Optimal power flow solution of an integrated power system using elephant herd optimization algorithm incorporating stochastic wind and solar power

Abstract

ABSTRACT In this paper an improved optimization strategy is proposed to address issues related to uncertainty of the optimum power flow (OPF) considering the cost analysis. Elephant Herd Optimization (EHO) method roused by the crowding conduct of elephant gathering has been modified to achieve the minimization of the objective functions. Initially, the OPF is analyzed with the Newton-Raphson (N-R) approach by considering only conventional resources, later on the wind and PV-based power scheduling is performed. The output power of the wind and PV systems is computed from the Weibull probability distribution function (PDF) and Lognormal PDF. Initially, the objective function is defined to analyze the power loss, voltage deviation, carbon emissions and generation cost. The defined multi-objective function is solved for PV and Wind power generation costs, emission, voltage stability, and losses. The considered constraints are the cost of generation and the risks associated with the renewable energy sources apart from voltage and reactive power limits. Moreover, the penalty deviation charges have also been considered during extreme conditions for sustainable power sources. The proposed approach has been applied to IEEE 57 bus system and the resulting emissions, generation cost, losses and voltage deviation are evaluated and the direct, reserve, and penalty costs of wind and PV are analyzed It is contrasted to standard approaches such as Differential Evolutionary (DE) and Firefly Algorithm (FA) to substantiate the efficiency of the EHO method. The proposed approach is deployed in the MATLAB for various cases.