Energy Trilemma Index-Based Multiobjective Optimal Sizing of PV-Battery System for a Building in Tropical Savanna Climate

Abstract

Traditional buildings are responsible for carbonizing the environment by consuming a substantial amount of overall energy generation and are held accountable for violating the indexes of energy trilemma (ET). Optimally sizing the renewable energy sources (RES) with a battery system enhances the energy reliability and affordability that can address the challenges of ET indexes (ETI). To that end, an ETI-based multiobjective optimal sizing (MOOS) of PV-battery systems for a building situated in a tropical savanna climate (TSC) has been proposed. Objective functions based on ETI viz., loss of power supply probability (LPSP) that ensures energy security/reliability and levelized cost of energy (LCOE) that indicates energy affordability are formulated. An additional index named surplus energy generation (SEG) that minimizes the excess energy generated (EEG) from the PV which could not be sold back to the utility grid is also formulated. A multiobjective grey wolf optimization algorithm (MOGWO) for optimal sizing of PV-battery systems is utilized to resolve the challenges of the ETI. The Euclidean distance-based sorting approach is implemented to find the best optimal solution from the Pareto front of the MOGWO. Further, a techno-economic and reliability analysis of the optimized PV battery system is presented.