Summary:A novel Smart Charging strategy, based on low base allowances per charger combined with 1. clustering of chargers on the same part of the grid and 2. dynamic non guaranteed allowance, is presented in this paper. This manner of Smart Charging will allow more than 3 times the amount of chargers to be installed in the existing grid, even when the grid is already congested. The system also improves the usage of available flexibility in EV charging compared to other Smart Charging strategies. The required algorithms are tested on public chargers in Amsterdam, in some of the most intensely used parts of the Dutch grid.
The ever-increasing electrification of society has been a cause of utility grid issues in many regions around the world. With the increased adoption of electric vehicles (EVs) in the Netherlands, many new charge points (CPs) are required. A common installation practice of CPs is to group multiple CPs together on a single grid connection, the so-called charging hub. To further ensure EVs are adequately charged, various control strategies can be employed, or a stationary battery can be connected to this network. A pilot project in Amsterdam was used as a case study to validate the Python model developed in this study using the measured data. This paper presents an optimisation of the battery energy storage capacity and the grid connection capacity for such a P&R-based charging hub with various load profiles and various battery system costs. A variety of battery control strategies were simulated using both the optimal system sizing and the case study sizing. A recommendation for a control strategy is proposed.
This study used historical data from a Park & Ride facility in Amsterdam to build a validated computer (Python) model to optimize battery and grid connection sizing. The case study modelled is equipped with 8 EV chargers (16 connections), an on-site supplementary battery, and a limited capacity grid connection. This model was then used to optimize the battery energy storage capacity and grid connection capacity for minimal annualized investment, using a future proof monthly load profile. A variety of battery control strategies were simulated using both the optimal system sizing and the current system sizing. The results were compared and a recommended control strategy presented, considering a number of performance metrics.
MULTIFILE
