基于AMOWOA的区域综合能源系统运行优化调度

韩永明; 王新鲁; 耿志强; 朱群雄; 毕帅; 张红斌

doi:10.16383/j.aas.c211146

基于AMOWOA的区域综合能源系统运行优化调度

doi: 10.16383/j.aas.c211146

韩永明^{1, 2,},
王新鲁^{1, 2,},
耿志强^{1, 2,},
朱群雄^{1, 2,},
毕帅^{1, 2,},
张红斌^3,

1.
北京化工大学信息科学与技术学院北京 100029
2.
智能过程系统工程教育部工程研究中心北京 100029
3.
国网经济技术研究院有限公司北京 102209

基金项目: 国家自然科学基金(21978013), 中央高校基本科研业务费专项资金(XK1802-4)资助

详细信息

作者简介:
韩永明：北京化工大学信息科学与技术学院教授. 分别于2009年和2014年获得北京化工大学学士学位和博士学位. 主要研究方向为知识图谱分析, 神经网络, 智能计算, 数据挖掘和分析. E-mail: hanym@mail.buct.edu.cn

王新鲁：北京化工大学硕士研究生. 2018 年获得北京化工大学学士学位. 主要研究方向为食品安全风险预测预警, 多目标优化. E-mail: wangxinlu_9102@126.com

耿志强：北京化工大学信息科学与技术学院教授. 1997年和2002年分别获得郑州大学学士学位和硕士学位. 2005年获得北京化工大学博士学位. 主要研究方向为神经网络, 智能计算, 数据挖掘, 知识管理与过程建模. 本文通信作者. E-mail: gengzhiqiang@mail.buct.edu.cn

朱群雄：北京化工大学信息科学与技术学院教授. 主要研究方向为计算智能与工业应用, 过程建模与系统优化, 故障诊断与报警管理, 虚拟现实与数字孪生. E-mail: zhuqx@mail.buct.edu.cn

毕帅：2021年获得北京化工大学硕士学位. 主要研究方向为智能优化. E-mail: bishuai@vip.qq.com

张红斌：博士, 国网经济技术研究院有限公司教授级高级工程师. 主要研究方向为智能配电网以及综合能源规划. E-mail: hongbin09172015@163.com

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出版历程
- 收稿日期: 2021-12-03
- 录用日期: 2022-03-01
- 网络出版日期: 2022-09-29
- 刊出日期: 2024-03-29

Optimal Scheduling for Regional Integrated Energy System Operation Based on the AMOWOA

HAN Yong-Ming^{1, 2
,},
WANG Xin-Lu^{1, 2
,},
GENG Zhi-Qiang^{1, 2
,},
ZHU Qun-Xiong^{1, 2
,},
BI Shuai^{1, 2
,},
ZHANG Hong-Bin^3
,

1.
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029
2.
Engineering Research Center of Intelligent Process Systems Engineering (PSE), Ministry of Education, Beijing 100029
3.
State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209

Funds: Supported by National Natural Science Foundation of China (21978013) and Fundamental Research Funds for the Central Universities (XK1802-4)

More Information

Author Bio:
HAN Yong-Ming　Professor at the College of Information Science and Technology, Beijing University of Chemical Technology. He received his bachelor degree and Ph.D. degree from Beijing University of Chemical Technology, in 2009 and 2014, respectively. His research interest covers knowledge map analysis, neural network, intelligent computing, data mining and analysis

WANG Xin-Lu　 Master student at Beijing University of Chemical Technology. He received his bachelor degree from Beijing University of Chemical Technology in 2018. His research interest covers food safety risk prediction and early warning and multi-objective optimization

GENG Zhi-Qiang　Professor at the College of Information Science and Technology, Beijing University of Chemical Technology. He received his bachelor degree and master degree from Zhengzhou University in 1997 and 2002, respectively. He received his Ph.D. degree from Beijing University of Chemical Technology in 2005. His research interest covers neural network, intelligent computing, data mining, knowledge management, and process modeling. Corresponding author of this paper

ZHU Qun-Xiong　Professor at the College of Information Science and Technology, Beijing University of Chemical Technology. His research interest covers computational intelligence and industrial applications, process modeling and system optimization, fault diagnosis and alarm management, virtual reality and digital twinning

BI Shuai　Received his master degree from Beijing University of Chemical Technology in 2021. His main research interest is intelligent optimization

