Multi-Objective Optimal Power Flow including Wind and Solar Generation Uncertainty Using New Hybrid Evolutionary Algorithm with Efficient Constraint Handling Method

Abstract

A new hybrid decomposition-based multiobjective evolutionary algorithm is proposed for optimal power flow (OPF) including wind and solar generation uncertainty. This study recommends a novel constraint-handling method, which adaptively adds the penalty function and eliminates the parameter dependency on penalty function evaluation. The summation-based sorting and improved diversified selection methods are utilized to enhance the diversity of multiobjective optimization algorithms. The OPF problem is modeled as a multiobjective optimization problem with four objectives such as minimizing (i) total fuel cost (TC) including the cost of renewable energy source (RES), (ii) total emission (TE), (iii) active power loss (APL), and (iv) voltage magnitude deviation (VMD). The impact of RESs such as wind and solar energy sources on integration is considered in optimal power flow cost analysis. The costs of RESs are considered in the OPF problem to minimize the overall cost so that the impact of intermittence and uncertainty of renewable sources is studied in terms of cost and operation wise. The uncertainty of wind and solar energy sources is described using probability distribution functions (PDFs) such as Weibull and lognormal distributions. The efficiency of the algorithm is tested on IEEE 30-, IEEE 57-, and IEEE 118-bus systems for all possible conditions of renewable sources using Monte Carlo simulations.