The impact of electric vehicle penetration and charging patterns on the management of energy hub - A multi-agent system simulation

In this paper, a multi-agent system (MAS) was developed to simulate the operation of an energy hub (EH) with different penetration rates (PRs) and various charging patterns of electric vehicle (EV). Three charging patterns, namely uncontrolled charging pattern (UCP), rapid charging pattern (RCP) and smart charging pattern (SCP), together with vehicle to grid (V2G), were simulated in the MAS. The EV penetration rates (EV-PRs), from 10% to 90% with a step of 20%, are considered in this study. Under the UCP, the peak load increases by 3.4-17.1% compared to the case without EVs, which is the reference case in this study. A main part of the increased electricity demand can be supplied by the gas turbine (GT) when the PR is lower, i.e. 71.7% under 10% PR and 37.4% under 50% PR. Under the SCP, the charging load of EVs is shifted to the valley period and thus the energy dispatch of the EH at 07:00-23:00 remain the same as that in the reference case. When V2G is considered, the electricity demand from the grid becomes the largest in all of the cases, e.g. the demand with 50% PR doubles the electricity demand in the reference case. However, the GT output decreases by 2.9-15.7% at 07:00-23:00 due to the effect of V2G. The variations in the EH's operation further raise the changes in energy cost, i.e. the electricity and cooling prices are lowered by 18.3% and 33.8% due to the availability of V2G and the heating and cooling prices increase by 3.5% and 4.3% under the UCP with the PR of 50%. Regarding the V2G capacity, near 39% of the EVs' battery capacity can be discharged via V2G. In addition, the paper also produced a V2G potential line, which is an effective tool to provide the maximum potential of the EVs for peak shaving at any specific time.