Design of experiments in H2I

Design of experiments in H2I#

One of the key features of H2Integrate is the ability to perform a design of experiments (DOE) for hybrid energy systems.

The design of experiments process uses the driver_config.yaml file to define the design sweep, including the design variables, constraints, and objective functions. Detailed information on setting up the driver_config.yaml file can be found here

Driver config file#

The driver config file defines the analysis type and the optimization or design of experiments settings. For completeness, here is an example of a driver config file for a design of experiments problem:

name: "driver_config"
description: "Runs a design sweep"

general:
  folder_output: outputs

driver:
  design_of_experiments:
    flag: True
    generator: "csvgen" #type of generator to use
    filename: "" #this input is specific to the "csvgen" generator
    debug_print: False

design_variables:
  solar:
    capacity_kWdc:
      flag: true
      lower: 5000.0
      upper: 5000000.0
      units: "kW"
  electrolyzer:
    n_clusters:
      flag: true
      lower: 1
      upper: 25
      units: "unitless"

constraints: #constraints are not used within the design of experiments run
# but are accessible in the recorder file as constraint variables

objective: #the objective is not used within the design of experiments run
# but is accessible in the recorder file as the objective
  name: finance_subgroup_hydrogen.LCOH

recorder:
  file: "cases.sql"
  flag: True
  includes: ["*"]
  excludes: ['*_resource*']

Types of Generators#

H2Integrate currently supports the following types of generators:

Documentation for each generator type can be found on OpenMDAO's documentation page.

Uniform#

driver:
  design_of_experiments:
    flag: True
    generator: "uniform" #type of generator to use
    num_samples: 10 #input is specific to this generator
    seed: #input is specific to this generator

FullFactorial#

driver:
  design_of_experiments:
    flag: True
    generator: "fullfact" #type of generator to use
    levels: 2 #input is specific to this generator

The levels input is the number of evenly spaced levels between each design variable lower and upper bound.

You can check the values that will be used for a specific design variable by running:

import numpy as np
design_variable_values = np.linspace(lower_bound,upper_bound,levels)

PlackettBurman#

driver:
  design_of_experiments:
    flag: True
    generator: "plackettburman" #type of generator to use

BoxBehnken#

driver:
  design_of_experiments:
    flag: True
    generator: "boxbehnken" #type of generator to use

LatinHypercube#

driver:
  design_of_experiments:
    flag: True
    generator: "latinhypercube" #type of generator to use
    num_samples:  10 #input is specific to this generator
    criterion: "center"  #input is specific to this generator
    seed: #input is specific to this generator

CSV#

This method is useful if there are specific combinations of designs variables that you want to sweep. An example is shown here:

driver:
  design_of_experiments:
    flag: True
    generator: "csvgen" #type of generator to use
    filename: "cases_to_run.csv" #input is specific to this generator

The filename input is the filepath to the csv file to read cases from. The first row of the csv file should contain the names of the design variables. The rest of the rows should contain the values of that design variable you want to run (such as solar.capacity_kWdc or electrolyzer.n_clusters). The values in the csv file are expected to be in the same units specified for that design variable.

Note

You should check the csv file for potential formatting issues before running a simulation. This can be done using the check_file_format_for_csv_generator method in h2integrate/core/utilities.py. Usage of this method is shown in the 20_solar_electrolyzer_doe example in the examples folder.

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