Multidimensional Analysis and Optimization of Bus Loads for Enhanced Renewable Energy Integration in Power Systems

Adopting an innovative framework, this study transcends traditional weak bus identification, exploring the interplay and causality among buses beyond direct connections. This multidimensional approach enhances system planning and operation by facilitating a comprehensive understanding of system load changes and elucidating the impact of a single-bus load alteration across the entire system. This methodology could underpin optimal renewable technology allocation in diverse contexts, promoting holistic system analysis. The research employed a combination of sensitivity and causality analysis to identify the most critical buses in the system, extending the analysis to the entire system rather than just neighboring buses. A web-based simulator was developed to predict the future values of the system’s most critical bus, “B03”, by considering the influence of other impactful buses under two conditions: their value in a previous time period (t-1) and their steady-state value before the simulation. Furthermore, an optimization process was performed to minimize the load on the critical B03 bus. By optimally distributing the load across the system based on the loadability of the entire system, the load at B03 was reduced from an initial 11.12–10.83 kWh. The neural network model, with a lower error rate of 3.85%, was more accurate than the baseline model in predicting the load on bus B03. The optimization process further enhanced the system’s ability to integrate renewable energy sources, contributing to a balanced and resilient power system. The proposed methodology’s superiority has been confirmed through experimental analysis of a sizable dataset from Iowa’s 240-bus power system. An adaptable framework, strengthened by various tools and techniques, can be successfully customized for a wide range of applications. This approach offers a promising pathway for the optimal allocation of renewable technologies, contributing to the development of more sustainable and resilient power systems.