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Vehicle to grid - Monte Carlo simulations for optimal aggregator strategies
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.ORCID iD: 0000-0002-9860-4472
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.ORCID iD: 0000-0003-2017-7914
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.ORCID iD: 0000-0003-3014-5609
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2010 (English)In: 2010 International Conference on Power System Technology: Technological Innovations Making Power Grid Smarter, POWERCON2010, 2010Conference paper, Published paper (Refereed)
Abstract [en]

Previous work has shown that it could be profitable on some control markets to use Plug-in Hybrid Electric Vehicles (PHEV) as control power resources. This concept, where battery driven vehicles such as PHEV s provide ancillary service to the grid is commonly referred to as Vehicle to Grid (V2G). The idea is to sell the capacity and energy of the parked PHEVs on the control market. Due to the fact that cars on average are parked 92% of the day, the availability of this capacity could be very high, even though it will be highly dependent on commuting patterns in peak hours. However, as each PHEV has a very small capacity from a grid perspective, it is necessary to implement an aggregating control system, managing a large number of vehicles. This paper presents strategies for an Aggregator to fulfill control bids on the German control markets. These strategies are tested with respect to reliability, efficiency and profitability in a Monte Carlo simulation model. The model is based on available data on the distributions of commuting departure times and travel distances, as well as average driving power consumption, PHEV battery capacities and the market constraints of the secondary control market in Germany. 

Place, publisher, year, edition, pages
2010.
Keyword [en]
Aggregator, Control market, Monte Carlo simulations, Plug-in hybrid electric vehicles, Vehicle to grid, Ancillary service, Battery capacity, Control power, Departure time, Driving power, Germany, Market constraints, Monte Carlo Simulation, Number of vehicles, Plug-In Hybrid Electric Vehicle, Secondary control, Travel distance, Vehicle to grids, Automobiles, Commerce, Computer simulation, Cosmic ray detectors, Electric vehicles, Innovation, Power generation, Profitability, Secondary batteries, Monte Carlo methods
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-49935DOI: 10.1109/POWERCON.2010.5666607Scopus ID: 2-s2.0-78751525352ISBN: 9781424459407 (print)OAI: oai:DiVA.org:kth-49935DiVA: diva2:460684
Conference
2010 International Conference on Power System Technology: Technological Innovations Making Power Grid Smarter, POWERCON2010. Hangzhou, China 24 October 2010 - 28 October 2010
Note

QC 20150707

Available from: 2011-11-30 Created: 2011-11-30 Last updated: 2015-07-07Bibliographically approved

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Sandels, ClaesFranke, UlrikNordström, Lars

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CiteExportLink to record
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Citation style
  • apa
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