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Updating the electrolyzer plant model for v2 and new electrolyzer code release (#180)
* Adding wind and hydrogen example * Initial electrolyzer example * Electrolyzer reference signal * Update with documentation * Adding plots for electrolyzer * Updating example and plotting outputs, adding verbose print out statements * Update readme for example 06 * Remove unused imports * Fix ruff formatting * Fix electrolyzer implementation test * Install main electrolyzer repo (main branch) * Update initialization to not require all electrolyzer input values and update regression test values for better initialization * Fix ruff formatting --------- Co-authored-by: misi9170 <michael.sinner@nrel.gov>
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‎.github/workflows/continuous-integration-workflow.yaml‎

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run: |
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python -m pip install --upgrade pip
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pip install -e ".[develop]"
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pip install git+https://github.com/NREL/electrolyzer.git@638d890
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pip install git+https://github.com/NREL/electrolyzer.git
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pip install https://github.com/NREL/SEAS/blob/v1/SEAS.tar.gz?raw=true
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# - uses: pre-commit/action@v3.0.0
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- name: Run ruff

‎docs/_toc.yml‎

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- file: wind
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- file: battery
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- file: solar_pv
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- file: electrolyzer
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- caption: Usage
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chapters:
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- file: order_of_op

‎docs/electrolyzer.md‎

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# Electrolyzer Plant
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The hydrogen electrolyzer modules use the [electrolyzer](https://github.com/NREL/electrolyzer) package developed by the National Renewable Energy Laboratory to predict hydrogen output of hydrogen electrolyzer plants. This repot contains models for PEM and Alkaline electrolyzer cell types.
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To create a hydrogen electrolzer plant, set `component_type` = `ElectrolyzerPlant` in the input dictionary (.yaml file).
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## Inputs
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#### Required Parameters
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The parameters listed below are required unless otherwise specifice as *Optional*.
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- `general`: General simulation parameters.
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- `initial_conditions`: Initial conditions for the simulation including:
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- `power_available_kw`: Initial power available to the electrolyzer [kW]
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- `electrolyzer`: Electrolyzer plant specific parameters including:
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- `initialize`: boolean. Whether to initialize the electrolyzer.
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- `initial_power_kW`: Initial power input to the electrolyzer [kW].
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- `supervisor`:
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- `system_rating_MW`: Total system rating in MW.
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- `n_stacks`: Number of electrolyzer stacks in the plant.
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- `stack`: Electrolyzer stack parameters including:
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- `cell_type`: Type of electrolyzer cell (e.g., PEM, Alkaline).
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- `max_current`: Maximum current of the stack [A].
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- `temperature`: Stack operating temperature [degC].
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- `n_cells`: Number of cells per stack.
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- `min_power`: Minimum power for electrolyzer operation [kW].
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- `stack_rating_kW`: Stack rated power [kW].
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- `include_degradation_penalty`: *Optional* Whether to include degradation penalty.
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- `hydrogen_degradation_penalty`: *Optional* boolean, wether degradation is applied to hydrogen (True) or power (False)
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- `cell_params`: Electrolyzer cell parameters including:
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- `cell_area`: Area of individual cells in the stack [cm^2].
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- `turndown_ratio`: Minimum turndown ratio for stack operation [between 0 and 1].
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- `max current_density`: Maximum current density [A/cm^2].
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- `p_anode`: Anode operating pressure [bar].
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- `p_cathode`: Cathode operating pressure [bar].
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- `alpha_a`: anode charge transfer coefficient.
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- `alpha_c`: cathode charge transfer coefficient.
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- `i_0_a`: anode exchange current density [A/cm^2].
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- `i_0_c`: cathode exchange current density [A/cm^2].
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- `e_m`: membrane thickness [cm].
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- `R_ohmic_elec`: electrolyte resistance [A*cm^2].
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- `f_1`: faradaic coefficien [mA^2/cm^4].
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- `f_2`: faradaic coefficien [mA^2/cm^4].
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- `degradation`: Electrolyzer degradation parameters including:
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- `eol_eff_percent_loss`: End of life efficiency percent loss [%].
