Useful multi-objective hybrid evolutionary approach to optimal power flow

Abstract

Optimal power flow (OPF) in electric power systems is a static, non-linear, multi-objective optimisation problem of determining the optimal settings of control variables for minimising the cost of generation, emissions, transmission losses and voltage and power flow deviations. OPF is an important problem in power systems operation not only due to operational security considerations but also because even a small saving per hour translates to a large annual saving. The solution of the OPF problem, with a simultaneous and adequate consideration of all its facets within reasonable computing time, is still to be achieved. A multi-objective hybrid evolutionary strategy (MOHES) is presented for the solution of the comprehensive model for OPF. The hybridisation of GA with SA effects a beneficial synergism of both. MOHES concentrates on the ‘better’ areas of the search space. The greater modelling power of the method enables representation of non-linear and discontinuous functions and discrete variables easily without involving approximations, and its enhanced search capabilities lead to better solutions. A complete set of non-inferior solutions representing the trade-off between various objectives is provided in a single run. MOHES has been designed to use the small perturbation analysis to avoid computing a complete load flow in every fitness evaluation. This results in considerable savings in computational expense. Test results provided on standard systems reported in the literature clearly indicate its efficacy.