Cooperative Operation Optimization for Multi-region Hydrogen-Integrated Integrated Energy System Based on Hybrid Wasserstein Two-Stage Distributionally Robust

doi:10.11930/j.issn.1004-9649.202410026

Abstract

Abstract:

An integrated energy system (IES) coupled with hydrogen energy is significantly influenced by source-load uncertainties when operating alone. Traditional robust optimization techniques are overly conservative, which impedes the economic performance. In order to enhance the robustness and economy of each local IES under multi-region power interactions, this paper puts forward a cooperative operation optimization strategy for multi-region hydrogen-integrated IES based on hybrid Wasserstein two-stage distributionally robust. Firstly, a min-max-min three-layer two-stage distributionally robust optimization model was established based on the fuzzy set of the probability distribution of Wasserstein distance. Secondly, the collaborative cost minimization and optimal pricing problems are formulated by applying enhanced Nash bargaining principles to incentivize active participation from all entities. Finally, we proposed an ADMM-nested distributed C&CG algorithm to solve the problems while preserving privacy. Case study shows that the proposed strategy exhibits good robust performance while enhancing the conservatism of traditional robust optimization techniques, and improves economic efficiency through cooperative operation.

Key words: Wasserstein distance, two-stage distributionally robust, hydrogen-integrated integrated energy system, cooperative operation, enhanced Nash bargaining, ADMM-nested distributed C&CG

DING Xiaoqiang, YUAN Zhi, LI Ji. Cooperative Operation Optimization for Multi-region Hydrogen-Integrated Integrated Energy System Based on Hybrid Wasserstein Two-Stage Distributionally Robust[J]. Electric Power, 2025, 58(7): 1-14.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202410026

https://www.electricpower.com.cn/EN/Y2025/V58/I7/1

Figures/Tables 17

Fig.1 Overall architecture of multi-region IES energy interaction

Fig.2 IES structure of each region

Fig.3 C&CG algorithm flowchart

Fig.4 ADMM-nested parallelizable C&CG algorithm iteration flowchart

Fig.5 Historical data of source-load

Table 1 Time of use electricity prices for external power grids 单位：元/(kW·h)

时段	购电电价	售电电价
23:00—次日07:00	0.40	0.20
07:00—11:00	0.75	0.40
11:00—14:00	1.20	0.60
14:00—18:00	0.75	0.40
18:00—23:00	1.20	0.60

Fig.6 Iterative diagram of C&CG algorithm and ADMM algorithm

Fig.7 Iterative diagram of ADMM-nested C&CG algorithm

Fig.8 Energy exchange between IES in each region

Fig.9 Transaction electricity prices between IES in various regions

Fig.10 Power energy dispatch plan for PV Zone 1

Fig.11 Thermal energy dispatch plan for PV Zone 1

Fig.12 Hydrogen energy dispatch plan for PV Zone 1

Fig.13 Relationship between Wasserstein sphere radius variation and cost growth

Fig.14 The influence of source-load uncertainties

Table 2 Strategy settings

策略	合作运行	处理源荷不确定性
1	否	否
2	是	否
3	否	传统鲁棒优化
4	否	分布鲁棒优化
5	是	分布鲁棒优化

Table 3 Cost comparison of various regions under different strategies 单位：元

策略	区域	能量交互成本	运行成本	区域总成本	策略总成本
1	1	0	33605.2	33605.2	101177.9
	2	0	33407.8	33407.8
	3	0	34164.9	34164.9
2	1	2063.0	31225.8	33288.8	99257.7
	2	–2418.0	34480.8	32062.8
	3	355.0	33551.1	33906.1
3	1	0	39452.8	39452.8
	2	0	38212.4	38212.4	116334.5
	3	0	38669.3	38669.3
4	1	0	38746.7	38746.7	114879.6
	2	0	37550.7	37550.7
	3	0	38582.2	38582.2
5	1	1512.5	36116.2	37628.7	111639.1
	2	–1620.7	37668.5	36047.8
	3	108.2	37854.4	37958.6

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