In this paper, we propose a new algorithm for global minimization of functions represented as a difference of two convex functions. The proposed method is a derivative free method and it is designed by adapting the extended cutting angle method. We present preliminary results of numerical experiments using test problems with difference of convex objective functions and box-constraints. We also compare the proposed algorithm with a classical one that uses prismatical subdivisions.
Electrification systems based on the use of renewable energy sources are suitable
for providing electricity to isolated communities autonomously. Specifically, electrification by wind power is one of the technological options that have been used recently in projects implemented in the Andean highlands of Peru. To date, these projects have tended to install
individual microwind turbines at each demand point. Alternatively, we propose a solution
that considers both individual generators and microgrids.We develop a mathematical model that gives the location and size or type of the wind turbines and the design of the microgrids, taking into account the demand of the consumption points and the wind potential. The criterion is the minimization of the initial investment cost required to meet the demand. The model is validated by application to a real case in the northern highlands of Peru. Results
show that microgrids are used despite the village dispersion, and the solutions significantly reduce the initial investment costs.