Simulation of Multi-objective Low-carbon Economic Scheduling Model Based on Big Data Algorithm

With the global warming, energy and environmental problems have become increasingly prominent, and the problem of excessive CO
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emission has attracted wide attention from all countries in the world. As a renewable energy, wind power has the characteristics of zero emission and wide sources, but the problem of abandoning wind and absorbing it hinders the development of wind power. Because the quality of scheduling strategy affects the resource utilization rate and job execution efficiency in cloud environment to a great extent, and the jobs scheduled by scheduling algorithms in homogeneous cloud environment are usually independent of each other, which obviously cannot meet the growing and widely used trend of cloud computing, it is very necessary to develop a workflow job scheduling algorithm that can adapt to different resource requirements or depend on each other in large-scale heterogeneous cloud environment. This article first introduces the basic concepts and optimization algorithms of economic dispatch involving wind farms, including the establishment of objective functions and the selection of constraints, commonly used mathematical models for dynamic economic dispatch in power systems, commonly used evolutionary algorithms, and multi-objective optimization models. Finally, a numerical simulation was conducted to analyze the economic benefits and emission reduction effects of using heat storage devices to dissipate wind power in the carbon market environment, as well as the impact of different carbon trading prices on the system’s CO
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emission reduction.