中文摘要：

建立了计及碳责任分摊的碳能复合流优化模型,并提出了一种网格化知识迁移学习算法,以便实现电网的低碳、经济、安全最优运行。算法采用二值编码的方式实现连续-离散空间的转换,以解决连续状态-动作空间的学习和维数灾难问题;从优化任务的状态信息和最优Q值之间的关系从发,构建了知识迁移的基本框架;为了避免在弱联系环境下,整体性提取状态特征信息给学习网络带来干扰,影响迁移学习的准确性,提出了一种网格化信息提取方式,分散式地对各局部特征进行提取和迁移。最后,通过IEEE 118节点系统的碳能复合流优化仿真验证了算法的有效性。

英文摘要：

This paper establishes a carbon-energy combined-flow optimization model with carbon responsibility sharing,and proposes a grid knowledge transfer learning algorithm to realize the low-carbon,economical and safe optimal operation of power grid. The algorithm uses the binary coding method to realize the continuous-discrete space conversion,in order to solve the continuous state-action space learning and dimension disaster problem. This paper constructs the basic framework of know ledge migration from the relationship betw een the state information of the optimization task and the optimal Q value. In order to avoid the interference of the state feature information in the weak connection environment to the learning network,which affects the accuracy of the migration learning,this paper proposes a kind of grid information extraction method for decentralized extraction and migration of each local feature. Finally,the effectiveness of this algorithm is verified by the carbon-energy combined-flow optimization model of IEEE 118-bus system.