基于全局灵敏度分析的综合能源设备响应价值量化方法

doi:10.16183/j.cnki.jsjtu.2023.590

上海交通大学学报 ›› 2025, Vol. 59 ›› Issue (5): 569-579.doi: 10.16183/j.cnki.jsjtu.2023.590

• 新型电力系统与综合能源 • 上一篇下一篇

基于全局灵敏度分析的综合能源设备响应价值量化方法

黄逸翔¹, 窦迅¹, 李林溪¹, 杨函煜¹(), 于建成², 霍现旭³

1.南京工业大学电气工程与控制科学学院,南京 211816
2.国网天津市电力公司电力科学研究院,天津 300384
3.国网天津市电力公司,天津 300010

收稿日期:2023-11-20 修回日期:2024-01-03 接受日期:2024-01-24 出版日期:2025-05-28 发布日期:2025-06-05
通讯作者: 杨函煜,讲师;E-mail:hyang73@outlook.com.
作者简介:黄逸翔(1999—),硕士生,从事综合能源系统研究.
基金资助:
国家电网公司总部科技项目(5108-202218280A-2-244-XG)

Quantitative Method of Response Value of Integrated Energy Equipment Based on Global Sensitivity Analysis

HUANG Yixiang¹, DOU Xun¹, LI Linxi¹, YANG Hanyu¹(), YU Jiancheng², HUO Xianxu³

1. College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China
2. State Grid Tianjin Electric Power Company Electric Power Research Institute, Tianjin 300384, China
3. State Grid Tianjin Electric Power Company, Tianjin 300010, China

Received:2023-11-20 Revised:2024-01-03 Accepted:2024-01-24 Online:2025-05-28 Published:2025-06-05

摘要/Abstract

摘要：

综合需求响应作为提升能源利用效率,促进可再生清洁能源消纳的有效途径之一,其本质是通过综合能源设备的多能耦合能力引导用户参与源荷双向互动.为提升综合能源系统的运行控制水平,需要准确评估综合能源设备的响应价值.因此,提出一种基于Sobol全局灵敏度分析的综合能源设备响应价值量化方法.首先,以总运行成本最小为目标函数,考虑多类型需求响应,建立综合能源系统泛化优化模型,并构建基于粒子群-反向传播神经网络的综合能源系统优化代理模型.然后,采用Sobol全局灵敏度方法量化设备效率参数对成本、用户满意度、综合能源利用率以及电能替代率的全局灵敏度指标,用于评估综合能源设备的响应价值并且辨识影响系统状态的关键设备.最后,通过对江苏省某商业园区进行仿真,获得各综合能源设备效率的全局灵敏度系数,分析不同设备效率对系统状态的影响,准确量化综合能源设备的响应价值,验证了所提方法的有效性.

关键词: 全局灵敏度分析, 综合需求响应价值, 综合能源设备, 综合能源系统, 粒子群-反向传播神经网络

Abstract:

Integrated demand response is an effective strategy for improving energy efficiency and facilitating the integration of renewable clean energy, of which the essence is to guide users into bidirectional interactions between source and load by the multi-energy coupling capabilities of integrated energy devices. Accurately evaluating the response values of integrated energy devices is essential for improving the operational control level of integrated energy systems. Therefore, this paper proposes a method for quantifying the response value of integrated energy devices based on Sobol’s global sensitivity analysis. First, an integrated energy system generalized optimization model is developed with the objective function of minimizing total operating costs while considering multiple types of integrated demand response. A surrogate model for the integrated energy system is conducted based on particle swarm optimization-backpropagation (PSO-BP) neural network. The Sobol’s global sensitivity analysis is then applied to quantify the global sensitivity indices of device efficiency parameters in terms of cost, user satisfaction, integrated energy utilization rate, and electricity substitution rate, which are used to assess the response value of integrated energy devices and identify key devices influencing system states. Finally, simulations based on a commercial park in Jiangsu Province are conducted to determine the global sensitivity coefficients of the efficiency for each integrated energy device. The effect of various device efficiencies on system states is investigated with the response value of integrated energy devices calculated precisely, and the effectiveness of the proposed method is verified.

Key words: global sensitivity analysis, value of integrated demand response, integrated energy equipment, integrated energy system, particle swarm optimization-backpropagation (PSO-BP) neural network

中图分类号:

TM732
TK01+9

黄逸翔, 窦迅, 李林溪, 杨函煜, 于建成, 霍现旭. 基于全局灵敏度分析的综合能源设备响应价值量化方法[J]. 上海交通大学学报, 2025, 59(5): 569-579.

