Stochastic Stability Analysis of Renewable-Rich Power Systems
Creators: name: Yang, Shichen
Publisher: name: RMIT University
Year: 2025
Subjects: classificationCode: 400899
subject: Electrical engineering not elsewhere classified
classificationCode: 400805
subject: Electrical energy transmission, networks and systems

description: The increasing integration of intermittent renewable energy sources, such as wind and solar power, into power systems has introduced significant challenges to power system stability. These sources, while efficient in power generation, exhibit unpredictable generation patterns that lead to supply fluctuations, thereby threatening the reliability of energy systems. This research project addresses these challenges through a multifaceted analysis of the impacts of intermittent renewable energy on power system stability, with a focus on identifying research gaps and proposing novel solutions. The significance of this study lies in its comprehensive approach to understanding and mitigating the instability caused by renewable energy sources. Despite the growing adoption of renewables, there remains a critical gap in the literature regarding the probabilistic modelling of short-term generation patterns and their impact on system stability. This research fills this gap by conducting a short-term probabilistic analysis using three years of wind and photovoltaic generation data from Australian farms. Probability density distribution functions are developed based on realistic mathematical models, providing an accurate representation of renewable energy generation patterns. Furthermore, the study identifies continuous zero-generation conditions as a key factor that undermines the accuracy of short-term wind generation predictions, offering new insights into the variability of renewable energy sources. A novel contribution of this research is the exploration of steep short-term variations in renewable energy generation, known as steep variations, and their impact on system stability. By analysing daily generation data using probability density functions, the study identifies patterns of steep variations and conducts simulations to examine transient and frequency stability under extreme conditions. This analysis provides a deeper understanding of system behaviour and identifies potential causes of instability, which are critical for ensuring the reliability of power systems with high renewable penetration. The third stage of the study introduces a simulation model developed using the DIgSILENT PowerFactory platform, which assesses the stability of single and multiple WFs under extreme conditions. A mathematical model is also incorporated to evaluate system stability under varying levels of renewable penetration, with a focus on curtailing semi-scheduled renewable sources. This approach offers a novel framework for assessing and mitigating the risks associated with high renewable energy integration. The final phase of the research employs Monte Carlo simulations to determine the maximum stable deviation under complex conditions, integrating and validating data from previous stages. Additionally, the study utilises the Recurrent Neural Network (RNN) algorithm to explore how short-term generation changes affect system stability, providing advanced predictive insights. The analysis is extended to the Australian Power Grid, with corresponding models developed for authenticity testing, ensuring the practical applicability of the findings. In conclusion, this study synthesises its findings to derive a value range correlated with the estimation of maximum stable deviation, influenced by standard deviations and clustering effects. The research not only highlights the challenges posed by intermittent renewable energy but also offers innovative solutions to enhance power system stability, contributing to the sustainable integration of renewable energy sources into modern power grids.