Abstract | ||
---|---|---|
This paper proposes a topology-aware vehicle-to-grid (V2G) energy trading mechanism that uses charging/discharging powers of electric vehicles (EVs) to regulate voltage deviations of an active distribution system. To incentivize EVs to trade charging/discharging energy, auction theory is employed to guarantee three economic properties: 1) truthfulness; 2) individual rationality; and 3) social cost minimization. The mechanism is designed according to the topology of an active distribution system, in which multiple microgrids (MGs) are networked through a distribution network (DN). Two types of V2G auctions are proposed for the MG and DN, respectively. In each auction, the auctioneer optimizes control of EVs’ charging/discharging by solving a social cost minimization problem subject to constraints of power network topology. Further, the mechanism uses analytic target cascading framework, allowing the MG and DN to set up their own V2G auctions separately, and meanwhile enabling them to coordinate with each other to determine DN–MG power exchange. It is theoretically proved that the mechanism’s three economic properties hold in both the cases of grid-tied MGs and islanding MGs. Simulation results show that the proposed mechanism can regulate voltages of the distribution system to a secure range. Theoretic analysis of the economic properties is verified as well. |
Year | DOI | Venue |
---|---|---|
2019 | 10.1109/TSG.2018.2789940 | IEEE Transactions on Smart Grid |
Keywords | Field | DocType |
Network topology,Power markets,Minimization,Voltage control,Power system stability | Topology,Voltage,Distribution system,Network topology,Auction theory,Common value auction,Minification,Engineering,Islanding,Vehicle-to-grid | Journal |
Volume | Issue | ISSN |
10 | 2 | 1949-3053 |
Citations | PageRank | References |
5 | 0.43 | 0 |
Authors | ||
5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Weifeng Zhong | 1 | 99 | 10.88 |
Kan Xie | 2 | 351 | 28.49 |
Yi Liu | 3 | 466 | 27.80 |
Chao Yang | 4 | 51 | 12.05 |
Shengli Xie | 5 | 2530 | 161.51 |