Microgrids adoption around all world is increasing with different aims as to integrate renewable generation, providing energy to rural zones, reducing CO2 emissions, or simply as an alternative for energy independence. However, the planning and operation of microgrids can be a challenging task because it involves many economical, technical, environmental aspects. This work presents a methodology for planning and validating a grid-connected microgrid using two well-known software that facilitates the design. The planning process is performed in HOMER Pro to size and select appropriate generation sources, while the possible implementation on a real distribution network is validated in DigSILENT. Numerical results show the optimal sizing process of a grid-connected microgrid for a university campus based on real data and the technical validation based on Colombian regulatory aspects.

This work presents a distributed in-line strategy to manage an isolated microgrid by optimizing active and reactive power dispatch. The proposed objective function leads to minimize the operation costs and addresses some technical requirements such as diminishing power losses and voltage deviation. Additionally, the strategy deals with temporal multi-scale goals, i.e., robustness to demand disturbances and variation of renewable resources (a short-term objective), and preservation of the health of battery-based storage systems (a long-term objective). The technique uses alternating directions method of multipliers (ADMM), accelerated consensus, and a novel battery degradation model (Quadratic AH-Throughput model). We test the proposed solution in a case study that includes renewable resources and lead-acid and lithium batteries. To obtain the results of the case study, we employ a co-simulation scheme that uses Matlab and DIgSILENT. Finally, the performance of the method is compared with a centralized optimization technique.

Context: This work presents different models based on artificial neural networks, among them NNARX, for estimating global solar radiation from UV index measurements. The objective is to determine the efficiency of the models studied to estimate global solar radiation in terms of the coefficient of determination (R2), the root-mean-square error (RMSE), and the mean absolute error (MAE). Methodology: It is divided into four stages: i) conformation of the training dataset (in this case, it uses a training set of 213.019 data collected over five years in the city of Pasto, Colombia, with the Davis Vantage Pro 2.0 station); ii) pre-processing of data to remove erroneous and unusual data; iii) definition of models based on recurrent and conventional artificial neural networks according to an analysis of topologies, e.g. NNFIR and NNARX; iv) training of the models and evaluation of the estimation efficiency through metrics such as R2, RMSE, and MAE. To validate the model, a new dataset collected during the last year was used, which was not included in the data training. Results: The global solar radiation estimation models based on NNARX show the best estimation efficiency compared to conventional neural networks. The NNARX221 model has an RMSE of 54,32 and a MAE of 18,06 w/m2. Conclusions: NNARX models are highly efficient at estimating global solar radiation, with a coefficient of determination of 0,9697 in the best of cases. The most efficient models are characterized by using two past times and the current UV index instant, and they feed from two past times of their own estimated radiation output. Furthermore, the numerical results show that the contribution of temperature and relative humidity is not relevant to improving the efficiency of the estimation of global solar radiation. These models can be particularly important since they only use measurements made with UV index sensors, which are less expensive than solar radiation ones.

A model for the electricity demand in a rural area of Colombia is developed. The methodology used to build this model is based on the exploration of cross-categorical static and linear models based on robust regression, and using the most representative variables of the processes of electricity consumption in the region. The transversal observations used in the regressions were retrieved from the energy survey conducted in Nariño's rural area in 2013. The demand model will allow, in the future, proposing policies to increase coverage and/or use of alternative energies.