Distributed robust low-carbon optimization model for integrated energy systems driven by historical data

doi:10.3969/j.issn.2097-0706.2026.03.005

Abstract

Abstract:

To effectively improve the economic benefits of integrated energy systems （IES）， reduce carbon emissions， promote the efficient utilization of hydrogen energy， and mitigate the uncertainty of renewable energy output， a distributed robust low-carbon optimization model for IES driven by historical data was proposed. A data-driven uncertainty quantification method was used to construct a probability distribution ambiguity set with joint constraints of 1-norm and ∞-norm based on historical renewable energy output data. By identifying the worst-case probability distribution scenario， a min-max-min three-layer distributed robust optimization framework was constructed， ensuring robustness while reducing scheduling conservatism. An integrated tiered carbon trading mechanism and power-to-gas （P2G） technology were adopted to incentivize carbon reduction through phased carbon pricing， achieving closed-loop utilization of carbon elements within the system while balancing environmental benefits and economic costs. The distributed robust model was solved using the column-and-constraint generation （C&CG） algorithm. The results showed that the daily carbon trading cost obtained based on the hybrid-norm ambiguity set was approximately 1.6% lower than that derived from the single-norm ambiguity set， further reducing the conservatism of the renewable energy output model. Meanwhile， with a reasonable division of tiered intervals， carbon emissions and carbon trading costs were reduced by 16.35% and 22.35%， respectively， achieving a relative balance between carbon emissions and carbon trading costs. The experimental results verified the advantage of the model in reducing carbon trading costs. The proposed model provides theoretical support and practical reference for the planning and operation of IES.

Key words: integrated energy system, distributed robustness, tiered carbon trading mechanism, renewable energy, carbon emission, carbon trading cost, joint constraints

CLC Number:

TK 01：TM 73

WANG Zixuan, HAO Yu, LIU Xingchen, SUN Weinan, LIU Lin, ZHANG Yi. Distributed robust low-carbon optimization model for integrated energy systems driven by historical data[J]. Integrated Intelligent Energy, 2026, 48(3): 47-55.

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耗电型模型参数			耗气型模型参数
a₁	b₁	c₁	a₂	b₂	c₂
36	-0.38	0.003 4	3	-0.004	0.001

设备	容量/kW	转换效率/%	爬坡约束/%
EL	500	87	20
MR	250	60	20
HFC	250	95	20
GB	800	95	20
CHP系统	600	92	20
CCS系统	250	60	20

参数	模式1	模式2	模式3
碳排放量/kg	12 758	10 671	25 792
碳交易成本/元	6 379	4 953	16 197
购电成本/元	2 408	2 822	2 379
购气成本/元	7 740	7 538	9 410
弃风成本/元	0	0	0
弃光成本/元	0	0	0
碳捕集成本/元	512	502	0
总成本/元	17 039	15 815	27 986

场景	总成本
场景	模式1	模式2	模式3
1	17 652	15 659	28 698
2	17 571	15 818	28 532
3	18 128	15 777	28 508
4	17 924	15 749	29 148
5	17 300	15 024	28 012

场景	日碳排放总量
场景	模式1	模式2	模式3
1	13 271	11 662	26 746
2	13 065	11 816	26 002
3	13 587	12 000	26 048
4	13 432	11 846	26 712
5	12 680	11 123	26 598