Distributed Optimal Dispatching of Multi-Entity Distribution Network With Demand Response and Edge Computing

被引:20
作者
Wang, Jingting [1 ]
Peng, Yuehui [1 ]
机构
[1] North China Elect Power Univ, Baoding 071003, Peoples R China
关键词
Edge computing; Optimal scheduling; Collaboration; Dispatching; Computational modeling; Optimization methods; Demand response; edge computing; distributed optimization; KKT transformation; optimal dispatching; ECONOMIC-DISPATCH; STRATEGY;
D O I
10.1109/ACCESS.2020.3013231
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With large-scale penetrations of distributed generation (DG) and flexible loads, there will be multiple entities in traditional distribution network, such as distribution network operator (DNO), DG owner, and prosumers. Aiming at the collaborative optimization problem of distribution network among multiple entities, a distributed optimal scheduling approach of distribution network considering demand response and edge computing is proposed in this paper. Firstly, the virtual region decomposition method is proposed to divide the original distribution network into multiple regions according to different entities, and the bi-level optimization framework based on edge computing is constructed. Secondly, the optimal models of DNO, DG owner, and prosumers are established respectively, and the distributed optimal scheduling approach of distribution network with collaboration of control center and edge nodes is proposed. Then, the KKT conditions are adopted to realize the transformation of optimal models of DG owner and prosumers. Finally, the proposed distributed optimization scheduling approach is verified based on the modified IEEE33-node system. The results show that the proposed distributed optimal scheduling method can achieve better collaborative optimization among different entities in distribution network compared with the centralized optimization method.
引用
收藏
页码:141923 / 141931
页数:9
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共 24 条
  • [1] A Two-Stage Robust Optimization for Centralized-Optimal Dispatch of Photovoltaic Inverters in Active Distribution Networks
    Ding, Tao
    Li, Cheng
    Yang, Yongheng
    Jiang, Jiangfeng
    Bie, Zhaohong
    Blaabjerg, Frede
    [J]. IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2017, 8 (02) : 744 - 754
  • [2] A Bi-Level Linearized Dispatching Model of Active Distribution Network With Multi-Stakeholder Participation Based on Analytical Target Cascading
    Du, Puliang
    Chen, Zhong
    Chen, Yanxi
    Ma, Ziwen
    Ding, Hongen
    [J]. IEEE ACCESS, 2019, 7 : 154844 - 154858
  • [3] Optimal Operation Scheduling of a Microgrid Incorporating Battery Swapping Stations
    Esmaeili, Saeid
    Anvari-Moghaddam, Amjad
    Jadid, Shahram
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2019, 34 (06) : 5063 - 5072
  • [4] Decentralized Energy Management for Networked Microgrids in Future Distribution Systems
    Gao, Hongjun
    Liu, Junyong
    Wang, Lingfeng
    Wei, Zhenbo
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2018, 33 (04) : 3599 - 3610
  • [5] UAV-Enhanced Intelligent Offloading for Internet of Things at the Edge
    Guo, Hongzhi
    Liu, Jiajia
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2020, 16 (04) : 2737 - 2746
  • [6] Coordinated Multi-Area Economic Dispatch via Critical Region Projection
    Guo, Ye
    Tong, Lang
    Wu, Wenchuan
    Zhang, Boming
    Sun, Hongbin
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2017, 32 (05) : 3736 - 3746
  • [7] Cost-Benefit Analyses of Active Distribution Network Management, Part I: Annual Benefit Analysis
    Hu, Zechun
    Li, Furong
    [J]. IEEE TRANSACTIONS ON SMART GRID, 2012, 3 (03) : 1067 - 1074
  • [8] Multi-Area Interchange Scheduling Under Uncertainty
    Ji, Yuting
    Tong, Lang
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2018, 33 (02) : 1659 - 1669
  • [9] Decentralized Multi-Area Dynamic Economic Dispatch Using Modified Generalized Benders Decomposition
    Li, Zhigang
    Wu, Wenchuan
    Zhang, Boming
    Wang, Bin
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2016, 31 (01) : 526 - 538
  • [10] A Time-Driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing
    Lin, Bing
    Zhu, Fangning
    Zhang, Jianshan
    Chen, Jiaqing
    Chen, Xing
    Xiong, Naixue N.
    Mauri, Jaime Lloret
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2019, 15 (07) : 4254 - 4265