A methodology for the generation of synthetic input data for hybrid microgrid simulations

Access to electricity is still one of the main issues in the world, especially in developing countries. In many cases rural villages are un-electrified and distant from existing national grid networks and they are thinly populated. As a consequence the costs of connection to the grid networks are very high and uneconomical. An established solution is represented by hybrid stand-alone microgrids, that are able to provide electricity to the community exploiting local renewable sources. The most of the times, during the preliminary study of the feasibility and operation of a microgrid in an isolated rural location (simulation phase), precise data regarding load demand are not available. Also local renewable sources as irradiation and wind speed profiles, recorded from the past years, are difficult to be found due to the lack of information and measure equipments. The purpose of this work is to elaborate a general methodology, focused on rural villages of developing countries but with validity extensible in every location on the earth’s surface, that starting from latitude and longitude coordinates and global datasets, is able to generate hourly annual profiles of load demand and renewable sources power output from the solar PV and wind turbine. These profiles are required as input during the simulation phase. As regard renewable energy sources, the proposed methods are synthetic data generation models that starting from monthly mean values taken from datasets, they generate 8760 hourly data for the entire year, for the desired location; while regarding load demand, a method for the generation of daily load profiles based on a big amount of assumptions and datasets is suggested; loads are divided into basic needs, agriculture, water needs and productive activities. All these calculations are implemented with a very flexible and organized Matlab algorithm that requires latitude and longitude as input, and on the basis of many assumptions regarding the type of village, people’s habits, agriculture, productive activities and generators’ characteristics, gives back these three typical annual profiles in addition to other useful information concerning biomass residues.