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

Yiwei Qiu, Buxiang Zhou, Tianlei Zang, Yi Zhou, Shi Chen, Ruomei Qi, Jiarong Li, Jin Lin

TL;DR

This work tackles the challenge of extending load flexibility for utility-scale P2H plants by incorporating dynamic multiphysics constraints—specifically, temperature dynamics and HTO impurity crossover—into the production scheduling problem. It presents a multiphysics-aware MILP model and a decomposition-based solution (SDM-GS-ALM) to solve large-scale, nonconvex problems by decoupling electrolyzers and coordinating them via a coupling constraint. Case studies up to 22 electrolyzers show that accounting for thermal and impurity dynamics improves hydrogen output and profitability compared to traditional schedules, with average hydrogen/profit gains of up to about 1.4% and 1.8% in wind- and solar-powered scenarios. The approach enhances renewable integration and grid services for P2H while maintaining computational tractability, paving the way for industrial-scale deployment and future stochastic extensions.

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.

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

TL;DR

This work tackles the challenge of extending load flexibility for utility-scale P2H plants by incorporating dynamic multiphysics constraints—specifically, temperature dynamics and HTO impurity crossover—into the production scheduling problem. It presents a multiphysics-aware MILP model and a decomposition-based solution (SDM-GS-ALM) to solve large-scale, nonconvex problems by decoupling electrolyzers and coordinating them via a coupling constraint. Case studies up to 22 electrolyzers show that accounting for thermal and impurity dynamics improves hydrogen output and profitability compared to traditional schedules, with average hydrogen/profit gains of up to about 1.4% and 1.8% in wind- and solar-powered scenarios. The approach enhances renewable integration and grid services for P2H while maintaining computational tractability, paving the way for industrial-scale deployment and future stochastic extensions.

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.
Paper Structure (26 sections, 33 equations, 16 figures, 4 tables, 1 algorithm)

This paper contains 26 sections, 33 equations, 16 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Motivation
  3. A Brief Literature Review
  4. Contributions of This Work
  5. Modeling Dynamic Multiphysics Constraints of an Alkaline Electrolyzer for Scheduling
  6. Production/Idle/Standby State Transition of Electrolyzers
  7. Hydrogen Production and Power Consumption
  8. Temperature Dynamic Model and Constraints
  9. Hydrogen-to-Oxygen Crossover Dynamics and Constraints
  10. The Production Scheduling Problem and Decomposition-Based Solution Method
  11. The P2H Plant Production Scheduling Model
  12. Decision variables and scheduling horizon
  13. Objective
  14. Power supply constraints
  15. Operational feasibility constraints of the electrolyzers
  16. ...and 11 more sections

Figures (16)

  • Figure 1: The conceptual schematic of utility-scale hydrogen production.
  • Figure 2: The conceptual structure of this paper.
  • Figure 3: Illustrative schematic of an alkaline electrolyzer.
  • Figure 4: State transition diagram of the electrolyzer varela2021modelingzheng2022optimal.
  • Figure 5: Approximate production function of a 1000 Nm$^3$/hr-rated electrolyzer.
  • ...and 11 more figures

Theorems & Definitions (4)

  • Remark 1
  • Remark 2
  • Remark 3
  • Remark 4