Due to technological improvement and development of the vehicle-to-home (V2H) concept, electric vehicle (EV) can be considered as an active component of net-zero energy buildings (NZEBs). However, to achieve more dependable results, proper energy analysis is needed to take into consideration the stochastic behavior of renewable energy, energy consumption in the building and vehicle use pattern. This study aims to stochastically model a building integrating photovoltaic panels as a microgeneration technology and EVs to meet NZEB requirements.
First, a multiobjective nondominated sorting genetic algorithm (NSGA-II) was developed to optimize the building energy performance considering panels installed on the façade. Next, a dynamic solution is implemented in MATLAB to stochastically model electricity generation using solar panels as well as building and EV energy consumption. Besides, the Monte Carlo simulation method is used for quantifying the uncertainty of NZEB performance. To investigate the impact of weather on both energy consumption and generation, the model is tested in five different climatic zones in Iran.
The results show that the stochastic simulation provides building designers with a variety of convenient options to select the best design based on level of confidence and desired budget. Furthermore, economic evaluation signifies that investing in all studied cities is profitable.
Considering the uncertainty in building energy demand and PV power generation as well as EV mobility and the charging–discharging power profile for evaluating building energy performance is the main contribution of this study.
