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Cooperative Control of Charging Stations for an EV Park with Stochastic Dynamic Programming

 
: Aragon, G.; Gümrükcü, E.; Pandian, V.; Werner-Kytölä, O.

:

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-:
IECON 2019, IEEE 45th Annual Conference of the Industrial Electronics Society. Proceedings : October 14-17, 2019, Lisbon, Portugal
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-4878-6
ISBN: 978-1-7281-4879-3
pp.6649-6654
IEEE Industrial Electronics Society (IECON Annual Conference) <45, 2019, Lisbon>
English
Conference Paper
Fraunhofer FIT ()

Abstract
An increasing penetration of EVs and their charging impose challenges to the energy grid stability. As a consequence, an optimal management of EV charging in parking lots becomes essential. This work presents an approach of a cooperative control of charging stations based on a stochastic optimization model for the energy management of a group of charging stations. Uncertainties regarding the number of charging EVs at each time step were modelled using a Markovian process, while the probability mass function was generated using a Monte Carlo simulation. Furthermore, the concept prioritizes the exploitation of local renewable resources and energy storage for EV charging. The stochastic optimization model was integrated into our own developed Stochastic Optimization Software Framework (SOFW), which deploys the application as Model Predictive Control (MPC) in the real-time scenario using dynamic programming. The cooperative control of charging stations presented in this work was evaluated successfully with a variety of EV driving scenarios. The approach will be validated in the future on the field in a car park of a DSO company including renewable generation and energy storage system.

: http://publica.fraunhofer.de/documents/N-629218.html