HIL Demonstration of Energy Management Strategy for Real World Extreme Fast Charging Stations with Local Battery Energy Storage Systems

Extreme Fast Charging (XFC) infrastructure is crucial for an increase in electric vehicle (EV) adoption. However, an unmanaged implementation may lead to negative grid impacts and huge power costs. This paper presents an optimal energy management strategy to utilize grid-connected Energy Storage Systems (ESS) integrated with XFC stations to mitigate these grid impacts and peak demand charges. To achieve this goal, an algorithm that controls the charge and discharge of ESS based on an optimal power threshold is developed. The optimal power threshold is determined to carry out maximum peak shaving for given battery size and SOC constraints. To validate the effectiveness of the developed strategies and algorithms at the distribution network level, real-time power simulations are performed with a modified IEEE 37-bus test feeder model and loads, including a real-world energy plaza at Argonne National Laboratory (ANL), 4 XFC-ESS sets, 4 commercial and 6 workplace nodes with both level 2 and XFC charging. 3 commercial, 2 workplace, and 8 residential nodes with only level 2 charging. The grid simulates a total of 83 XFC and 300 level 2 stations. To realistically estimate the charging power demand of ANL XFC stations a statistical approach using the probability distribution model of the ANL historical dataset is employed. Unlike other studies, the scope of the paper is not just limited to the simulation study, but it discusses and compares the results with the experimental testing performed using real-time communication with two sets of XFC and ESS at ANL. Furthermore, the peak shaving threshold determination discussed in this paper works irrespective of the generic profile, it considers both charging and discharging of the ESS. The simulation results demonstrate the effectiveness of the presented algorithms to improve voltage distortion by carrying out maximum peak shaving for given battery size and SOC constraints.