📝 SummarXiv

Abstract

This paper proposes a novel privacy-preserving uncertainty disclosure framework, enabling system operators to release marginal value function bounds to reduce the conservativeness of interval forecast and mitigate excessive withholding, thereby enhancing storage dispatch and social welfare. We propose a risk-averse analytical storage arbitrage model based on stochastic dynamic programming and explicitly account for uncertainty intervals in value function training. We derive real-time marginal value function bounds using a rolling-horizon chance-constrained economic dispatch formulation. We rigorously prove that the bounds reliably cap the true opportunity cost and dynamically converge to the hindsight value. We verify that both the marginal value function and its bounds monotonically decrease with the state of charge and increase with uncertainty, providing a theoretical basis for risk-averse strategic behaviors and SoC-dependent designs. We validate the effectiveness of the proposed framework via an agent-based simulation on the ISO-NE test system. Under 50% renewable capacity and 35% storage capacity, the proposed bounds enhance storage response by 38.91% and reduce the optimality gap to 3.91% through improved interval predictions. Additionally, by mitigating excessive withholding, the bounds yield an average system cost reduction of 0.23% and an average storage profit increase of 13.22%. These benefits further scale with higher prediction conservativeness, storage capacity, and system uncertainty.