rt from the optimal real-time electricity price to buy the electricity, the optimal, time dependent, capacity contracted with the DSO is of crucial importance for concerted charging of electric vehicles in a parking garage. The battery management system, on its turn, imposes constraints on the sequence of steps in which power is transmitted. Maximum power in individual charging steps has to vary as a function of the state-of-charge to keep an optimal state-of-health of the battery. Finally, the mobility wishes of the car user, given by the desired departure time and SOC will vary. In the PowerMatchingCity Smart Grid living lab  a strategy has been developed to optimize the charging strategies of a collection of cars by using a combination of agent-based optimization, using the PowerMatcher , and constrained, combinatorial optimization. In this article, this solution approach, the algorithms and the configuration are described. Furthermore, the implementation in the PowerMatchingCity [3,4] virtual power plant configuration with a fleet of 10 vehicles is discussed. First simulation results of constrained optimization for forecasting are analysed.
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