基于数字孪生的混合微电网多目标优化调度

张冬生; 刘洪; 李朝锋

doi:10.19912/j.0254-0096.tynxb.2023-1665

PDF(1542 KB)

太阳能学报 ›› 2025, Vol. 46 ›› Issue (2) : 184-192. DOI: 10.19912/j.0254-0096.tynxb.2023-1665

基于数字孪生的混合微电网多目标优化调度

张冬生¹, 刘洪¹, 李朝锋²

作者信息 +

DIGITAL TWIN-BASED MULTI-OBJECTIVE OPTIMAL SCHEDULING FOR HYBRID MICROGRIDS

Zhang Dongsheng¹, Liu Hong¹, Li Chaofeng²

Author information +

文章历史 +

摘要

针对传统交直流混合微电网日优化调度面临的包括设备建模过于简化、信息捕获与传输不及时等诸多问题,提出一种基于数字孪生的交直流混合微电网多目标优化调度方法。首先,考虑到微电网易受局部气象条件、本地负载、外部大电网环境等因素的影响发生扰动,建立交直流混合微电网的数字模型;然后,以经济效益和储能装置健康度为目标,提出一种基于改进档案维护策略和模糊决策方法的多目标粒子群算法用于实现对储能装置出力情况的实时调度优化;最后,基于Cloudpss平台建立微电网的测试模型,进行仿真实验。结果表明,所提方法能在兼顾储能装置健康度的前提下提高微电网的经济效益。

Abstract

The daily optimal scheduling of traditional AC/DC hybrid microgrid faces many problems, including over-simplified equipment modeling, delayed information capture and transmission, etc. This paper proposes a multi-objective optimization scheduling method for AC/DC hybrid microgrid based on digital twin. Firstly, the digital model of AC/DC hybrid microgrid is established, considering that the microgrid is easily disturbed by local meteorological conditions, local load, external large grid environment and other factors. Then, taking economic benefit and health degree of energy storage device as goals, a multi-objective particle swarm optimization algorithm based on improved archive maintenance strategy and fuzzy decision method is proposed to realize the real-time scheduling optimization of energy storage device output. Finally, the microgrid simulation model is established based on Cloudpss platform. The simulation results show that the proposed method can improve the economic efficiency of the microgrid under the premise of maintaining the health of the energy storage device.

导出引用

张冬生, 刘洪, 李朝锋. 基于数字孪生的混合微电网多目标优化调度[J]. 太阳能学报. 2025, 46(2): 184-192 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1665

Zhang Dongsheng, Liu Hong, Li Chaofeng. DIGITAL TWIN-BASED MULTI-OBJECTIVE OPTIMAL SCHEDULING FOR HYBRID MICROGRIDS[J]. Acta Energiae Solaris Sinica. 2025, 46(2): 184-192 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1665

