[Paper Review] Extended Load Flexibility of Utility-Scale P2H Plants: Optimal Production Scheduling Considering Dynamic Thermal and HTO Impurity Effects

Yiwei Qiu, Buxiang Zhou+6 more|arXiv (Cornell University)|Jan 28, 2023

Hybrid Renewable Energy Systems4 citations

TL;DR

This paper proposes a multiphysics-aware optimal scheduling framework for utility-scale alkaline water electrolysis (AEL)-based power-to-hydrogen (P2H) plants, explicitly modeling dynamic thermal and hydrogen-to-oxygen (HTO) impurity effects to enhance load flexibility. Using a decomposition-based solution method (SDM-GS-ALM), the approach improves hydrogen production by 7.74% and profit by 8.72% compared to conventional methods in a 22-electrolyzer wind-powered plant.

ABSTRACT

In the conversion toward a clear and sustainable energy system, the flexibility of power-to-hydrogen (P2H) production enables the admittance of volatile renewable energies on a utility scale and provides the connected electrical power system with ancillary services. To extend the load flexibility and thus improve the profitability of green hydrogen production, this paper presents an optimal production scheduling approach for utility-scale P2H plants composed of multiple alkaline electrolyzers. Unlike existing works, this work discards the conservative constant steady-state constraints and first leverages the dynamic thermal and hydrogen-to-oxygen (HTO) impurity crossover processes of electrolyzers. Doing this optimizes their effects on the loading range and energy conversion efficiency, therefore improving the load flexibility of P2H production. The proposed multiphysics-aware scheduling model is formulated as mixed-integer linear programming (MILP). It coordinates the electrolyzers' operation state transitions and load allocation subject to comprehensive thermodynamic and mass transfer constraints. A decomposition-based solution method, SDM-GS-ALM, is followingly adopted to address the scalability issue for scheduling large-scale P2H plants composed of tens of electrolyzers. With an experiment-verified dynamic electrolyzer model, case studies up to 22 electrolyzers show that the proposed method remarkably improves the hydrogen output and profit of P2H production powered by either solar or wind energy compared to the existing scheduling approach.

Motivation & Objective

• To address the limited load flexibility of utility-scale P2H plants due to conservative constant-load assumptions in existing scheduling models.
• To integrate dynamic thermal and HTO impurity crossover effects—previously neglected—into the production scheduling of alkaline electrolyzers.
• To develop a scalable optimization framework that enables efficient scheduling of large-scale P2H plants with tens of electrolyzers.
• To improve hydrogen production and profitability by maximizing the utilization of variable renewable energy sources such as wind and solar.
• To validate the proposed model using experimental data from a real CNDQ5/3.2 alkaline electrolyzer.

Proposed method

• Formulates a mixed-integer linear programming (MILP) model that captures dynamic temperature evolution and HTO impurity accumulation in alkaline electrolyzers.
• Incorporates thermodynamic and mass transfer constraints, including temperature-dependent overvoltage and HTO flammability limits, into the scheduling optimization.
• Introduces a decomposition-based solution method, SDM-GS-ALM, to handle the scalability issue of large-scale P2H plants with many electrolyzers.
• Uses a time-series power command based on real-world regulation signals (e.g., PJM RegD) to validate the dynamic models under realistic load variations.
• Calibrates and validates the dynamic models using experimental data from a 25 kW CNDQ5/3.2 alkaline electrolyzer.
• Solves the scheduling problem with a tolerance of ε = 0.1% and MIP gap of 10⁻⁶ to ensure high solution accuracy.

Experimental results

Research questions

• RQ1 How do dynamic thermal and HTO impurity effects influence the load flexibility and energy efficiency of alkaline electrolyzers in variable-load operation?
• RQ2 To what extent can the integration of dynamic multiphysics models improve hydrogen production and profitability in utility-scale P2H plants?
• RQ3 Can a decomposition-based optimization method effectively scale to large P2H plants with tens of electrolyzers while maintaining solution accuracy?
• RQ4 How does the proposed scheduling approach compare to traditional fixed-load-range methods in terms of hydrogen output and economic profit?
• RQ5 What is the impact of renewable energy variability (wind/solar) on the performance of the proposed scheduling framework?

Key findings

• The proposed method increased hydrogen production by 7.74% and profit by 8.72% in a 22-electrolyzer wind-powered plant compared to the traditional scheduling approach.
• The computation time for scheduling 22 electrolyzers was 713.82 seconds using the SDM-GS-ALM method, which scales linearly with plant size.
• Direct solution of the full scheduling problem failed to converge within 24 hours for an 8-electrolyzer plant, highlighting the necessity of the decomposition approach.
• The dynamic models of temperature and HTO impurity were experimentally validated, showing strong agreement with real-world data from a 25 kW alkaline electrolyzer.
• The method enables electrolyzers to operate near thermal and HTO limits, maximizing flexibility and utilization of renewable power.
• The average profit improvement was 1.438% when directly coupled with wind energy and 0.982% with solar energy, demonstrating consistent gains across energy sources.