Renewable energy plays a very important role in solving energy problems, and solar energy is one of the most important renewable sources, especially in sunny countries.
This paper deals with two problems: The first one is about optimal sizing in a photovoltaic panel (PVP)-battery system, and the second consists in energy management in smart grids. To achieve the first objective, an adaptive neuro-fuzzy inference system (ANFIS) estimation algorithm is developed in order to estimate a database of instantaneous photovoltaic power. The estimated instantaneous photovoltaic power is used in an optimal algorithm to size a PVP-battery power station to supply a 1.5 kW AC load. For the second objective, a deep learning forecasting algorithm is realized to estimate the smart grid parameters so as to optimize the consumption energy. All results are checked carrying out Matlab simulation using real weather data. The simulation results give a good performance of our proposed sizing and management systems.
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