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

Pedro Luis Camuñas García-Miguel, Donato Zarilli, Jaime Alonso-Martinez, Manuel García Plaza and Santiago Arnaltes Gómez ()
Additional contact information
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

Sustainability, 2024, vol. 16, issue 7, 1-22

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.

Keywords: electrolyzer; batteries; optimization; degradation; WTG; EMS (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/7/2856/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/7/2856/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:7:p:2856-:d:1366415

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().