MULTI-OBJECTIVE BUILDING ENERGY SYSTEM OPTIMIZATION CONSIDERING EV INFRASTRUCTURE

Abstract

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 and infrastructure, building energy systems should incorporate the ZEB concept and the increasing electricity for EV charging. 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 Algorithm and PROBID method are applied to optimize the Total Annual Cost (TAC) and Self-Energy Sufficiency Ratio. Using the proposed method, the building owner can determine the optimal capacity of an energy system considering economic and ZEB conditions, contributing to the future ZEB and transportation systems.