数据与模型混合驱动的区域综合能源系统双层优化调度决策方法

Hybrid Data-driven and Model-driven Bi-level Optimal Scheduling Decision for Regional Integrated Energy Systems

在高比例可再生能源接入以及多种能源耦合网络快速发展的背景下,基于模型驱动的传统调度方法将难以满足区域综合能源系统实时优化调度决策的速度需求。因此,研究具有高智能性和快速决策能力的智能调度决策方法具有重要的意义。该文提出了一种数据与模型混合驱动的区域综合能源双层优化调度决策方法。上层使用混合整数线性规划(mix integer linear programming,MILP)求解得到日前调度计划,为日内滚动优化提供参考,下层将卷积神经网络(convolutional neural network,CNN)与门控循环单元(gated recurrent unit,GRU)相结合进行日内滚动优化决策,使用自适应功率修正模型对其输出进行微调得到精确解。最后,通过算例分析验证了本文所提方法的有效性。

Under the background of the high percentage renewable energy access and the rapid development of multiple energy coupling networks,it is difficult for a single model-driven methodology to satisfy the speed requirements of the real-time optimal scheduling decisions for the integrated regional energy systems.Therefore,it is significant to study the scheduling decision methods with high intelligence and fast decision-making capabilities.In this paper,a data-and model-driven method for a bi-level optimal scheduling of the regional integrated energy systems is proposed.On the upper layer,the mixed integer linear programming(MILP)is used to obtain a day-ahead scheduling plan which provides reference values for the within-day rolling optimization.The lower layer combines the Convolutional Neural Network(CNN)with the Gated Recurrent Unit(GRU)for the within-day rolling optimization decision making.Using the adaptive power correction model it finely tunes its outputs to obtain the accurate solution.Finally,the effectiveness of the method proposed in this paper is demonstrated by means of example analysis.