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Offshore wind is playing an increasingly important role in our energy mix as it provides the opportunity to increase the penetration of wind energy considerably beyond the current capacity of land-based wind resources. While most planned offshore wind projects consider constructing the wind farms relatively close to the coast, there is vast untapped potential over the open ocean where wind velocities are significantly higher. However, transmitting electricity generated far from shore to onshore demand points is a major challenge since using submarine power cables for long distances can be prohibitively expensive. To address this challenge, we consider using offshore wind energy to directly produce green ammonia that can then be transported to shore via ships or pipelines. This is technically feasible since electricity-based production of ammonia only requires water and air as input materials. We perform a comprehensive techno-economic analysis for such green offshore ammonia plants, determining the minimum achievable levelized costs of ammonia for various wind profiles, plant capacities, distances to shore, and water depths. Our results indicate that the proposed approach has the promise to be cost-competitive, especially when considering expected cost reductions in offshore wind turbines in the foreseeable future.

As the basis for virtually any form of nitrogen fertilizers, ammonia plays a vital role in agriculture; in addition, there has been an increased interest in its use as a carbon-free energy carrier. However, ammonia is also associated with two major environmental concerns: CO 2 emissions from the conventional production process and nitrogen pollution from the excessive use of ammoniabased fertilizers. To mitigate these environmental impacts, we develop an optimization framework for the design of a sustainable ammonia-based agricultural system that synergistically integrates the production of ammonia from renewable resources and effective measures for nitrogen management. The proposed model captures the effect of intermittency by incorporating both design and detailed operational decisions. By applying a multiscale time representation that reduces the problem size and a tailored surrogate model that accurately approximates model nonlinearity, we are able to achieve optimal solutions within reasonable computation times. A computational case study is conducted using real-world data from a local farm in Morris, Minnesota, and the results indicate the trade-off between cost and nitrogen loss. Importantly, we show that practicing effective nitrogen management can significantly reduce the nitrogen loss with only a small increase in net present cost.

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