[关键词]
[摘要]
微能源系统是城市配网终端的重要聚合部分,其应对源荷随机特性的能力为城市配网稳定运行提供了有效支撑。针对城市工业园微能源系统提出一种考虑源荷随机波动的动态调度方法。考虑工业园多种可调度资源对微能源系统的经济调度构建数学模型,然后将构建的微能源系统经济调度模型表示为具有连读动作调节的深度强化学习(DRL)模型,最后采用双延迟深度确定性策略梯度算法获取DRL模型下的动态连续调度策略。所提方法不仅避免对源荷随机波动的不确定性进行建模,同时也避免了离散Q学习的可调节设备出力不连续性。仿真结果表明所提出的动态调度方法具有更好的经济性和自适应性。
[Key word]
[Abstract]
Micro-energy system is an important aggregation part of urban distribution network terminals, and its ability to cope with the random characteristics of source-load provides an effective support for stable operation of urban distribution network. An intelligent dynamic scheduling method considering the random fluctuation of source-load is proposed for the micro-energy system in urban industrial park. A mathematical model is constructed for the economic dispatch of micro-energy system considering multiple dispatchable resources in the industrial park. Then, the constructed economic dispatch model of micro-energy system is represented as a deep reinforcement learning model with continuous action regulation. Finally, a dual delayed deep deterministic policy gradient algorithm is used to obtain the dynamic continuous dispatch policy under the deep reinforcement learning model. The proposed method not only avoids modeling the uncertainty of random fluctuation of source-load, but also avoids the discontinuity of adjustable equipment output with discrete Q-learning. Simulation results show that the proposed dynamic scheduling method has better economy and adaptivity.
[中图分类号]
[基金项目]
国网宁夏电力有限公司科技项目(5229XT20003T)