A Novel Global Optimization Algorithm for Power Carbon Emissions Trading Processes in Power Systems

Conventional global optimization of power system electricity/carbon emissions trading process is mostly a singlelevel matching process, and the coverage of optimization is limited, which leads to the final carbon emission effect of loads being difficult to meet the standard. For this reason, the design and practice of the global optimization algorithm for the new power system power/carbon emissions trading process is proposed. At present, the characteristics of power/carbon emission analysis and the global optimization goal of the trading process are clarified first, and a multi-level approach is adopted to break the limitation of the optimization coverage and build a multi-level process optimization matching process. Based on this, the global optimization measurement model of power/carbon emission trading process is established, and the dynamic scheduling adjustment is adopted to realize the global optimization. The test results show that, compared with the optimization of power grid carbon emission process from the dual perspective of time and space, the global optimization algorithms can achieve the optimization ratio of load carbon flow rate of more than 6.5, which indicates that the global optimization algorithms of the power system power/carbon emission trading process designed this time are more efficient, specific, stable and easy to measure. and the algorithm has been practically verified in the current test context.