<p class="Default">Normally, the character of the wind energy as a renewable energy sources has uncertainty in generation. To resolve the Optimal Power Flow (OPF) drawback, this paper proposed a replacement Hybrid Multi Objective Artificial Physical Optimization (HMOAPO) algorithmic rule, which does not require any management parameters compared to different meta-heuristic algorithms within the literature. Artificial Physical Optimization (APO), a moderately new population-based intelligence algorithm, shows fine performance on improvement issues. Moreover, this paper presents hybrid variety of Animal Migration Optimization (AMO) algorithmic rule to express the convergence characteristic of APO. The OPF drawback is taken into account with six totally different check cases, the effectiveness of the proposed HMOAPO technique is tested on IEEE 30-bus, IEEE 118-bus and IEEE 300-bus check system. The obtained results from the HMOAPO algorithm is compared with the other improvement techniques within the literature. The obtained comparison results indicate that proposed technique is effective to succeed in best resolution for the OPF drawback.</p>
Stochastic Dynamic Optimal Power Flow Integrated with Wind Energy Using Generalized Dynamic Factor Model
