renewable power generation and energy storage systems (ESS). Firstly, the tool provides accurate tracking of the SI in real-time settings using recursive least square identification and Kalman filtering to provide accurate and computationally efficient results. Secondly, the tool estimates the FR of the whole system in a virtual environment for different contingencies and inertia levels. The accuracy of the FR is improved by classifying the frequency response models (FRMs) based on the SI levels. Lastly, the proposed tool computes the optimal additional estimated reserve power (ERP) required from PV and battery energy storage systems (BESS) to provide inertial and primary frequency support to the power grid at the onset of a contingency. In addition, a reserve power allocation and load-shedding strategies are proposed and implemented to dynamically adjust the reserve power of PV power plants. This dynamic control of the PV power plant reserve capacity guarantees the availability of adequate reserves for addressing unforeseen systems contingencies. Moreover, dedicated FR controllers for PV and BESS are employed to emulate the synchronous machine’s inertial response (IR) and primary frequency control (PFC). The performance of the proposed tool and the FR controllers are tested and validated on a generic power grid and the IEEE 39 bus system using MATLAB Simulink and the OPAL-RT real-time simulation software.