cronian 0.3.2

Build energy models with flexible demand from simple configuration files.

Cronian

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Cronian is a Python package for building copper plate optimization models in Pyomo to optimize flexibility scheduling in multi-carrier integrated energy systems. You can specify your entire co-optimization model using simple YAML and CSV files. Alternatively, you can use the modular architecture to build standalone models of generators and consumers/prosumers to use as part of other methods such as distributed optimization or agent-based simulations.

Installation

You can install Cronian directly from PyPI:

python3 -m pip install cronian[gurobi]

Note: this includes the optional gurobi dependency, since the Gurobi solver is used by default. If omitted, please make sure to pass your own preferred solver instead when solving through Cronian.

Usage

Creating Models

To create a model, you have to specify the desired generators and prosumers in YAML format. To start, generators are fairly simple:

# Generator01.yaml
Generators:  # Top level key must be 'Generators'
  # Required keys
  name: WindOffshore
  id: G05  # Must be unique
  marginal_cost_quadratic: 0.007
  marginal_cost_linear: 5
  installed_capacity: 6000
  # Optional keys
  availability_factor: WIND_OFFSHORE  # Timeseries column name

The name under the optional availability_factor key will be used to look up a timeseries from the dataframe that is passed in separately, otherwise availability will be set to 1 at all times t. The production gen_power[t] of the specified generator will then be constrained by:

0 <= gen_power[t] <= installed_capacity * availability_factor[t]

A simple prosumer with just some fixed demand would look like this:

# Prosumer01.yaml
Prosumers:
  name: Simple_demand
  id: P01
  demand:
    commercial:
      carrier: electricity
      base:
        peak: 100

We can make prosumers more interesting by adding flexible demand and assets. Multiple demands can be added, each with their own flexible and base (inflexible) components. Furthermore, there are three kinds of assets: generator, converter and storage. Here's a more complex prosumer with flexible demand and one of each asset type:

# Prosumer01.yaml
Prosumers:
  name: Heat_system_operator
  id: P02
  demand: 
    hot_water:
      carrier: heat  # Must be 'electricity', or output of an asset
      # At least `base` or `flexible` demand must be present 
      base:
        peak: 300
      flexible:  # `flex+N` encodes a demand with `N` timesteps of flexibility
        flex+2:
          peak: 125000
          n_profile: P02-flexible-demand  # similar to `availability_factor` for generators
        flex+3:
          peak: 50
  assets:
    solar_pv:
      behavior_type: generator
      input: light
      output: electricity
      installed_capacity: 1000
      availability_factor: SOLAR_WEST  # Optional
      operational_costs:
        marginal_cost_quadratic: 0.007
        marginal_cost_linear: 5
    chp:
      behavior_type: converter
      # Can convert 1-to-1, 1-to-many or many-to-1
      input: methane
      output:
        - heat
        - electricity
      installed_capacity: 1000
      # if converting 1-to-many or many-to-1, specify efficiency for each of the 'many'
      efficiency:
        heat: 0.4
        electricity: 0.4
    heat_storage:
      behavior_type: storage
      input: heat
      output: heat
      energy_capacity: 5000
      initial_energy: 50
      charge_capacity: 500
      discharge_capacity: 500
      charge_efficiency: 0.8
      discharge_efficiency: 0.65

Performing Co-Optimization

To use Cronian for a co-optimization run, you first have to specify an additional general_config.yaml file, and place it in the same folder as your generator and prosumer configuration files.

# general_config.yaml
General:
  number_of_timesteps: 24

Then in code you can create and run the model as follows:

from pathlib import Path
from cronian.run_co_optimization import main
import pandas as pd

main(
    configurations_folder=Path("/path/to/configurations/"),
    timeseries_data=pd.read_csv("/path/to/availability/factors/timeseries.csv", index_col=0, parse_dates=True),
    price_timeseries=pd.read_csv("/path/to/prices/for/external/carriers.csv", index_col=0, parse_dates=True),
    explicit_prosumer_configuration=None,
    explicit_prosumer_timeseries_data=None,
    number_of_timesteps=None,
    include_base_load=True,
    results_folder=Path("/path/to/folder/for/output"),
)

Some intermediate information will be printed on stdout, and all final results will be stored in the specified results_folder.

External Usage Examples

Cronian can also be used to build smaller models to be used by other methods.

• Distributed optimization: demoses-ADMM
• Agent-based simulation: Annular

Contributing

If you want to contribute to the development of Cronian software, have a look at the contribution guidelines.

Further instructions can be found inREADME.dev.md

NOTE

The code is tested and compatible with python versions 3.10 and 3.11.

Citation

For citation information, see CITATION.cff