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.

► A fuzzy based hybrid PSO method was presented for solving the OPF problem with uncertainties.
► To take the errors in forecast load demand and wind speed into account, membership functions were derived for the total fuel cost, total emission, total real power loss, real and reactive power demands and wind speed using fussy set notations.
► With these membership functions, a PSO incorporating a local random search was presented to reach the optimal setting for real power generation levels, voltage magnitudes, and LTC tap positions under a fuzzy environment.
► The results revealed that the proposed fuzzy based hybrid PSO method is very effective in reaching a proper setting when imprecision in load demands and wind speed is considered.
► The proposed fuzzy based hybrid PSO method has good performance and ability to obtain the solution of the OPF problem.