Storing energy is a major challenge in achieving a 100 % renewable energy system. One promising approach is the production of green hydrogen from wind power. This work proposes a method for optimizing the design of wind–hydrogen systems for existing onshore wind farms in order to achieve the lowest possible levelized cost of hydrogen (LCoH). This is done by the application of a novel Python-based optimization model that iteratively determines the optimal electrolyzer position and distribution mode of hydrogen for given wind farm layouts. The model includes the costs of all required infrastructure components. It considers peripheral factors such as existing and new roads, necessary power cables and pipelines, wage and fuel costs for truck transportation, and the distance to the point of demand (POD). Based on the results, a decision can be made whether to distribute the hydrogen to the POD by truck or pipeline. For a 23.4 MW onshore wind farm in Germany, a minimal LCoH of EUR 4.58 ${{\mathrm{kg}}_{{\mathrm{H}}_{\mathrm{2}}}}^{-\mathrm{1}}$ at an annual hydrogen production of 241.4 ${\mathrm{t}}_{{\mathrm{H}}_{\mathrm{2}}}{\mathrm{a}}^{-\mathrm{1}}$ is computed. These results are significantly affected by the position of the electrolyzer, the distribution mode, varying wind farm and electrolyzer sizes, and the distance to the POD. The influence of the ratio of electrolyzer power to wind farm power is also investigated. The ideal ratio between the rated power of the electrolyzer and the wind farm lies at around 10 %, with a resulting capacity factor of 78 % for the given case. The new model can be used by system planners and researchers to improve and accelerate the planning process for wind–hydrogen systems. Additionally, the economic efficiency, hence competitiveness, of wind–hydrogen systems is increased, which contributes to an urgently needed accelerated expansion of electrolyzers. The results of the influencing parameters on the LCoH will help to set development goals and indicate a path towards a cost-competitive green wind–hydrogen system.

Green hydrogen is crucial for achieving climate neutrality and replacing fossil fuels in processes that are hard to electrify. Wind farms producing electricity and hydrogen can help mitigate stress on electricity grids and enable new markets for operators. While optimizing wind farms for electricity production is well-established, optimizing combined wind–hydrogen systems is a relatively new research field. This study examines the potential profit of wind–hydrogen systems by conducting a case study of an onshore wind farm near the North Sea. Varying turbine types from high wind-speed turbines (with high annual energy production) to low wind-speed turbines (with high full-load hours) are examined. Findings indicate that in a combined hydrogen system, the low wind-speed turbines, which are sub-optimal for mere electricity production, yield lower levelized costs of hydrogen at a higher hydrogen production. Although high wind-speed turbines generate higher profits under current market conditions, at high hydrogen prices and low electricity prices, low wind-speed turbines can yield higher total profit at this site. Therefore, an integrated optimization approach of wind–hydrogen systems can, in certain cases, lead to better results compared to an isolated, sequential optimization of each individual system.

The expansion of volatile renewable energy sources leads to increased electricity price volatility and cannibalization effects, intensifying the economic pressure on project developers. Decentralized hybrid energy systems for renewable hydrogen production offer a solution to exploit these price fluctuations and counteract curtailment during hours of low or negative electricity prices. However, the design and operation of these systems are inherently coupled and significantly influenced by external factors, such as electricity and hydrogen prices that can be achieved over the lifetime. To determine an economically optimal design, specifically the power of an electrolyzer and the capacity of a battery, both site-specific and plant-specific characteristics must be considered. First, this paper presents a methodology for determining the optimal electrolyzer rated power and lithium-ion buffer battery capacity for a 68 MW wind farm in northwestern Germany. The approach extends an existing site-specific design method by introducing a battery energy storage system (BESS) and enhancing the electrolyzer model with part-load efficiency and operating-mode-dependent degradation. Results indicate that neglecting degradation leads to an underestimation of levelized cost of hydrogen (LCOH) by EUR 1.2 kg ⁻¹ , corresponding to 21 %, while neglecting both degradation and part-load efficiency increases this underestimation to 35 %. Concurrently, the inclusion of the BESS can reduce electrolyzer degradation by one-fifth and increase the annual operational profit by 7 %, while the LCOH remains constant. For the design phase, a price-independent operational strategy aiming to maximize renewable hydrogen yield was implemented, representing a conservative operation simplification. In a second step, this operational assumption within the design phase was compared to a mixed-integer linear (MIL) operational optimization. This assessment reveals two key findings: firstly, the assumption of a constant achievable electricity price over the system's lifetime leads to a 23 % overestimation of annual operational profits when compared to the more realistic electricity sales on the German day-ahead market in 2024. Secondly, the operation heuristic of the design method demonstrates high economic competitiveness, deviating by only 2 %. However, reducing the hydrogen price considerably increases this deviation, highlighting the strong price-dependence of the operation strategy and demonstrating that the determined operation assumptions within the design method significantly underestimate potential profits under altered price conditions. Nevertheless, this performance may be site-specific, as integrated optimization may yield significantly higher added value in markets characterized by greater price volatility or different meteorological profiles. Beyond these specific results, the model showcases the critical importance of integrating high-fidelity physical effects for electrolyzer models, alongside the strategic inclusion of battery storage. Furthermore, it demonstrates that a rigorous consideration of the operational strategy is necessary for a reliable system assessment to account for volatile external factors. Overall, the proposed method provides wind farm developers with a tool to evaluate and optimize site-specific wind–hydrogen-battery systems to derive strategic investment decisions.

