A Two-Timescale Decision-Hazard-Decision Formulation for Storage Usage Values Calculation

TL;DR

This work addresses the challenge of computing storage usage values under uncertainty by introducing a two-timescale decision-hazard-decision (DHD) information structure that separates planning (nonanticipative) from recourse decisions. It develops a stochastic dynamic programming framework that handles binary on/off decisions for slow thermal units without state extension, and demonstrates how information disclosure timing alters usage values and the resulting dispatch. Theoretical comparisons show that hazard-decision (HD) is a relaxation of DHD, and numerical results reveal meaningful differences in utilization of storage and merit order depending on the chosen information structure. The study highlights the practical significance of selecting appropriate information structures in prospective storage management and lays groundwork for future scalable techniques such as spatial decomposition.

Abstract

The penetration of renewable energies requires additional storages to deal with intermittency. Accordingly, there is growing interest in evaluating the opportunity cost (usage value) associated with stored energy in large storages, a cost obtained by solving a multistage stochastic optimization problem. Today, to compute usage values under uncertainties, an adequacy resource problem is solved using stochastic dynamic programming assuming a hazard-decision information structure. This modelling assumes complete knowledge of the coming week uncertainties, which is not adapted to the system operation as the intermittency occurs at smaller timescale. We equip the twotimescale problem with a new information structure considering planning and recourse decisions: decision-hazard-decision. This structure is used to decompose the multistage decision-making process into a nonanticipative planning step in which the on/off decisions for the thermal units are made, and a recourse step in which the power modulation decisions are made once the uncertainties have been disclosed. In a numerical case, we illustrate how usage values are sensitive as how the disclosure of information is modelled.

Figures (8)

• Figure 1: Sketch of information structure with weekly planning decisions and hourly recourses. An arrow maps the available information towards the decision, so that, here, the information structure is nonanticipative as all arrows go either down or from the left to the right.
• Figure 2: Decision-hazard-decision information structure at the weekly timescale. An arrow maps the available information towards the decision, so that, here, the information structure is partly anticipative as some arrows go from the right to the left.
• Figure 3: Weekly hazard-decision information structure. An arrow maps the available information towards the decision, so that, here, the information structure is partly anticipative as some arrows go from the right to the left.
• Figure 4: Weekly decision-hazard-decision information structure. An arrow maps the available information towards the decision, so that, here, the information structure is partly anticipative as some arrows go from the right to the left.
• Figure 5: Prices and usage values comparison for week 20, depending on the information structure ($\mathsf{DHD}$ or $\mathsf{HD}$) considered in the Bellman equations

Theorems & Definitions (1)

• Remark 1