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Exploiting Electrolyzer Flexibility via Multiscale Model Predictive Control Cross Heterogeneous Energy Markets

Zhichao Chen, Hongyuan Sheng, Hao Wang, Jiaze Ma

TL;DR

This paper tackles the high electricity cost barrier in PEM-based green hydrogen production by enabling electrolyzers to actively participate in multi-scale electricity markets (DAM and RTM). It introduces a rolling-horizon optimization framework tightly integrated with a high-fidelity PEM electrolyzer model that includes membrane degradation, co-optimizing bidding and operation to exploit inter-market price differentials. Results show that HF-MS strategies substantially reduce total hydrogen costs and can achieve near-zero or negative net electricity costs, outperforming single-scale and lower-fidelity approaches by delaying or mitigating membrane degradation while capitalizing on DAM-RTM arbitrage. The work demonstrates a financially compelling pathway for scalable green hydrogen deployment and enhanced grid flexibility, with practical implications for market design and asset longevity. The approach is validated with real-market data from Houston and California and is extendable to other regions and renewable-forecast-informed operation.

Abstract

Green hydrogen production via electrolysis is crucial for decarbonization but faces significant economic hurdles primarily due to the high cost of the electricity. However, current electrolyzer-based hydrogen production processes predominantly rely on the single-scale Day-Ahead Market (DAM) for electricity procurement, failing to fully exploit the economic benefits offered by multi-scale electricity market that integrates both the DAM and the Real-Time Market (RTM), thereby eliminating the opportunity to reduce the overall cost. To mitigate this technical gap, this research investigates a dynamic operational strategy enabling electrolyzers to strategically navigate between the DAM and RTM to minimize net operation costs. Using a rolling horizon optimization framework to coordinate bidding and operation, we demonstrate a strategy where electrolyzers secure primary energy via exclusive DAM purchases, then actively engage the RTM to buy supplemental energy cheaply or, critically, sell procured DAM energy back at a profit during high RTM price periods. Our analysis reveals that this coordinated multi-scale electricity market participation strategy can dramatically reduce net electricity expenditures, achieving near-zero or even negative effective electricity costs for green hydrogen production under realistic market scenarios, effectively meaning the operation can profit from its electricity market interactions. By transforming electrolyzers from simple price-takers into active participants capable of arbitrage between market timescales, this approach unlocks a financially compelling pathway for green hydrogen, accelerating its deployment while simultaneously enhancing power grid flexibility.

Exploiting Electrolyzer Flexibility via Multiscale Model Predictive Control Cross Heterogeneous Energy Markets

TL;DR

This paper tackles the high electricity cost barrier in PEM-based green hydrogen production by enabling electrolyzers to actively participate in multi-scale electricity markets (DAM and RTM). It introduces a rolling-horizon optimization framework tightly integrated with a high-fidelity PEM electrolyzer model that includes membrane degradation, co-optimizing bidding and operation to exploit inter-market price differentials. Results show that HF-MS strategies substantially reduce total hydrogen costs and can achieve near-zero or negative net electricity costs, outperforming single-scale and lower-fidelity approaches by delaying or mitigating membrane degradation while capitalizing on DAM-RTM arbitrage. The work demonstrates a financially compelling pathway for scalable green hydrogen deployment and enhanced grid flexibility, with practical implications for market design and asset longevity. The approach is validated with real-market data from Houston and California and is extendable to other regions and renewable-forecast-informed operation.

Abstract

Green hydrogen production via electrolysis is crucial for decarbonization but faces significant economic hurdles primarily due to the high cost of the electricity. However, current electrolyzer-based hydrogen production processes predominantly rely on the single-scale Day-Ahead Market (DAM) for electricity procurement, failing to fully exploit the economic benefits offered by multi-scale electricity market that integrates both the DAM and the Real-Time Market (RTM), thereby eliminating the opportunity to reduce the overall cost. To mitigate this technical gap, this research investigates a dynamic operational strategy enabling electrolyzers to strategically navigate between the DAM and RTM to minimize net operation costs. Using a rolling horizon optimization framework to coordinate bidding and operation, we demonstrate a strategy where electrolyzers secure primary energy via exclusive DAM purchases, then actively engage the RTM to buy supplemental energy cheaply or, critically, sell procured DAM energy back at a profit during high RTM price periods. Our analysis reveals that this coordinated multi-scale electricity market participation strategy can dramatically reduce net electricity expenditures, achieving near-zero or even negative effective electricity costs for green hydrogen production under realistic market scenarios, effectively meaning the operation can profit from its electricity market interactions. By transforming electrolyzers from simple price-takers into active participants capable of arbitrage between market timescales, this approach unlocks a financially compelling pathway for green hydrogen, accelerating its deployment while simultaneously enhancing power grid flexibility.
Paper Structure (16 sections, 32 equations, 10 figures, 1 table)

This paper contains 16 sections, 32 equations, 10 figures, 1 table.

Table of Contents

  1. Introduction
  2. Experimental Results
  3. Discussions
  4. Methods
  5. Rolling Horizon Optimization Approach
  6. Additional Controller and Simulator Information
  7. Plant Model
  8. Mass Conservation of PEM Electrolyzer
  9. Energy Conservation of the Whole Plant
  10. Membrane Degradation Model
  11. Controller Model
  12. Electricity Market Bidding
  13. Cost Computation
  14. Simulator Model
  15. Overall Workflow for Rolling Horizon Optimization
  16. ...and 1 more sections

Figures (10)

  • Figure 1: Bidding from the DAM and RTM with a flexible PEM.
  • Figure 2: DAM traded power, RTM traded power, and $\text{H}_{\text{2}}$ storage levels for the HF-MS (light blue line) and HF-SS (gray line) strategies. Data correspond to four weeks in Houston, January 2022. The red line indicates the price differential (RTM Price minus DAM Price).
  • Figure 3: DAM traded power, RTM traded power, and $\text{H}_{\text{2}}$ storage levels for the HF-MS (light blue line) and LF-MS (gray line) strategies. Data correspond to four weeks in Houston, January 2022. The red line indicates the price differential (RTM Price minus DAM Price).
  • Figure 4: Cost comparison results.
  • Figure 5: Membrane degradation rate and operating temperature.
  • ...and 5 more figures