Price Information Is Not Enough: Ordering and Decision Rules in Storage Bidding
arXiv:2608.08377
2026
Training
2 ideas extracted · analyzed Sep 1, 2026
What the math gives to ML
The paper offers a transferable decision-focused mechanism: forecast information or pointwise accuracy is not sufficient to predict the value of a downstream policy when the policy depends on ordinal structure. For storage-like decisions, feasible schedules are determined largely by profitable price orderings and pairwise price differences, while arbitrary ordering errors can destroy revenue. The most promising neural-network transfer is to train forecasters and decision policies with economically weighted ranking losses and explicit permutation-sensitivity tests, then evaluate the resulting feasible actions rather than relying only on forecast error.
Ideas from this paper
✗ Failed on benchmark
2026
Train a neural forecaster or policy network to preserve the pairwise ordering that determines profitable charge and discharge decisions, rather than optimizing only pointwise forecast error. Combine a conventional prediction loss with a pairwise ranking loss weighted by the economic price gap, then pass the prediction through a feasibility-aware storage scheduler.
Useful8/10
Difficulty5/10
Novelty5/10
✗ Failed on benchmark
2026
Regularize a neural decision policy against economically harmful changes in its action when the predicted price ordering is perturbed. Targeted swaps of extrema and threshold-adjacent entries directly test the paper’s mechanism that a small number of ordering mistakes can cause a disproportionate revenue loss.
Useful7/10
Difficulty6/10
Novelty7/10