Evolutionary optimization of multi-agent controlstrategies for electric vehicle charging

  • Authors:

  • Stephan Hutterer;Michael Affenzeller;Franz Auinger

  • Affiliations:

  • University of Applied Sciences Upper Austria, School of Engineering and Environmental Sciences, Wels, Austria;University of Applied Sciences Upper Austria, School of Informatics/Communication/Media, Hagenberg, Austria;University of Applied Sciences Upper Austria, School of Engineering and Environmental Sciences, Wels, Austria

  • Venue:
  • Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
  • Year:
  • 2012

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Abstract

While an increasing share of intermittent and non- dispatchable renewable energy plants cause probabilistic behavior at the power grids' supply side, the expected penetration of electric mobility at the demand side offers the opportunity of controllable load. Their optimal coordination is one major concern for future smart grids. Therefore, a multi-agent system will be proposed where each electric vehicle (agent) acts in response to dynamic conditions in its environment according to a given strategy. Optimizing these strategies will be the core of this paper, while evolutionary computation will be used for optimization. Here, simulation models will be applied for problem representation and solution evaluation. Thus, simulation allows modeling of complex as well as probabilistic systems, necessary for the herein tackled probem. In the end, the optimized strategies determine electric vehicles' charging behavior such that end-users' energy demand is satisfied and secure power grid operation is guaranteed throughout the considered grid using power from renewable plants. For solution representation, two different approaches will be compared concerning reachable solution quality as well as problem-specific metrics.