Optimized strategy for emergency load shedding in virtual power plants based on dynamic elastic grading and 5G communication delay compensation

doi:10.3969/j.issn.2097-0706.2025.11.006

Abstract

Abstract:

Emergency load shedding in virtual power plants（VPPs） faces command deviations due to inaccurate load identification and communication delays. Therefore， an optimized model for emergency load shedding in VPPs based on dynamic elastic grading and 5G communication delay compensation was proposed. A dynamic elastic grading mechanism was introduced to accurately identify load characteristics， providing a basis for load shedding operations. A 5G communication delay compensation mechanism was incorporated to effectively reduce command execution deviations caused by communication delays， thereby improving the execution accuracy of load shedding commands. An objective function with the goal of minimizing operational costs was constructed， and the model was solved through multi-mechanism collaborative optimization. Case study results demonstrated that the two mechanisms above helped fully leverage the advantages of the optimized model， significantly improving the execution accuracy of load shedding commands while reducing system carbon emissions. The optimized model could reduce the execution error of load shedding commands from 0.73 MW to 0.28 MW， decrease the total system cost by 13.3%， and lower carbon emissions by 32.0%. The proposed model effectively enhances the operational precision， environmental performance， and economic efficiency of the system， providing a novel solution with both theoretical and practical value for emergency load shedding decision-making in VPPs.

Key words: virtual power plant, emergency load shedding, dynamic elastic grading, 5G communication delay compensation, demand response

CLC Number:

TK01：TM37

BIAN Lijie, MA Gang, CHEN Xudong, ZHAN Xiaosheng. Optimized strategy for emergency load shedding in virtual power plants based on dynamic elastic grading and 5G communication delay compensation[J]. Integrated Intelligent Energy, 2025, 47(11): 62-71.

Figures/Tables 10

Fig.1

Fig. 2

Table 1

Parameter setting

参数	数值	参数	数值
$k$	2.5	$h$	0.3
$c$ /(m·s^-1)	8	n	0.5
$Δ P b a s e / M W$	0.75	$θ 1,0 / °$	30
$v r a t e d$ /(m·s^-1)	20	$m$	0.95
$α$	0.05	$λ$	0.1
$β$	0.2	$γ$	1.5/0.6
$c 1$ /(元·kW^-1）	200	$η$	0.95
$c 2$ /(元·kW^-1）	50	$c c e r t$ /(元·本^-1)	500
$c 3$ /(元·kW^-1）	10	$c c a r b o n / 元 ⋅ k g - 1$	60
$c b u y$ /[元·（kW·h）^-1]	0.6	$c e l e /$ [元·（kW·h）^-1]	0.5
$c c u r$ /[元·（kW·h）^-1]	0.3

Table 1

Table 2

Table 3

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

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场景	动态弹性分级	5G通信延迟补偿	DR
1	×	×	×
2	×	×	√
3	√	×	√
4	√	√	√

场景	切负荷成本	碳排放成本	补偿费用	DR成本	绿证收益
1	10 173.20	2 123.50	3 000.06	0	823.50
2	9 785.23	1 987.40	2 865.11	1 628.39	1 156.74
3	8 763.62	1 865.05	2 689.74	1 265.97	1 198.50
4	8 379.24	1 710.34	2 552.43	1 174.30	1 274.38