Two-Stage Robust Optimization Scheduling of Park-Level Hydrogen-Electric Coupling Systems With Demand Response and Carbon Trading

doi:10.12096/j.2096-4528.pgt.24162

Abstract

Abstract:

Objectives Hydrogen energy, as a clean energy source with high energy density and zero carbon emissions, is an important component of future energy systems. For park-level hydrogen-electric coupling systems (HECS), a two-stage robust optimization scheduling model that considers demand response and tiered carbon trading is proposed. Methods First, a park-level HECS is established, consisting of wind and solar power generation units, backup generation units, energy storage systems, and hydrogen-electric conversion devices. Then, demand response and tiered carbon trading are integrated into the model, aiming to minimize the total costs of system energy procurement, operation and maintenance, and carbon emissions, thereby establishing a deterministic optimization model for the system. Finally, a source-load uncertainty set is incorporated into the deterministic optimization model to mitigate the effect of source-load uncertainty on scheduling results, forming a two-stage robust optimization model. This model is re-established using a master-slave framework and solved using the column-and-constraint generation method. Results With source-load uncertainty coefficients of 12 and 6, demand response loads with an adjustable ratio below 0.5 can reduce the system's operating costs by 1.6%. The introduction of tiered carbon trading can reduce carbon emissions by 604.9 kg. Conclusions The proposed model can improve the risk resilience of park-level HECS, and the integration of demand response and tiered carbon trading can ensure the economic and low-carbon operation of HECS.

Key words: hydrogen energy, dual carbon, hydrogen-electric coupling system (HECS), two-stage robust optimization, tiered carbon trading, uncertainty, demand response

CLC Number:

TK 01

Jiaxin ZHANG, Yonggang PENG, Jing SUN. Two-Stage Robust Optimization Scheduling of Park-Level Hydrogen-Electric Coupling Systems With Demand Response and Carbon Trading[J]. Power Generation Technology, 2025, 46(2): 252-262.

Figures/Tables 19

Fig. 1 Structure of park-level HECS

Fig. 2 Electricity purchase and sale prices

Tab. 1 Operating parameters of system equipment

参数	数值	参数	数值
$p m a x s t o r$ /kW	1 000	$p m a x e l e (p m i n e l e)$ /kW	1 500 $($ 400 $)$
$η s t o r$	0.95	$η e l e (η f c)$	0.8 $($ 0.7 $)$
$I s o c, t = i n i$ /kW	2 000	$H d r$ /kg	33.6
$p m a x m$ /kW	1 500	$I s o p, t = i n i$ /kg	33
$P d r$ /kW	1 470	$H H 2$ /[(kW⋅h)/kg]	33

Tab. 1 Operating parameters of system equipment

参数	数值	参数	数值
$p m a x s t o r$ /kW	1 000	$p m a x e l e (p m i n e l e)$ /kW	1 500 $($ 400 $)$
$η s t o r$	0.95	$η e l e (η f c)$	0.8 $($ 0.7 $)$
$I s o c, t = i n i$ /kW	2 000	$H d r$ /kg	33.6
$p m a x m$ /kW	1 500	$I s o p, t = i n i$ /kg	33
$P d r$ /kW	1 470	$H H 2$ /[(kW⋅h)/kg]	33

Fig. 3 Wind and solar uncertainty set

Fig. 4 Electrical load uncertainty set

Fig. 5 Hydrogen load uncertainty set

Tab. 2 Comparison of optimization results under different models

模型	日前调度成本/元	碳排放成本/元	碳排放量/kg	IDR成本/元	购电成本/元	售电收益/元
1	2 019.1	0	2 736.8	0	3 595.3	3 158.2
2	2 690.4	660.4	2 366.7	0	2 356.6	2 910.1
3	1 937.1	0	3 096.6	100.3	4 102.4	4 087.7
4	2 649.2	699.4	2 491.7	74.7	2 561.3	3 092.0

Fig. 6 Electrical power balance under deterministic optimization

Fig. 7 Hydrogen energy flow balance under deterministic optimization

Fig. 8 Electrical power balance under robust optimization

Fig. 9 Hydrogen energy flow balance under robust optimization

Fig. 10 Electrical load demand response

Fig. 11 Hydrogen load demand response

Fig. 12 Operating costs under different uncertainty adjustment coefficients

Fig. 13 Carbon emissions under different uncertainty adjustment coefficients

Tab. 3 Operating costs at different DR ratios

项目	IDR可调占比
项目	0	0.1	0.3	0.5	0.7
日前调度成本/元	2 690.1	2 681.1	2 663.5	2 649.2	2 627.8
IDR成本/元	0	15.9	40.3	74.7	75.4
备用发电成本/元	2 286.5	2 248.6	2 167.9	2 038.7	2 005.4
碳排放量/kg	2 381.4	2 407.3	2 447.8	2 491.7	2 509.6
碳交易成本/元	665.0	673.1	685.7	699.4	705.0
购电成本/元	2 365.6	2 399.1	2 471.6	2 561.3	2 592.6
售电收益/元	2 986.3	3 021.3	3 067.6	3 092.0	3 116.3

Fig. 14 Effect of carbon base price on system operating costs and carbon emissions

Fig. 15 Effect of carbon price growth rate on system operating costs and carbon emissions

Tab. 4 Comparison of operating costs under different optimization algorithms

优化算法		日前调控成本/元	日前售能收益/元	日内平衡成本/元	总成本/元
确定性优化		3 961.7	3 393.7	4 200.3	4 768.3
随机优化		4 418.9	3 751.1	3 919.1	4 586.9
两阶段鲁棒优化	$Г p v, p w$ =24 $Г p l, h l$ =12	4 548.4	3 168.3	2 821.1	4 201.6
	$Г p v, p w$ =12 $Г p l, h l$ =6	5 741.2	3 092	1 265.3	3 914.5
	$Г p v, p w$ =6 $Г p l, h l$ =0	6 900.5	3 046.9	-278.3	3 575.3

Tab. 4 Comparison of operating costs under different optimization algorithms

优化算法		日前调控成本/元	日前售能收益/元	日内平衡成本/元	总成本/元
确定性优化		3 961.7	3 393.7	4 200.3	4 768.3
随机优化		4 418.9	3 751.1	3 919.1	4 586.9
两阶段鲁棒优化	$Г p v, p w$ =24 $Г p l, h l$ =12	4 548.4	3 168.3	2 821.1	4 201.6
	$Г p v, p w$ =12 $Г p l, h l$ =6	5 741.2	3 092	1 265.3	3 914.5
	$Г p v, p w$ =6 $Г p l, h l$ =0	6 900.5	3 046.9	-278.3	3 575.3

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