基于知识迁移Q学习算法的多能源系统联合优化调度

Knowledge Transfer Based Q-learning Algorithm for Optimal Dispatch of Multi-energy System

随着能源互联网的提出,传统的单一能源优化利用模式正在发生变革,多种能源网络协调优化模式展现出广阔的发展前景。在此背景下,首先以能源中心建模方法建立了多能源系统的联合优化调度框架,在此基础上构建了计及含阀点效应供能成本和碳排放目标的典型多能源系统联合优化调度模型。其次,对于此不连续可微、非凸的非线性问题,以知识迁移Q学习算法和内点法构成级联式算法进行求解,即上层Q学习以机组有功功率作为动作变量,下层以内点法求解机组有功功率确定后的多能源系统优化模型,并通过知识迁移提高求解效率。最后,以33能源中心测试系统为算例的仿真分析,验证了所提模型及算法的有效性。

The recent development of the Energy Internet has urged the conventional inefficient utilization of single energy to change towards the more developed energy usage of optimal dispatch of the multi-energy system. Against the above-mentioned background, an optimal joint dispatch of multi-energy system model framework is firstly proposed based on the energy hub modeling approach. Then a typical multi-energy system model is developed considering carbon emission and energy supply costs with valve point effect. To solve this non-linear problem with non-convex, discontinuously differentiable characteristic, the cascaded algorithm combined with the knowledge transfer based Q-learning algorithm and interior point method is applied on the model. That is, the active power of generators is taken as an action variable of Q-learning in the upper structure and solve the multi-energy system model with the interior point method in the lower structure. Meanwhile, the efficiency is greatly improved by knowledge transfer. Case studies have been carried out on a 33 energy hubs test system to verify the effectiveness of the proposed model and algorithm. This work is supported by National Basic Research Program of China （973 Program） （No. 2013CB228205） and National Natural Science Foundation of China （No. 51477055）.