The growing uncertainty and the variability arising from the continuous proliferation of variable renewable deployment in electric vehicle charging stations (EVCSs) and power distribution networks (PDNs) have posed inevitable cost and operation risks to EVCSs and PDNs, respectively. In such an intricate uncertain environment, how to coordinate the peer-to-peer (P2P) transactive energy (TE) trading among EVCSs is still a challenge. Therefore, this article proposes a risk-aware P2P-TE coordination framework for EVCSs in constrained urban transportation network (UTN) and PDN to handle various uncertainties. The electric vehicle (EV) charging load simulation is implemented in UTN using the Monte Carlo method (MCM) to obtain the expected value and various scenarios, which are used for EVCS scheduling and uncertainty set construction. The autonomous and privacy-protected P2P-TE trading, which is modeled as a distributionally robust optimization (DRO) problem, is designed among EVCSs considering the uncertainties of charging load, renewable generation, and PDN electricity price. After receiving the trading results, a three-phase unbalanced probabilistic optimal power flow based on distributionally robust chance constraint (DR-CC) is executed by distribution system operator (DSO) for security operation, where the affine policy and system-wide response functions are developed to reformulate the original DR-CC model into tractable second-order cone programming (SOCP) form. If any PDN constraints are violated, a trading adjustment signal will be returned to EVCSs in an iterative process. Numerical tests are carried out on a 12-node UTN and the modified IEEE 33-bus PDN to validate the effectiveness of the proposed risk-aware P2P-TE coordination scheme, where the total operation cost of EVCSs is reduced by 26.65% and the tradeoff between conservatism and optimism against uncertainties is guaranteed.
Risk-Aware Hierarchical Coordination of Peer-to-Peer Energy Trading for Electric Vehicle Charging Stations in Constrained Power Distribution and Urban Transportation Networks Under Uncertainties
IEEE Transactions on Transportation Electrification ; 10 , 4 ; 9264-9280
2024-12-01
16396849 byte
Article (Journal)
Electronic Resource
English