A collaborative matching method for multi-energy supply systems in office buildings considering the random characteristics of electric vehicles

Abstract

Multi-energy supply systems for office buildings have been rapidly developed. Although some combinations of distributed energy sources have been applied in office buildings, there is a lack of research on the collaborative matching of office building multi-energy supply systems that integrate random electric vehicles (EVs), stationary battery (STB), photovoltaic (PV), and the power grid (PG). Therefore, in this study, we propose a method for the collaborative matching of supply and demand for multi-energy systems integrated with random EVs, STB, PV, and PG in office buildings to minimize the net operating cost of the system. To this end, we first constructed an objective function, including system operating cost and operating revenue. We then modeled the supply and demand sides of the system. On the supply side, the travel parameters of EVs around the office building were investigated, and the randomness of EVs was quantified using the Monte Carlo algorithm. On this basis, the charging and discharging capacity of EVs was determined. Additionally, an STB charging and discharging model and PV power generation model were established. On the demand side, a prediction model of office building power consumption was constructed using the Random forest (RF) algorithm. Next, the energy supply and demand of office buildings for the following 24 h were matched collaboratively using nonlinear programming. Finally, the proposed collaborative matching method was applied to a real case study. The results showed that the matching method can reduce the net operating cost of the system by up to 92% compared with the Basic scenario.