Optimal Management of Wind Energy with Storage: Structural Implications for Policy and Market Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is well known that the generation resource uncertainty induced by significant wind capacity raises concerns about grid security, price stability, and revenue adequacy. One of the most promising solutions is the use of utility-scale energy storage, although the question of general implementation of this strategy remains unanswered. This paper uses a simplified model to show that simple rules exist that govern the decision to generate or store energy from a hybrid wind-storage system. The heuristics developed consider the combination of storage efficiency, electricity price, and shortfall penalty and wind forecast characteristics to guide the decision of whether to bid energy into the electricity market or not. Specifically, this paper develops the optimal strategy for use of a simplified system of an energy storage unit with a wind generator. The solution is analyzed using a dynamic programming formulation in a simplified framework over a multiperiod planning horizon. The analysis of the solution under all regimes yields insightful structural solutions regarding the conditions under which the wind generator should bid into the energy market and when it should not. The results also provide insight into the specific implications of forecast accuracy and market design on the need for storage. This analysis allows additional conclusions to be drawn about the value of various storage technologies based on their capacity and efficiency characteristics. However, the most important contribution of this work is the understanding of the importance of market penalties in encouraging participants to either improve forecasting ability or, perhaps more realistically, contract storage to mitigate shortfall risk. Improving both forecasting accuracy and storage capabilities results in value reduction for both.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it