The hybridization of renewable energy resources is a known topic in sustainable technology. Many projects are going on based on the topic. The use of Photovoltaic, wind energy, and other renewable resources can be helpful to optimize the load in the utility grid. Countries like Europe and other western countries have electricity storage, whether the developing countries are still struggling to make sure the stable utility grid connection to the distribution network system. In this research, we would like to discuss the different energy production processes sustainably. As we know, the energy sources are volatile and cannot always assure stable production to keep the requirements or demand properly. We want to use the combination of the sources in a way so that we can make the balance between the demand and the supply system. This research will be an overview in terms of technical and financial sites. Also, by using the different combinations of the Internet of things and data analysis method, we will see the correlation between the different sources and their production. Based on the production data, we can determine the financial feasibility and the outcome of the system. The main problem of renewable energy sources is uncertainty. In terms of wind energy, the velocity is also not stable according to the location. We want to show a predictive model by using the intelligent formula by which we can maintain the hybrid system. The production data from different sources will tell us their contribution to the system. This contribution will help us monitor the system and control which sources have more contribution on the demand side. The predictive model will have consisted of renewable sources such as photovoltaic, wind, utility grid, and inverter systems. In the research, the tool such as Artificial Intelligence can be implemented by sustainable management. The arrangement information is prepared to extricate data and based on resources. The renewable sources data are variant according to their location and it has an impact in terms of energy production. Data acquisition and analysis could help the current technologies such as smart grid, microgrid, and their control systems. This exploration aims to introduce a predictive foundation for the management of enormous volumes of data through large Information instruments (sensors) to help the coordination of environmentally friendly power. The main difference between the conventional electricity system and the renewable energy system is the variability of sources, with conventional sources such as utility grids and diesel generators and renewable sources consisting of photovoltaic (PV), wind, etc.
Microgrids incorporating Renewable Energy (RE) sources are being used nowadays to overcome the lack of electric power supply or grid instabilities in rural areas. Microgrids are decentralized and performant solutions to distribute electric power and to supply the consumers of a community with energy. They can be used to provide stable electrical energy to hospitals, companies and residential areas and therefore, they can contribute significantly to rural development. Based on renewable sources, they are climate neutral as well. Very often, in regular operation, a Microgrid is connected with an utility national or another distributed grid. In case of an utility grid fault occurrence, the Microgrid can still provide power since it incorporates renewable sources. However, since renewable sources like photovoltaics or wind power are volatile in supply, grid instabilities, voltage and frequency fluctuations and harmonic distortions in the Microgrid can occur. This paper focuses on developing a Microgrid (M.G.) model using MATLAB Simulink and analyzing its issues at different operational modes assuming a photovoltaic generator and a coupling to an utility grid as power sources. In order to analyze and predict the behavior of the Microgrid, deep learning methods based on Auto Regressive Moving Average (ARIMA) and Artificial Neural Networks (ANN) will be applied. It is shown that these methods allow to optimize the operation modes of the Microgrid. For instance, a balance between power supply and demand at different times could be reached and lead to economic efficiency and feasibility.