ZHANG Hong-Bin　Ph.D., Professor-level senior engineer at the State Grid Economic and Technological Research Institute Co., Ltd.. His research interest covers intelligent distribution network and integrated energy planning

摘要

摘要: 目前, 智能优化算法已广泛应用于工程优化中, 在当前多能耦合与互补的能源发展趋势下, 仅考虑系统经济指标的单目标优化模式已经不再适用于目前区域综合能源系统(Integrated energy system, IES)的运行优化调度, 需要研究一种多目标运行策略来解决区域综合能源系统的运行优化调度问题. 首先综合考虑经济与能源利用两个指标并结合商业住宅区域的特性, 以系统日运行收益和一次能源利用率为优化目标构建商业住宅区域综合能源系统多目标运行优化调度模型. 其次由于传统多目标智能优化算法缺乏一种最优解综合评价方法, 基于非支配排序以及拥挤度计算的多目标算法框架, 提出一种利用模糊一致矩阵选取全局最优解的多目标鲸鱼优化算法(A multi-objective whale optimization algorithm, AMOWOA), 并将提出算法对商住区域综合能源系统多目标运行优化调度模型进行求解. 最后以华东某商业住宅区域综合能源系统为例进行仿真, 验证了该方法的有效性和可行性.
- 多目标优化 /
- 综合能源系统 /
- 动态层次分析 /
- 鲸鱼优化算法
Abstract: Now intelligent optimization algorithms have been widely used in engineering optimization. Under the current energy development trend of multi-energy coupling and complementation, the single-objective optimization model that only considers system economic indicators is no longer applicable to the optimal scheduling for integrated energy system (IES) operation, it is necessary to study a multi-objective operation strategy to solve operation optimization and scheduling of regional integrated energy system problem. First, considering the two indicators of economy and energy utilization and combining the characteristics of commercial and residential areas, a multi-objective operation optimization and scheduling model of an integrated energy system for commercial and residential areas with system daily operating income and primary energy utilization as the optimization goals is constructed. Secondly, since the traditional multi-objective intelligent optimization algorithm lacks a comprehensive evaluation method of optimal solutions, based on the framework of multi-objective algorithm with non-dominated sorting and congestion calculation, a multi-objective whale optimization algorithm (AMOWOA) is proposed to select the optimal solution using fuzzy consistency matrix, and then the optimal solution will be evaluated by AMOWOA, and then the optimal solution will be selected by AMOWOA. AMOWOA is proposed to select the global optimal solution using a fuzzy consistency matrix, and the proposed algorithm is used to solve the multi-objective operation optimisation scheduling model of an integrated energysystem in a commercial and residential area. The proposed algorithm is used to solve the multi-objective operation optimisation model of commercial and residential energy system. Finally, a commercial and residential area integrated energy system in East China is used as an example to verify the effectiveness and feasibility of the proposed method.
- Multi-objective optimization /
- integrated energy system (IES) /
- dynamic analytic hierarchy process /
- whale optimization algorithm (WOA)

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图 1 商业住宅区域综合能源系统架构

Fig. 1 Integrated energy system architecture for commercial and residential area

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图 2 ZDT1优化结果

Fig. 2 ZDT1 optimization results

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图 3 ZDT2优化结果

Fig. 3 ZDT2 optimization results

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图 4 ZDT3优化结果

Fig. 4 ZDT3 optimization results

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图 5 日均冷负荷与光伏预测功率曲线

Fig. 5 Average daily cooling load and photovoltaic predicted power curves

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图 6 日均电负荷与日均热负荷曲线

Fig. 6 Daily average electric load and daily average heat load curve

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图 7 Pareto分布对比

Fig. 7 Pareto distribution of contrast

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图 8 结果对比

Fig. 8 Comparison of results

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图 9 优化前后内燃机出力对比

Fig. 9 Comparison of internal combustion engines before and after optimize output

