This paper addresses the optimal location and sizing of photovoltaic (PV) sources in isolated direct current (DC) electrical networks, considering time-varying load and renewable generation curves. The mathematical formulation of this problem corresponds to mixed-integer nonlinear programming (MINLP), which is reformulated via mixed-integer convex optimization: This ensures the global optimum solving the resulting optimization model via branch & bound and interior-point methods. The main idea of including PV sources in the DC grid is to minimize the daily energy losses and greenhouse emissions produced by diesel generators in isolated areas. The GAMS package is employed to solve the MINLP model, using mixed and integer variables; also, the CVX and MOSEK solvers are used to obtain solutions from the proposed mixed-integer convex model in the MATLAB. Numerical results demonstrate important reductions in the daily energy losses and the harmful gas emissions when PV sources are optimally integrated into DC grid.
The Colombian power system is facing a transition from hydro-thermal generation to a diversified mix of hydro, solar, and wind energy. This paper presents an overview of the current situation and the challenges of transitioning to a more sustainable power system. This review includes data up to June 2022 about the level of renewable power generation and the introduction of modern technologies such as hydrogen and electric vehicles.
A large percentage of Colombia’s economic activity corresponds to the agricultural sector. In this sector, plantains rank second in production and planted area. However this crop is affected by different diseases, among which The Black Sigatoka stands out, caused by the fungus Mycosphaerella fijiensis. The disease highly reduces the production level of the crop and although there are prevention measures that allow reducing the incidence of the disease, there’s a lack of support for small producers in Colombia, who do not have technological tools to support the disease detection processes. This article outlines the development of a support system for the detection of black sigatoka using digital images. For this, a characterization process of the agricultural user is carried out, then, a machine learning methodology is implemented to classify the disease on a mobile device. The support system is validated through laboratory tests, field tests and the feedback from the agricultural user.
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