Computational Intelligence-Based Optimal Power Flow Methods-A Review

The key objective of the power system is to supply reliable power at the lowest possible cost and one of the most important and technical tools to achieve this is the optimal power flow (OPF) that determines the best operating conditions for power systems while taking into account system constraints including generator capabilities, line capacity, bus voltages, and line power flow limits. Traditional methods fail to meet the required objectives because they are ineffective in dealing with quantitative objectives, exhibit local convergence, and require a significant amount of computational time if the system is large, complex and hybrid. Moreover, the problem of OPF has grown more complicated as a result of a paradigm shift in the power industry that established competition in the electricity sector. Artificial Intelligence (AI) technologies have emerged in recent years that can address extremely complicated OPF problems. Because of their versatility, such AI approaches may deal with qualitative constraints in a seamless and efficient manner. The current study provides a comprehensive review of various computational intelligence-based optimization methodologies for solving OPF problems, with a focus on the modifications introduced over time and the computational features of each for various test cases. The recent evolution of population-based search algorithms and their hybridization for addressing OPF problems, particularly over the last two decades, has been investigated in this article.