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

doi:10.16183/j.cnki.jsjtu.2023.590

Abstract

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

CLC Number:

TM732
TK01+9

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.

Figures/Tables 15

Fig.1

Fig.2

Tab.1

Parameters of campus equipment

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

Tab.1

Tab.2

Parameters related to demand response

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]

Tab.2

Tab.3

Fig.3

Fig.4

Tab.4

Tab.5

Tab.6

Tab.7

Tab.8

Fig.5

Tab.9

Fig.6

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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