Microgrid dispatch is crucial for enhancing the technical reliability and environmental value of microgrids. Whereas traditional master-slave games coordinate grid revenue and user costs through dynamic pricing, they overlook the risk of exceeding carbon emission thresholds. To address this, this study proposes a master-slave game strategy integrating a conditional value-at-risk-based-carbon emission system with a carbon reduction subsidy mechanism, combined with an adaptive differential evolution algorithm employing threshold switching variability and crossover modes to jointly resolve the conflict between grid revenue and user costs. In this model, the upper layer acts as a leader to maximize grid revenue, formulates dynamic pricing strategies via an adaptive differential evolution algorithm, and transmits pricing information to the lower layer. The lower layer acts as a follower to minimize user costs and optimizes energy consumption behavior while accounting for uncertainties in carbon emission costs and carbon reduction subsidies, thereby transmitting user-side information to the upper layer. The electricity pricing of the superior layer influences the energy consumption decisions of the subordinate layer and carbon emission outcomes, whereas the response of the subordinate layer in turn affects grid revenue, forming bidirectional coupling to realize a master-slave game. Numerical simulations demonstrate that the proposed strategy exhibits superior adaptability and robust convergence characteristics in terms of algorithmic performance. Regarding dispatch outcomes across multiple scenarios, the proposed strategy improves economic performance while maintaining grid operation. Correspondingly, user-side expenditures are reduced, while peak carbon emissions decrease and tail-risk exposure is limited. By controlling carbon emission variations, this approach maintains a balance between economic performance and environmental sustainability.
Wu et al. (2026) studied this question.