Intelligent hierarchical energy and power management to control the voltage and frequency of micro-grids based on power uncertainties and communication latency

This paper presents an intelligent energy management method to control the voltage and frequency at the primary and secondary control levels of micro-grids. The proposed model is based on the model predictive control (MPC) at the primary and intelligent neural network (INN) at the secondary. The purpose of this paper is to control the voltage and frequency based on the uncertainties of power generation resources and loads. In order to validate, the results of the proposed model are compared in different scenarios with other methods such as Fuzzy and PID controllers. The performance of the proposed method in primary and secondary as well as the coordination of this method with a high degree of sensitivity along with HESS and diesel generator units, in addition to increasing stability and reliability, protects the operation of the micro-grid against fluctuations based on uncertainties. The results show that this method, in addition to accuracy, has a higher operating speed than other controllers. As a result, the voltage and frequency restoration in the primary and the compensation of deviations from the reference in the secondary are associated with acceptable results. In order to validate the simulation results and experimental results are also presented.

Automated summary

This paper introduces an intelligent energy management method to control the voltage and frequency of micro-grids at primary and secondary control levels. The proposed model, based on model predictive control and intelligent neural network, demonstrates superior performance, stability, and reliability in protecting micro-grids against uncertainties. Simulation and experimental results validate the effectiveness of the method.