Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm

This paper focuses on minimizing the annual operative costs in monopolar DC distribution networks with inclusion of solar photovoltaic (PV) generators while considering a planning period 20 years. problem is formulated through mixed-integer nonlinear programming (MINLP) model, which binary variables define nodes where PV must be located, and continuous are related to power flow solution optimal sizes sources. The implementation master–slave optimization approach proposed order address complexity MINLP formulation. In master stage, discrete-continuous generalized normal optimizer (DCGNDO) implemented for sources along their sizes. slave stage corresponds specialized known as successive approximation method, helps determine total energy generation at substation terminals its expected period. Numerical results 33- 69-bus grids demonstrate effectiveness DCGNDO compared versions Chu Beasley genetic algorithm vortex search algorithm.