Iterative non-deterministic algorithms in optimal power flow : a comparative study

Abstract EN

In this paper we have investigated three iterative non-deterministic algorithms (met heuristics) for solving the Optimal Power Flow (OPF) problem.

The three approaches are Differential Evolution (DE) algorithm, Particle Swarms Optimization (PSO) and Artificial Bee Colony (ABC).

The purpose of an electric power system is to deliver real power to the greatest number of users at the lowest possible cost all the time.

So the objective is to minimize the total fuel cost of the generating units and also maintaining an acceptable system performance in terms of limits on generator reactive power outputs, bus voltages, Static VAR Compensator (SVC) parameters and overload in transmission lines.

CPU times can be reduced by decomposing the problem in two sub problems, the first sub problem minimize the fuel cost of generation and the second sub problem is a reactive power dispatch so optimum bus voltages can be determined and reduce the losses by controlling tap changes of the transformers and the static Vat Compensators (SVC).

This paper compares the main features of the above and reviews their methodologies.

OPF algorithms with the three approaches DE, PSO and ABC have been developed and applied to the Algerian network with 114 buses, 175 branches (lines and transformers) and 15 generators.