Electric vehicles (EVs) have great potential for implementing demand response and regulating power loads. However

existing methods mainly rely on price control and load baselines

with single response objectives

and do not fully consider user-specific needs and system uncertainties

making it difficult to achieve desired user incentives. To address this

an EV demand response optimization method based on electricity-carbon load baselines is proposed. First

an EV node electricity-carbon coupling demand response model is developed

considering parking demand variations

charging randomness

and dynamic carbon emission factors. Then

a customer directrix load dual-objective game rolling optimization framework is introduced

incorporating a day-ahead and intra-day rolling optimization model and a dual-objective leader-follower game between the distribution network and EV users to set more precise customer directrix load. A joint game optimization algorithm is proposed to reduce computational complexity and iteration time while ensuring global optimality and overall solution effectiveness. Case studies show that the method optimizes electricity-carbon balance in the distribution network

effectively reduces EV user response difficulty

ensures mutual benefits

and provide a reference for accurate baseline setting.