A Nested Game Model for Real-Time Pricing of Smart Grid under Dual-Carbon Target

With the advancement of China's dual-carbon goal strategy, large-scale integration of renewable energy into power grid has posed significant challenges.To address these challenges effectively, it is crucial to encourage users to actively adjust their electricity consumption according to price signals and prioritise renewable energy.This can not only ensure the stability of the power grid but also improve economic efficiency.A smart grid system, consisting of an aggregated power supplier and multiple residential users, is considered.In this system, all users form a population, and the supplier offers electricity to users in two forms: traditional and renewable energy power.The supplier is responsible for setting prices and incentives, while users independently make decisions regarding their power demand based on the information provided by the supplier.Different from the existing literature, this study also incorporates the guilt and discomfort cost, which reflects users' psychological responses to different types of power.As a result, a nested game model is established.Specifically, a Stackelberg game is played between the supplier and the user population, and an evolutionary game occurs within the user population.To solve this model, two iterative algorithms are developed.Moreover, numerical analysis is carried out to verify the rationality of the model and the effectiveness of the algorithms.The results indicate that renewable power is consistently cheaper.Both traditional and renewable power can achieve supply-demand equilibrium quickly.Additionally, the residential demand for renewable power accounts for 17.26% of total residential demand, which can help achieve China's binding targets for renewable energy consumption.