Dynamic Multi-Period Intelligent Traffic Allocation Based on Multi-Objective Data Mining

This study develops a sophisticated dynamic, multi-period intelligent traffic allocation algorithm using multi-objective data mining techniques, designed to optimize the incorporation of renewable energy systems (RESs) and electric vehicles (EVs) within electrical power grids.Given the inherent intermittency and unpredictability associated with RESs and EVs, the algorithm utilizes Dynamic Optimal Network Reconfiguration (DONR) and Capacitor Bank Switching (CBS) to address these challenges effectively.This integrated approach aims to enhance grid stability and operational efficiency, focusing on reducing energy losses, improving voltage profiles, and achieving financial savings through optimized 24-hour grid operations.The core innovation of this research is the application of the Artificial Hummingbird Algorithm (AHA), which has been adapted for the first time to tackle this multi-faceted optimization problem.By considering the impacts of variable solar generation and the demands of diverse load profiles, including substantial EV penetrations, the AHA navigates complex decision spaces to find optimal solutions.This methodology was rigorously tested using an enhanced IEEE 33-bus benchmark system, where various scenarios were simulated to evaluate the computational effectiveness of the AHA compared to other prevailing methods.The results from these simulations clearly demonstrate the superior performance of the integrated DONR and CBS strategy, particularly in managing the dynamic and stochastic nature of load demands and renewable energy inputs in real-time scenarios.The method by dynamic reconfiguration may boost the overall savings to 6903.03 $/h and decrease inefficiencies at (87.95 kW + j64.72 kVAr).