基于迁移多搜索器Q学习算法的碳能复合流无功优化

Reactive Power Optimization of Carbon-energy Composite Flow Based on Transfer Multi-searcher Q-learning

传统的碳排放计算模型,发电侧承担着主要的责任。但是在碳排放流的理论中,认为电网侧和用户侧才是碳排放的主要来源,需要承担主要责任。因此,有必要利用碳排放流分析方法将发电侧的碳足迹转移到电网侧和用户侧,从而制定出更有效的节能减排策略,减少二氧化碳的排放。因此,为了实现电力系统的低碳、节能和经济运行,本文把碳-能复合流放进了无功优化的目标函数中。为了对碳能复合流无功优化的模型进行求解以及证明迁移多搜索器Q学习算法的优越性,本文在IEEE 118节点系统上设计了碳-能复合流优化模型,并将GA等6种算法加进来进行对比仿真实验。算例仿真验证结果表明,所提模型和算法能够实现电力系统经济、低碳、安全运行。

In the traditional carbon emission calculation model,the generation side takes primary responsibility. However,in the theory of carbon emission flow,it is believed that the grid side and the user side are the main sources of carbon emissions and need taking primary responsibility. Therefore,it is necessary to use the carbon emission flow analysis method to transfer the carbon footprint of the power generation side to the grid side and the user side,so as to develop more effective energy saving and emission reduction strategies and reduce emission of carbon dioxide. Therefore,in order to achieve low-carbon,energy-saving and economic operation of the power system,the carbon-energy composite flow is put into the objective function of reactive power optimization in this paper. In order to solve the reactive power optimization model of the carbon-energy composite flow and prove the superiority of the migration multi-searcher Q learning algorithm,the carbon-energy composite flow optimization model is designed on the IEEE 118 node system,and such six kinds of algorithms as GA are added for comparison simulation experiment. The simulation verification results of calculation examples show that the proposed model and algorithm can achieve economic,low-carbon,and safe operation of the power system.