With the development of the green economy and world energy transformations, the widespread application of renewable energy and the gradual improvement of carbon emission trading market have become one of the key paths to address climate change. Investigating the cooperative operating mode of renewable energy storage systems and carbon emission trading markets is crucial from both a theoretical and practical standpoint in order to further increase the overall efficiency of the energy system. This paper aims to study the coordinated scheduling and optimization strategy between energy storage system and carbon market through computer simulation analysis, so as to improve the comprehensive performance of the system in terms of economic benefits, carbon emission control and energy utilization efficiency. To this end, this paper introduces computer simulation analysis method and constructs a mathematical model of renewable energy storage system and carbon emission trading market. Through a refined simulation process, the methods for powering up and disconnecting of the energy storage system, price fluctuations in the electricity market, changes in renewable energy generation, and the supply and demand of the carbon trading market are controlled in detail, and the interplay between the carbon emission rights market and the electrical power storage system under different scenarios is analyzed. Specifically, by dynamically adjusting the energy preservation method’s capacity to charge and discharge, the power purchase and sales strategies, carbon emissions, and price fluctuations in the carbon market, computer simulation provides data support for various optimization strategies. The results show that when the electricity price rises by 10%, the total economic benefits increase by 85,000 yuan, while carbon emissions decrease by 300 tons and the life of the energy storage system is extended by 0.5 years. This shows that higher electricity prices can improve the market competitiveness of renewable energy and promote low-carbon scheduling. Computer simulation analysis can effectively control detailed factors and thus optimize system operation.