HUANG Yixiang, DOU Xun, LI Linxi, YANG Hanyu, YU Jiancheng, HUO Xianxu. Quantitative Method of Response Value of Integrated Energy Equipment Based on Global Sensitivity Analysis[J]. Journal of Shanghai Jiao Tong University, 2025, 59(5): 569-579.

图/表 15

图1

图2

表1

园区设备参数

参数	取值	参数	取值
$P m a x G T$ , $H m a x G T$ , $H m a x G B$ /MW	2,3,4	η^GB	[0.7,0.9]
$P m a x c E E S$ , $P m a x d E E S$ /MW	0.2	η^LBR	[0.8,1]
$H m a x c H S$ , $H m a x d H S$ /MW	0.5	η^AC	[3,4]
[ $E m i n b a t t$ , $E m a x b a t t$ ]/(MW·h)	[0.2,2]	η^HRB	[0.8,1]
[ $θ m i n H S$ , $θ m a x H S$ ]/(MW·h)	[0.5,2]	η^ESS	[0.8,1]
Δ $P m a x G T$ , $P m a x P G$ /MW	0.3,5	η^HS	[0.8,1]
$V m a x G a s$ /m³	500	η^GTe	0.35

表1

表2

需求响应相关参数

负荷种类	$P t, i m o v e . m a x$ /%	$P t, i c u t . m a x$ /%	[ $L i, I D R t, m i n$ , $L i, I D R t, m a x$ ]
电负荷	5	5	[0.6,1.4]
热负荷	10	5	[0.7,1.3]
冷负荷	10	5	[0.6,1.4]

