A Comprehensive Review of Optimal Power Flow in Integrated Energy Systems: A Shift from Traditional to Data-Driven Technologies

This review delineates the evolution of Optimal Power Flow (OPF) within Integrated Energy Systems (IESs), marking the transition from conventional to advanced data-driven methodologies. Traditional OPF models, constrained by static assumptions and linear approximations, often falter under the dynamic and multifaceted nature of IESs. In contrast, modern data-driven approaches leverage machine learning and real-time data analytics to enhance precision, adaptability, and resilience in energy management. This paper critically examines these methodologies, highlighting significant advancements and comparing their capabilities to meet the challenges posed by increased renewable integration and digital transformation. Through this exploration, the review underscores the transformative potential of data-driven innovations in optimizing power flow, thereby fostering more efficient and sustainable energy systems.