Optimal production strategies for manufacturer with renewable energy supply fluctuations and financial risk mitigation
Bibliographic record
Abstract
The global imperative to mitigate climate change underscores the critical importance of transitioning from conventional fossil fuels to sustainable energy sources. However, the integration of renewable energy into industrial operations presents substantial challenges, notably supply fluctuations. Simultaneously, manufacturers must navigate time-based energy pricing mechanisms (TEPM), which dynamically adjust electricity prices based on demand cycles, creating complex incentives for energy procurement. To address these challenges, this study develops a game-theoretic framework involving renewable energy suppliers, conventional energy suppliers, and manufacturers, aiming to identify optimal procurement strategies across different demand phases. Our findings show that manufacturers prefer a hybrid (renewable and conventional) energy strategy when renewable capacity and market demand are high. The profit gap between demand phases depends on renewable energy’s market share. Renewable adoption also helps manufacturers reduce financial risks, especially when spot prices are volatile and contracts provide price protection. From a consumer perspective, hybrid energy strategies enhance welfare when renewable spot prices are low, while high prices and stable output incentivize risk-averse supplier behaviour. These findings enrich the theoretical discourse on energy transition under operational constraints and provide practical implications for manufacturers, energy providers, and policymakers seeking to balance cost efficiency, environmental sustainability, and market stability.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".