基于数据更新长短期记忆网络的多能源微网集群优化调度方法

邱实; 张琨; 程嵩晴; 陈哲

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

PDF(1170 KB)

太阳能学报 ›› 2025, Vol. 46 ›› Issue (4) : 193-199. DOI: 10.19912/j.0254-0096.tynxb.2023-2144

基于数据更新长短期记忆网络的多能源微网集群优化调度方法

邱实¹, 张琨¹, 程嵩晴¹, 陈哲^1,2

作者信息 +

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 +

文章历史 +

摘要

为实现多能源微网集群海量存储数据的准确分析和利用,提出一种利用数据更新长短期记忆网络的多能源微网集群优化调度方法。首先,研究多能源微网间的能量交互机理,建立多能源微网集群能量交互模型。然后,基于多能源微网间的能量传输,综合考虑负荷需求和新能源出力历史数据对多能源微网集群调度决策的影响,建立基于长短期记忆网络的多能源微网集群优化调度模型。最后,对提出的多能源微网集群优化调度方法进行算例验证,并与传统微网调度方法进行对比,结果表明提出的调度方法可有效提升多能源微网集群调度的经济性和精准性。

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.

导出引用

邱实, 张琨, 程嵩晴, 陈哲. 基于数据更新长短期记忆网络的多能源微网集群优化调度方法[J]. 太阳能学报. 2025, 46(4): 193-199 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2144

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

中图分类号： TM73

参考文献

[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.