How to set up an analysis

How to set up an analysis#

H2Integrate is designed so that you can run a basic analysis or design problem without extensive Python experience. The key inputs for the analysis are the configuration files, which are in YAML format. This doc page will walk you through the steps to set up a basic analysis, focusing on the different types of configuration files and how use them.

Top-level config file#

The top-level config file is the main entry point for H2Integrate. Its main purpose is to define the analysis type and the configuration files for the different components of the analysis. Here is an example of a top-level config file:

name: H2Integrate_config

system_summary: This reference hybrid plant is located in Minnesota and contains wind, solar, and battery storage technologies. The system is designed to produce hydrogen using an electrolyzer and also produce steel using a grid-connected plant.

driver_config: driver_config.yaml
technology_config: tech_config.yaml
plant_config: plant_config.yaml

The top-level config file contains the following keys:

  • name: (optional) A name for the analysis. This is used to identify the analysis in the output files.

  • system_summary: (optional) A summary of the analysis. This helpful for quickly describing the analysis for documentation purposes.

  • driver_config: The path to the driver config file. This file defines the analysis type and the optimization settings.

  • technology_config: The path to the technology config file. This file defines the technologies included in the analysis, their modeling parameters, and the performance, cost, and financial models used for each technology.

  • plant_config: The path to the plant config file. This file defines the system configuration and how the technologies are connected together.

The goal of the organization of the top-level config file is that it is easy to swap out different configurations for the analysis without having to change the code. For example, if you had different optimization problems, you could have different driver config files for each optimization problem and just change the driver_config key in the top-level config file to point to the new file. This allows you to quickly test different configurations and see how they affect the results.

Driver config file#

The driver config file defines the analysis type and the optimization settings. If you are running a basic analysis and not an optimization, the driver config file is quite straightforward and might look like this:

name: driver_config
description: This analysis runs a hybrid plant to match the first example in H2Integrate

general:
  folder_output: outputs

If you are running an optimization, the driver config file will contain additional keys to define the optimization settings, including design variables, constraints, and objective functions. Further details of more complex instances of the driver config file can be found in more advanced examples as they are developed.

Technology config file#

The technology config file defines the technologies included in the analysis, their modeling parameters, and the performance, cost, and financial models used for each technology. The yaml file is organized into sections for each technology included in the analysis under the technologies heading. Here is an example of part of a technology config that is defining an energy system with only one technology, an electrolyzer:

name: technology_config
description: This hybrid plant produces steel

technologies:
  electrolyzer:
    performance_model:
      model: eco_pem_electrolyzer_performance
    cost_model:
      model: eco_pem_electrolyzer_cost
    model_inputs:
      shared_parameters:
        rating: 1125.165 # MW
        location: onshore
        electrolyzer_capex: 2000 # $/kW overnight installed capital costs

      performance_parameters:
        sizing:
          resize_for_enduse: False
          size_for: BOL #'BOL' (generous) or 'EOL' (conservative)
          hydrogen_dmd:
        cluster_rating_MW: 40
        pem_control_type: basic
        eol_eff_percent_loss: 13 #eol defined as x% change in efficiency from bol
        uptime_hours_until_eol: 77600 #number of 'on' hours until electrolyzer reaches eol
        include_degradation_penalty: True #include degradation
        turndown_ratio: 0.1 #turndown_ratio = minimum_cluster_power/cluster_rating_MW

      cost_parameters:
        cost_model: singlitico2021

      financial_parameters:
        replacement_cost_percent: 0.15 # percent of capex - H2A default case

Here, we have defined a electrolyzer model that uses the built-in eco_pem_electrolyzer_performance and eco_pem_electrolyzer_cost models. The performance_model and cost_model keys define the models used for the performance and cost calculations, respectively. The model_inputs key contains the inputs for the models, which are organized into sections for shared parameters, performance parameters, cost parameters, and financial parameters. Here, we do not define the financial_model key, so the default financial model from ProFAST is used.

The shared_parameters section contains parameters that are common to all models, such as the rating and location of the technology. These values are defined once in the shared_parameters section and are used by all models that reference them. The performance_parameters section contains parameters that are specific to the performance model, such as the sizing and efficiency of the technology. The cost_parameters section contains parameters that are specific to the cost model, such as the capital costs and replacement costs. The financial_parameters section contains parameters that are specific to the financial model, such as the replacement costs and financing terms.

Note

There are no default values for the parameters in the technology config file. You must define all the parameters for the models you are using in the analysis.

Based on which models you choose to use, the inputs will vary. Each model has its own set of inputs, which are defined in the source code for the model. Because there are no default values for the parameters, we suggest you look at an existing example that uses the model you are interested in to see what inputs are required or look at the source code for the model. The different models are defined in the supported_models.py file in the h2integrate package.

Plant config file#

The plant config file defines the system configuration, any parameters that might be shared across technologies, and how the technologies are connected together.

Here is a snippet of an example plant config file:

name: plant_config
description: This plant is located in MN, USA

site:
  latitude: 47.5233
  longitude: -92.5366
  elevation_m: 439.0
  time_zone: -5

# array of arrays containing left-to-right technology
# interconnections; can support bidirectional connections
# with the reverse definition.
# this will naturally grow as we mature the interconnected tech
technology_interconnections: [
  ["wind", "electrolyzer", "electricity", "cable"],
  ["electrolyzer", "h2_storage", "efficiency"],
  ["electrolyzer", "h2_storage", "hydrogen", "pipe"],
  ["financials_group_1", "steel", "LCOH"],
  ["wind", "steel", "electricity", "cable"],
  # etc
]

plant:
  plant_life: 30
  grid_connection: False # option, can be turned on or off
  ppa_price: 0.025 # $/kWh
  hybrid_electricity_estimated_cf: 0.492
  atb_year: 2030
  cost_year: 2022
  installation_time: 36 # months

finance_parameters:
  costing_general_inflation: 0.025 # used to adjust modeled costs to cost_year
  profast_general_inflation: 0.0 # 0 for nominal analysis
...

The site section contains the site parameters, such as the latitude, longitude, elevation, and time zone. The plant section contains the plant parameters, such as the plant life, grid connection, PPA price, and installation time. The finance_parameters section contains the financial parameters used across the plant, such as the inflation rates, financing terms, and other financial parameters.

The technology_interconnections section contains the interconnections between the technologies in the system and is the most complex part of the plant config file. The interconnections are defined as an list of lists, where each sub-list defines a connection between two technologies. The first entry in the list is the technology that is providing the input to the next technology in the list. If the list is length 4, then the third entry in the list is what's passing passed via a transporter of the type defined in the fourth entry. If the list is length 3, then the third entry in the list is what is connected directly between the technologies.

Running the analysis#

Once you have the config files defined, you can run the analysis using a simple Python script that inputs the top-level config yaml like this:

from h2integrate.core.h2integrate_model import H2IntegrateModel


# Create a H2Integrate model
model = H2IntegrateModel("top_level_config.yaml")

# Run the model
model.run()

model.post_process()

This will run the analysis defined in the config files and generate the output files in the through the post_process method.

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