Autonomous fuzzy controller design for the utilization of hybrid pv-wind energy resources in demand side management environment

• Main Authors: Anthony, Mohanasundaram, Prasad, Valsalal, Kannadasan, Raju, Mekhilef, Saad, Alsharif, Mohammed H., Kim, Mun-Kyeom, Jahid, Abu, Aly, Ayman A.
• Format: Article
• Published: MDPI 2021
• Subjects: QA75 Electronic computers. Computer science; QC Physics; TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.um.edu.my/33911/

This work describes an optimum utilization of hybrid photovoltaic (PV)-wind energy for residential buildings on its occurrence with a newly proposed autonomous fuzzy controller (AuFuCo). In this regard, a virtual model of a vertical axis wind turbine (VAWT) and PV system (each rated at 2 kW) are constructed in a MATLAB Simulink environment. An autonomous fuzzy inference system is applied to model primary units of the controller such as load forecasting (LF), grid power selection (GPS) switch, renewable energy management system (REMS), and fuzzy load switch (FLS). The residential load consumption pattern (4 kW of connected load) is allowed to consume energy from the grid and hybrid resources located at the demand side and classified as base, priority, short-term, and schedulable loads. The simulation results identify that the proposed controller manages the demand side management (DSM) techniques for peak load shifting and valley filling effectively with renewable sources. Also, energy costs and savings for the home environment are evaluated using the proposed controller. Further, the energy conservation technique is studied by increasing renewable conversion efficiency (18% to 23% for PV and 35% to 45% for the VAWT model), which reduces the spending of 0.5% in energy cost and a 1.25% reduction in grid demand for 24-time units/day of the simulation study. Additionally, the proposed controller is adapted for computing energy cost (considering the same load pattern) for future demand, and it is exposed that the PV-wind energy cost reduced to 6.9% but 30.6% increase of coal energy cost due to its rise in the Indian energy market by 2030.