Abstract
This study addresses the challenges associated with optimal power flow (OPF) management in hybrid power systems incorporating diverse energy sources, particularly focusing on the unpredictability of renewable energy sources (RESs). A novel analytics approach is introduced using Multi-Objective Thermal Exchange Optimization (MOTEO). MOTEO is based on modeling energy transfer grounded on Newton’s Law of Cooling. The model integrates innovative non-dominated sorting and crowing distance strategies to effectively solve the multi-objective optimization problem. The proposed hybrid OPF model incorporates four primary types of energy resources: thermal, wind, solar, and small-hydro, offering a holistic approach to power management in hybrid systems. Our model’s practical applicability and efficiency are validated through rigorous testing on a modified IEEE 30-Bus system, benchmarked against other contemporary optimization methodologies. The results demonstrate that the MOTEO model successfully identifies optimal solutions for the multi-objective optimal power flow (MOOPF) problem while maintaining compliance with stringent power system constraints. This novel contribution enhances the field of analytics by providing a robust and efficient model to handle the complex problem of OPF in hybrid systems, thereby ensuring increased system reliability.
Published Version
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