Multi-objective techno-economic-environmental optimization of the building integrated photovoltaic (BIPV) system: A high-rise building

This paper presents a multi-objective optimization framework for the optimal design of building integrated photovoltaic (BIPV) systems. This approach is conducted using the non-dominated sorting genetic algorithm (NSGA-II) to find the best solution that satisfies the technical, economic, and environmental criteria formulated in this study. The optimization process is performed using EnergyPlus and jEPlus+EA software. Decision variables are the building orientation, depth of overhang panels, angle of overhang panels, the width of facade panels, and panel technology that can vary in every target surface. Also, the occupants’ thermal discomfort hours, system return on investment (ROI), and carbon equivalent of pollution are assumed as objective functions. The energy required to produce PV panels is considered in formulating environmental factors to present a more comprehensive approach. The final optimal solution is selected using the weighted sum method. Based on the results, implementation of the optimal system not only supplies ۸۶۷ MWh of electrical energy in the first year (equivalent to ۲۳% of total energy consumption) but also reduces the HVAC energy consumption by ۱۶%. The optimized system's return on investment (ROI) in the first year of operation is ۲۱.۷%. Furthermore, for the final optimal BIPV system, the occupants’ thermal discomfort time, total energy consumption, and pollutant emission were reduced by ۳۳%, ۵.۷%, and ۲۷%, respectively, compared to the initial case. The energy and carbon payback times of the optimized case are estimated as ۶ and ۲.۵ years, respectively. The results show that facade-integrated solar systems are technically, economically, and environmentally justifiable. They can decrease the energy consumption and emission of the building while increasing the occupants’ thermal comfort.

کلیدواژه ها:

Building Integrated Photovoltaic (BIPV) ، Multi-objective optimization ، Payback Time ، Building Simulation ، EnergyPlus