表2

表3

图3

图4

表4

表5

表6

表7

表8

图5

表9

图6

参考文献 20

[1]	辛保安, 陈梅, 赵鹏, 等. 碳中和目标下考虑供电安全约束的我国煤电退减路径研究[J]. 中国电机工程学报, 2022, 42(19): 6919-6931.
	XIN Baoan, CHEN Mei, ZHAO Peng, et al. Research on coal power generation reduction path considering power supply adequacy constraints under carbon neutrality target in China[J]. Proceedings of the CSEE, 2022, 42(19): 6919-6931.
[2]	司方远, 张宁, 韩英华, 等. 面向多元灵活资源聚合的区域综合能源系统主动调节能力评估与优化: 关键问题与研究架构[J]. 中国电机工程学报, 2024, 44(6): 2097-2119.
	SI Fangyuan, ZHANG Ning, HAN Yinghua, et al. Fundamental problems and research framework for assessment and optimization of the functional regulation capacity of the regional integrated energy system under the aggregation of diversified and flexible resources[J]. Proceedings of the CSEE, 2024, 44(6): 2097-2119.
[3]	黄武靖, 张宁, 董瑞彪, 等. 多能源网络与能量枢纽联合规划方法[J]. 中国电机工程学报, 2018, 38(18): 5425-5437.
	HUANG Wujing, ZHANG Ning, DONG Ruibiao, et al. Coordinated planning of multiple energy networks and energy hubs[J]. Proceedings of the CSEE, 2018, 38(18): 5425-5437.
[4]	LIU N, WANG J, WANG L F. Hybrid energy sharing for multiple microgrids in an integrated heat-electricity energy system[J]. IEEE Transactions on Sustainable Energy, 2019, 10(3): 1139-1151.
[5]	韩肖清, 李廷钧, 张东霞, 等. 双碳目标下的新型电力系统规划新问题及关键技术[J]. 高电压技术, 2021, 47(9): 3036-3046.
	HAN Xiaoqing, LI Tingjun, ZHANG Dongxia, et al. New issues and key technologies of new power system planning under double carbon goals[J]. High Voltage Engineering, 2021, 47(9): 3036-3046.
[6]	KARIMI H, JADID S. Optimal energy management for multi-microgrid considering demand response programs: A stochastic multi-objective framework[J]. Energy, 2020, 195: 116992.
[7]	赵海彭, 苗世洪, 李超, 等. 考虑冷热电需求耦合响应特性的园区综合能源系统优化运行策略研究[J]. 中国电机工程学报, 2022, 42(2): 573-589.
	ZHAO Haipeng, MIAO Shihong, LI Chao, et al. Research on optimal operation strategy for park-level integrated energy system considering cold-heat-electric demand coupling response characteristics[J]. Proceedings of the CSEE, 2022, 42(2): 573-589.
[8]	WANG L, HOU C Q, YE B, et al. Optimal operation analysis of integrated community energy system considering the uncertainty of demand response[J]. IEEE Transactions on Power Systems, 2021, 36(4): 3681-3691.
[9]	李天格, 胡志坚, 陈志, 等. 计及电-气-热-氢需求响应的综合能源系统多时间尺度低碳运行优化策略[J]. 电力自动化设备, 2023, 43(1): 16-24.
	LI Tiange, HU Zhijian, CHEN Zhi, et al. Multi-time scale low-carbon operation optimization strategy of integrated energy system considering electricity-gas-heat-hydrogen demand response[J]. Electric Power Automation Equipment, 2023, 43(1): 16-24.
[10]	WANG L Y, LIN J L, DONG H Q, et al. Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system[J]. Energy, 2023, 270: 126893.
[11]	程杉, 陈诺, 徐建宇, 等. 考虑综合需求响应的楼宇综合能源系统能量管理优化[J]. 电力工程技术, 2023, 42(2): 40-47.
	CHENG Shan, CHEN Nuo, XU Jianyu, et al. Optimal energy management of residential integrated energy system with consideration of integrated demand response[J]. Electric Power Engineering Technology, 2023, 42(2): 40-47.
[12]	PANG Y X, HE Y X, JIAO J, et al. Power load demand response potential of secondary sectors in China: The case of western Inner Mongolia[J]. Energy, 2020, 192: 116669.
[13]	ZHU J, NIU J D, TIAN Z, et al. Rapid quantification of demand response potential of building HAVC system via data-driven model[J]. Applied Energy, 2022, 325: 119796.
[14]	WANG Y L, LI F, YANG J L, et al. Demand response evaluation of RIES based on improved matter-element extension model[J]. Energy, 2020, 212: 118121.
[15]	黄莉, 周赣, 张娅楠, 等. 考虑贡献度的聚合商需求响应精准评估与动态激励决策[J]. 电力工程技术, 2022, 41(6): 21-29.
	HUANG Li, ZHOU Gan, ZHANG Yanan, et al. Accurate evaluation and dynamic incentive decision of aggregators’ demand response considering contribution degree[J]. Electric Power Engineering Technology, 2022, 41(6): 21-29.
[16]	郭尊, 李庚银, 周明, 等. 计及综合需求响应的商业园区能量枢纽优化运行[J]. 电网技术, 2018, 42(8): 2439-2448.
	GUO Zun, LI Gengyin, ZHOU Ming, et al. Optimal operation of energy hub in business park considering integrated demand response[J]. Power System Technology, 2018, 42(8): 2439-2448.
[17]	程浩忠, 胡枭, 王莉, 等. 区域综合能源系统规划研究综述[J]. 电力系统自动化, 2019, 43(7): 2-13.
	CHENG Haozhong, HU Xiao, WANG Li, et al. Review on research of regional integrated energy system planning[J]. Automation of Electric Power Systems, 2019, 43(7): 2-13.
[18]	李平姣, 钟文琪, 陈晓乐, 等. 600 MW S-CO₂循环燃煤流化床锅炉热量分布及锅炉效率[J]. 中国电机工程学报, 2019, 39(7): 2080-2093.
	LI Pingjiao, ZHONG Wenqi, CHEN Xiaole, et al. Heat distribution and boiler efficiency of 600 MW coal-fired CFB boiler with S-CO₂ power cycle[J]. Proceedings of the CSEE, 2019, 39(7): 2080-2093.
[19]	TANG Y, REED P, WAGENER T, et al. Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation[J]. Hydrology & Earth System Sciences, 2007, 11(2): 793-817.
[20]	杨晶, 赵津蔓, 孟润泉, 等. 基于粒子群优化和卷积神经网络的电力系统运行状态辨识[J]. 电网技术, 2024, 48(1): 315-324.
	YANG Jing, ZHAO Jinman, MENG Runquan, et al. Power system operation state identification based on particle swarm optimization and convolutional neural network[J]. Power System Technology, 2024, 48(1): 315-324.

设备	FSC	TSC
AC	0.1267	0.1215
LBR	0.2975	0.3214
GB	0.0025	0.0027
HS	0.0257	0.0206
EES	0.2695	0.2690
HRB	0.2766	0.2862

设备	FSC	TSC
AC	0.0561	0.0651
LBR	0.0529	0.0929
GB	0.0037	0.0093
HS	0.0050	0.0223
EES	0.0006	0.0023
HRB	0.8329	0.8684

设备	α₁=0.8, α₂=0.2	α₁=0.2, α₂=0.8
AC	0.0890	0.0598
LBR	0.1094	0.0765
GB	0.0502	0.0082
HS	0.0390	0.0069
EES	0.0495	0.0022
HRB	0.6238	0.9160

