Open Access

ARTICLE

Adaptive Neuro-Fuzzy Based Load Frequency Control in Presence of Energy Storage Devices

Pankaj Jood^*, Sanjeev Kumar Aggarwal, Vikram Chopra

Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, 147004, India

* Corresponding Author: Pankaj Jood. Email:

Intelligent Automation & Soft Computing 2022, 34(2), 785-804. https://doi.org/10.32604/iasc.2022.025217

Received 16 November 2021; Accepted 12 January 2022; Issue published 03 May 2022

Abstract

Energy storage technologies are utilized for improving the primary frequency control in complex electrical systems. In this paper, the modeling and simulation of a two-area power system is done to evaluate and compare the impact of three different energy storage applications on load frequency control performance. Capacitive energy storage (CES), battery energy storage (BES), and superconducting magnetic energy storage (SMES) are considered for the study. On the basis of peak overshoot and settling time, the performance of these energy storage devices is compared. The power system consists of thermal, wind, and solar resources. All nonlinearities are incorporated in the system model. Both conventional and artificial intelligence (AI) based controllers are tested for the case study. The simulations are carried out under multiple operating conditions and penetration levels of renewable sources. It is found that the primary frequency response of the test system is improved in the presence of the storage facilities and the neuro-fuzzy controller. The simulation results exhibit that the SMES displays the best performance indices. The prime contribution of the paper is to investigate and compare the response of the storage devices in a realistically modeled power system with renewable resources using both conventional and AI controllers.

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