Low-carbon Distributed Scheduling Optimization for Multi-park Stochastic Situations Based on a Multi-stage Dual Game

To achieve the integrated energy system's low-carbon development, it is essential to coordinate the conflict of interest distribution under various park scenarios on the basis of protecting information privacy. This paper analyzes the principles of carbon emission trading and green certificate trading to build a two-tier trading framework of green card-carbon emission linkage theory. With the aim of setting the electricity selling and purchasing price and directing the park in setting the unit production plan, the Stackelberg theory is employed to create a leader-follower relationship. For the purpose of realizing the bargaining and cooperative game among multi-parks and equitable distribution among multi-parks, the Nash theory is combined on this basis. Then, using the stochastic scenario method and conditional value at risk theory, a multi-stage dual game approach for multi-park stochastic scenario low-carbon distributed dispatch optimization is proposed. And the alternating direction multiplier method is combined with the differential evolution method to decouple various park systems and safeguard the privacy of each participant. Finally, the proposed scheduling method can improve the overall advantages of multi-park and lower carbon emissions while optimizing the competing interests of parks under new energy multi-scenarios through the case analysis.