Two-Stage Optimal Scheduling Strategy of Microgrid Distribution Network Considering Multi-Source Agricultural Load Aggregation

被引:0
作者
Ma, Guozhen [1 ]
Pang, Ning [1 ]
Wang, Yunjia [1 ]
Hu, Shiyao [1 ]
Xu, Xiaobin [1 ]
Zhang, Zeya [1 ]
Wang, Changhong [2 ]
Gao, Liai [2 ,3 ]
机构
[1] State Grid Hebei Elect Power Co Ltd, Econ & Technol Res Inst, Shijiazhuang 050021, Peoples R China
[2] Hebei Agr Univ, Coll Mech & Elect Engn, Baoding 071000, Peoples R China
[3] Baoding Key Lab Precis Control & Clean Energy Supp, Baoding 071000, Peoples R China
关键词
multi-source agricultural load; distribution network; optimize scheduling; reactive power optimization; agricultural park; DISPATCH;
D O I
10.3390/en17215429
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
With the proposed "double carbon" target for the power system, large-scale distributed energy access poses a major challenge to the way the distribution grid operates. The rural distribution network (DN) will transform into a new local power system primarily driven by distributed renewable energy sources and energy storage, while also being interconnected with the larger power grid. The development of the rural DN will heavily rely on the construction and efficient planning of the microgrid (MG) within the agricultural park. Based on this, this paper proposes a two-stage optimal scheduling model and solution strategy for the microgrid distribution network with multi-source agricultural load aggregation. First, in the first stage, considering the flexible agricultural load and the market time-of-use electricity price, the economic optimization is realized by optimizing the operation of the schedulable resources of the park. The linear model in this stage is solved by the Lingo algorithm with fast solution speed and high accuracy. In the second stage, the power interaction between the MG and the DN in the agricultural park is considered. By optimising the output of the reactive power compensation device, the operating state of the DN is optimised. At this stage, the non-linear and convex optimization problems are solved by the particle swarm optimization algorithm. Finally, the example analysis shows that the proposed method can effectively improve the feasible region of safe operation of the distribution network in rural areas and improve the operating income of a multi-source agricultural load aggregation agricultural park.
引用
收藏
页数:16
相关论文
共 33 条
  • [1] [Anonymous], 2010, Proc. CSEE
  • [2] A Distributed Control Strategy for Reactive Power Compensation in Smart Microgrids
    Bolognani, Saverio
    Zampieri, Sandro
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2013, 58 (11) : 2818 - 2833
  • [3] [陈璨 Chen Can], 2022, [电网技术, Power System Technology], V46, P3786
  • [4] Three-stage relaxation-weightsum-correction based probabilistic reactive power optimization in the distribution network with multiple wind generators
    Chen, Shuheng
    Hu, Weihao
    Du, Yuefang
    Wang, Shixing
    Zhang, Chenxuan
    Chen, Zhe
    [J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2022, 141
  • [5] Reactive Power Optimization and Price Management in Microgrid Enabled with Blockchain
    Danalakshmi, D.
    Gopi, R.
    Hariharasudan, A.
    Otola, Iwona
    Bilan, Yuriy
    [J]. ENERGIES, 2020, 13 (23)
  • [6] A coordinated active and reactive power optimization approach for multi-microgrids connected to distribution networks with multi-actor-attention-critic deep reinforcement learning
    Dong, Lei
    Lin, Hao
    Qiao, Ji
    Zhang, Tao
    Zhang, Shiming
    Pu, Tianjiao
    [J]. APPLIED ENERGY, 2024, 373
  • [7] Stochastic optimal design of a rural microgrid with hybrid storage system including hydrogen and electric cars using vehicle-to-grid technology
    Er, Gulfem
    Soykan, Gurkan
    Canakoglu, Ethem
    [J]. JOURNAL OF ENERGY STORAGE, 2024, 75
  • [8] Multi-objective Optimal Model of Rural Multi-energy Complementary System with Biogas Cogeneration and Electric Vehicle Considering Carbon Emission and Satisfaction
    Fan, Wei
    Huang, Liling
    Tan, Zhongfu
    Xue, Fan
    De, Gejirifu
    Song, Xueying
    Cong, Biao
    [J]. SUSTAINABLE CITIES AND SOCIETY, 2021, 74
  • [9] Collaborative Optimization of PV Greenhouses and Clean Energy Systems in Rural Areas
    Fu, Xueqian
    Zhou, Yazhong
    [J]. IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2023, 14 (01) : 642 - 656
  • [10] Demand-side Response Strategy of Multi-microgrids Based on an Improved Co-evolution Algorithm
    Gao, Yang
    Ai, Qian
    [J]. CSEE JOURNAL OF POWER AND ENERGY SYSTEMS, 2021, 7 (05): : 903 - 910