Integrated energy microgrids optimization considering hydrogen energy sharing and energy price correction under gaming strategy
|更新时间:2026-10-15
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Integrated energy microgrids optimization considering hydrogen energy sharing and energy price correction under gaming strategy
Vol. 45, Issue 9, (2026)
作者机构:
1. 天津理工大学电气工程与自动化学院,天津,300384
2. 天津市新能源电力变换传输与智能控制重点实验室,天津,300384
作者简介:
基金信息:
DOI:
CLC:TM73
Published:2026
稿件说明:
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WU Yanjuan, JIAO Zhijie. Integrated energy microgrids optimization considering hydrogen energy sharing and energy price correction under gaming strategy[J]. 2026, 45(9).
DOI:
WU Yanjuan, JIAO Zhijie. Integrated energy microgrids optimization considering hydrogen energy sharing and energy price correction under gaming strategy[J]. 2026, 45(9).DOI:
Integrated energy microgrids optimization considering hydrogen energy sharing and energy price correction under gaming strategy
With the deepening advancement of the "dual carbon" goals
constructing a multi-energy coupling system has become an urgent requirement to promote renewable energy accommodation. Owing to its flexibility and cleanliness
hydrogen has gradually become a key medium coupling traditional and renewable energy
driving the deep low-carbon transition of energy systems. To address the energy supply-demand imbalance and enhance system economic and low-carbon performance
an optimal scheduling strategy is proposed for multi-integrated energy microgrids with electricity-heat-cooling-gas-hydrogen coupling based on master-slave game. Firstly
a hydrogen sharing and multi-energy complementarity system model considering source-load flexible response is established. On the hydrogen sharing operator side
refined modeling is conducted for the integrated hydrogen production-storage process and the hydrogen liquefaction-storage link. On the integrated energy microgrids aggregator side
a comprehensive demand response model is established. Secondly
a master-slave game between hydrogen energy sharing operators and integrated energy microgrids aggregators is introduced
and a dynamic energy price correction mechanism is proposed to optimize the operator's pricing strategy. Finally
genetic algorithm combined with mixed integer programming is employed to obtain the optimal pricing and energy utilization strategies. Simulation results demonstrate that the proposed game model and scheduling strategy can balance the interests of both parties
reduce scheduling costs
meet the energy load demands of each microgrid
significantly enhance local wind and solar resource accommodation