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The growing integration of renewable energy production and important loads such as electric vehicles bring a lot of challenges to today’s electric systems. Demand-side management strategies are often presented as one of the solutions to these problems. In this study, a fuzzy logic supervision system is proposed to minimize energy transmission costs in distribution grids by controlling electric vehicle charging and electric water heater activation. The supervision system takes into account the production of renewable energy within the grid, the load consumption and other parameters including user’s constraints. A genetic algorithm is used to optimize the supervisor’s parameters. Finally, the system’s performance is confirmed through simulation by testing it on real distribution grid data.

Known for being a reliable alternative, microgrids have been widely deployed recently in power distribution field in order to guarantee a constant power supply especially in isolated zones. Moreover, microgrids have increasingly known the penetration of renewable energy as environmentally friendly energy sources. However, the intermittency of these sources oblige specialists to think about tools allowing determining their potential, over a predetermined time interval, in order to ensure energy security. For this reason, renewable energy forecasting is crucial. Thus, in this paper, a feed forward back-propagation neural network is used to forecast next 24 hours photovoltaic (PV) power of one of the catholic university buildings "îlot RIZOMM". The accuracy of the model built is evaluated with some performance metrics. Thereafter, prediction results of PV power are compared to those provided by SteadySat, an industrial solution developed by the company SteadySun. It is shown that the prediction Mean Absolute Errors (MAEs) of the model are of 3.05% in a clear sky day, 4.95% in a cloudy day and 5.98% in a partly cloudy day.