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Preventing Distribution Grid Congestion by Integrating Indirect Control in a Hierarchical Electric Vehicles’ Management System

In this paper, a hierarchical management system is proposed to integrate electric vehicles (EVs) into a distribution grid. Three types of actors are included in the system: distribution system operators (DSOs), fleet operators (FOs), and EV owners. In contrast to a typical hierarchical control system where the upper level controller directly controls the lower level subordinated nodes, this study aims to integrate two common indirect control methods: market-based control and price-based control into the hierarchical EVs’ management system. Specifically, on the lower level of the hierarchy, the FOs coordinate the charging behaviors of their EV users using a price-based control method. A parametric utility model is used on the lower level to characterize price elasticity of EVs and thus used by the FO to coordinate the individual EV charging. On the upper level of the hierarchy, the DSO uses the market-based control strategy to coordinate the limited power capacity of power transformer with FOs. To facilitate the application of the two indirect control methods into the system, a model describing decision tasks in control is used to specify the essential functions that are needed in the control system. The simulations illustrate the effectiveness of the proposed solutions.
- Technical University of Denmark Denmark
- University of Shanghai for Science and Technology China (People's Republic of)
- Agency for Science, Technology and Research Singapore
- Institute for Infocomm Research Singapore
- University of Shanghai for Science and Technology China (People's Republic of)
Hierarchical control, Electric vehicles, Indirect control, Congestion prevention, Price elasticity model
Hierarchical control, Electric vehicles, Indirect control, Congestion prevention, Price elasticity model
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).42 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
