An Effective Optimal Economic Sustainable Clean Energy Solution With Reduced Carbon Capturing/Carbon Utilization/ Carbon Footprint for Grid Integrated Hybrid System

Abstract

The integration of conventional sources with the grid has many challenges, like carbon emission, optimal cost of the system, and power quality issues. All these shortcomings create a non-sustainability in the environment, which is of great concern. In order to overcome such issues, a hybrid system is designed that is composed of various components or sources like wind energy, solar photovoltaic energy, thermal energy, and battery energy storage with the purpose of providing an environmentally friendly, economically viable, sustainable, and reliable solution. The objective is to reduce the carbon capture, carbon utilization, and carbon footprint. The carbon footprint is measured as the optimal difference between carbon capture (CC) and its utilization (CU), and carbon emission is represented as loss of carbon emission (LCE). Another objective is to reduce the optimal size of components, the distortion level, and the optimal cost in terms of loss of cost of energy (LCOE) for the various values of loss of power supply probability (LPSP). All the above objectives are accomplished by designing a nonlinear multi-objective problem. The designed nonlinear multi-objective function is based on a hybrid hysteresis fuzzy algorithm. The proposed algorithm is a combination of both fuzzy logic controllers and the hysteresis band method. The effectiveness of the proposed topology is tested on an IEEE standard 9 bus system. It is observed that a nonlinear hybrid hysteresis fuzzy algorithm provides a reliable and sustainable solution for optimal cost with a reduced effective carbon footprint and minimal distortion by maintaining the proper balance between carbon capturing and carbon utilization. The average values of LCOE,LCE, CU, and CC with the proposed method for various LPSP are found to be 0.5926 $/KWh, 70.64 g CO2/kWh, 89 g CO2/kWh, and 159 g CO2/kWh, which are the least among all methods.