Carbon-aware Coordination of Active Distribution Network and Large-scale Electric Vehicles under Source-load Uncertainties

With the popularity of electric vehicles (EVs) in the context of carbon neutrality, the effective utilization of their charging flexibility in active distribution networks (ADNs) has drawn wide attentions. This paper proposes a carbon-aware bilevel model for the ADNs with multiple charging stations (CSs) with consideration of multiple source-load uncertainties. In the upper level, the distribution system operator (DSO) minimizes the carbon-related operation cost of the ADN with uncertainties of renewable energy. In the lower level, the EV aggregator (EVA) optimizes EV charging based on the carbon intensity released from the DSO and sets optimal incentives for EV drivers under their responsivity uncertainties. The source-load uncertainties are modeled by distributionally robust chance constraints (DRCCs) and their tractable reformulations are derived. Moreover, a heuristic iterative algorithm is designed to solve the established bilevel model efficiently. Case studies demonstrate the effectiveness and computational efficiency of the proposed model and algorithm for improving the system economy and reducing carbon emission.