赵奇,吕洋,王毅,庄卫金,田江,王彪,陈中
ZHAO Qi,LYU Yang,WANG Yi,ZHUANG Weijin,TIAN Jiang,WANG Biao,CHEN Zhong

• 1:国网江苏省电力有限公司苏州供电分公司
• 2:国电南瑞科技股份有限公司
• 3:中国电力科学研究院
• 4:东南大学电气工程学院

摘要(Abstract)：

针对分布式新能源广泛接入下计及不确定性的ADN(主动配电网)与MG(微电网)协同调度问题,提出了一种基于MG灵活调节能力的ADN鲁棒优化方法。建立光伏、储能、燃气轮机和柔性负荷等分布式资源的灵活调节模型,在鲁棒优化的基础上,对MG灵活调节能力进行了描述。提出MG-ADN的交互方法,分析了MG不确定性及灵活调节能力对ADN的影响,并基于二阶锥松弛对AND进行鲁棒优化调度。设计基于灵活调节资源价格的ADN-MG迭代方法,并采用C&CG(列和约束生成算法)对模型进行了求解。基于改进的IEEE 33节点配电系统的仿真实验表明,所提策略能够有效挖掘MG内部的灵活调节资源,提升ADN级鲁棒优化效果。
A robust optimization method for ADN(active distribution network)based on the flexible regulation capability of MG(microgrid)is proposed given the coordinated dispatching of uncertain ADN and MG with large-scale distributed new energies integrated. A flexible regulation model of distributed energy resources such as PV,energy storage,gas turbine and flexible load is established,and the flexible regulation capability of MG is described on the basis of robust optimization. An MG-ADN interaction method is proposed,and the influence of uncertainty and flexible regulation capability of MG on ADN is analyzed. Moreover,the robust optimization dispatching of AND is implemented based on the second-order cone relaxation. An ADN-MG iterative method based on flexible resource price regulation is designed,and the model is solved using a C&CG(column-and-constraint generation algorithm). Simulation experiments based on the improved IEEE 33-node power distribution system show that the proposed strategy can effectively tap the flexible regulation resources within MG and enhance the robust optimization effect at the ADN level.

关键词(KeyWords)： 主动配电网;鲁棒优化;灵活调节;协同调度
active distribution network;robust optimization;flexible regulation;coordinated dispatching

Abstract:

Keywords:

基金项目(Foundation): 国家电网有限公司总部科技项目（5108-202018026A-0-0-00）

作者(Author): 赵奇,吕洋,王毅,庄卫金,田江,王彪,陈中
ZHAO Qi,LYU Yang,WANG Yi,ZHUANG Weijin,TIAN Jiang,WANG Biao,CHEN Zhong

DOI: 10.19585/j.jzdl.202201008

参考文献(References)：

• [1]蒲天骄，刘克文，陈乃仕，等.基于主动配电网的城市能源互联网体系架构及其关键技术[J].中国电机工程学报，2015,35(14):3511-3521.
• [2]高红均，刘俊勇，魏震波，等.主动配电网分层鲁棒规划模型及其求解方法[J].中国电机工程学报，2017,37(5):1389-1401.
• [3]尤毅，刘东，钟清，等.主动配电网优化调度策略研究[J].电力系统自动化，2014,38(9):177-183.
• [4]BAYAT A,BAGHERI A.Optimal active and reactive power allocation in distribution networks using a novel heuristic approach[J].Applied Energy,2019,233/234:71-85.
• [5]刘东，陈云辉，黄玉辉，等.主动配电网的分层能量管理与协调控制[J].中国电机工程学报，2014,34(31):5500-5506.
• [6]邓振立，张涛，李荣，等.基于ε-松弛的主动配电网有功-无功协调调度求解方法[J].电力工程技术，2020,39(4):104-111.
• [7]TACBATABAEE S,MORTAZAVI S S,NIKNAM T.Stochastic scheduling of local distribution systems considering high penetration of plug-in electric vehicles and renewable energy sources[J].Energy,2017,121:480-490.
• [8]康重庆，姚良忠.高比例可再生能源电力系统的关键科学问题与理论研究框架[J].电力系统自动化，2017,41(9):2-11.
• [9]任佳依，顾伟，王勇，等.基于模型预测控制的主动配电网多时间尺度有功无功协调调度[J].中国电机工程学报，2018,38(5):1397-1407.
• [10]董雷，刘梦夏，陈乃仕，等.基于随机模型预测控制的分布式能源协调优化控制[J].电网技术，2018,42(10):3219-3227.
• [11]DONG L,LI J,PU TJ,et al.Distributionally robust optimization model of active distribution network considering uncertainties of source and load[J].Journal of Modern Power Systems and Clean Energy,2019,7(6):1585-1595.
• [12]HIR SCH A,PARAG Y,GU ERR ERO J.Microgrids:a review of technologies,key drivers,and outstanding issues[J].Renewable and Sustainable Energy Reviews,2018,90:402-411.
• [13]桑博，张涛，刘亚杰，等.多微电网能量管理系统研究综述[J].中国电机工程学报，2020,40(10):3077-3093.
• [14]吕天光，艾芊，孙树敏，等.含多微网的主动配电系统综合优化运行行为分析与建模[J].中国电机工程学报，2016,36(1):122-132.
• [15]黄伟，李宁坤，李玟萱，等.考虑多利益主体参与的主动配电网双层联合优化调度[J].中国电机工程学报，2017,37(12):3418-3428.
• [16]WANG L L,ZHU Z,JIANG C W,et al.Bi-level robust optimization for distribution system with multiple microgrids considering uncertainty distribution locational marginal price[J].IEEE Transactions on Smart Grid.2021,12(2):1104-1117.
• [17]于丹文，杨明，翟鹤峰，等.鲁棒优化在电力系统调度决策中的应用研究综述[J].电力系统自动化，2016,40(7):134-143.
• [18]KALANTAR-NEYESTANAKI M,SOSSAN F,BOZORG M,et al.Characterizing the reserve provision capability area of active distribution networks:a linear robust optimization method[J].IEEE Transactions on Smart Grid.2020,11(3):2464-2475.
• [19]涂京，周明，宋旭帆，等.居民用户参与电网调峰激励机制及优化用电策略研究[J].电网技术，2019,43(2):443-453.
• [20]FARIVAR M,LOW SH.Branch flow model:relaxations and convexification-part I[J].IEEE Transactions on Power Systems.2013,28(3):2554-2564.
• [21]ZENG B,ZHAO L.Solving two-stage robust optimization problems by a constraint-and-column generation method[J].Operations Research Letters.2013,41(5):457-461.
• [22]张甜，赵奇，陈中，等.基于深度强化学习的家庭能量管理分层优化策略[J].电力系统自动化，2021,45(21):149-158.
• [23]张福兴.能源互联网系统能量管理中的大规模优化问题研究[D].长沙：国防科技大学，2017.