Optimal power flow by a fuzzy based hybrid particle swarm optimization approach

This paper presents a fuzzy based hybrid particle swarm optimization (PSO) approach for solving the optimal power flow (OPF) problem with uncertainties. Wind energy systems are being considered in the study power systems. OPF is an optimization problem which minimizes the total thermal unit fuel cost, total emission, and total real power loss while satisfying physical and technical constraints on the network. When performing the OPF problem in conventional methods, the load demand and wind speed must be forecasted to prevent errors. However, actually there are always errors in these forecasted values. A characteristic feature of the proposed fuzzy based hybrid PSO method is that the forecast load demand and wind speed errors can be taken into account using fuzzy sets. Fuzzy set notations in the load demand, wind speed, total fuel cost, total emission, and total real power loss are developed to obtain the optimal setting under an uncertain environment. To demonstrate the effectiveness of the proposed method, the OPF problem is performed on the IEEE 30- and 118-Bus test systems.