(Created page with " == Abstract == 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...") |
m (Scipediacontent moved page Draft Content 684088965 to MacDougall et al 2013a) |
(No difference)
| |
Latest revision as of 09:43, 8 February 2021
Abstract
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 [1] 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 [2], 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.
Original document
The different versions of the original document can be found in:
Document information
Published on 31/12/12
Accepted on 31/12/12
Submitted on 31/12/12
Volume 2013, 2013
DOI: 10.1109/ptc.2013.6652148
Licence: CC BY-NC-SA license
Share this document
Keywords
claim authorship
Are you one of the authors of this document?