GBO Algorithm Application for Solving OPF Problem Considering Renewable Energy Uncertainty

this research deals with the optimal power flow (OPF) problem from the uncertainty perspective which arises due to the high penetration levels of renewable energy sources (RESs) in recent years. In this work, RESs are represented by wind and solar PV generators and their uncertain outputs are modeled by weibull and lognormal probability density functions (PDFs), respectively. From economic point of view, the uncertain output of wind and solar power is translated into the total power cost in form of reserve or penalty cost based on the situation of their output. The IEEE-30 bus and 57 bus power systems are adjusted to involve wind and solar PV generators. Gradient based optimization (GBO) algorithm is employed for solving the OPF problem in these circumstances. The obtained results have been compared with the results of other optimization algorithms presented in literature. GBO has achieved the minimum total power cost for both modified IEEE-30 and 57 bus power systems, 781.5504 $/h, and 20233.5012 $/h, respectively with low computation time and fast convergence of solution.