Within-day rolling optimal scheduling problem for active distribution networks by multi-objective evolutionary algorithm based on decomposition integrating with thought of simulated annealing

被引:21
|
作者
Zhang, Jingrui [1 ,2 ]
Li, Zhuoyun [1 ]
Wang, Beibei [1 ]
机构
[1] Xiamen Univ, Dept Instrumental & Elect Engn, Xiamen 361005, Peoples R China
[2] Xiamen Univ, Shenzhen Res Inst, Shenzhen 518000, Peoples R China
关键词
Rolling optimal dispatch; Multi-objective optimal dispatch; Active distribution network; MOEA; D-TSA; Simulated annealing algorithm; PARTICLE SWARM OPTIMIZATION; DEMAND RESPONSE PROGRAM; OPTIMAL POWER-FLOW; DIFFERENTIAL EVOLUTION; OPTIMAL OPERATION; STRATEGY; PENETRATION; PERFORMANCE; DISPATCH; SYSTEMS;
D O I
10.1016/j.energy.2021.120027
中图分类号
O414.1 [热力学];
学科分类号
摘要
The prediction accuracy plays a very important role in the optimal dispatch problem of active distribution networks (ADN). Basing on the fact that the prediction accuracy will be greatly improved when approaching prediction domains and decreasing prediction time periods, rolling optimization provides an alternative approach to handle uncertainty of renewable sources in ADN. Considering the characteristics of power supplies and loads in ADN, a comprehensive model of rolling multi-objective optimal dispatch is established. Four objective functions of minimizing the ADN operation cost, minimizing adjustment of the active power outputs to the day-ahead plan, minimizing the total active power loss, and minimizing the total voltage deviation of the system are considered simultaneously. Then, the thought of simulated annealing is integrated into the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to change the process of generating new solutions and the neighborhood updating process. Therefore, the proposed MOEA/D with the thought of simulated annealing (MOEA/DTSA) is adopted to solve this rolling multi-objective optimal dispatch problem. In the proposed approach, a dynamic probability value is employed to select which neighbor subproblem should be updated using the new generated solution in order to balance global and local searching ability of the algorithm. Finally, the performance of the proposed approach is tested and verified on an improved IEEE 33 node test system. The comparisons of the proposed method with the original MOEA/D and NSGA II on non dominated solutions, extreme solutions, optimal compromise solution (OCS) and statistical indicators are well illustrated. The results show that MOEA/D-TSA algorithm has better convergence and comprehensive performance than other considered algorithms and its application in rolling dispatch problem is also presented through this test system. ? 2021 Elsevier Ltd. All rights reserved.
引用
收藏
页数:18
相关论文
共 50 条
  • [1] Hybridizing a multi-objective simulated annealing algorithm with a multi-objective evolutionary algorithm to solve a multi-objective project scheduling problem
    Yannibelli, Virginia
    Amandi, Analia
    EXPERT SYSTEMS WITH APPLICATIONS, 2013, 40 (07) : 2421 - 2434
  • [2] Dynamic Multi-Objective Evolutionary Algorithm Based on Decomposition for Test Task Scheduling Problem
    Lu, Hui
    Xu, Xin
    Zhang, Mengmeng
    Yin, Lijuan
    2015 SIXTH INTERNATIONAL CONFERENCE ON INTELLIGENT CONTROL AND INFORMATION PROCESSING (ICICIP), 2015, : 11 - 18
  • [3] Decomposition of Multi-Objective Evolutionary Algorithm based on Estimation of Distribution
    Zhang, Jian-Qiu
    Xu, Feng
    Fang, Xian-Wen
    APPLIED MATHEMATICS & INFORMATION SCIENCES, 2014, 8 (01): : 249 - 254
  • [4] A multi-objective optimisation model to integrating flexible process planning and scheduling based on hybrid multi-objective simulated annealing
    Mohammadi, Ghorbanali
    Karampourhaghghi, Ali
    Samaei, Farshid
    INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2012, 50 (18) : 5063 - 5076
  • [5] Decomposition multi-objective evolutionary algorithm based photolithography area scheduling method
    Zhang P.
    Zhang J.
    Wang Z.
    Sun K.
    Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition), 2022, 50 (04): : 26 - 32
  • [6] Hybrid flow shop scheduling problem based on evolutionary multi-objective algorithm
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
    Nanjing Li Gong Daxue Xuebao, 2006, 3 (327-331):
  • [7] Optimal allocation of water resources by multi-objective evolutionary algorithm based on decomposition
    Wang W.
    Wang H.
    Wang, Hui (huiwang@whu.edu.cn), 1600, Inderscience Publishers (18): : 339 - 342
  • [8] A hybrid multi-objective evolutionary algorithm based on decomposition for green permutation flow-shop scheduling problem
    Luo, Cong
    Gong, Wen-Yin
    Kongzhi yu Juece/Control and Decision, 2024, 39 (08): : 2737 - 2745
  • [9] TOPSIS Based on Parallel Simulated Annealing Algorithm for Multi-Objective Aircraft Landing Problem
    Yang, Qunting
    Ye, Zhijian
    Yang, Qian
    SEVENTH INTERNATIONAL CONFERENCE ON TRAFFIC ENGINEERING AND TRANSPORTATION SYSTEM, ICTETS 2023, 2024, 13064
  • [10] A multi-objective evolutionary algorithm based on decomposition and constraint programming for the multi-objective team orienteering problem with time windows
    Hu, Wanzhe
    Fathi, Mahdi
    Pardalos, Panos M.
    APPLIED SOFT COMPUTING, 2018, 73 : 383 - 393