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Optimal Operation and Market Integration of a Hybrid Farm with Green Hydrogen and Energy Storage: A Stochastic Approach Considering Wind and Electricity Price Uncertainties

Author

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  • Pedro Luis Camuñas García-Miguel

    (Electrical Engineering Department, Carlos III University, 28911 Leganés, Madrid, Spain)

  • Donato Zarilli

    (Siemens Gamesa Renewable Energy, 28043 Madrid, Spain)

  • Jaime Alonso-Martinez

    (Electrical Engineering Department, Carlos III University, 28911 Leganés, Madrid, Spain)

  • Manuel García Plaza

    (Siemens Gamesa Renewable Energy, 28043 Madrid, Spain)

  • Santiago Arnaltes Gómez

    (Electrical Engineering Department, Carlos III University, 28911 Leganés, Madrid, Spain)

Abstract

In recent years, growing interest has emerged in investigating the integration of energy storage and green hydrogen production systems with renewable energy generators. These integrated systems address uncertainties related to renewable resource availability and electricity prices, mitigating profit loss caused by forecasting errors. This paper focuses on the operation of a hybrid farm (HF), combining an alkaline electrolyzer (AEL) and a battery energy storage system (BESS) with a wind turbine to form a comprehensive HF. The HF operates in both hydrogen and day-ahead electricity markets. A linear mathematical model is proposed to optimize energy management, considering electrolyzer operation at partial loads and accounting for degradation costs while maintaining a straightforward formulation for power system optimization. Day-ahead market scheduling and real-time operation are formulated as a progressive mixed-integer linear program (MILP), extended to address uncertainties in wind speed and electricity prices through a two-stage stochastic optimization model. A bootstrap sampling strategy is introduced to enhance the stochastic model’s performance using the same sampled data. Results demonstrate how the strategies outperform traditional Monte Carlo and deterministic approaches in handling uncertainties, increasing profits up to 4% per year. Additionally, a simulation framework has been developed for validating this approach and conducting different case studies.

Suggested Citation

  • Pedro Luis Camuñas García-Miguel & Donato Zarilli & Jaime Alonso-Martinez & Manuel García Plaza & Santiago Arnaltes Gómez, 2024. "Optimal Operation and Market Integration of a Hybrid Farm with Green Hydrogen and Energy Storage: A Stochastic Approach Considering Wind and Electricity Price Uncertainties," Sustainability, MDPI, vol. 16(7), pages 1-22, March.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:7:p:2856-:d:1366415
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    References listed on IDEAS

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    1. Qiu, Haifeng & Gu, Wei & Liu, Pengxiang & Sun, Qirun & Wu, Zhi & Lu, Xi, 2022. "Application of two-stage robust optimization theory in power system scheduling under uncertainties: A review and perspective," Energy, Elsevier, vol. 251(C).
    2. M. Hasni & M.S. Aguir & M.Z. Babai & Z. Jemai, 2019. "Spare parts demand forecasting: a review on bootstrapping methods," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 4791-4804, August.
    3. Maheshwari, Arpit & Paterakis, Nikolaos G. & Santarelli, Massimo & Gibescu, Madeleine, 2020. "Optimizing the operation of energy storage using a non-linear lithium-ion battery degradation model," Applied Energy, Elsevier, vol. 261(C).
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    Cited by:

    1. Ricardo Raineri, 2025. "Power Shift: Decarbonization and the New Dynamics of Energy Markets," Energies, MDPI, vol. 18(3), pages 1-52, February.

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