基于改进秃鹰算法的微电网群经济优化调度研究

周辉; 张玉; 肖烈禧; 赵冠皓

doi:10.19912/j.0254-0096.tynxb.2022-1531

PDF(1774 KB)

太阳能学报 ›› 2024, Vol. 45 ›› Issue (2) : 328-335. DOI: 10.19912/j.0254-0096.tynxb.2022-1531

基于改进秃鹰算法的微电网群经济优化调度研究

周辉¹, 张玉^1,2, 肖烈禧¹, 赵冠皓¹

作者信息 +

RESEARCH ON ECONOMIC OPTIMAL DISPATCHING OF MICROGRID CLUSTERS BASED ON IMPROVED BALD EAGLE SEARCH ALGORITHM

Zhou Hui¹, Zhang Yu^1,2, Xiao Liexi¹, Zhao Guanhao¹

Author information +

文章历史 +

摘要

传统优化算法相较于智能优化算法在求解微电网群优化调度问题中较难寻出可行解或最优解,对此提出一种基于融合反向学习和柯西变异改进的秃鹰算法（IBES）,在秃鹰搜索空间猎物阶段采用融合反向学习和柯西变异策略,使得秃鹰算法有效跳出局部最优,解决算法求解精度低等问题。通过与粒子群算法（PSO）、麻雀算法（SSA）、鲸鱼算法（WOA）进行对比,仿真结果表明IBES寻优精度更高,可有效减少微电网群系统的经济成本。

Abstract

Compared with the intelligent optimization algorithm, the traditional optimization algorithm is more difficult to find a feasible or optimal solution in solving the optimal dispatch problem of microgrid clusters. This paper presents an improved bald eagle search algorithm （IBES） based on fusion reverse learning and Cauchy mutation, which uses the fusion reverse learning and Cauchy mutation strategy in the bald eagle search space prey stage to make BES jump out of local optimization effectively and solve the problem of low accuracy in solving the algorithm. By comparing with particle swarm optimization （PSO）, sparrow search algorithm （SSA）, and whale optimization algorithm （WOA）, the simulation results show that IBES is more accurate and can effectively reduce the economic cost of microgrid cluster system.

导出引用

周辉, 张玉, 肖烈禧, 赵冠皓. 基于改进秃鹰算法的微电网群经济优化调度研究[J]. 太阳能学报. 2024, 45(2): 328-335 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1531

Zhou Hui, Zhang Yu, Xiao Liexi, Zhao Guanhao. RESEARCH ON ECONOMIC OPTIMAL DISPATCHING OF MICROGRID CLUSTERS BASED ON IMPROVED BALD EAGLE SEARCH ALGORITHM[J]. Acta Energiae Solaris Sinica. 2024, 45(2): 328-335 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1531

