A smart charging profile was implemented on 39 public charging stations in Amsterdam on which the current level available for electric vehicle (EV) charging was limited during peak hours on the electricity grid (07:00-08:30 and 17:00-20:00) and was increased during the rest of the day. The impact of this profile was measured on three indicators: average charging power, amount of transferred energy and share of positively and negatively affected sessions. The results are distinguished for different categories of electric vehicles with different charging characteristics (number of phases and maximum current). The results depend heavily on this categorisation and are a realistic measurement of the impact of smart charging under real world conditions. The average charging power increased as a result of the new profile and a reduction in the amount of transferred energy was detected during the evening hours, causing outstanding demand which was solved at an accelerated rate after limitations were lifted. For the whole population, 4% of the sessions were positively affected (charged a larger volume of energy) and 5% were negatively affected. These numbers are dominated by the large share of plug-in hybrid electric vehicles (PHEVs) in Amsterdam which are technically not able to profit from the higher current levels. For new generation electric vehicles, 14% of the sessions were positively affected and the percentage of negatively affected sessions was 5%.

The energy system is changing due to a steady increase in electric vehicles on the demand side and local production (mostly through solar panels) on the production side. Both developments can put the energy grid under stress during certain timeframes, while there might be enough capacity on the grid most of the day. Smart charging of electric vehicles might be a solution to time dependent congestion. In this study, a smart charging strategy was developed and tested in large scale with 1000 public chargers, operated in the real word. We developed and tested protocols to temporarily limit the charger capacity based on the transformer data and the number of running sessions. Over 150,000 sessions were handled, of which almost half were influenced by the smart charging strategy applied. We found that we were able to keep within the grid limits by using these controls, without hindering the driver experience. Further improvements to the smart charging strategy can be made as soon as car manufacturers share information about the car battery such as the state of charge.

Flexible charging can be applied to avoid peak loads on the electricity grid by curbing demand of electric vehicle chargers as well as matching charging power with availability of sustainable energy. This paper presents results of a large-scale demonstration project “Flexpower” where time-dependent charging profiles are applied to 432 public charging stations in the city of Amsterdam between November 2019 and March 2020. The charging current on Flexpower stations is reduced during household peak consumption hours (18:00–21:00), increased during the night-time, and dynamically linked to solar intensity levels during the day. The results show that the EV contribution to the grid peak load can be reduced by 1.2 kW per charging station with very limited user impact. The increased charging current during sunny conditions does not lead to a significantly higher energy transfer during the day because of lack of demand and technical limitations in the vehicles. A simulation model is presented based on empirical power measurements over a wide range of conditions combining the flexibility provided by simulations with the power of real-world data. The model was validated by comparing aggregated results to actual measurements and was used to evaluate the impact of different smart charging profiles in the Amsterdam context.

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This paper exposes a flexibility management algorithm to optimize the operation of behind-the-meter charging infrastructure in a building including external flexibility requests from the local distribution system operation. It includes the electricity cost minimization including drivers' comfort cost and it uses the limited information available in conventional slow charging points like electricity consumption and charging point status. Index Terms-Electric vehicle; Smart charging; Energy management system; demand response;I. NOMENCLATURE A. SetsSet of time periods in the planning horizon Sub-set of constrained periods according to the DSO request Set of electric vehicle charging points Set of photovoltaic generation units N Set of charging point sessions per charging pointB. Parameters Prosumer model parameters: − Price at energy part of retail contract for buying electricity in period t [€/kWh] − Price at energy part of grid contract for buying electricity in period t [€/kWh] Parameter that adds VAT to the amount bought [fraction] − Price at energy part of retail contract for selling electricity in period t [€/kWh] − Price at energy part of grid contract for selling electricity in period t [€/kWh] − Maximum import capacity [kWh] − Maximum export capacity [kWh] ℎ Periods per hour [#] Charging point model parameters: , Baseline charging schedule for EV v in period t [kWh] , , Arrival time of each EV charging point v of session n [#] , , Departure time of each EV charging point v of session n [#] , ℎ Price for deferring 1 kWh energy demand for one time period for charging point v [€/kWh] , , , ℎ Cost for shifting EV charging [€] , Cost for non-supplied EV charging [€]