Optimal Scheduling Strategy of Virtual Power Plant with Carbon Emission and Carbon Penalty Considering Uncertainty of Wind Power and Photovoltaic Power

doi:10.16182/j.issn1004731x.joss.23-0840

Abstract

Abstract:

To better meet the development needs of China's new power system, an optimal scheduling strategy of virtual power plant(VPP) with carbon emission and carbon penalty considering the uncertainty of wind power and photovoltaic power is proposed. The mathematical description of photovoltaic(PV), wind turbine(WT), combined heat and power(CHP) unit and energy storage system(ESS) is carried out, and a wind-solar output model considering the uncertainty is established. The scenario generation and reduction method is used to generate the typical scenario. To maximize the overall operation benefit of VPP, considering carbon emission cost and carbon penalty, an optimal scheduling model of VPP in typical scenario is established. Example results show that the proposed strategy is conducive to tapping the potential of ESS, improving the system economy and reducing the carbon emission. The example built on CloudPSS simulation platform further verifies the effectiveness of the strategy. It provides a feasible way for the subsequent optimization scheduling of virtual power plants.

Key words: virtual power plant, optimal scheduling, uncertainty, scenario generation and reduction, CloudPSS

CLC Number:

TP391.9

Shui Jijun, Peng Daogang, Song Yankan, Zhou Qiang. Optimal Scheduling Strategy of Virtual Power Plant with Carbon Emission and Carbon Penalty Considering Uncertainty of Wind Power and Photovoltaic Power[J]. Journal of System Simulation, 2024, 36(2): 305-319.

Figures/Tables 19

Fig. 1

Fig. 2

Table 1

Fig. 3

Table 2

Parameters of PV

$V M P P$ /V	$I M P P$ /A	$V o c$ /V	$I s c$ /A	$N P V$
37	8.38	45.9	8.96	2 000

Table 2

Table 3

Parameters of WT

$P r W T$ /kW	$v r$ /(m/s)	$v i n$ /(m/s)	$v o u t$ /(m/s)	$N W T$
300	8	4	20	3

Table 3

Table 4

Parameters of CHP unit

P m a x C H P

/kW

P m i n C H P

/kW

P C C H P

/kW

$C p o t e n t i a l$ /

(g/( $k W ⋅ h$ ))

η e C H P

η h C H P

1 000

100

500

0.8

0.5

Table 4

Table 5

Parameters of ESS

$P m a x E S S$ /kW	$Q m a x E S S$ /( $k W ⋅ h$ )	$η C, η D$ /%	$λ C D e e$	$C E S S, m a x$	$C E S S, m i n$	$C E S S, 0$	$σ s$ /%
300	1 000	90	0.1	0.95	0.2	0.5	5

Table 5

Fig. 4

Fig. 5

Table 6

Details of VPP benefits in different scenarios

场景	$F$	$F 1$	$k e q P V$	$k e q W T$	$k e q E S S$	$k e q C H P$	$k r C H P$	$k b G$	$k c o s C$	$k p u b C$	$π s G$	$π e$	$π h$
I	5 147.0	14 520.6	87.1	206.2	20.8	292.5	9 212.9	2 209.7	6 695.4	2 678.2	46.8	25 662.0	840.9
II	4 662.9	14 693.9	87.1	206.2	9.7	313.0	9 859.0	1 977.7	7 165.0	2 866.0	584.6	25 662.0	899.9
III	4 173.1	14 461.8	83.5	206.2	—	321.0	10 112.3	1 887.8	7 349.1	2 939.6	487.6	25 662.0	923.0
IV	4 795.2	14 614.3	87.1	206.2	10.5	306.4	9 650.7	2 057.9	7 013.6	2 805.4	390.2	25 662.0	880.9
V	5 319.0	14 551.6	87.1	206.2	25.6	288.1	9 074.3	2 304.3	6 594.7	2 637.9	46.8	25 662.0	828.3
VI	5 423.9	14 600.7	87.1	206.2	28.1	286.3	9 019.4	2 304.3	6 554.8	2 621.9	46.8	25 662.0	823.3

Table 6

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Fig. 13

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场景	本文	文献[ 7]	文献]12]	文献[ 19]
I	18.18	18.18	19.70	19.70
II	19.70	24.24	10.61	24.24
III	12.12	24.24	21.21	12.12
IV	24.24	12.12	24.24	13.64
V	13.64	9.09	9.09	18.18
VI	12.12	12.12	15.15	12.12

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