Topology optimization and collaborative development planning of electro-hydrogen coupling based on multi-objective solution algorithms

Authors

  • Xiaowei Li Guangxi Power Grid Co., Ltd.
  • Guoxian Luo Baise Power Supply Bureau
  • Dandan Li Guangxi Power Grid Co., Ltd.
  • Peng Yan China Energy Engineering Group Guangdong Electric Power Design Institute Co., Ltd.

DOI:

https://doi.org/10.4108/ew.12030

Keywords:

electro-hydrogen coupling, microgrid, multi-objective, optimization, topological planning, NSGA-II, consumption of renewable energy

Abstract

INTRODUCTION: Renewable energy microgrids need planning methods to manage volatility and balance performance.

OBJECTIVES: Develop a collaborative planning method for electro-hydrogen coupling systems, optimizing economy, environment and reliability.

METHODS: Build a unified planning model for siting, capacity and topology. Solve using improved NSGA-II with adaptive operators and constraint handling.

RESULTS: Based on 8760-hour data, the system increased energy self-sufficiency from 52% to >95%. With 28% extra investment, carbon emissions fell 54% and reliability rose 55%. Lower electrolyzer cost further cut emissions; carbon price at 300 CNY/ton improved scheme competitiveness.

CONCLUSION: Electro-hydrogen coupling enhances microgrid performance. Multi-objective optimization finds the best trade-off. Falling costs and carbon policies will promote system application, aiding low-carbon transformation.

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References

[1] Islam M M, Yu T, Giannoccaro G, et al. Improving reliability and stability of the power systems: A comprehensive review on the role of energy storage systems to enhance flexibility[J]. IEEE Access, 2024, 12: 152738-152765.

[2] Meng Q, Tong X, Hussain S, et al. Enhancing distribution system stability and efficiency through multi‐power supply startup optimization for new energy integration[J]. IET Generation, Transmission & Distribution, 2024, 18(21): 3487-3500.

[3] Yang B, Tao J, Yin X, et al. Large-signal stability analysis and enhancement strategy of ITER power supply system[J]. International Journal of Electrical Power & Energy Systems, 2025, 172: 111217.

[4] Banihabib R, Fadnes F S, Assadi M. Techno-economic optimization of microgrid operation with integration of renewable energy, hydrogen storage, and micro gas turbine[J]. Renewable Energy, 2024, 237: 121708.

[5] Zhang C, Rezgui Y, Luo Z, et al. Simultaneous community energy supply-demand optimization by microgrid operation scheduling optimization and occupant-oriented flexible energy-use regulation[J]. Applied Energy, 2024, 373: 123922.

[6] Ahsan S M, Musilek P. Optimizing Multi-Microgrid Operations with Battery Energy Storage and Electric Vehicle Integration: A Comparative Analysis of Strategies[J]. Batteries, 2025, 11(4): 129.

[7] Goyal A, Bhattacharya K. Optimal design of a decarbonized sector-coupled microgrid: electricity-heat-hydrogen-transport sectors[J]. IEEE Access, 2024, 12: 38399-38409.

[8] Shi T, Zhou H, Shi T, et al. Research on energy management in hydrogen–electric coupled microgrids based on deep reinforcement learning[J]. Electronics, 2024, 13(17): 3389.

[9] Balu V, Krishnaveni K, Malla P, et al. Improving the power quality and hydrogen production from renewable energy sources based microgrid[J]. Engineering Research Express, 2023, 5(3): 035037.

[10] Alsalloum H, Merghem‐Boulahia L, Rahim R. A systematical analysis on the dynamic pricing strategies and optimization methods for energy trading in smart grids[J]. International Transactions on Electrical Energy Systems, 2020, 30(9): e12404.

[11] Temiz M, Dincer I. Development and assessment of an onshore wind and concentrated solar based power, heat, cooling and hydrogen energy system for remote communities[J]. Journal of Cleaner Production, 2022, 374: 134067.

[12] Moritz M, Schönfisch M, Schulte S. Estimating global production and supply costs for green hydrogen and hydrogen-based green energy commodities[J]. International Journal of Hydrogen Energy, 2023, 48(25): 9139-9154.

[13] Quan S, Wang Y X, Xiao X, et al. Real-time energy management for fuel cell electric vehicle using speed prediction-based model predictive control considering performance degradation[J]. Applied Energy, 2021, 304: 117845.

[14] Teo T T, Logenthiran T, Woo W L, et al. Optimization of fuzzy energy-management system for grid-connected microgrid using NSGA-II[J]. IEEE transactions on cybernetics, 2020, 51(11): 5375-5386.

[15] Su R, He G, Su S, et al. Optimal placement and capacity sizing of energy storage systems via NSGA-II in active distribution network[J]. Frontiers in Energy Research, 2023, 10: 1073194.

[16] Egeland-Eriksen T, Hajizadeh A, Sartori S. Hydrogen-based systems for integration of renewable energy in power systems: Achievements and perspectives[J]. International journal of hydrogen energy, 2021, 46(63): 31963-31983.

[17] Blackhurst M, Venkatesh A, Sinha A, et al. Marginal abatement costs for greenhouse gas emissions in the United States using an energy systems approach[J]. Environmental Research: Energy, 2025, 2(1): 015012.

[18] Bin S, Sun G. Research on the influence maximization problem in social networks based on the multi-functional complex networks model[J]. Journal of Organizational and End User Computing (JOEUC), 2022, 34(3): 1-17.

[19] Russo C, Cirillo V, Pollaro N, et al. The global energy challenge: second-generation feedstocks on marginal lands for a sustainable biofuel production[J]. Chemical and Biological Technologies in Agriculture, 2025, 12(1): 10.

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Published

31-03-2026

How to Cite

1.
Li X, Luo G, Li D, Yan P. Topology optimization and collaborative development planning of electro-hydrogen coupling based on multi-objective solution algorithms. EAI Endorsed Trans Energy Web [Internet]. 2026 Mar. 31 [cited 2026 Apr. 1];12. Available from: https://publications.eai.eu/index.php/ew/article/view/12030