Seasonal storage and grid balancing through power-to-methanol and a “Biomass Battery”
Bibliographic record
Abstract
The world is witnessing a rise in the use of renewable energy sources for electricity generation. However, these sources rely on weather and seasonal conditions, affecting grid stability and energy supply security. This study introduces a system enabling seasonal energy storage and synthesis of e-methanol using biomass as an energy reservoir. Biogas drives an oxy-fuel combustion power cycle, generating electricity during periods with low wind/solar electricity production. The captured biogenic CO 2 is stored and later used to produce e-methanol, with a solid oxide electrolyzer providing hydrogen when there is excess grid electricity. The feasibility of the system is examined from a techno-economic standpoint, including calculations of exergy efficiency and the Levelized Cost of Methanol (LCOM). The LCOM is estimated to be about €900/ton for conditions applicable in 2023 using a multi-objective optimization method. It is predicted to reach approximately €500/ton by 2040, based on various assumptions about future developments in green electricity costs and the technologies involved. With projected electricity prices, the proposed system can become economically attractive by 2040, as the profits can outweigh the electricity costs. The CO 2 tax thresholds required for e-methanol to be economically competitive with traditional fossil-based methanol under various electricity tariff models are also analyzed. It is found that by 2040, a CO 2 tax of €258/tCO 2 would match the production cost of e-methanol compared to its non-renewable counterpart, assuming a grid variable tariff model expected in that year.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".