设备	FSC	TSC
AC	0.1893	0.1944
LBR	0.4333	0.4840
GB	0.0124	0.0245
HS	0.0430	0.0452
EES	0.0108	0.0049
HRB	0.2877	0.2928

设备	FSC	TSC
AC	0.0127	0.0085
LBR	0.0529	0.0468
GB	0.0132	0.0092
HS	0.0149	0.0040
EES	0.0553	0.0447
HRB	0.8988	0.8932

基于全局灵敏度分析的综合能源设备响应价值量化方法

Quantitative Method of Response Value of Integrated Energy Equipment Based on Global Sensitivity Analysis

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 15

参考文献 20

相关文章 15

编辑推荐

Metrics

本文评价

场景	运行成本/元				变化率/%
场景	购电成本	购气成本	IDR成本	总成本	变化率/%
4	42410.38	26148.94	2825.77	71385.09	0
5	41310.18	27827.83	2844.77	71982.79	0.83
6	43064.70	25913.03	2844.77	71822.51	0.61
7	42754.38	26148.94	2825.77	71729.09	0.48
8	42610.02	26148.94	2852.42	71611.38	0.31
9	42480.52	26194.22	2825.77	71500.51	0.16
10	42806.76	25752.60	2825.77	71385.14	0

[1]	. 区域综合能源系统响应-恢复融合阶段弹性提升策略[J]. J Shanghai Jiaotong Univ Sci, 2026, 31(2): 515-527.
[2]	. 基于知识-数据融合模型的综合能源系统多元负荷短期预测[J]. J Shanghai Jiaotong Univ Sci, 2026, 31(2): 499-514.
[3]	刘东林, 周霞, 戴剑丰, 解相朋, 汤奕, 李隽诗. 考虑虚拟储能的建筑综合能源系统双层优化调度策略[J]. 上海交通大学学报, 2026, 60(1): 61-73.
[4]	林嘉瑜, 韩俊涛, 王永真, 韩恺, 韩艺博, 李健. 计及㶲效率的低碳数据中心算力综合能源系统规划及运行优化[J]. 上海交通大学学报, 2025, 59(9): 1327-1337.
[5]	彭楚轩, 边晓燕, 金海翔, 林顺富, 徐波, 赵健. 基于多能灵活性约束的含富氧燃烧机组综合能源系统两阶段优化调度[J]. 上海交通大学学报, 2025, 59(9): 1281-1291.
[6]	沈赋, 杨志文, 徐潇源, 张微, 王哲, 曹旸. 基于统计聚类的综合能源系统基础单元模型[J]. 上海交通大学学报, 2025, 59(9): 1348-1358.
[7]	邓倩文, 李奇, 邱宜彬, 李豆萌, 霍莎莎, 陈维荣. 考虑多灵活性资源联合运行的综合能源系统优化配置方法[J]. 上海交通大学学报, 2025, 59(7): 912-922.
[8]	张辰微, 王颖, 李亚平, 张凯锋. 基于带权重介数的综合能源系统脆弱环节防护优化模型[J]. 上海交通大学学报, 2025, 59(7): 923-937.
[9]	鲁斌, 王伊晓, 濮川苘, 陈云辉, 陈波波, 樊飞龙. 区域多主体综合能源系统电压偏差异步协调控制方法[J]. 上海交通大学学报, 2025, 59(6): 758-767.
[10]	高波, 李飞, 史轮, 陶鹏, 石振刚, 张超, 彭杰, 赵一伊. 基于实时碳强度评估的社区综合能源系统低碳互动管理策略[J]. 上海交通大学学报, 2025, 59(5): 580-591.
[11]	李建林, 张则栋, 梁策, 曾飞. 计及源-荷不确定性的综合能源系统多目标鲁棒优化调度[J]. 上海交通大学学报, 2025, 59(2): 175-185.
[12]	李冰洁, 袁晓昀, 史静, 徐华池, 罗子萱. 综合能源系统电气热多能量流建模及优化[J]. 上海交通大学学报, 2024, 58(9): 1297-1308.
[13]	林森, 文书礼, 朱淼, 戴群, 鄢伦, 赵耀, 叶惠丽. 考虑碳交易机制的海港综合能源系统电-热混合储能优化配置[J]. 上海交通大学学报, 2024, 58(9): 1344-1356.
[14]	周思怡, 杨欢红, 黄文焘, 周泽, 焦伟, 杨镇瑜. 集装箱港口综合能源系统日前-日内两阶段滚动优化调度[J]. 上海交通大学学报, 2024, 58(9): 1357-1369.
[15]	王金锋, 王琪, 任正某, 孙晓晨, 孙毅, 赵一伊. 基于联邦强化学习的电热综合能源系统能量管理策略[J]. 上海交通大学学报, 2024, 58(6): 904-915.