Revolutionizing power resilience: innovative optimization for distributed generation integration

Ensuring resilience in power systems against extreme events such as hurricanes is a critical challenge in modern grid management. This study introduces an innovative optimization framework for Distributed Generation (DG) integration, utilizing Evolutionary Programming (EP) and Artificial Immune System (AIS) techniques to enhance power system performance. These methods determine the optimal placement and sizing of DG units to minimize power losses and bolster system resilience. The framework was validated on the IEEE 30-Bus Reliability Test System (RTS), simulating hurricane-induced disruptions. Results demonstrated significant reductions in power losses and notable improvements in resilience indices across various scenarios, with AIS outperforming EP in most cases. The study also highlighted the ability of DG to mitigate the impact of line outages and maintain system stability under varying reactive power loads. Key advantages of this approach include precise optimization, adaptability to extreme events, and scalability for larger networks. Socio-economically, the framework supports energy security and promotes sustainability through efficient DG integration, reducing reliance on centralized power sources. With its robustness and commercialization potential, this innovation offers utility providers a cost-effective solution for grid modernization and resilience enhancement, addressing the growing demand for reliable and sustainable energy systems in a dynamic environment