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How NREL uses ETAP electrical digital twin modeling of fuel cells and electrolyzer systems for standards-based optimization of microgrids

The ETAP AI-based data-driven model allows us to reproduce manufacturer polarization curves, estimate internal parameters, and use these models for steady-state and dynamic studies.
By Ahmed Saber, PhD, VP Optimization and AI, ETAP

The National Renewable Energy Laboratory (NREL) collaborated with ETAP to advance fuel cell modeling by developing data-driven, AI-based fuel cell and electrolyzer models. This presentation outlines the methodology for creating a realistic digital model of a fuel cell, validating it using manufacturer I-V curves, and applying it to system-level studies such as hosting capacity, optimization, and quasi-static time-series analysis.

The National Renewable Energy Laboratory is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy LLC. NREL has decades of focused leadership in clean energy research, development, and deployment. No other institution has the long-standing expertise and breadth of knowledge that will form the foundation of the clean energy transition.

Location: Golden, Colorado, USA

Year: 2023

Data-driven modeling of fuel cells and electrolyzers

Challenges

1. Manufacturers do not supply circuit-equivalent models
Fuel cell and electrolyzer vendors typically provide only polarization I-V curves, not electrical models.

2. Traditional assumptions like constant-voltage or constant-current do not apply
Fuel cell curves have nonlinear regions (activation, Ohmic, concentration) with no constant-voltage or constant-current behavior.

3. Highly nonlinear and hysteretic behavior in electrolyzers
Electrolyzer characteristics do not mirror fuel cells; they introduce hysteresis and nonlinearity that must be captured accurately.

4. Temperature and pressure significantly affect fuel cell output
Voltage must be corrected based on operating temperature and pressure deviations.

5. Safety constraints are essential to model
Fuel cells must operate within limits for:
  • Temperature
  • Pressure
  • Voltage
  • Current
Outside these bounds, the fuel cell must enter a cutoff region.

Which solutions did they use for research?

Selected applications

The ETAP Digital Twin and ETAP Power Simulator modeling optimization platform enables:
  • Data-driven modeling of fuel cells & electrolyzers
  • Three-phase and unified AC/DC load flow
  • Quasi-static time-series (QSTS) analysis
  • Hosting capacity evaluation
  • Microgrid and resource optimization

Why do they use ETAP?

Main customer benefits

1. AI-based parameter estimation from manufacturer I-V curves

ETAP uses proprietary AI/ML algorithms to extract internal parameters (E鈧�, A, B, C, etc.) from digitized I-V curves, creating accurate electrical models of fuel cells and electrolyzers.

2. Ability to model all operating regions of a fuel cell

The model accurately represents:
  • Activation loss region
  • Ohmic region (normal operating zone)
  • Concentration loss region

3. Temperature and pressure compensation

Fuel cell voltage can be adjusted based on:
  • Logarithmic pressure correction
  • Linear temperature correction

4. Integration into system-level studies

Once modeled, fuel cells can be used in:
  • Load flow and AC/DC unified load flow
  • QSTS simulations
  • Hosting capacity analysis
  • Nonlinear microgrid optimization

5. Fuel Cell Management System (FCMS) behavior

Voltage, current, temperature, and pressure limits ensure operation stays within safe boundaries.

 

What studies were implemented in ETAP?

1. Load flow & unified AC/DC load flow

Fuel cells and electrolyzers were modeled using ETAP steady-state and unified AC/DC engines to observe voltage, current, power flow, reactive power, and system losses.

2. Quasi-static time-series (QSTS)

Hourly or annual simulations provide:
  • Voltage and current trends
  • Power and reactive power profiles
  • System loss variations
  • Over-/under-voltage and overload detection

3. Hosting capacity analysis

Determines the maximum number or size of fuel cells or electrolyzers that can be added without violating:
  • Thermal limits
  • Voltage limits (95%–105%)
  • Harmonic constraints

A demonstration showed that adding three electrolyzers caused undervoltage (94.95%) and transformer overload; reducing to two electrolyzers restored compliance.

4. Microgrid optimization

A nonlinear optimization minimizes total cost while respecting:
  • Technical constraints
  • Net-zero emission goals
  • Load profile requirements
  • Thermal and voltage limits
  • Harmonic constraints
Decision variables include fuel cell size, PV size, and other DER capacities.

What do they think about ETAP?

Customer perspective

This case study benefitted from the assistance of Dr. Kumaraguru Prabakar from NREL, who contributed to this DOE-sponsored project focusing on hydrogen and fuel cell systems.

— By Ahmed Saber, PhD, VP Optimization and AI, ETAP


Videos

Fuel cell and electrolyzer systems, employed from small-scale to grid-level applications, serve various grid support functions and energy optimization objectives, relying on intelligent energy management systems (EMS) and control strategies, yet face challenges due to the absence of standardized distributed energy resources sizing and lack of high-fidelity circuit model data, which will be addressed in the upcoming presentation, alongside discussions on cell-level fuel cell modeling in ETAP and its application in micro-grid optimization for net zero energy-emission adhering to relevant standards/grid-codes.


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