基于自适应学习率萤火虫算法的多能源系统联合优化调度

doi:10.3969/j.issn.2097-0706.2022.07.006

综合智慧能源 ›› 2022, Vol. 44 ›› Issue (7): 49-57.doi: 10.3969/j.issn.2097-0706.2022.07.006

基于自适应学习率萤火虫算法的多能源系统联合优化调度

张荣权¹(), 李刚强²^,^*(), 卜思齐³(), 刘芳²(), 朱玉祥²()

1.南昌交通学院交通运输学院,南昌 330100
2.河南省智能机器人行为优化控制国际联合实验室（黄淮学院）,河南驻马店 463000
3.香港理工大学电机工程学系,香港 999077

收稿日期:2022-03-01 修回日期:2022-04-24 出版日期:2022-07-25 发布日期:2022-07-19
通讯作者: 李刚强
作者简介:张荣权（1993）,男,讲师,硕士,从事电力市场、数据挖掘、综合能源调度等方面的研究, zhangrq19931102@163.com;
卜思齐（1984）,男,副教授,博士生导师,博士,从事电力系统稳定控制分析与运行规划等方面的研究, siqi.bu@polyu.edu.hk;
刘芳（1973）,女,教授,博士,从事电力分布式系统优化、控制等方面的研究, liufang@huanghuai.edu.cn;
朱玉祥（1990）,男,讲师,博士,从事数据分析、电力能源管理等方面的研究, zhuyuxiangcn@163.com。
基金资助:
国家自然科学基金项目(61973177);河南省自然科学基金项目(212102210142);河南省科技攻关项目(212102210516)

Economic operation of a multi-energy system based on adaptive learning rate firefly algorithm

ZHANG Rongquan¹(), LI Gangqiang²^,^*(), BU Siqi³(), LIU Fang²(), ZHU Yuxiang²()

1. College of Transportation and Communication, Nanchang Jiaotong Institute, Nanchang 330100, China
2. Henan International Joint Laboratory of Behavior Optimization Control for Smart Robots（Huanghuai University）, Zhumadian 463000, China
3. Department of Electrical Engineering,The Hong Kong Polytechnic University, HongKong 999077, China

Received:2022-03-01 Revised:2022-04-24 Online:2022-07-25 Published:2022-07-19
Contact: LI Gangqiang

摘要/Abstract

摘要：

随着碳中和目标下分布式新能源发电的快速增长以及多能协同技术的不断成熟,多能源系统（MES）得到了快速发展,成为未来能源的主要承载形式。但MES包括生产、存储、消费等复杂环节,其经济运行面临挑战。在MES框架下构建了包含新能源发电站、电池储能装置和冷热电联供装置的经济优化模型,以弃风弃光惩罚成本、电池储能装置放电损耗成本、燃气轮机燃气成本、碳排放惩罚成本等最小为目标函数,以电池储能装置的充放电特性、光伏与风力发电机组的出力特性、冷热电平衡等为约束条件,采用一种新型的自适应学习率萤火虫算法（ALRFA）对优化模型进行求解,通过引入自适应学习率参数,可避免陷入局部最优、收敛速度慢等问题。以某园区的用户冷热电负荷为例,验证了所提模型和优化算法的有效性和可行性。

关键词: 碳中和, 新能源, 多能源系统, 冷热电联供, 联合优化, 电池储能, 碳排放惩罚, 自适应学习率萤火虫算法

Abstract:

With the extensive application of renewable energy as power sources and the stepwise maturity of synergetic techniques, multi-energy system（MES） is drawing increasing attention,turning into the main carrier of energy on the way to achieving carbon neutrality. However, the economic operation of MES is confronting challenges due to the complexity of energy production, energy storage and energy consumption in the system. To tackle the problem, an economic optimization model of an MES including new energy power plants, a battery energy storage system（BESS） and a combined cooling heating and power（CCHP） system is formulated, taking minimizing the penalty of abandoning wind and solar power, power discharge loss of BESS, fuel costs of gas turbines, carbon emission penalty and other costs as the objective function. The optimization model is solved with the charging and discharging characteristics of BESS, the output characteristics of photovoltaic units and wind turbines and the balance between cooling, heating and electricity load as constraints. Afterward, a novel adaptive learning rate firefly algorithm（ALRFA） is proposed to prevent the problem solving from falling into local optimum and slow convergence by taking adaptive learning rate. Taking the cooling, heating and power loads of an industrial park as study case,the effectiveness and feasibility of the proposed model and algorithm is verified.

Key words: carbon neutrality, renewable energy, multi-energy system, CCHP, comprehensive optimization, battery energy storage system, carbon emission penalty, adaptive learning rate firefly algorithm

中图分类号:

TK018：TP301.6

张荣权, 李刚强, 卜思齐, 刘芳, 朱玉祥. 基于自适应学习率萤火虫算法的多能源系统联合优化调度[J]. 综合智慧能源, 2022, 44(7): 49-57.

ZHANG Rongquan, LI Gangqiang, BU Siqi, LIU Fang, ZHU Yuxiang. Economic operation of a multi-energy system based on adaptive learning rate firefly algorithm[J]. Integrated Intelligent Energy, 2022, 44(7): 49-57.

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算法	最小值	最大值	均值	方差
PSO	44 487	44 552	44 525	1 158
FA	44 474	44 534	44 509	674
MFA	44 524	44 539	44 531	55
ALRFA	44 350	44 353	44 352	3

时刻	MES	MESWBESS	MESWREG	CCHP
01:00	1 830	1 831	1 854	1 838
02:00	1 810	1 796	1 822	1 826
03:00	1 778	1 772	1 794	1 817
04:00	1 712	1 705	1 700	1 742
05:00	1 750	1 762	1 758	1 765
06:00	1 703	1 642	1 675	1 696
07:00	1 752	1 690	1 754	1 758
08:00	1 771	1 756	1 782	1 823
09:00	1 800	1 793	1 865	1 880
10:00	1 756	1 760	2 000	2 019
11:00	1 754	1 796	2 132	2 177
12:00	1 967	1 945	2 245	2 236
13:00	1 969	1 995	2 423	2 379
14:00	1 971	1 973	2 272	2 322
15:00	1 905	1 961	2 191	2 241
16:00	1 868	1 830	2 093	2 141
17:00	1 284	1 362	1 666	1 756
18:00	1 588	1 645	1 837	1 901
19:00	2 144	2 142	2 218	2 205
20:00	2 145	2 157	2 174	2 181
21:00	2 104	2 129	2 138	2 149
22:00	2 057	2 055	2 052	2 069
23:00	2 009	1 991	1 994	1 997
24:00	1 923	1 942	1 945	1 964
合计	44 350	44 430	47 384	47 882

基于自适应学习率萤火虫算法的多能源系统联合优化调度

Economic operation of a multi-energy system based on adaptive learning rate firefly algorithm

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