Implicit Incorporation of Heuristics in MPC-Based Control of a Hydrogen Plant

TL;DR

This paper presents a novel MPC-based control strategy for a hydrogen plant that implicitly incorporates complex heuristics, improving energy management and operational validity over traditional rule-based methods.

Contribution

It introduces an implicit heuristic incorporation method into MPC for hydrogen plant control, handling complex physical constraints without explicit modeling.

Findings

Enhanced control validity with heuristic constraints

Improved energy management over rule-based control

Successful simulation with real-world data

Abstract

The replacement of fossil fuels in combination with an increasing share of renewable energy sources leads to an increased focus on decentralized microgrids. One option is the local production of green hydrogen in combination with fuel cell vehicles (FCVs). In this paper, we develop a control strategy based on Model Predictive Control (MPC) for an energy management system (EMS) of a hydrogen plant, which is currently under installation in Offenbach, Germany. The plant includes an electrolyzer, a compressor, a low pressure storage tank, and six medium pressure storage tanks with complex heuristic physical coupling during the filling and extraction of hydrogen. Since these heuristics are too complex to be incorporated into the optimal control problem (OCP) explicitly, we propose a novel approach to do so implicitly. First, the MPC is executed without considering them. Then, the so-called allocator uses a heuristic model (of arbitrary complexity) to verify whether the MPC's plan is valid. If not, it introduces additional constraints to the MPC's OCP to implicitly respect the tanks' pressure levels. The MPC is executed again and the new plan is applied to the plant. Simulation results with real-world measurement data of the facility's energy management and realistic fueling scenarios show its advantages over rule-based control.