basic_example

Complexity

Total Complexity

8

Size/Duplication

Total Lines	339
Duplicated Lines	0 %

Importance

Changes

0

3 Functions

Rating	Name	Duplication	Size	Complexity
A	get_data_from_file_path()	0	7	2
A	plot_figures_for()	0	11	1
C	main()	0	230	5

# -*- coding: utf-8 -*-

"""
General description
-------------------

A basic example to show how to model a simple energy system with oemof.solph.

The following energy system is modeled:

.. code-block:: text

                     input/output  bgas     bel
                         |          |        |
                         |          |        |
     wind(FixedSource)   |------------------>|
                         |          |        |
     pv(FixedSource)     |------------------>|
                         |          |        |
     rgas(Commodity)     |--------->|        |
                         |          |        |
     demand(Sink)        |<------------------|
                         |          |        |
                         |          |        |
     pp_gas(Converter)   |<---------|        |
                         |------------------>|
                         |          |        |
     storage(Storage)    |<------------------|
                         |------------------>|

Code
----
Download source code: :download:`basic_example.py </../examples/basic_example/basic_example.py>`

.. dropdown:: Click to display code

    .. literalinclude:: /../examples/basic_example/basic_example.py
        :language: python
        :lines: 61-

Data
----
Download data: :download:`basic_example.csv </../examples/basic_example/basic_example.csv>`

Installation requirements
-------------------------
This example requires oemof.solph (v0.5.x), install by:

.. code:: bash

    pip install oemof.solph[examples]

License
-------
`MIT license <https://github.com/oemof/oemof-solph/blob/dev/LICENSE>`_
"""
###########################################################################
# imports
###########################################################################

import logging
import os
import pprint as pp
from datetime import datetime

import matplotlib.pyplot as plt
import pandas as pd
from oemof.tools import logger

from oemof.solph import EnergySystem
from oemof.solph import Model
from oemof.solph import buses
from oemof.solph import components
from oemof.solph import create_time_index
from oemof.solph import flows
from oemof.solph import helpers
from oemof.solph import processing
from oemof.solph import views

STORAGE_LABEL = "battery_storage"


def get_data_from_file_path(file_path: str) -> pd.DataFrame:
    try:
        data = pd.read_csv(file_path)
    except FileNotFoundError:
        dir = os.path.dirname(os.path.abspath(__file__))
        data = pd.read_csv(dir + "/" + file_path)
    return data


def plot_figures_for(element: dict) -> None:
    figure, axes = plt.subplots(figsize=(10, 5))
    element["sequences"].plot(ax=axes, kind="line", drawstyle="steps-post")
    plt.legend(
        loc="upper center",
        prop={"size": 8},
        bbox_to_anchor=(0.5, 1.25),
        ncol=2,
    )
    figure.subplots_adjust(top=0.8)
    plt.show()


def main(dump_and_restore=False, optimize=True):
    # For models that need a long time to optimise, saving and loading the
    # EnergySystem might be advised. By default, we do not do this here. Feel
    # free to experiment with this once you understood the rest of the code.
    dump_results = restore_results = dump_and_restore

    # *************************************************************************
    # ********** PART 1 - Define and optimise the energy system ***************
    # *************************************************************************

    # Read data file
    file_name = "basic_example.csv"
    data = get_data_from_file_path(file_name)

    solver = "cbc"  # 'glpk', 'gurobi',....
    debug = False  # Set number_of_timesteps to 3 to get a readable lp-file.
    number_of_time_steps = len(data)
    solver_verbose = False  # show/hide solver output

    # initiate the logger (see the API docs for more information)
    logger.define_logging(
        logfile="oemof_example.log",
        screen_level=logging.INFO,
        file_level=logging.INFO,
    )

    logging.info("Initialize the energy system")
    date_time_index = create_time_index(2012, number=number_of_time_steps)

    # create the energysystem and assign the time index
    energysystem = EnergySystem(
        timeindex=date_time_index, infer_last_interval=False
    )

    ##########################################################################
    # Create oemof objects
    ##########################################################################

    logging.info("Create oemof objects")

    # The bus objects were assigned to variables which makes it easier to
    # connect components to these buses (see below).

