A GAN‑and‑Transformer‑Assisted Scheduling Approach for Hydrogen‑Based Multi‑Energy Microgrid

Against the backdrop of ever‑increasing energy demand and growing awareness of en‑ vironmental protection, the research and optimization of hydrogen‑related multi‑energy systems have become a key and hot issue due to their zero‑carbon and clean characteristics. In the scheduling of such multi‑energy systems, a typical problem is how to describe and deal with the uncertainties of multiple types of energy. Scenario‑based methods and ro‑ bust optimization methods are the two most widely used methods. The first one combines probability to describe uncertainties with typical scenarios, and the second one essentially selects the worst scenario in the uncertainty set to characterize uncertainties. The selection of these scenarios is essentially a trade‑off between the economy and robustness of the so‑ lution. In this paper, to achieve a better balance between economy and robustness while avoiding the complex min‑max structure in robust optimization, we leverage artificial in‑ telligence (AI) technology to generate enough scenarios, from which economic scenarios and feasible scenarios are screened out. While applying a simple single‑layer framework of scenario‑based methods, it also achieves both economy and robustness. Specifically, first, a Transformer architecture is used to predict uncertainty realizations. Then, a Gener‑ ative Adversarial Network (GAN) is employed to generate enough uncertainty scenarios satisfying the actual operation. Finally, based on the forecast data, the economic scenar‑ ios and feasible scenarios are sequentially screened out from the large number of gener‑ ated scenarios, and a balance between economy and robustness is maintained. On this ba‑ sis, a multi‑energy collaborative optimization method is proposed for a hydrogen‑based multi‑energy microgrid with consideration of the coupling relationships between energy sources. The effectiveness of this method has been fully verified through numerical exper‑ iments. Data show that on the premise of ensuring scheduling feasibility, the economic cost of the proposed method is 0.67% higher than that of the method considering only eco‑ nomic scenarios. It not only has a certain degree of robustness but also possesses good economic performance.