摘要
将大量小规模的分布式风电、光伏发电和储能等单元并网的一种有效手段是将它们通过能源聚合商聚合之后参与虚拟电厂(VPP)的优化调度,然而VPP调度中心常难以对能源聚合商的总体出力特性进行精确的建模及预测,给传统的集中式VPP优化调度模式带来挑战。针对含分布式风-光-储单元的VPP,提出一种基于强化学习的交互式优化调度模型。VPP调度中心通过与各聚合商的在线信息交互,逐步学习得到VPP中各类单元的聚合出力及VPP对大电网的购售电量决策。该优化模型采用深度确定性策略梯度(DDPG)算法求解。基于真实新能源出力数据的仿真算例验证了该方法的有效性。与线性规划求解器得到的传统集中式调度结果对比,表明本所提法有助于改善VPP运行总效益,尤其是提高新能源利用率。
One effective way to accommodate the large number of small-scale distributed wind power generation,photovoltaic power generation and energy storage units is to aggregate them by energy aggregators,and to participate in virtual power plants(VPPs)for optimal dispatch.However,it is usually difficult for VPP dispatch center to build detailed models and make accurate forecasts for the overall output characteristics of the aggregators,which brings challenges to the traditional centralized dispatch of VPPs.An interactive dispatch model based on deep reinforcement learning(DRL)is presented for VPPs containing distributed wind generation units,distributed photovoltaic generation units and distributed energy storage units.Through the online information interaction with the aggregators,the VPP dispatch center gradually learns the aggregate output of various units in VPP and the purchase and sale decision of VPP for the large power grid.The dispatch model is solved by using deep deterministic policy gradient(DDPG)algorithm.Examples based on real data are presented to demonstrate the effectiveness of proposed method.By comparing the results of our method with those of the traditional centralized dispatch obtained by CBC linear programming solver,it is shown that the proposed method is helpful in increasing total benefits of VPP,especially in improving the rate of renewable power utilization.
作者
李明扬
张智
LI Mingyang;ZHANG Zhi(School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China)
出处
《智慧电力》
北大核心
2024年第8期50-56,共7页
Smart Power
基金
国家自然科学基金资助项目(62073182)。
关键词
虚拟电厂
分布式新能源
储能
强化学习
DDPG
virtual power plant
distributed renewable energy resources
energy storage
reinforcement learning
DDPG
