Work

Data-informed scenario generation for statistically stable energy storage sizing in isolated power systems

Year: 2022

Type: article

Abstract: We consider the problem of how to generate uncertainty scenarios to be used in energy storage sizing problems in isolated power systems. More precisely, we consider storage sizing formulations where both loads and generation are stochastic, where no closed form analytical expression is available, and where the presence of multiple discrete random variables makes the sizing problem mixed integer and with combinatorial search spaces. We thus propose and characterize a data-driven scenarios selection strategy that mitigates the computational issues associated with these types of storage sizing problem formulations while guaranteeing statically stable optimal solutions. Specifically, the approach works by first learning the distribution of the uncertainties of the loads and generation starting from field data, and then generating, through the learned distribution, an optimal set of uncertainty scenarios that are subsequently used in a two-stage stochastic programming reformulation of the original sizing problem. The workflow does not impose arbitrary structures to the correlation among the uncertainties, nor does it lump these in a single parameter; thus, it is suitable for systems with any load characteristics. Moreover, the approach ensures to reach a solution that is statistically close to the one that would be computed if the original problem was solvable and not computationally intractable. As a case study, we analyze the problem of designing an energy storage system for a wind powered oil and gas platform to minimize the expected daily system operational costs. Numerical simulations showed that the proposed methodology leads to higher quality solutions compared with other scenario selection strategies. This reveals realistic estimations of the expected benefits, while also highlighting the risk-management limitations, when solving the risk-constrained version of the problem.

Source: Journal of Energy Storage

DOI: 10.1016/j.est.2022.104311

Authors: Spyridon Chapaloglou, Damiano Varagnolo, Francesco Marra, Elisabetta Tedeschi

Institutions: Norwegian University of Science and Technology, University of Padua, Equinor (Norway), University of Trento

Language: English

Topic: Electric Power System Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: hybrid

Estimated APC paid: $2,440