OPTIMAL SCHEDULING OF MICROGRIDS BASED ON IDBO

Dai Wenzhi; Cui Tianyu

doi:10.19912/j.0254-0096.tynxb.2025-0309

PDF(1169 KB)

Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (7) : 653-662. DOI: 10.19912/j.0254-0096.tynxb.2025-0309

OPTIMAL SCHEDULING OF MICROGRIDS BASED ON IDBO

Dai Wenzhi, Cui Tianyu

Author information +

History +

Abstract

To realize the efficient and economical operation of a combined cooling, heating and power （CCHP） system, an optimal scheduling mathematical model is established with the minimum sum of operationmaintenance cost and environmental cost as the objective. Aiming at the problems of weak global search capability and low optimization efficiency in the traditional dung beetle optimizer （DBO）, an improved dung beetle optimizer is proposed. Chaotic initialization is introduced to enhance population diversity, goldensine strategy and nonlinear control factors are adopted to improve particle search performance, and a mutation strategy is utilized to prevent the algorithm from falling into local optima. Unimodal and multimodal functions are selected to test the performance of the improved dung beetle optimizer. Comparative results with other algorithms show that the improved algorithm achieves better convergence speed and convergence precision. Rank tests are conducted on the simulation results, which verify the statistical significance of the proposed algorithm. When applied to the CCHP system, the algorithm can improve the operational and environmental benefits of the microgrid.

Key words

dung beetle algorithm / cogeneration of cold heat and electricity / microgrid / optimized scheduling / chaotic mapping

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Dai Wenzhi, Cui Tianyu. OPTIMAL SCHEDULING OF MICROGRIDS BASED ON IDBO[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 653-662 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0309

References

[1] Jia J X, Zang G Y, PAUL M C.Energy, exergy, and economic (3E) evaluation of aCCHPsystem with biomass gasifier, solid oxide fuel cells, micro-gas turbine, and absorption chiller[J]. International Journal of Energy Research, 2021, 45(10): 15182-15199.
[2] 林顺富, 刘持涛, 李东东, 等. 考虑电能交互的冷热电区域多微网系统双层多场景协同优化配置[J]. 中国电机工程学报, 2020, 40(5): 1409-1421.
Lin S F, Liu C T, Li D D, et al.Bi-level multiple scenarios collaborative optimization configuration of CCHP regional multi-microgrid system considering power interaction among microgrids[J]. Proceedings of the CSEE, 2020, 40(5): 1409-1421.
[3] Gao L, Yunho Y, Cao T.An overview of optimization technologies applied in combined cooling, heating and power systems[J]. Renewable and Sustainable Energy Reviews, 2019, 114: 109344.
[4] 丁泽宇, 侯宏娟, 段立强. 基于线性规划的太阳能辅助热电联供机组运行优化研究[J]. 热力发电, 2021, 50(6): 33-39.
Ding Z Y, Hou H J, Duan L Q.Study on operation optimization of solar-aided combined heat and power unit based on linear programming[J]. Thermal power Generation, 2021, 50(6): 33-39.
[5] 王帅飞, 王维庆, 王海云, 等. 计及联络线功率波动的CCHP容量配置[J]. 太阳能学报, 2021, 42(12): 381-387.
WANG S F, WANG W Q, WANG H Y, et al.CCHP capacity configuration considering power fluctuation of Tie lines[J]. Acta Energiae Solaris Sinica, 2021, 42(12): 381-387.
[6] 和树森, 刘天羽. 基于改进蝴蝶算法的冷热电联供微网日前优化调度研究[J]. 电气技术, 2021, 22(3): 14-19.
He S S, Liu T Y.Research on optimized scheduling of combined cooling heating and power microgrid based on improved butterfly algorithm[J]. Electrical Engineering, 2021, 22(3): 14-19.
[7] Mahdavi N, Mojaver P, Khalilarya S.Multi-objective optimization of power, CO₂ emission and exergy efficiency of a novel solar-assisted CCHP system using RSM and TOPSIS coupled method[J]. Renewable Energy, 2022, 185: 506-524.
[8] Xue J K, Shen B.Dung beetle optimizer: a new meta-heuristic algorithm for global optimization[J]. The Journal of Supercomputing, 2023, 79(7): 7305-7336.
[9] 刘仙萍, 田东, 雷豫豪, 等. 光伏/光热-地源热泵联合供热系统运行性能研究[J]. 太阳能学报, 2022, 43(9): 88-97.
Liu X P, Tian D, Lei Y H, et al.Performance analysis for solar photovoltaic/thermalground source heat pump hybrid heating system[J]. Acta Energiae Solaris Sinica, 2022, 43(9): 88-97.
[10] 张少丰, 李书琴. 引入精英反向学习和柯西变异的混沌蜉蝣算法[J]. 计算机工程与设计, 2024, 45(1): 187-196.
Zhang S F, Li S Q.Chaos mayfly algorithm combining elite reverse learning and Cauchy mutation[J]. Computer Engineering and Design, 2024, 45(1): 187-196.
[11] 王宁, 何庆. 融合黄金正弦与sigmoid连续化的海鸥优化算法[J]. 计算机应用研究, 2022, 39(1): 157-162, 169.
Wang N, He Q.Seagull optimization algorithm combining golden sine and sigmoid continuity[J]. Application Research of Computers, 2022, 39(1): 157-162, 169.
[12] Mirjalili S, Mirjalili S M, Lewis A.Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61.
[13] Mirjalili S, Lewis A.The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67.
[14] 李大海, 詹美欣, 王振东. 混合策略改进的麻雀搜索算法及其应用[J]. 计算机应用研究, 2023, 40(2): 404-412.
Li D H, Zhan M X, Wang Z D.Improved sparrow search algorithm with mixed strategy and its application[J]. Application Research of Computers, 2023, 40(2): 404-412.