城市智能光储充电塔自适应鲁棒日前优化调度  

Adaptive Robust Day-ahead Optimal Dispatch for Urban Smart Photovoltaic Storage and Charging Tower

作　　者：高昇宇 柳志航 卫志农[2] 成乐祥 张凌浩 谈康 GAO Shengyu;LIU Zhihang;WEI Zhinong;CHENG Leociang;ZHANG Linghao;TAN Kang(Nanjing Power Supply Company of State Grid Jiangsu Electric Power Co.,Ltd.,Nanjing 210019,China;College of Energy and Electrical Engineering,Hohai University,Nanjing 211100,China)

机构地区：[1]国网江苏省电力有限公司南京供电公司,江苏省南京市210019 [2]河海大学能源与电气学院,江苏省南京市211100

出　　处：《电力系统自动化》2019年第20期39-48,共10页Automation of Electric Power Systems

基　　金：国家自然科学基金资助项目（51277052）;国网江苏省电力有限公司重点科技项目（J2017142）~~

摘　　要：智能光储充电塔子系统间的协调优化调度可以实现充电塔经济高效运行。考虑不确定性对系统调度影响,文中提出一种光储充电塔自适应鲁棒日前能量-备用协同优化调度方法。首先,以日运行总成本最小为目标,日前能量-备用调度和实时能量平衡调整分别为第1、第2阶段决策,建立光储充系统自适应鲁棒三层优化调度模型。然后,针对三层优化模型难以直接求解的问题,采用代表性场景描述不确定性,同时引入辅助变量代替最恶劣场景运行成本,实现内层解耦替换,转化为含有限数量场景的单层鲁棒优化模型。接着,提出一种关键场景辨识的迭代算法以提高模型求解效率,其中主问题为考虑少数关键场景的单层鲁棒模型,子问题则辨识关键场景。最后,以南京某实际光储充电塔为例进行算例分析,验证了所提算法的有效性。The co-optimization dispatch of sub-systems for smart photovoltaic(PV) storage and charging tower contributes to the realization of economical and efficient operation for the charging tower. Considering the influence of uncertainty on day-ahead dispatching, an adaptive robust day-ahead energy-reserve co-optimization dispatching method for PV storage and charging tower is proposed. Firstly, aiming at the minimum of daily operation cost, the day-ahead energy-reserve dispatch and real-time energy balance regulation are employed as first-and second-stage decisions, respectively, a tri-level optimization dispatch model of adaptive robust for the PV storage and charging system is established. Then, aiming at the problem that it is difficult to solve the tri-level optimization model directly, representative scenarios are used to describe the uncertainty, auxiliary variable is introduced to replace the worst-scene operation cost to deal with the inner decoupling replacement, so the tri-level model is transformed into the single-level robust optimization model with a limited number of scenarios. Moreover, an iterative algorithm for binding scenario identification is proposed to promote the computational efficiency of the model, where the master problem is the single-level robust model considering a few binding scenarios, which are identified by the sub-problem. Finally, the effectiveness of the algorithm is verified by the simulation of practical PV storage and charging tower in Nanjing, China.

关 键 词：光储充电塔 日前能量-备用协同调度 不确定性 自适应鲁棒优化 双层解耦替换 关键场景辨识 

分 类 号：TM73[电气工程—电力系统及自动化]