The expansion of volatile renewable energy sources leads to increased electricity price volatility and cannibalization effects, intensifying the economic pressure on project developers. Decentralized hybrid energy systems for renewable hydrogen production offer a solution to exploit these price fluctuations and counteract curtailment during hours of low or negative electricity prices. However, the design and operation of those systems are inherently coupled and significantly influenced by external factors, such as electricity and hydrogen prices that can be achieved over the lifetime. To determine an economically optimal design, specifically the power of an electrolyzers and the capacity of a battery, both site-specific and plant-specific characteristics must be considered. First, this paper presents a methodology for determining the optimal electrolyzer rated power and lithium-ion buffer battery capacity size for a 68 MW wind farm in north-western Germany. The approach extends an existing site-specific design method by introducing a battery storage system and enhancing the electrolyzer model with part-load efficiency and operating-mode-dependent degradation. Results indicate that neglecting degradation leads to an underestimation of the levelized cost of hydrogen (LCOH) by 1.2 € kg⁻¹, corresponding 21 %, while neglecting both degradation and part-load efficiency increases this underestimation to 35 %. Concurrently, the inclusion of the battery energy storage system (BESS) can reduce electrolyzer degradation by more than one-third and increase the annual operational profit by 5 %, while it leads to a marginal LCOH increase of 1 % due to higher capital expenditures. For the design phase, a price-independent operational strategy aiming to maximizing renewable hydrogen yield was implemented, representing a necessary simplification. This operational assumption within the design phase was validated in a second step through a mixed-integer linear (MIL) operational optimization. This assessment reveals two key findings: Firstly, the assumption of a constant archivable electricity price over the systems lifetime leads to a 23 % overestimation of annual operational profits when compared to the more realistic electricity sales at the German day-ahead market in 2024. Secondly, the operation heuristic of the design method demonstrates high economic competitiveness, deviating by only 4.5 % from the theoretical MIL optimum even under reduced hydrogen pricing. Nevertheless, this performance may be site-specific, as integrated optimization may yield significantly higher added value in markets characterized by greater price volatility or different meteorological profiles. Beyond these specific results, the model showcases the critical importance of integrating high-fidelity physical effects for electrolyzer models, alongside the strategic inclusion of battery storage. Furthermore, it demonstrates that a rigorous consideration of the operational strategy is necessary for a robust and reliable system assessment to account for volatile external factors. Overall, the proposed method provides wind farm developers with a tool to evaluate and optimize site-specific wind-hydrogen-battery systems to derive well-founded strategic investment decisions.

Storing energy is a major challenge in achieving a 100 % renewable energy system. One promising approach is the production of green hydrogen from wind power. This work proposes a method for optimizing the design of wind-hydrogen systems for existing onshore wind farms in order to achieve the lowest possible Levelized Cost of Hydrogen (LCoH). This is done by application of a novel Python-based optimization model, that iteratively determines the optimal electrolyzer position and distribution mode of Hydrogen for given wind farm layouts. The model includes the costs of all required infrastructure components. It considers peripheral factors such as existing and new roads, necessary power cables and pipelines, wage and fuel costs for truck transportation and the distance to the Point of Demand (POD). Based on the results, a decision can be made whether to distribute the hydrogen to the POD by truck or pipeline. For a 23.8 MW onshore wind farm in Germany, minimal LCoH of 4.58 € kg⁻¹_H2 at an annual hydrogen production of 241.4 t_H2 a⁻¹ are computed. These results are significantly affected by the position of the electrolyzer, the distribution mode, varying wind farm and electrolyzer sizes, as well as distance to POD. The influence of the ratio of electrolyzer power to wind farm power is also investigated. The ideal ratio between rated power of electrolyzer and wind farm lies at around 10 % and a resulting capacity factor of 78 % for the given case. The new model can be used by system planners and researchers to improve and accelerate the planning process for wind-hydrogen systems. Additionally, the economic efficiency, hence competitiveness of wind-hydrogen systems is increased, which contributes to an urgently needed accelerated expansion of electrolyzers. The results of the influencing parameters on LCoH will help to set development goals and indicate a path towards cost-competitive green wind-hydrogen.