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- `PEM_params` or `ALK_params`: Degradation parameters specific to PEM or Alkaline cells:
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- `rate_steady`: Rate of voltage degradation under steady operation alone
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- `rate_fatigue`: Rate of voltage degradation under variable operation alone
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- `rate_onoff`: Rate of voltage degradation per on/off cycle
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- `controller`: Electrolyzer control parameters including:
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- `control_type`: Controller type for electrolyzer plant operation.
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- `costs`: *Optional* Cost parameters for the electrolyzer plant including:
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- `plant_params`:
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- `plant_life`: integer, Plant life in years
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- `pem_location`: Location of the PEM electrolyzer. Options are
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[onshore, offshore, in-turbine]
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- `grid_connected`: boolean, Whether the plant is connected to the grid or not
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- `feedstock`: Parameters related to the feedstock including:
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- `water_feedstock_cost`: Cost of water per kg of water
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- `water_per_kgH2`: Amount of water required per kg of hydrogen produced
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- `opex`: Operational expenditure parameters including:
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- `var_OM`: Variable operation and maintenance cost per kW
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- `fixed_OM`: Fixed operation and maintenance cost per kW-year
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- `stack_replacement`: Parameters related to stack replacement costs including:
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- `d_eol`: End of life cell voltage value [V]
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- `stack_replacement_percent`: Stack replacement cost as a percentage of CapEx [0,1]
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- `capex`: Capital expenditure parameters including:
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- `capex_learning_rate`: Capital expenditure learning rate.
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- `ref_cost_bop`: Reference cost of balance of plant per kW.
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- `ref_size_bop`: Reference size of balance of plant in kW.
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- `ref_cost_pem`: Reference cost of PEM electrolyzer stack per kW.
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- `ref_size_pem`: Reference size of PEM electrolyzer stack in kW.
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- `finances`: Financial parameters including:
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- `discount_rate`: Discount rate for financial calculations [%].
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- `install_factor`: Installation factor for capital expenditure [0,1].
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- `log_channels`: List of output channels to log (see [Logging Configuration](elec-logging-configuration) below)
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## Outputs
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(elec-logging-configuration)=
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**Logging Configuration**
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The `log_channels` parameter controls which outputs are written to the HDF5 output file. This is a list of channel names. The `power` channel is always logged, even if not explicitly specified.
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### Available Channels
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**Scalar Channels:**
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- `H2_output`: Total hydrogen produced during the last timestep
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- `H2_mfr`: Mass flow rate of the hydrogen production in kg/s
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- `power`: Power that the electrolyzer plant used to create hydrogen (kW). This follows the convention that power consumed is negative power.
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- `Power_input_kw`: Power allocated to the electrolyzer plant to use (kW)
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- `stacks_on`: Total number of stacks producing hydrogen
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**Array Channels:**
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- `stacks_waiting`: Boolean list of the stacks that are waiting to start producing hydrogen (True for stacks waiting, False for stacks not waiting)
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**Example:**
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```yaml
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ele:
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component_type: ElectrolyzerPlant
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log_channels:
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- power
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- H2_output
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- H2_mfr
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initial_conditions:
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- power_available_kw: 3000
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electrolyzer:
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# ... other parameters
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```
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If `log_channels` is not specified, only `power` will be logged.
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## References
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1. Z. Tully, G. Starke, K. Johnson and J. King, "An Investigation of Heuristic Control Strategies for Multi-Electrolyzer Wind-Hydrogen Systems Considering Degradation," 2023 IEEE Conference on Control Technology and Applications (CCTA), Bridgetown, Barbados, 2023, pp. 817-822, doi: 10.1109/CCTA54093.2023.10252187.

