9th EAI International Conference on Performance Evaluation Methodologies and Tools

Research Article

Optimization of Charging Strategies for Electric Vehicles in PowerMatcher-Driven Smart Energy Grids

  • @INPROCEEDINGS{10.4108/eai.4-1-2016.151091,
        author={Pia  Kempker and Nico van Dijk and Werner Scheinhardt and Hans van den Berg and Johann Hurink},
        title={Optimization of Charging Strategies for Electric Vehicles in PowerMatcher-Driven Smart Energy Grids},
        proceedings={9th EAI International Conference on Performance Evaluation Methodologies and Tools},
        publisher={ACM},
        proceedings_a={VALUETOOLS},
        year={2016},
        month={2},
        keywords={smart grids stochastic dynamic programming market-based coordination electric vehicles PowerMatcher},
        doi={10.4108/eai.4-1-2016.151091}
    }
    
  • Pia Kempker
    Nico van Dijk
    Werner Scheinhardt
    Hans van den Berg
    Johann Hurink
    Year: 2016
    Optimization of Charging Strategies for Electric Vehicles in PowerMatcher-Driven Smart Energy Grids
    VALUETOOLS
    ICST
    DOI: 10.4108/eai.4-1-2016.151091
Pia Kempker1,*, Nico van Dijk2, Werner Scheinhardt2, Hans van den Berg3, Johann Hurink2
  • 1: TNO, Performance of Networks and Systems, The Netherlands
  • 2: University of Twente, Dept. of Applied Mathematics, The Netherlands
  • 3: TNO, Performance of, Networks and Systems,, University of Twente, Dept. of Computer Science
*Contact email: pia.kempker@tno.nl

Abstract

A crucial challenge in future smart energy grids is the large-scale coordination of distributed energy demand and generation. The well-known PowerMatcher is a promising approach that integrates demand and supply flexibility in the operation of the electricity system through dynamic pricing and a hierarchical bidding coordination scheme. However, as the PowerMatcher focuses on short-term coordination of demand and supply, it cannot fully exploit the flexibility of e.g. electric vehicles over longer periods of time. In this paper, we propose an extension of the PowerMatcher comprising a planning module, which provides coordinated predictions of demand/price over longer times as input to the users for determining their short-term bids. The optimal short-term bidding strategy minimizing a user's costs is then formulated as a Stochastic Dynamic Programming (SDP) problem. We derive an analytic solution for this SDP problem leading to a simple short-term bidding strategy. Numerical results using real-world data show a substantial performance improvement compared to the standard PowerMatcher, without significant additional complexity.