Temporal and Spatial Decomposition for Prospective Studies in Energy Systems under Uncertainty

TL;DR

This paper introduces a spatio-temporal decomposition approach combined with Dual Approximate Dynamic Programming to efficiently evaluate energy storage opportunity costs in large, uncertain European energy systems, addressing complexity across multiple dimensions.

Contribution

It develops a novel decomposition scheme and applies DADP to compute localized usage values in large-scale, stochastic energy system models, improving computational tractability.

Findings

DADP provides competitive results compared to SDDP.

The method effectively handles large-scale, multi-node energy systems.

Localized usage values are computed independently of other nodes.

Abstract

The increasing penetration of renewable energy requires greater use of storage resources to manage system intermittency. As a result, there is growing interest in evaluating the opportunity cost of stored energy, or usage values, which can be derived by solving a multistage stochastic optimization problem. Stochasticity arises from net demand (the aggregation of demand and non-dispatchable generation), the availability of dispatchable generation, and inflows when the storage facilities considered are hydroelectric dams. We aim to compute these usage values for each market zone of the interconnected European electricity system, in the context of prospective studies currently conducted by RTE, the French TSO. The energy system is mathematically modelled as a directed graph, where nodes represent market zones and arcs represent interconnection links. In large energy systems, spatial complexity (thirty nodes in the system, each with at most one aggregated storage unit) compounds temporal complexity (a one-year horizon modelled with two timescales: weekly subproblems with hourly time steps). This work addresses three main sources of complexity: temporal, spatial, and stochastic. We tackle the multinode multistage stochastic optimisation problem by incorporating a spatio-temporal decomposition scheme. To efficiently compute usage values, we apply Dual Approximate Dynamic Programming (DADP), which enables tractable decomposition across both time and space. This approach yields nodal usage values that depend solely on the local state of each node, independently of the others. We conduct numerical studies on a realistic system composed of thirty nodes (modelling part of Europe) and show that DADP obtains competitive results when comparing with traditional methods like Stochastic Dual Dynamic Programming (SDDP).