Self-scheduling for a hydrogen-based virtual power plant in day-ahead energy and reserve electricity markets

Abstract

This study presents an innovative optimization model for the self-scheduling of a hydrogen-based virtual power plant (H2-VPP) that aims to thrive in day-ahead energy and reserve markets. At its core, the model seeks to optimize profits by integrating a mix of renewable sources, battery storage, electrolyzers, and hydrogen storage, highlighting the model’s focus on both electricity and hydrogen networks within a unified operational framework. Designed to navigate the complexities of a VPP, the model excels at strategically managing diverse resources for energy and reserve markets, emphasizing optimal operation of all assets. It accounts for the interplay between electricity and hydrogen production, storage, and demand, and addresses the time constraints critical to increasing revenues and ensuring balanced supply. A case study demonstrates the model’s effectiveness, highlighting the role of hydrogen storage in increasing renewable integration and revenues. This underscores the model’s ability to leverage the unique dynamics of electricity and hydrogen within the H2-VPP, confirming its potential in a rapidly evolving energy landscape.