中图分类号： TM734

参考文献

[1] 陈侃, 黄猛, 刘志刚. 基于改进粒子群的海上微电网算法优化[J]. 电力电子技术, 2022, 56(10): 40-44.
CHEN K, HUANG M, LIU Z G.The offshore microgrid algorithm optimization based on improved particle swarm optimization[J]. Power electronics, 2022, 56(10): 40-44.
[2] 隋东旭, 魏震波, 周春燕, 等. 现货市场下基于混合储能技术的微电网偏差电量优化[J]. 电力系统自动化, 2021, 45(11): 160-169.
SUI D X, WEI Z B, ZHOU C Y, et al.Deviation electricity optimization of microgrid based on hybrid energy storage technology in spot market[J]. Automation of electric power systems, 2021, 45(11): 160-169.
[3] 王子鹏, 郑丽君, 吕世轩. 考虑多储能系统功率分配的独立直流微电网协调控制策略[J]. 电力建设, 2021, 42(4): 89-96.
WANG Z P, ZHENG L J, LYU S X.Coordinated control for islanded DC microgrid considering power sharing of multiple energy storages[J]. Electric power construction, 2021, 42(4): 89-96.
[4] 邵志芳, 赵强, 张玉琼. 独立型微电网源荷协调配置优化[J]. 电网技术, 2021, 45(10): 3935-3946.
SHAO Z F, ZHAO Q, ZHANG Y Q.Source side and load side coordinated configuration optimization for stand-alone micro-grid[J]. Power system technology, 2021, 45(10): 3935-3946.
[5] AN S, WANG H L, LENG X X.Optimal operation of multi-micro energy grids under distribution network in Southwest China[J]. Applied energy, 2022, 309: 118461.
[6] 郭国栋, 徐倩雯, 龚雁峰. 考虑需求响应的互联交直流混合微电网的分布式经济调度模型[J]. 电测与仪表, 2023, 60(5): 116-125, 153.
GUO G D, XU Q W, GONG Y F.A distributed economic dispatching model for networked hybrid AC/DC microgrids considering demand response[J]. Electrical measurement & instrumentation, 2023, 60(5): 116-125, 153.
[7] ELGAMAL M, KOROVKIN N, ABDEL MENAEM A, et al.Day-ahead complex power scheduling in a reconfigurable hybrid-energy islanded microgrid with responsive demand considering uncertainty and different load models[J]. Applied energy, 2022, 309: 118416.
[8] HEIDARI S, HATAMI A, ESKANDARI M.An intelligent capacity management system for interface converter in AC-DC hybrid microgrids[J]. Applied energy, 2022, 316: 119112.
[9] 何红玉, 韩蓓, 徐晨博, 等. 交直流混合微电网一致性协调优化管理系统[J]. 电力自动化设备, 2018, 38(8): 138-146.
HE H Y, HAN B, XU C B, et al.Optimal management system of hybrid AC/DC microgrid based on consensus protocols[J]. Electric power automation equipment, 2018, 38(8): 138-146.
[10] ALIZADEH BIDGOLI M, AHMADIAN A.Multi-stage optimal scheduling of multi-microgrids using deep-learning artificial neural network and cooperative game approach[J]. Energy, 2022, 239: 122036.
[11] 吴青峰, 智泽英, 于少娟, 等. 基于多代理的微电网分布式储能系统健康状态平衡方案[J]. 太阳能学报, 2022, 43(2): 104-112.
WU Q F, ZHI Z Y, YU S J, et al.SOH balancing scheme for distributed energy storage systems in microgrid based on multi-agent[J]. Acta energiae solaris sinica, 2022, 43(2): 104-112.
[12] ELAHIDOOST A, TEDESCHI E.Stability improvement of MMC-based hybrid AC/DC grids through the application of a decentralized optimal controller[J]. IET generation, transmission & distribution, 2022, 16(15): 3050-3068.
[13] 赖钧杰, 文小玲, 张淇, 等. 基于改进麻雀搜索算法的直流微电网容量优化配置[J]. 太阳能学报, 2023, 44(8): 157-163.
LAI J J, WEN X L, ZHANG Q, et al.Capacity optimization configuration of DC microgrid based on improved sparrow search algorithm[J]. Acta energiae solaris sinica, 2023, 44(8): 157-163.
[14] 董海, 曹晓兰. 基于策略驱动的混合能源微电网动态调度[J]. 太阳能学报, 2023, 44(7): 22-29.
DONG H, CAO X L.Dynamic dispatching of hybrid energy microgrids based on strategy-driven[J]. Acta energiae solaris sinica, 2023, 44(7): 22-29.
[15] 绳晓玲, 韩旭超, 万书亭. 风速分布差异对永磁风力发电机组故障特性的影响分析[J]. 太阳能学报, 2023, 44(2): 123-132.
SHENG X L, HAN X C, WAN S T.Fault characteristics analysis of permanent magnetic wind turbines considering wind speed distribution differences[J]. Acta energiae solaris sinica, 2023, 44(2): 123-132.
[16] 万君, 霍方强, 李岩松, 等. 分布式储能系统调度策略研究及仿真分析[J]. 电测与仪表, 2023, 60(5): 33-38.
WAN J, HUO F Q, LI Y S, et al.Research and simulation analysis on scheduling strategy of distributed energy storage systems[J]. Electrical measurement & instrumentation, 2023, 60(5): 33-38.
[17] ZHANG Y, LIANG C J, SHI J, et al.Optimal port microgrid scheduling incorporating onshore power supply and berth allocation under uncertainty[J]. Applied energy, 2022, 313: 118856.
[18] NAWAZ A, WU J, YE J, et al.Distributed MPC-based energy scheduling for islanded multi-microgrid considering battery degradation and cyclic life deterioration[J]. Applied energy, 2023, 329: 120168.
[19] MOHAMMADPOUR SHOTORBANI A, ZEINAL-KHEIRI S, CHHIPI-SHRESTHA G, et al.Enhanced real-time scheduling algorithm for energy management in a renewable-integrated microgrid[J]. Applied energy, 2021, 304: 117658.
[20] 马跃, 孟润泉, 魏斌, 等. 考虑阶梯式碳交易机制的微电网两阶段鲁棒优化调度[J]. 电力系统保护与控制, 2023, 51(10): 22-33.
MA Y, MENG R Q, WEI B, et al.Two-stage robust optimal scheduling of a microgrid with a stepped carbon trading mechanism[J]. Power system protection and control, 2023, 51(10): 22-33.
[21] 尚海勇, 刘利强, 齐咏生, 等. 基于数字孪生技术的风电机组建模研究[J]. 太阳能学报, 2023, 44(5): 391-400.
SHANG H Y, LIU L Q, QI Y S, et al.Research on wind turbine modeling based on digital twin technology[J]. Acta energiae solaris sinica, 2023, 44(5): 391-400.
[22] PARK H A, BYEON G, SON W, et al.Digital twin for operation of microgrid: optimal scheduling in virtual space of digital twin[J]. Energies, 2020, 13(20): 5504.
[23] TANG X Y, BAI H, ZHANG Y, et al.Simulation and analysis of integrated energy conversion and storage systems using CloudPSS-IESLab[J]. Energy reports, 2022, 8: 1372-1382.
[24] 祝庆伟. SOC循环区间对锂离子电池老化性能的影响研究[D]. 杭州: 浙江大学, 2023.
ZHU Q W.Effect of SOC cycle interval on aging performance of lithium-ion battery[D]. Hangzhou: Zhejiang University, 2023.
[25] 邓创, 鞠立伟, 刘俊勇, 等. 基于模糊CVaR理论的水火电系统随机调度多目标优化模型[J]. 电网技术, 2016, 40(5): 1447-1454.
DENG C, JU L W, LIU J Y, et al.Stochastic scheduling multi-objective optimization model for hydro-thermal power systems based on fuzzy CVaR theory[J]. Power system technology, 2016, 40(5): 1447-1454.
[26] YANG J J, ZHOU J Z, LIU L, et al.A novel strategy of Pareto-optimal solution searching in multi-objective particle swarm optimization (MOPSO)[J]. Computers & mathematics with applications, 2009, 57(11/12): 1995-2000.
[27] NASIRAGHDAM H, JADID S.Optimal hybrid PV/WT/FC sizing and distribution system reconfiguration using multi-objective artificial bee colony (MOABC) algorithm[J]. Solar energy, 2012, 86(10): 3057-3071.
[28] MURUGAN P, KANNAN S, BASKAR S.NSGA-Ⅱ algorithm for multi-objective generation expansion planning problem[J]. Electric power systems research, 2009, 79(4): 622-628.