Effective Energy Management Scheme by IMPC

Abstract

The primary purpose of the Energy Management Scheme (EMS) is to monitor the energy fluctuations present in the load profile. In this paper, the improved model predictive controller is adopted for the EMS in the power system. Emperor Penguin Optimization (EPO) algorithm optimized Artificial Neural Network (ANN) with Model Predictive Control (MPC) scheme for accurate prediction of load and power forecasting at the time of pre-optimizing EMS is presented. For the power generation, Renewable Energy Sources (RES) such as photo voltaic (PV) and wind turbine (WT) are utilized along with that the fuel cell is also presented in case of failure by the RES. Such a setup is connected with the grid and applies to the household appliances. In improved model predictive control (IMPC), the set of constraints for the power flow in the system is optimized by the ANN, which is trained by EPO. Such a tuning based prediction model is presented in the IMPC technique. The proposed work is implemented in the MATLAB/Simulink platform. The energy management capability of the proposed system is analyzed for different atmospheric conditions. The total system cost, life cycle cost and annualized cost for IMPC are 48%, 45% and 15%, respectively. From the performance analysis, the cost obtained by the proposed method is very low compared to that obtained by the existing techniques.