Multi-objective building energy system optimization considering EV infrastructure

With increasing concerns over carbon dioxide emissions, the concept of Zero Energy Building (ZEB) has emerged. Electric Vehicles (EVs) are also considered environmentally friendly since they reduce greenhouse gas emissions, with a rapidly growing market. With these global trends of increasing EV amount and infrastructure, building energy systems should incorporate the ZEB concept and the increasing electricity requirements for EV charging. However, it is unclear how EV charging demand can affect building energy system design while aligning with ZEB requirements. Therefore, this paper develops a new framework to find the optimal energy system design that meets EV charging demand and ZEB requirements. The charging demand for EVs is predicted by the machine learning model, which combines the building energy demand from EnergyPlus. Ultimately, the Genetic Algo-rithm and PROBID method are applied to optimize the Total Annual Cost (TAC) and Self-Energy Sufficiency Ratio. EV charging demand has been found to affect energy system design, especially in small-size buildings. Using the proposed method, the building owner can determine the optimal capacity of an energy system based on economic and ZEB conditions, contributing to the future net ZEB and transportation systems.