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图 10 储能设备负荷对比

Fig. 10 Load comparison of energy storage equipment

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表 1 收敛度对比

Table 1 Convergence contrast

算法	指标	ZDT1	ZDT2	ZDT3
AMOWOA	M	9.41${\times{10^{-4}}}$	9.59${\times{10^{-4}}}$	9.68${\times{10^{-4}}}$
AMOWOA	V	2.26${\times{10^{-5}}}$	3.41${\times{10^{-5}}}$	2.16${\times{10^{-5}}}$
NSGA-II	M	9.79${\times{10^{-4}}}$	9.68${\times{10^{-4}}}$	9.84${\times{10^{-4}}}$
NSGA-II	V	4.88${\times{10^{-5}}}$	5.84${\times{10^{-5}}}$	3.63${\times{10^{-5}}}$
MOPSO	M	9.46${\times{10^{-4}}}$	1.42${\times{10^{-3}}}$	9.73${\times{10^{-4}}}$
MOPSO	V	3.42${\times{10^{-5}}}$	8.26${\times{10^{-5}}}$	3.79${\times{10^{-5}}}$
PESA-II	M	1.05${\times{10^{-3}}}$	7.40${\times{10^{-4}}}$	7.89${\times{10^{-3}}}$
PESA-II	V	0.00	0.00	1.10${\times{10^{-4}}}$
NSPSO	M	6.42${\times{10^{-3}}}$	9.51${\times{10^{-3}}}$	4.91${\times{10^{-3}}}$
NSPSO	V	0.00	0.00	0.00

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表 2 多样度对比

Table 2 Diversity contrast

算法	指标	ZDT1	ZDT2	ZDT3
AMOWOA	M	0.65560	0.74680	0.79080
AMOWOA	V	0.02109	0.03116	0.02679
NSGA-II	M	0.74470	0.87290	0.78760
NSGA-II	V	0.02901	0.05793	0.06771
MOPSO	M	0.75250	0.93860	0.95170
MOPSO	V	0.03574	0.06475	0.01563
PESA-II	M	0.84810	0.89290	1.22730
PESA-II	V	0.00287	0.05740	0.02930
NSPSO	M	0.90700	0.92200	0.06210
NSPSO	V	0.00	1.20${\times{10^{-4}}}$	6.90${\times{10^{-4}}}$

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表 3 设备规格

Table 3 Specification of equipment

设备	配置容量	能效系数 (COP)
内燃机	10 000 kW	—
光伏	7 100 kW	—
电制冷机	2 000 kW	3.1
热泵	5 000 kW	4.4 (热)/5 (冷)
溴化锂余	8 000 kW	1.0
	热机组
蓄电池	6 000 kWh	0.9 (充/放)
储热设备	5 000 kWh	0.9 (充/放)
储冷设备	2 000 kWh	0.9 (充/放)

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表 4 模型参数

Table 4 Model parameter

参数	数值
内燃机电效率	41.33%
内燃机热效率	40.54%
内燃机燃料热耗率	7 962.726 kJ/kWh
电网输电效率	92%
发电厂发电效率	37%

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表 5 初始运行条件

Table 5 Initial operating conditions

时段	内燃机出力 (kW)
1 (0:00−4:00)	4 000
2 (4:00−8:00)	4 000
3 (8:00−12:00)	8 000
4 (12:00−16:00)	8 000
5 (16:00−20:00)	8 000
6 (20:00−24:00)	4 000

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A1 多目标优化标准测试函数表达式

A1 Multi-objective optimization standard test functions expression

测试函数	表达式
ZDT1	$\left\{\begin{aligned} &\min{f}_{1}\left({x}_{1}\right)={x}_{1}\\& \mathrm{min}{f}_{2}\left(x\right)=g\left(1-\sqrt{\frac{ {f}_{1} }{g}}\right)\\ &g\left(x\right)=1 +\frac{9\sum\limits _{i=2}^{m}{x}_{i} }{m-1}\end{aligned}\right.$
ZDT2	$\left\{\begin{aligned} &\min{f}_{1}\left({x}_{1}\right)={x}_{1}\\& \mathrm{min}{f}_{2}\left(x\right)=g\left(1-{\left(\frac{ {f}_{1} }{g}\right)}^{2}\right)\\& g\left(x\right)=1 +\frac{9\sum\limits _{i=2}^{m}{x}_{i} }{m-1}\end{aligned}\right.$
ZDT3	$\left\{\begin{aligned} &\min{f}_{1}\left({x}_{1}\right)={x}_{1}\\& \mathrm{min}{f}_{2}\left(x\right)=g\left(1-\sqrt{ \frac{ {f}_{1} }{g} }-\left(\frac{ {f}_{1} }{g}\right)\mathrm{sin}\left(10\pi {f}_{1}\right)\right)\\& g\left(x\right)=1 +\frac{9\sum\limits _{i=2}^{m}{x}_{i} }{m-1}\end{aligned}\right.$

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