Towards Electricity Cost Alleviation by Integrating RERs in a Smart Community: A Case Study

The Renewable Energy Resources (RERs) are advantageous in decreasing the carbon emission and energy bill of the users by empowering them to produce their own green energy. However, energy users are not able to sufficiently take paybacks from the RERs without advanced technologies. With the advent of Smart Grids, the potential benefits of RERs and dynamic pricing schemes can be fully exploited. Nonetheless, the big issue is the precise prediction of produced energy by RERs. In current work, we propose an efficient framework which is based on the integration of RERs in a smart community. This framework will be helpful and can be applied for energy management at a community level. We applied the Artificial Neural Network (ANN) model for precise and accurate prediction of produced energy by RERs. Moreover, the considered smart community consists of eighty smart homes and it is also assumed that every consumer has installed RERs including solar panels and wind turbine. Our obtained results show that our proposed framework is suitable for decreasing the energy bill of the smart community. Numerical results indicate that the energy cost of the end customer is reduced by 35 % by installing RERs in smart homes.