中图分类号： TM732

参考文献

[1] 魏震波, 张海涛, 魏平桉, 等. 考虑动态激励型需求响应的微电网两阶段优化调度[J]. 电力系统保护与控制, 2021, 49(19): 1-10.
WEI Z B, ZHANG H T, WEI P A, et al.Two-stage optimal dispatching for microgrid considering dynamic incentive-based demand response[J]. Power system protection and control, 2021, 49(19): 1-10.
[2] 季颖, 王建辉. 基于深度强化学习的微电网在线优化调度[J]. 控制与决策, 2022, 37(7): 1675-1684.
JI Y, WANG J H.Online optimal scheduling of a microgrid based on deep reinforcement learning[J]. Control and decision, 2022, 37(7): 1675-1684.
[3] 齐志远, 李晓文, 张如意, 等. 基于动态回馈修正的独立微电网协调优化调度[J]. 高电压技术, 2022, 48(3): 938-947.
QI Z Y, LI X W, ZHANG R Y, et al.Coordinated optimal scheduling of stand-alone micro-grid based on dynamic feedback correction[J]. High voltage engineering, 2022, 48(3): 938-947.
[4] 陈寒, 唐忠, 鲁家阳, 等. 基于CVaR量化不确定性的微电网优化调度研究[J]. 电力系统保护与控制, 2021, 49(5): 105-115.
CHEN H, TANG Z, LU J Y, et al.Research on optimal dispatch of a microgrid based on CVaR quantitative uncertainty[J]. Power system protection and control, 2021, 49(5): 105-115.
[5] 冉金周, 李华强, 李彦君, 等. 考虑灵活性供需匹配的孤岛微网优化调度策略[J]. 太阳能学报, 2022, 43(5): 36-44.
RAN J Z, LI H Q, LI Y J, et al.Optimal scheduling of isolated microgrid considering flexible power supply and demand[J]. Acta energiae solaris sinica, 2022, 43(5): 36-44.
[6] 李翔宇, 赵冬梅. 市场机制下多微电网多时间尺度优化调度研究[J]. 电机与控制学报, 2020, 24(7): 29-38.
LI X Y, ZHAO D M.Multi-time scale optimal scheduling strategy of multi-microgrid based on market mechanism[J]. Electric machines and control, 2020, 24(7): 29-38.
[7] 程逸帆, 乔飞, 侯珂, 等. 区域微电网群两级能量调度策略优化研究[J]. 仪器仪表学报, 2019, 40(5): 68-77.
CHENG Y F, QIAO F, HOU K, et al.Research on bi-level energy dispatching strategy optimization for regional microgrid cluster[J]. Chinese journal of scientific instrument, 2019, 40(5): 68-77.
[8] 路畅, 郭力, 刘一欣, 等. 基于柔性互联的独立微电网分布式优化调度方法[J]. 电网技术, 2019, 43(5): 1512-1519.
LU C, GUO L, LIU Y X, et al.Distributed optimal dispatching method for independent microgrids based on flexible interconnection[J]. Power system technology, 2019, 43(5): 1512-1519.
[9] 陈友芹, 蒋炯, 殷展翔, 等. 基于NCPSO的微网群优化调度策略研究[J]. 太阳能学报, 2022, 43(8): 477-483.
CHEN Y Q, JIANG J, YIN Z X, et al.Research on optimal scheduling strategy of microgrid clusters based on NCPSO[J]. Acta energiae solaris sinica, 2022, 43(8): 477-483.
[10] 宋扬, 石勇, 刘宝泉, 等. 基于反向变异麻雀搜索算法的微电网优化调度[J]. 电力工程技术, 2022, 41(2): 163-170.
SONG Y, SHI Y, LIU B Q, et al.Optimal dispatch of microgrid based on reverse mutation sparrow search algorithm[J]. Electric power engineering technology, 2022, 41(2): 163-170.
[11] 张林, 郭辉, 姚李孝. 基于改进蝙蝠算法的微电网优化研究[J]. 电网与清洁能源, 2021, 37(4): 122-126.
ZHANG L, GUO H, YAO L X.Research on microgrid optimization based on improved bat algorithm[J]. Power system and clean energy, 2021, 37(4): 122-126.
[12] 李星辰, 袁旭峰, 李沛然, 等. 基于改进QPSO算法的微电网多目标优化运行策略[J]. 电力科学与工程, 2020, 36(12): 22-29.
LI X C, YUAN X F, LI P R, et al.Multi-objective optimal operation strategy of microgrid based on improved QPSO algorithm[J]. Electric power science and engineering, 2020, 36(12): 22-29.
[13] 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, 68.
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, 68.
[14] 杨茂, 王金鑫. 考虑可再生能源出力不确定的孤岛型微电网优化调度[J]. 中国电机工程学报, 2021, 41(3): 973-985.
YANG M, WANG J X.Optimal scheduling of islanded microgrid considering uncertain output of renewable energy[J]. Proceedings of the CSEE, 2021, 41(3): 973-985.
[15] 朱张涛, 陈豪杰, 戴俊杰, 等. 基于空间变换的含风电场和电动汽车配电网概率潮流计算[J]. 储能科学与技术, 2017, 6(1): 127-134.
ZHU Z T, CHEN H J, DAI J J, et al.Probabilistic load flow calculation of distribution network with wind power and electric vehicles based on space transform[J]. Energy storage science and technology, 2017, 6(1): 127-134.
[16] ALSATTAR H A, ZAIDAN A A, ZAIDAN B B.Novel meta-heuristic bald eagle search optimisation algorithm[J]. Artificial intelligence review, 2020, 53(3): 2237-2264.
[17] 何庆, 林杰, 徐航. 混合柯西变异和均匀分布的蝗虫优化算法[J]. 控制与决策, 2021, 36(7): 1558-1568.
HE Q, LIN J, XU H.Hybrid Cauchy mutation and uniform distribution of grasshopper optimization algorithm[J]. Control and decision, 2021, 36(7): 1558-1568.