Modeling Key Players of Highway EMS as MetaMAS™ Agents that Interact with Traffic and Power Simulators

With the spread of electric vehicles (EVs), it will be necessary to find an effective way of charging EVs on highways. Otherwise, the problem of many EV drivers trying to charge at the same time and at the same place may arise, with the result that the EV drivers may have to wait for hours before charging at a public EV charging station. Therefore, a highway energy management system (highway EMS) is required that controls EV charging. In order to evaluate EV charging strategies, a simulator is needed that can model how the highway EMS affects EV drivers’ decision-making. Using a multiagent simulator (MAS), individuals’ decision-making and activities can be described. However, a traffic simulator and a power simulator are also required. In this paper, the authors focus on a new general-purpose meta-level multi-agent simulator, MetaMAS™. Unlike conventional MASs, MetaMAS™ can use several external domain-specific simulators. The MetaMAS™ agents, such as EV driver agents, communicate with other agents and interact with traffic and power simulators. In other words, individuals (agents) connect different simulation worlds (traffic and power simulators) from the bottom up. The main contributions of this paper are the modeling of the key players of the highway EMS as MetaMAS™ agents and the evaluation of some strategies of the highway EMS: the digital signage strategy, the energy reduction strategy, the digital signage and energy reduction strategy, the power reduction strategy, and the energy and power reduction strategy. The authors evaluate these strategies from the viewpoints of “average waiting time for charging and queuing per charge” and “average waiting time for charging and queuing per unit of energy.”


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  • Accession Number: 01601020
  • Record Type: Publication
  • Files: TRIS
  • Created Date: May 2 2016 5:56PM