Optimizing Electrical Vehicle Charging Cycle to Increase Efficiency of Electrical Market Participants

Y. Hermans (), S. Lannez (), B. Le Cun () and J. -C. Passelergue ()
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Y. Hermans: University of Versailles-SQY
S. Lannez: Alstom Grid Network Management Solutions
B. Le Cun: University Paris Ouest Nanterre La Défense
J. -C. Passelergue: Alstom Grid Network Management Solutions

A chapter in Operations Research Proceedings 2012, 2014, pp 189-194 from Springer

Abstract: Abstract Most European electricity markets know the principle of Balanced Responsible Parties (BRP) which are entities in charge of ensuring the energy balance over each settlement period on their balance area. We present one of the many challenging problems that must be solved to increase penetration of Electrical Vehicle (EV) in Smart Grids: the valorization of the storage capacity owned by an Electrical Vehicle Rental Service (EVRS). Our purpose is to present a workable business model in the context of European BRPs, and to describe an industrial optimization tool which conjointly minimizes EV charging cost and increases the revenue of a BRP. Electrical Vehicles are consuming power when they are used to transport people. During transport, the flexibility of the battery is not available for grid services. But when idle, the optimizer can define if the vehicle battery has to be charged for future transport or if it can be used to store energy for different potential future usages (either injection in the grid or transport). The decisions are based on the forecast of vehicles reservation (speculation about the transport service usage) and on the price the BRP is exchanging electricity. First, we describe an Electric Vehicle Fleet Optimizer (EVFO) used on the EVRS side to optimally schedule EV charging cycles by provinding a mathematical program. Then we present how to make it interact with the BRP side tool which usually consists in an optimization tool scheduling and dispatching their generation portfolio. We choose to use a demande-response scheme to achieve this since this is an interesting way to contribute relieving electricity industrial problems [4] with a real potential [5]. We finally conclude on providing some hypothesis under which we can ensure our demand-response scheme to converge.

Keywords: Marginal Cost; Smart Grid; Rebate Price; Load Forecast; Operational Bias (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-319-00795-3_28

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DOI: 10.1007/978-3-319-00795-3_28

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