Multi-Objective Optimization of a Photovoltaic-Based Energy System for Residential Power Supply

Abstract

Photovoltaic-based energy systems are increasingly recognized as sustainable options for decentralized power generation, particularly in high-irradiation regions such as Iran. However, the intermittent nature of solar energy poses challenges for continuous power supply. Hydrogen energy storage has been recently proposed as a promising alternative to traditional batteries for mitigating power shortages caused by fluctuating photovoltaic output. Despite the technological advancements in industrial solar-driven hydrogen production systems, robust sizing strategies for energy management in small-scale applications, such as residential buildings, remain scarce. This study presents a novel multi-objective optimization framework for sizing a photovoltaic–hydrogen energy system capable of fully supplying the energy demands of a residential building in Yazd, Iran. The system is dynamically modeled in TRNSYS, and optimization is carried out using an artificial neural network combined with genetic algorithms. Cost of energy, energy efficiency, and reliability are defined as key performance indicators and the objective is to find the best combination of them. The optimal system configuration consists of an 11-kW photovoltaic array, a 9.2-kW electrolyzer, a 3.7-kW fuel cell, and a 6.5-m³ hydrogen storage tank, resulting in a levelized cost of energy of $0.40/kWh and an overall energy efficiency of 8.53%. These findings highlight the potential of hydrogen energy storage to provide a reliable, efficient, and cost-competitive pathway toward sustainable residential energy supply.