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This paper deals with the optimal sizing of islanded microgrids (MGs), which use diesel generators to supply energy in off-grid areas. The MG under study integrates photovoltaic (PV) and diesel generation, a battery energy storage System (BESS), and an inverter for the connection between AC and DC voltage buses. Levelised cost of energy (LCOE) and annual system cost (ASC) are considered economic indicators, while the loss of power supply probability (LPSP) is used as a reliability indicator. Fiscal incentives such as the tax benefits and accelerated depreciation applied in Colombia are considered for the optimally sizing of each MG element. Solar measurements were taken at a weather station located in the main campus of Universidad de Antioquia in Medellin, Colombia at a latitude of 6.10 and longitude of −75.38. The objective function is the minimization of the total energy delivered from the power sources that successfully meets the load. The model was implemented in Python programming language considering several scenarios. Two cases were evaluated: the first one considered PV panels, a BESS and a diesel generator, while the second one only considered PV panels and a BESS. The option that does not include the diesel generator turned out to be the most expensive, since additional PV and BESS resources are required to meet the load profile. Furthermore, it was found that the LCOE was lower when tax benefits were taken into account. [ABSTRACT FROM AUTHOR]

This paper presents an electrical infrastructure planning method for transit systems that operate with partially grid-connected vehicles incorporating on-board batteries. First, the state-of-the-art of electric transit systems that combine grid-connected and battery-based operation is briefly described. Second, the benefits of combining a grid connection and battery supply in Bus Rapid Transit (BRT) systems are introduced. Finally, the planning method is explained and tested in a BRT route in Medellin, Colombia, using computational simulations in combination with real operational data from electric buses that are currently operating in this transit line. Unlike other methods and approaches for Battery Electric Bus (BEB) infrastructure planning, the proposed technique is system-focused, rather than solely limited to the vehicles. The objective of the technique, from the vehicle's side, is to assist the planner in the correct sizing of batteries and power train capacity, whereas from the system side the goal is to locate and size the route sections to be electrified. These decision variables are calculated with the objective of minimizing the installed battery and achieve minimum Medium Voltage (MV) network requirements, while meeting all technical and reliability conditions. The method proved to be useful to find a minimum feasible cost solution for partially electrifying a BRT line with In-motion Charging (IMC) technology. [ABSTRACT FROM AUTHOR]

Science seeks strategies to mitigate global warming and reduce the negative impacts of the long-term use of fossil fuels for power generation. In this sense, implementing and promoting renewable energy in different ways becomes one of the most effective solutions. The inaccuracy in the prediction of power generation from photovoltaic (PV) systems is a significant concern for the planning and operational stages of interconnected electric networks and the promotion of large-scale PV installations. This study proposes the use of Machine Learning techniques to model the photovoltaic power production for a system in Medellín, Colombia. Four forecasting models were generated from techniques compatible with Machine Learning and Artificial Intelligence methods: K-Nearest Neighbors (KNN), Linear Regression (LR), Artificial Neural Networks (ANN) and Support Vector Machines (SVM). The results obtained indicate that the four methods produced adequate estimations of photovoltaic energy generation. However, the best estimate according to RMSE and MAE is the ANN forecasting model. The proposed Machine Learning-based models were demonstrated to be practical and effective solutions to forecast PV power generation in Medellin. [ABSTRACT FROM AUTHOR]