Distributionally Robust Planning of Hydrogen-Electrical Microgrids for Sea Islands

TL;DR

This paper develops a novel distributionally robust optimization framework for planning hydrogen-electrical microgrids on sea islands, accounting for complex uncertainties and infrastructure decisions, demonstrating improved cost-effectiveness and resilience.

Contribution

It introduces a decision-dependent uncertainty model for sea island microgrids and a customized C&CG algorithm to solve the complex DRO problem effectively.

Findings

The proposed method improves planning cost and resilience.

The customized algorithm enhances solution accuracy and efficiency.

Numerical results validate the framework's effectiveness.

Abstract

This paper presents a distributionally robust planning method for hydrogen-electrical microgrids over islands, where the cross-island energy exchange is supported by a maritime hydrogen transport network. This planning problem is complicated due to heterogeneous off-shore wind-driven uncertainties (i.e., renewable power, transport availability, demand fluctuations, and grid faulting), a subset of which exhibit endogenous uncertainty, as they can be affected by proactive measures (e.g., grid hardening) or infrastructure investment. To capture these features, a two-stage distributionally robust optimization (DRO) model is developed considering decision-dependent uncertainty (DDU), which encompasses variation of the underlying distributional ambiguity due to the change of the first stage decisions. Notably, the complete recourse property is missing, which is often neglected in existing DRO studies. Nevertheless, different from the case for land-based microgrids, this issue is critical and fundamental for sea island systems due to their particular physical and logistical requirements. To address these issues, we develop a C&CG algorithm that is customized with strong cutting planes to handle DRO with a varying DDU ambiguity set and feasibility requirements. Numerical results demonstrate the cost-effectiveness and resilience of the proposed planning framework, along with the nontrivial improvements of the algorithm in both solution accuracy and computational efficiency.