Mitigating the Energy Market Death Spiral through Long-Term Volume Firming Contracts
Bibliographic record
Abstract
Increasing penetration of behind-the-meter (BTM) resources in distribution systems is a prominent factor for inducing the death spiral in retail markets. Owing to real-time deviations in BTM generation, the energy procured by retailers in the day-ahead markets may no longer be sufficient. Thus, the retailer must procure excess energy at spot prices to compensate these deviations, which in turn increases energy costs for the customers, driving more BTM resource adoption and reinforcing the death spiral. In this context, this paper develops a framework for the electric retailer to procure energy-storage-as-a-service (ESaaS) to mitigate the operational uncertainty. Volume firming contracts are established between the retailer and utility-scale energy storage operators to minimize the retailer’s energy procurement costs in spot markets, thereby limiting energy costs for the customers in spite of high uncertainty introduced by BTM resources. This paper develops the mathematical framework for the volume firming contracts, obtains conditions for optimality and profitability of the contracts, and discusses implications on social welfare. Simulation results verify the effectiveness of the developed framework in improving the financial situation of the retailers and ESaaS providers under uncertain generation conditions of residential BTM resources.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".