基于A(yíng)MOWOA的區域綜合能源系統運行優(yōu)化調度

韓永明; 王新魯; 耿志強; 朱群雄; 畢帥; 張紅斌

doi:10.16383/j.aas.c211146

基于A(yíng)MOWOA的區域綜合能源系統運行優(yōu)化調度

doi: 10.16383/j.aas.c211146

韓永明^{1, 2,},
王新魯^{1, 2,},
耿志強^{1, 2,},
朱群雄^{1, 2,},
畢帥^{1, 2,},
張紅斌^3,

1.
北京化工大學(xué)信息科學(xué)與技術(shù)學(xué)院北京 100029
2.
智能過(guò)程系統工程教育部工程研究中心北京 100029
3.
國網(wǎng)經(jīng)濟技術(shù)研究院有限公司北京 102209

基金項目: 國家自然科學(xué)基金(21978013), 中央高?；究蒲袠I(yè)務(wù)費專(zhuān)項資金(XK1802-4)資助

詳細信息

作者簡(jiǎn)介:
韓永明：北京化工大學(xué)信息科學(xué)與技術(shù)學(xué)院教授. 分別于2009年和2014年獲得北京化工大學(xué)學(xué)士學(xué)位和博士學(xué)位. 主要研究方向為知識圖譜分析, 神經(jīng)網(wǎng)絡(luò ), 智能計算, 數據挖掘和分析. E-mail: hanym@mail.buct.edu.cn

王新魯：北京化工大學(xué)碩士研究生. 2018 年獲得北京化工大學(xué)學(xué)士學(xué)位. 主要研究方向為食品安全風(fēng)險預測預警, 多目標優(yōu)化. E-mail: wangxinlu_9102@126.com

耿志強：北京化工大學(xué)信息科學(xué)與技術(shù)學(xué)院教授. 1997年和2002年分別獲得鄭州大學(xué)學(xué)士學(xué)位和碩士學(xué)位. 2005年獲得北京化工大學(xué)博士學(xué)位. 主要研究方向為神經(jīng)網(wǎng)絡(luò ), 智能計算, 數據挖掘, 知識管理與過(guò)程建模. 本文通信作者. E-mail: gengzhiqiang@mail.buct.edu.cn

朱群雄：北京化工大學(xué)信息科學(xué)與技術(shù)學(xué)院教授. 主要研究方向為計算智能與工業(yè)應用, 過(guò)程建模與系統優(yōu)化, 故障診斷與報警管理, 虛擬現實(shí)與數字孿生. E-mail: zhuqx@mail.buct.edu.cn

畢帥：2021年獲得北京化工大學(xué)碩士學(xué)位. 主要研究方向為智能優(yōu)化. E-mail: bishuai@vip.qq.com

張紅斌：博士, 國網(wǎng)經(jīng)濟技術(shù)研究院有限公司教授級高級工程師. 主要研究方向為智能配電網(wǎng)以及綜合能源規劃. E-mail: hongbin09172015@163.com

中圖分類(lèi)號: Y
計量
- 文章訪(fǎng)問(wèn)數: 581
- HTML全文瀏覽量: 186
- PDF下載量: 127
- 被引次數: 0
出版歷程
- 收稿日期: 2021-12-03
- 錄用日期: 2022-03-01
- 網(wǎng)絡(luò )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, 智能優(yōu)化算法已廣泛應用于工程優(yōu)化中, 在當前多能耦合與互補的能源發(fā)展趨勢下, 僅考慮系統經(jīng)濟指標的單目標優(yōu)化模式已經(jīng)不再適用于目前區域綜合能源系統(Integrated energy system, IES)的運行優(yōu)化調度, 需要研究一種多目標運行策略來(lái)解決區域綜合能源系統的運行優(yōu)化調度問(wèn)題. 首先綜合考慮經(jīng)濟與能源利用兩個(gè)指標并結合商業(yè)住宅區域的特性, 以系統日運行收益和一次能源利用率為優(yōu)化目標構建商業(yè)住宅區域綜合能源系統多目標運行優(yōu)化調度模型. 其次由于傳統多目標智能優(yōu)化算法缺乏一種最優(yōu)解綜合評價(jià)方法, 基于非支配排序以及擁擠度計算的多目標算法框架, 提出一種利用模糊一致矩陣選取全局最優(yōu)解的多目標鯨魚(yú)優(yōu)化算法(A multi-objective whale optimization algorithm, AMOWOA), 并將提出算法對商住區域綜合能源系統多目標運行優(yōu)化調度模型進(jìn)行求解. 最后以華東某商業(yè)住宅區域綜合能源系統為例進(jìn)行仿真, 驗證了該方法的有效性和可行性.
- 多目標優(yōu)化 /
- 綜合能源系統 /
- 動(dòng)態(tài)層次分析 /
- 鯨魚(yú)優(yōu)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)

HTML全文

圖 1 商業(yè)住宅區域綜合能源系統架構

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

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圖 2 ZDT1優(yōu)化結果

Fig. 2 ZDT1 optimization results

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圖 3 ZDT2優(yōu)化結果

Fig. 3 ZDT2 optimization results

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圖 4 ZDT3優(yōu)化結果

Fig. 4 ZDT3 optimization results

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圖 5 日均冷負荷與光伏預測功率曲線(xiàn)

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

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圖 6 日均電負荷與日均熱負荷曲線(xiàn)

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

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圖 7 Pareto分布對比

Fig. 7 Pareto distribution of contrast

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圖 8 結果對比

Fig. 8 Comparison of results

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圖 9 優(yōu)化前后內燃機出力對比

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

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圖 10 儲能設備負荷對比

Fig. 10 Load comparison of energy storage equipment

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表 1 收斂度對比

Table 1 Convergence contrast

算法	指標	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 多樣度對比

Table 2 Diversity contrast

算法	指標	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 設備規格

Table 3 Specification of equipment

設備	配置容量	能效系數 (COP)
內燃機	10 000 kW	—
光伏	7 100 kW	—
電制冷機	2 000 kW	3.1
熱泵	5 000 kW	4.4 (熱)/5 (冷)
溴化鋰余	8 000 kW	1.0
	熱機組
蓄電池	6 000 kWh	0.9 (充/放)
儲熱設備	5 000 kWh	0.9 (充/放)
儲冷設備	2 000 kWh	0.9 (充/放)

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表 4 模型參數

Table 4 Model parameter

參數	數值
內燃機電效率	41.33%
內燃機熱效率	40.54%
內燃機燃料熱耗率	7 962.726 kJ/kWh
電網(wǎng)輸電效率	92%
發(fā)電廠(chǎng)發(fā)電效率	37%

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表 5 初始運行條件

Table 5 Initial operating conditions

時(shí)段	內燃機出力 (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 多目標優(yōu)化標準測試函數表達式

A1 Multi-objective optimization standard test functions expression

測試函數	表達式
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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