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.

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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

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]

设备效率	取值	设备效率	取值
η^GTe	0.35	η^GB	0.8
η^LBR	0.9	η^AC	3.5
η^HRB	0.9	η^ESS	0.9
η^HS	0.9

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

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

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参考文献 20

相关文章 15

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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

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

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