A Novel Load Forecast, and Sizing Model of a Hybrid PV-Hydroelectric Microgrid System Using Python

Abstract

Abstract Conventional sources of energy have played major in burning diesel, petrol into carbon dioxide. Carbon and greenhouse emissions have immensely contributed toward global warming and thus face global criticism from environmental activists and the UN agencies. Therefore, it became imperative to reduce or cut these emissions. Renewable energy systems have over the years gained attention from researchers and environmentalists due to their clean nature. Thus, they are emission free and reduce the reduction of carbon emissions. Furthermore, the size of the energy system depends on the energy demand required by the load. In the foregoing, the demand of Nile university for one year was obtained weekly. Furthermore, a load forecasting model was developed using python for the prediction of the energy demand. An average model accuracy of 98% was obtained. However, the campus uses four synchronized generators as energy sources coupled with a grid-tied PV/solar system and a public utility energy grid. The greenhouse emissions because of using these generators was determined. Using an energy content factor (EC) of 38.6 GJ/kL and an emission factor (EF) of 69.5Kg CO2 -e per GJ the greenhouse gas emission is obtained as 21,008.22 tones. Similarly, the carbon saved from using the grid-tied solar system is obtained at 202.96 tones. Therefore, the need for replacing the generators with a renewable energy system is obvious. Hence, to reduce the carbon emissions by the diesel generators used by the campus, a hydroelectric energy system was sized based on existing models. A best- and worst-case scenarios were modelled and obtained. Results have shown that the River/Stream of water passing through Nile has potentials that can be harnessed to curb the issues of emission.