🤖 AI Summary
This study addresses the limitation of relying solely on price forecasting for optimizing energy storage bidding, as actual revenue depends critically on the interaction between decision rules and price signals. The work disentangles the theoretical value of price signals from the gains achievable through strategic implementation and proposes an ordinal bidding strategy that leverages only the ranking of prices—rather than their absolute accuracy—under daily throughput constraints. Through a conditional sub-Gaussian price model, mutual information analysis, and Gaussian perturbation experiments, the authors reveal a non-monotonic relationship between information content and realized revenue. Empirical results demonstrate that the ranking-based strategy captures 90% of the attainable revenue; even with perfectly accurate price vectors, swapping the highest and lowest prices reduces revenue by 53%; and current climate-aware benchmarks already achieve 78% of the revenue attainable under ideal forecasts.
📝 Abstract
Price forecasts are evaluated in EUR/MWh of error, while storage earns euros; their link depends on the decision rule. We separate the optimal value of a signal V(S|C) from the revenue J(g,S) achieved by an implemented policy. For a price-taking asset with daily throughput bound L and a conditionally sub-Gaussian price law of scale s, the optimal gain over climatology is at most L s sqrt(2 I(S;Pi|C)). This is a square-root envelope, not a prediction for an individual policy. On 939 French day-ahead days the tested bound sits at least two orders of magnitude above the reference uplift. Along a nested Gaussian garbling family, however, plug-in policy revenue is non-monotone even though signal information is monotone: at the residual-noise scale the best Gaussian-shrinkage rule achieves -46% of the uplift, below climatology. The mechanism is ordinal. On a relaxation, optimal schedules depend on interval ordering and profitable-pair tests. A constructed rank-only bid captures 90% of the uplift; exchanging the cheapest and dearest entries of an otherwise exact price vector cuts total revenue by 53%. Within the designed sweep, rank agreement has in-sample R^2 = 0.99, against 0.34 for the information upper bound. Climatology already earns 78% of perfect-foresight revenue. Information quantity alone therefore does not value a forecast-driven policy; realised schedules and ordering must be evaluated explicitly.