Abstract
This paper presents an efficient master–slave methodology to solve the problem of integrating photovoltaic (PV) generators into DC grids for a planning period of 20 years. The problem is mathematically formulated as Mixed-Integer Nonlinear Programming (MINLP) with the objective of minimizing the total annual operating cost. The main stage, consisting of a discrete-continuous version of the Crow search algorithm (DCCSA), is in charge of determining the installation positions of the PV generators and their corresponding power ratings. On the other hand, at the slave level, the successive approximation power flow method is used to determine the objective function value. Numerical results on 33- and 69-bus test systems demonstrate the applicability, efficiency and robustness of the developed approach with respect to different methodologies previously discussed in the scientific literature, such as the vortex search algorithm, the generalized normal distribution optimizer and the particle swarm optimization algorithm. Numerical tests are performed in the MATLAB programming environment using proprietary scripts.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
