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Spatial-Temporal Demand Management and Benefit Allocation for Geo-Distributed Charging Station and EV Aggregators

This article presents a spatial-temporal demand management scheme and cooperative benefit allocation method to optimize the operation of geodistributed electric vehicle (EV) charging station and EV aggregators. Considering the spatial-temporal characteristics of the EV charging demand and energy cost distribution, we first propose a collaborative demand management model to optimize the dispatch of the charging demand and maximize the social welfare. After that, a two-stage cooperative benefit allocation mechanism based on the Nash bargaining and Shapley value theory is proposed to allocate the incremental social welfare while avoiding the conflict between individual interests and collective interests. A case study with multiple testing scenarios is provided to evaluate the performance of the proposed demand management model and benefit allocation mechanism. The numerical results demonstrate the effectiveness of the proposed model, which not only improves the operation efficiency of charging stations and EV aggregators but also achieves a benefit allocation solution that guarantees individual and collective rationalities.
- North China Electric Power University China (People's Republic of)
- Kansas State University United States
- Kansas State University United States
- North China Electric Power University China (People's Republic of)
- The University of Texas at Arlington United States
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).26 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%
