Load Frequency Control of Renewable Penetrated Multi Area Power System with Intelligent Techniques

Abstract

In this work, the performance of a neuro-fuzzy controller is analyzed for load frequency control (LFC) problem in modern power systems. The recent changes in power system structure due to inclusion of renewable energy resources and energy storage devices have made their operation and control a difficult task. It has become challenging for a conventional controller to handle such type of power systems with increased levels of uncertainty and intermittency. Intelligent controllers because of their adaptable characteristics may be helpful in managing these systems. Moreover, their performance is yet to be evaluated in the presence of renewable resources and considering system non-linearities simultaneously. In order to evaluate the performance of intelligent controllers in this environment, a neuro-fuzzy based LFC technique has been applied. In this research work, the design and simulation of an adaptive neuro fuzzy inference system (ANFIS) based controller is presented for a power network. The training data set for the ANFIS controller is obtained by tuning a proportional integral (PI) controller using bode plot approach for a test case system. An interconnected two-area power system is modeled with all its non-linearities such as boiler dynamics, generation rate constraints, governor dead band and time delay. The system is integrated with wind and solar resources in the form of a time series data with a resolution of one second. The impact of these two renewable energy resources on the frequency response of the power system is analyzed in terms of maximum overshoot (MP) and settling time (ts). The multiple scenarios of wind and solar penetration levels are considered. Further, the energy storage technologies are utilized for improving the primary frequency control in complex electrical systems. The capacitive energy storage (CES), battery energy storage (BES), and superconducting magnetic energy storage (SMES) are considered for the study.