SearcharxivSearch

arXiv · 2406.00442

Optimizing hydrogen and e-methanol production through Power-to-X integration in biogas plants

Abstract

The European Union strategy for net zero emissions relies on developing hydrogen and electro fuels infrastructure. These fuels will be crucial as energy carriers and balancing agents for renewable energy variability. Large scale production requires more renewable capacity, and various Power to X (PtX) concepts are emerging in renewable rich countries. However, sourcing renewable carbon to scale carbon based electro fuels is a significant challenge. This study explores a PtX hub that sources renewable CO2 from biogas plants, integrating renewable energy, hydrogen production, and methanol synthesis on site. This concept creates an internal market for energy and materials, interfacing with the external energy system. The size and operation of the PtX hub were optimized, considering integration with local energy systems and a potential hydrogen grid. The levelized costs of hydrogen and methanol were estimated for a 2030 start, considering new legislation on renewable fuels of non biological origin (RFNBOs). Our results show the PtX hub can rely mainly on on site renewable energy, selling excess electricity to the grid. A local hydrogen grid connection improves operations, and the behind the meter market lowers energy prices, buffering against market variability. We found methanol costs could be below 650 euros per ton and hydrogen production costs below 3 euros per kg, with standalone methanol plants costing 23 per cent more. The CO2 recovery to methanol production ratio is crucial, with over 90 per cent recovery requiring significant investment in CO2 and H2 storage. Overall, our findings support planning PtX infrastructures integrated with the agricultural sector as a cost effective way to access renewable carbon.

Explore related subjects

Keep this discovery

BibTeXRIS

Alberto Alamia, Behzad Partoon, Eoghan Rattigan, Gorm Brunn Andresen. 2024-06-01. Optimizing hydrogen and e-methanol production through Power-to-X integration in biogas plants. https://arxiv.org/abs/2406.00442

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Identification in Linear Quantile Panel Models

This paper studies identification in linear quantile panel models with unrestricted individual heterogeneity when the number of time periods is fixed and small. We impose strict exogeneity, whereby the conditional quantile restriction holds given the individual's complete regressor history and latent individual effect, but otherwise allow the disturbances to be arbitrarily dependent over time.

econ.EM

Experimental Design for Policy Choice

We show how to optimally design experiments when the resulting data will be used to choose a welfare-maximizing policy subject to constraints. A decision maker seeks to maximize Bayes expected welfare by choosing a policy whose effects depend on an unknown finite-dimensional parameter. The decision maker has access to a first wave of experimental data with a fixed design but may choose the design of a second wave that will be collected before choosing the policy. The resulting experimental design--policy choice problem is a very high-dimensional dynamic program that is generally intractable in finite samples. We propose a tractable approximation based on the limit experiment and show it is asymptotically optimal using a new asymptotic representation theorem for adaptive experiments with continuous treatments. We apply the method to a conditional cash transfer experiment and demonstrate the potential for large gains from tailoring the experiment to the policy choice.

econ.EM

Designing Spatial Treatments

Spatial treatments are interventions assigned to locations potentially distinct from those of the responding units. We study their optimal design under a general model in which a unit's response diminishes with distance to a treated site. Our estimand of interest is an ``uncontaminated'' effect equal to the average impact of a single intervention site over all hypothetical sites. We propose a novel design based on a Mat\'{e}rn point process which separates treatments by a distance of at least $r$. A larger choice of $r$ reduces bias by separating interventions but increases variance by reducing their numerosity. We choose $r$ to maximize the rate of convergence of a Horvitz-Thompson estimator and prove that this is minimax rate-optimal. We provide weak conditions under which the estimator is asymptotically normal and propose a variance estimator.

econ.EM