‎examples/06_wind_and_hydrogen/README.md‎

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# Example 06: Wind and hydrogen hybrid plant
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## Description
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Example of a wind and hydrogen hybrid plant where power that the wind farm produces goes directly to hydrogen electrolysis
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## Setup
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No manual setup is required. The example automatically generates the necessary input files (wind data, FLORIS configuration, and turbine model) in the centralized `examples/inputs/` folder when first run.
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## Running
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To run the example, execute the following command in the terminal:
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```bash
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python hercules_runscript.py
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```
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## Outputs
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To plot the outputs run the following command in the terminal:
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```bash
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python plot_outputs.py
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```

‎examples/06_wind_and_hydrogen/hercules_input.yaml‎

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# Input YAML for hercules
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# Name
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name: example_06
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###
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# Describe this simulation setup
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description: Wind and hydrogen
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dt: 1.0
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starttime_utc: "2024-06-24T16:59:08Z" # Jun 24, 2024 16:59:08 UTC (Zulu time)
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endtime_utc: "2024-06-24T18:59:00Z" # ≈2 hours later (Jun 26, 2024 16:59:00 UTC)
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# endtime_utc: "2024-06-26T16:59:00Z" # ≈48 hours later (Jun 26, 2024 16:59:00 UTC)
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verbose: False
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plant:
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interconnect_limit: 45000 # kW
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wind_farm: # The name of the Wind_MesoToPower wind farm
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component_type: Wind_MesoToPowerPrecomFloris
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floris_input_file: ../inputs/floris_input_large.yaml
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wind_input_filename: ../inputs/wind_input_large.ftr
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turbine_file_name: ../inputs/turbine_filter_model.yaml
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log_file_name: outputs/log_wind_sim.log
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log_channels:
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- power
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- wind_speed_mean_background
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- wind_speed_mean_withwakes
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- wind_direction_mean
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- turbine_powers
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- wind_speeds_withwakes
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- wind_speeds_background
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- turbine_power_setpoints
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floris_update_time_s: 300.0 # Update wakes every 5 minutes
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# Electrolyzer plant input file
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electrolyzer: # The name of the py_sim object
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component_type: ElectrolyzerPlant
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general:
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verbose: True # default
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initial_conditions:
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# Initial power input to electrolyzer
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power_available_kw: 20000
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electrolyzer:
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initialize: True
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initial_power_kW: 20000
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supervisor:
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n_stacks: 40
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system_rating_MW: 40
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stack:
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cell_type: PEM
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# Maximum current of Stack (A)
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max_current: 2000
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# Stack operating temperature (degC)
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temperature: 60
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# Number of Cells per Stack
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n_cells: 100
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# Stack rated power
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stack_rating_kW: 1000
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# Determines whether degradation is applied to the Stack operation
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include_degradation_penalty: False
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# hydrogen_degradation_penalty: True
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# cell_params:
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# cell_type: PEM
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# max_current_density: 2
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# PEM_params:
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# cell_area: 1000
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# turndown_ratio: 0.1
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# max_current_density: 2.0
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# p_anode: 1.01325
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# p_cathode: 30
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# alpha_a: 2
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# alpha_c: 0.5
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# i_0_a: 2.0e-7
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# i_0_c: 2.0e-3
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# e_m: 0.02
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# R_ohmic_elec: 50.0e-3
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# f_1: 250
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# f_2: 0.996
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# degradation:
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# eol_eff_percent_loss: 20
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# PEM_params:
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# rate_steady: 1.42e-10
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# rate_fatigue: 3.33e-07
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# rate_onoff: 1.47e-04
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controller:
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# Controller type for electrolyzer plant operation
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control_type: PowerSharingRotation
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# policy:
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# eager_on: False
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# eager_off: False
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# sequential: False
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# even_dist: False
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# baseline: True
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# costs:
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log_channels:
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- power
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- H2_output
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- H2_mfr
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- power_input_kw
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controller:
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num_turbines: 9 # Should match AMR-Wind! Ideally, would come from AMR-wind
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nominal_plant_power_kW: 45000 # Plant power in kW
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nominal_hydrogen_rate_kgps: 0.208 # in kg/s [kg per day/24/3600 * stack number]
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hydrogen_controller_gain: 1
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external_data_file: inputs/hydrogen_ref_signal.csv
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‎examples/06_wind_and_hydrogen/hercules_runscript.py‎

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import numpy as np
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from hercules.hercules_model import HerculesModel
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from hercules.utilities_examples import ensure_example_inputs_exist, prepare_output_directory
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from whoc.controllers import (
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HydrogenPlantController,
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WindFarmPowerTrackingController,
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)
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from whoc.interfaces import HerculesV2Interface
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prepare_output_directory()
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# Ensure example inputs exist
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ensure_example_inputs_exist()
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# Initialize the Hercules model
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hmodel = HerculesModel("hercules_input.yaml")
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# Define a simple controller that sets all deratings to full rating
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# and then sets the derating of turbine 000 to 500, toggling every other 100 seconds.
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class ControllerLimitSolar:
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"""Limits the solar power to keep the total power below the interconnect limit."""
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def __init__(self, h_dict):
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"""Initialize the controller.
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Args:
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h_dict (dict): The hercules input dictionary.
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"""
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self.interconnect_limit = h_dict["plant"]["interconnect_limit"]
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pass
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def step(self, h_dict):
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"""Execute one control step.
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Args:
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h_dict (dict): The hercules input dictionary.
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Returns:
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dict: The updated hercules input dictionary.
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"""
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# Set wind turbine power setpoints to full rating
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h_dict["wind_farm"]["turbine_power_setpoints"] = 5000 * np.ones(
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h_dict["wind_farm"]["n_turbines"]
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)
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# Get the total wind farm power
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wind_farm_power = h_dict["wind_farm"]["power"]
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# Charge or discharge the battery, reversing every other hour
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# With hybrid plant sign convention: Positive = discharge, Negative = charge
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if h_dict["time"] % 3600 < 1800:
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h_dict["battery"]["power_setpoint"] = 10000 # Discharge the battery
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else:
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h_dict["battery"]["power_setpoint"] = -10000 # Charge the battery
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# Get the limit for battery power
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# Battery power + wind power must be less than the interconnect limit
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battery_power_upper_limit = self.interconnect_limit - wind_farm_power
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battery_power_lower_limit = -1 * self.interconnect_limit - wind_farm_power
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# Set the solar power limit
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h_dict["battery"]["power_setpoint"] = np.clip(
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h_dict["battery"]["power_setpoint"],
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battery_power_lower_limit,
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battery_power_upper_limit,
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)
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return h_dict
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# Establish controllers based on options
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interface = HerculesV2Interface(hmodel.h_dict)
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print("Setting up controller.")
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wind_controller = WindFarmPowerTrackingController(interface, hmodel.h_dict)
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# solar_controller = (
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# SolarPassthroughController(interface, hmodel.h_dict) if include_solar
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# else None
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# )
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# battery_controller = (
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# BatteryPassthroughController(interface, hmodel.h_dict) if include_battery
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# else None
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# )
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controller = HydrogenPlantController(
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interface,
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hmodel.h_dict,
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generator_controller=wind_controller
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)
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# Assign the controller to the Hercules model
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hmodel.assign_controller(controller)
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print("Controller assigned.")
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# Run the simulation
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hmodel.run()
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hmodel.logger.info("Process completed successfully")

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