Soft Computing Methodologies for Hybrid Renewable Energy Sources in Smart Grid Adaptive Control

Abstract

Owing to the evolution of the smart grid, the emergence of hybrid renewable energy system (HRES) and the proliferation of plug-in-hybrid electric vehicles (PHEVs), the development of efficient and robust maximum power point tracking (MPPT) algorithms for renewable energy sources due to their inherent intermittent nature has overwhelmed the power industry. The HRES is a looming power generation scheme due to the plentiful availability of renewable energy sources (RESs). The renewable energy sources are intermittent in nature due to uncertain meteorological conditions. The residential and charging station loads behave in an erratic manner. In this scenario, to maintain the balance between generation and demand, the development of an intelligent and adaptive control algorithm has preoccupied power engineers and researchers. This research work presents the indirect adaptive tracking control of renewable energy sources in a grid-connected hybrid renewable energy system. The instantaneous nonlinear dynamics need to be captured online to harvest the maximum power efficiently from renewable energy sources. An adaptive Chebyshev-wavelet embedded NeuroFuzzy indirect MPPT control (ACWNF-MPPT) paradigm is proposed for variable speed wind energy conversion system (VS-WECS). An adaptive feedback linearization-based NeuroFuzzy MPPT (AFBLNF-MPPT) algorithm for a photovoltaic energy conversion system (PVECS) and an adaptive Hermite-wavelet incorporated NeuroFuzzy indirect tracking control (AHWNF) scheme for Solid Oxide Fuel Cell (SOFC) are developed. The charging infrastructure plays a vital role in the healthy and rapid development of electric vehicles industry. The charging station (CS) which consists of five different PHEVs and a battery storage system (BSS) is integrated to a grid-connected HRES having wind turbine MPPT controlled subsystem, photovoltaic MPPT controlled subsystem and controlled SOFC with electrolyzer subsystem which are characterized as renewable energy sources. Adaptive PID (AdapPID) control paradigm is used for non-renewable energy source (micro-turbine), storage system (battery and super-capacitor), grid side inverter and the charging station (CS converter, battery storage system (BSS), PHEVs). A comprehensive simulation test-bed for a grid-connected HRES is developed in Matlab/Simulink. The performance of the stated indirect adaptive control paradigms are evaluated through simulation results by comparison with conventional and intelligent control schemes. The simulation results prove the effectiveness of the proposed control paradigms.