A surrogate framework for green Hydrogen plants using Conditional Wasserstein GANs with gradient penalty

Green Hydrogen (H2), produced through electrolysis using Renewable Energy Resource (RES), is among the critical factors for a transition towards a 100% green and sustainable community as it decarbonizes hard-to-electrify sectors, requiring significant investments in infrastructure, electrolyzer technology, and RES capacity to scale production and achieve cost competitiveness. Currently, most data analytics for these H2 production systems is conducted using various Artificial Intelligence (AI) strategies. However, these strategies primarily rely on available data for production forecasting and may encounter challenges when conditions are uncertain or data are insufficient. To this end, a novel Multi-Input Multi-Output (MIMO) Generative Artificial Intelligence (GAI)-based model, known as Conditional Wasserstein Generative Adversarial Network (cWGAN)-Gradient Penalty (GP) is investigated to analyze and augment data from a H2 production plant under varying solar shading conditions (no shade, partial shade (cloud cover), and full shade). The model incorporates critical plant parameters, including grid and Battery Energy Storage System (BESS) voltage profiles, current profiles of the grid, electrolyzer, and Photovoltaic (PV) systems, State-of-Charge (SoC), and the mass of H2 produced. By encoding shading conditions as conditional labels, the cWGAN-GP uses generator and critic structures to produce high-quality synthetic data that closely mimics real plant data. The model’s performance is evaluated using a comprehensive suite of statistical divergences, moment errors, and distributional metrics, compared with recent models from nine state-of-the-art conditional GAN variants and alternatives, demonstrating its ability to capture complex, multivariate system behaviors with minimal generation errors across seven parameters.