AI-Assisted Stability Analysis of Microgrids with Distributed Renewable Energy Sources
DOI:
https://doi.org/10.15662/IJEETR.2025.0701010Keywords:
Microgrids, Distributed Renewable Energy Sources (DRES),, Power System Stability, Voltage and Frequency Control, Inverter-Based Resources, Energy Storage Systems, Small-Signal AnalysisAbstract
As an increasing number of communities and industries transition toward cleaner energy alternatives, microgrids are increasingly relying on distributed renewable energy sources such as solar and wind. While this transition provides significant environmental and economic benefits, the intermittent and unpredictable nature of renewable energy generation introduces technical challenges, particularly in maintaining stable voltage and frequency operation. This study investigates the impact of distributed renewable energy integration on the stability of microgrids using an AI-assisted analytical approach. An adapted version of the IEEE 33-bus test system is employed to model different levels of renewable energy penetration and evaluate microgrid performance under varying loading conditions and renewable generation profiles. An artificial intelligence-based predictive model is incorporated to analyze operating conditions, identify potential voltage and frequency deviations, and assist in assessing the stability of the microgrid under changing renewable energy conditions. The investigation also considers inverter control strategies, energy storage dynamics, and the temporal characteristics of solar and wind power generation. The results indicate that higher levels of renewable energy penetration, in the absence of appropriate control and predictive mechanisms, can adversely affect microgrid stability. However, the integration of AI-assisted stability assessment with suitable inverter control and energy storage strategies can improve the prediction and management of voltage and frequency variations, thereby supporting more reliable and resilient microgrid operation. The findings demonstrate the potential of AI-assisted techniques as a practical decision-support approach for engineers and system planners to evaluate and enhance the stability of modern microgrids with increasing levels of distributed renewable energy integration
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