To enhance the deployment capability and low-carbon degree of virtual power plants (VPPs)

a novel optimized scheduling model is proposed in this paper for a multi-energy VPP. To explore the distribution potential of the VPP and bolster its multi-energy complementarity

an architecture integrated with electric vehicle (EV) charging stations is introduced

and a battery health degradation mechanism is constructed. To address the uncertainty exhibited by EV behaviors

a feature extraction method based on deep Q-network and maximum relevance-minimum redundancy (mRMR) is then proposed. This method optimizes the applicability of mRMR in large datasets

thereby improving the accuracy of charge behavior prediction. Next

to achieve a complex optimization dispatch

a twin delayed deep deterministic policy gradient algorithm is employed. The twin Q-value truncation mechanism and smooth regularization effectively suppress the issue of policy overestimation biases. Further-more

to validate the performance of the proposed model and algorithm

four different cases are designed

and the scheduling effects achieved for EVs are compared with those of the traditional battery energy storage system framework. The simulation results show that the proposed model significantly reduces both the operational cost and carbon emission level while slowing the battery health degradation process.