OPTIMAL DISPATCH METHOD OF MULTI-ENERGY MICROGRID CLUSTER BASED ON DATA UPDATING LONG SHORT-TERM MEMORY NETWORK

Qiu Shi; Zhang Kun; Cheng Songqing; Chen Zhe

doi:10.19912/j.0254-0096.tynxb.2023-2144

PDF(1170 KB)

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (4) : 193-199. DOI: 10.19912/j.0254-0096.tynxb.2023-2144

OPTIMAL DISPATCH METHOD OF MULTI-ENERGY MICROGRID CLUSTER BASED ON DATA UPDATING LONG SHORT-TERM MEMORY NETWORK

Qiu Shi¹, Zhang Kun¹, Cheng Songqing¹, Chen Zhe^1,2

Author information +

History +

Abstract

Considering the uncertainty brought by high proportion of new energy supply and change in multi-source loads to multi-energy microgrid, this paper proposed a method of using long short-term memory networks to solve the optimization scheduling problem of multi energy microgrid clusters based on historical operating data of multi-energy microgrids. Firstly, a multi-energy microgrid cluster model was established in this paper. Then, considering the impact of historical data on load demand and new energy output, a data updating long short-term memory network multi-energy microgrid cluster optimization scheduling model was established. Finally, the simulation result shows that the proposed scheduling method can effectively improve the economy and accuracy of multi-energy microgrid cluster scheduling compared to traditional physical scheduling methods.

Key words

multi-energy microgrid / new energy power supply / long short-term memory network / microgrid cluster / neural networks / power grid dispatch

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Qiu Shi, Zhang Kun, Cheng Songqing, Chen Zhe. OPTIMAL DISPATCH METHOD OF MULTI-ENERGY MICROGRID CLUSTER BASED ON DATA UPDATING LONG SHORT-TERM MEMORY NETWORK[J]. Acta Energiae Solaris Sinica. 2025, 46(4): 193-199 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2144

References

[1] 王珺, 顾伟, 陆帅, 等. 结合热网模型的多区域综合能源系统协同规划[J]. 电力系统自动化, 2016, 40(15): 17-24.
WANG J, GU W, LU S, et al.Coordinated planning of multi-district integrated energy system combining heating network model[J]. Automation of electric power systems, 2016, 40(15): 17-24.
[2] 李鹏, 吴迪凡, 李雨薇, 等. 基于综合需求响应和主从博弈的多微网综合能源系统优化调度策略[J]. 中国电机工程学报, 2021, 41(4): 1307-1321.
LI P, WU D F, LI Y W, et al.Optimal dispatch of multi-microgrids integrated energy system based on integrated demand response and stackelberg game[J]. Proceedings of the CSEE, 2021, 41(4): 1307-1321.
[3] 门向阳, 曹军, 王泽森, 等. 能源互联微网型多能互补系统的构建与储能模式分析[J]. 中国电机工程学报, 2018, 38(19): 5727-5737, 5929.
MEN X Y, CAO J, WANG Z S, et al.The constructing of multi-energy complementary system of energy internet microgrid and energy storage model analysis[J]. Proceedings of the CSEE, 2018, 38(19): 5727-5737, 5929.
[4] 申泽渊, 赵海波, 李伟康, 等. 面向偏远地区低碳发展的风-光-沼-储综合能源微网多目标规划方法[J]. 太阳能学报, 2023, 44(7): 71-79.
SHEN Z Y, ZHAO H B, LI W K, et al.Multi-objective optimization method for low-carbon development of wind-solar-biogas-storage integrated energy microgrids in remote regions[J]. Acta energiae solaris sinica, 2023, 44(7): 71-79.
[5] 司徒友, 周立德, 陈凤超, 等. 基于典型场景集的智能园区多能源微网多目标配置优化研究[J]. 太阳能学报, 2022, 43(9): 515-526.
SITU Y, ZHOU L D, CHEN F C, et al.Research on configuration of multi-energy microgrid in smart park based on typical scenarios[J]. Acta energiae solaris sinica, 2022, 43(9): 515-526.
[6] 姜恩宇, 陈周, 史雷敏, 等. 计及多重不确定性与综合贡献率的多微网合作运行策略[J]. 太阳能学报, 2023, 44(10): 80-89.
JIANG E Y, CHEN Z, SHI L M, et al.Cooperative operation strategy of multi-microgrids based on multiple uncertainties and comprehen sive contribution rate[J]. Acta energiae solaris sinica, 2023, 44(10): 80-89.
[7] 刘鑫蕊, 张明超, 王睿, 等. 基于一致性算法的城市微网群分层协同控制[J]. 电力系统自动化, 2022, 46(17): 65-73.
LIU X R, ZHANG M C, WANG R, et al.Hierarchical collaborative control of urban microgrid cluster based on consensus algorithm[J]. Automation of electric power systems, 2022, 46(17): 65-73.
[8] 祝荣, 任永峰, 孟庆天, 等. 基于合作博弈的综合能源系统电-热-气协同优化运行策略[J]. 太阳能学报, 2022, 43(4): 20-29.
ZHU R, REN Y F, MENG Q T, et al.Electricity-heat-gas cooperative optimal operation strategy of integrated energy system based on cooperative game[J]. Acta energiae solaris sinica, 2022, 43(4): 20-29.
[9] 朱振山, 刘秉庚, 郭磊. 考虑动态氢价机制的综合能源多微网系统分布式优化调度[J]. 电网技术, 2023, 47(12): 5036-5054.
ZHU Z S, LIU B G, GUO L.Distributed optimal dispatch of integrated energy multi-microgrid system considering dynamic hydrogen price mechanism[J]. Power system technology, 2023, 47(12): 5036-5054.
[10] 李薇, 包哲, 杨涵晟, 等. 基于区间数理论的园区分布式综合能源系统效益及影响因素分析[J]. 太阳能学报, 2020, 41(2): 339-346.
LI W, BAO Z, YANG H S, et al.Cost-benefit and influencing factors analysis of distributed comprehensive energy system based on interval number theory[J]. Acta energiae solaris sinica, 2020, 41(2): 339-346.
[11] 徐艳春, 章世聪, 张涛, 等. 基于混合两阶段鲁棒的多微网合作优化运行[J]. 电网技术, 2024, 48(1): 247-267.
XU Y C, ZHANG S C, ZHANG T, et al.Cooperative optimal operation of multi-microgrids based on hybrid two-staged robustness[J]. Power system technology, 2024, 48(1): 247-267.
[12] 蒙飞, 单连飞, 卢峰, 等. 基于输入更新长短期记忆网络的调度自适应学习模型[J]. 电力系统自动化, 2022, 46(24): 26-35.
MENG F, SHAN L F, LU F, et al.Adaptive learning dispatching model based on long short-term memory network with internal input update[J]. Automation of electric power systems, 2022, 46(24): 26-35.
[13] 黄弦超, 封钰, 丁肇豪. 多微网多时间尺度交易机制设计和交易策略优化[J]. 电力系统自动化, 2020, 44(24): 77-88.
HUANG X C, FENG Y, DING Z H.Design of multi-time scale trading mechanism and trading strategy optimization for multiple microgrids[J]. Automation of electric power systems, 2020, 44(24): 77-88.