Optimal Power Flow Solution Based on Jellyfish Search Optimization Considering Uncertainty of Renewable Energy Sources

Today’s electrical power system became more complex interconnected network that is expanding every day. The transmission lines of the are severely loaded than ever before. Hence, facing many problems such as losses increasing, voltage instability, line overloads, etc. optimization real and reactive powers due to installation energy resources at appropriate buses can minimize improve profile especially, for congested networks. As a result, optimal flow problem (OPF) considered important tool processes planning operation systems. OPF very significant operators meet electricity demand consumers efficiently, reliable system. However, incorporation renewable sources (RESs) into grid challenging their intermittent nature. In this paper, proposed model contains three different types sources: thermal generators representing conventional sources, wind (WPGs), solar photovoltaic (SPGs) RESs. Uncertain output from WPGs SPGs forecasted with aid Weibull lognormal probability distribution functions (PDF), respectively. under overestimation RESs taken consideration while formulating objective function through adding penalty reserve cost, Moreover, carbon tax imposed main help in reducing emissions. A jellyfish search optimizer (JS) employed reach modified IEEE 30-bus test validate its feasibility. To examine effectiveness JS algorithm, simulation results compared four other nature-inspired global algorithms. developed algorithm considers several practical cases generation uncertainty time-varying load ramp rate limits generators. show solving terms minimization total cost solution convergence.