基于改进蝴蝶算法的冷热电联供微网日前优化调度研究

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (1336 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Combined cooling heating and power microgrid is of great significance for the realization of renewable energy development and the construction of a low-carbon society. Aiming at the problem of poor system economics caused by the large number of internal devices and the complicated energy coupling relationship, a scheduling strategy based on optimal economics is proposed in this paper. Firstly, the impact of time-sharing electricity price on the system operation is fully considered, and a microgrid model of combined cooling heating and power is established including renewable energy, energy storage equipment, micro-turbine and absorption chiller. With the system operating cost as the objective function, the economic optimal model is constructed. The traditional butterfly algorithm is improved by the reverse learning strategy and Cauchy mutation, and the optimization accuracy of the algorithm is improved. Taking the traditional scheduling strategy as a comparison, the calculation results show that the proposed scheduling strategy can not only effectively reduce the operating cost, but also effectively absorb the new energy in the system, reducing the phenomenon of abandoning wind and light.

Key words： combined cooling heating and power (CCHP) microgrid optimal dispatch reverse learning Cauchy mutation butterfly algorithm

Received: 23 July 2020

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	HE Shusen
	LIU Tianyu

Cite this article:

HE Shusen,LIU Tianyu. Research on optimized scheduling of combined cooling heating and power microgrid based on improved butterfly algorithm[J]. Electrical Engineering, 2021, 22(3): 14-19.

URL:

http://dqjs.cesmedia.cn/EN/Y2021/V22/I3/14

[1] 胡荣, 马杰, 李振坤, 等. 分布式冷热电联供系统优化配置与适用性分析[J]. 电网技术, 2017, 41(2): 418-425.
[2] BASU A K, BHATTACHARYA A, CHOWDHURY S, et al.Planned scheduling for economic power sharing in a CHP-based microgrid[J]. IEEE Transactions on Power Systems, 2012, 27(1): 30-38.
[3] 李玉齐, 张健. 关于微网的新型配电系统研究概述与思考[J]. 电气技术, 2020, 21(5): 1-5, 14.
[4] 李正茂, 张峰, 梁军, 等. 含电热联合系统的微电网运行优化[J]. 中国电机工程学报, 2015, 35(14): 3569-3576.
[5] 熊焰, 吴杰康, 王强, 等. 风光气储互补发电的冷热电联供优化协调模型及求解方法[J]. 中国电机工程学报, 2015, 35(14): 3616-3625.
[6] 王成山, 武震, 李鹏. 微电网关键技术研究[J]. 电工技术学报, 2014, 29(2): 59-68.
[7] 黄炜果, 范译文, 姚元文, 等. 基于智能电网调度控制系统的配网合环操作研究分析[J]. 电气技术, 2018, 19(12): 35-39, 45.
[8] NOSAIR H, BOUFFARD F.Reconstructing operating reserve: flexibility for sustainable power systems[J]. IEEE Transactions on Sustainable Energy, 2015, 6(4): 1-14.
[9] GU Wei, WANG Zhihe, WU Zhi, et al.An online optimal dispatch CCHP microgrids based on model prediviteve control[J]. IEEE Transactionson Smart Grid, 2017, 8(5): 2332-2342.
[10] 陈柏翰, 冯伟, 孙凯, 等. 冷热电联供系统多元储能及孤岛运行优化调度方法[J]. 电工技术学报, 2019, 34(15): 3231-3243.
[11] 王成山, 洪博文, 郭力, 等. 冷热电联供微网优化调度通用建模方法[J]. 中国电机工程学报, 2013, 33(31): 26-33.
[12] 吴盛军, 刘建坤, 周前, 等. 考虑储能电站服务的冷热电多微网系统优化经济调度[J]. 电力系统自动化, 2019, 43(10): 10-18.
[13] 董宸, 夏彦辉, 孙丹. 基于全寿命周期理论的微电网优化配置[J]. 电气技术, 2020, 21(3): 59-63.
[14] 刘栋. 多能互补冷热电联供系统日前运行优化研究[D]. 济南: 山东大学, 2019.
[15] 张忠, 王建学, 曹晓宇. 基于负荷分类调度的孤岛型微网能量管理方法[J]. 电力系统自动化, 2015, 39(15): 17-23, 109.
[16] ARORA S, SINGH S.Butterfly optimization algorithm: a novel approach for global optimization[J]. Soft Computing, 2019, 23: 715-734.
[17] 曾敏, 赵志刚, 李智梅. 带柯西变异的自学习改进烟花算法[J]. 小型微型计算机系统, 2020, 41(2): 264-270.
[18] 肖子雅, 刘升, 韩斐斐, 等. 正弦余弦指引的乌鸦搜索算法研究[J]. 计算机工程与应用, 2019, 55(21): 51-58.
[19] 柳川, 王宇拓, 王林川, 等. 含冷热电联供系统微电网优化运行研究[J]. 电测与仪表, 2015, 52(2): 31-37.
[20] 徐青山, 曾艾东, 王凯, 等. 基于Hessian内点法的微型能源网日前冷热电联供经济优化调度[J]. 电网技术, 2016, 40(6): 1657-1665.