    # create natural gas bus
    bus_gas = buses.Bus(label="natural_gas")

    # create electricity bus
    bus_electricity = buses.Bus(label="electricity")

    # adding the buses to the energy system
    energysystem.add(bus_gas, bus_electricity)

    # create excess component for the electricity bus to allow overproduction
    energysystem.add(
        components.Sink(
            label="excess_bus_electricity",
            inputs={bus_electricity: flows.Flow()},
        )
    )

    # create source object representing the gas commodity
    energysystem.add(
        components.Source(
            label="rgas",
            outputs={bus_gas: flows.Flow()},
        )
    )

    # create fixed source object representing wind power plants
    energysystem.add(
        components.Source(
            label="wind",
            outputs={
                bus_electricity: flows.Flow(
                    fix=data["wind"], nominal_capacity=1000000
                )
            },
        )
    )

    # create fixed source object representing pv power plants
    energysystem.add(
        components.Source(
            label="pv",
            outputs={
                bus_electricity: flows.Flow(
                    fix=data["pv"], nominal_capacity=582000
                )
            },
        )
    )

    # create simple sink object representing the electrical demand
    # nominal_capacity is set to 1 because demand_el is not a normalised series
    energysystem.add(
        components.Sink(
            label="demand",
            inputs={
                bus_electricity: flows.Flow(
                    fix=data["demand_el"], nominal_capacity=1
                )
            },
        )
    )

    # create simple converter object representing a gas power plant
    energysystem.add(
        components.Converter(
            label="pp_gas",
            inputs={bus_gas: flows.Flow()},
            outputs={
                bus_electricity: flows.Flow(
                    nominal_capacity=10e10, variable_costs=50
                )
            },
            conversion_factors={bus_electricity: 0.58},
        )
    )

    # create storage object representing a battery
    nominal_capacity = 10077997
    nominal_capacity = nominal_capacity / 6

    battery_storage = components.GenericStorage(
        nominal_capacity=nominal_capacity,
        label=STORAGE_LABEL,
        inputs={
            bus_electricity: flows.Flow(nominal_capacity=nominal_capacity)
        },
        outputs={
            bus_electricity: flows.Flow(
                nominal_capacity=nominal_capacity, variable_costs=0.001
            )
        },
        loss_rate=0.00,
        initial_storage_level=None,
        inflow_conversion_factor=1,
        outflow_conversion_factor=0.8,
    )

    energysystem.add(battery_storage)

    ##########################################################################
    # Optimise the energy system and plot the results
    ##########################################################################

    if optimize is False:
        return energysystem

    logging.info("Optimise the energy system")

    # initialise the operational model
    energysystem_model = Model(energysystem)

    # This is for debugging only. It is not(!) necessary to solve the problem
    # and should be set to False to save time and disc space in normal use. For
    # debugging the timesteps should be set to 3, to increase the readability
    # of the lp-file.
    if debug:
        file_path = os.path.join(
            helpers.extend_basic_path("lp_files"), "basic_example.lp"
        )
        logging.info(f"Store lp-file in {file_path}.")
        io_option = {"symbolic_solver_labels": True}
        energysystem_model.write(file_path, io_options=io_option)

    # if tee_switch is true solver messages will be displayed
    logging.info("Solve the optimization problem")
    energysystem_model.solve(
        solver=solver, solve_kwargs={"tee": solver_verbose}
    )

    logging.info("Store the energy system with the results.")

    # The processing module of the outputlib can be used to extract the results
    # from the model transfer them into a homogeneous structured dictionary.

    # add results to the energy system to make it possible to store them.
    energysystem.results["main"] = processing.results(energysystem_model)
    energysystem.results["meta"] = processing.meta_results(energysystem_model)

    # The default path is the '.oemof' folder in your $HOME directory.
    # The default filename is 'es_dump.oemof'.
    # You can omit the attributes (as None is the default value) for testing
    # cases. You should use unique names/folders for valuable results to avoid
    # overwriting.
    if dump_results:
        energysystem.dump(dpath=None, filename=None)

    # *************************************************************************
    # ********** PART 2 - Processing the results ******************************
    # *************************************************************************

    # Saved data can be restored in a second script. So you can work on the
    # data analysis without re-running the optimisation every time. If you do
    # so, make sure that you really load the results you want. For example,
    # if dumping fails, you might exidentially load outdated results.
    if restore_results:
        logging.info("**** The script can be divided into two parts here.")
        logging.info("Restore the energy system and the results.")

        energysystem = EnergySystem()
        energysystem.restore(dpath=None, filename=None)

    # define an alias for shorter calls below (optional)
    results = energysystem.results["main"]
    storage = energysystem.groups[STORAGE_LABEL]

    # print a time slice of the state of charge
    start_time = datetime(2012, 2, 25, 8, 0, 0)
    end_time = datetime(2012, 2, 25, 17, 0, 0)

    print("\n********* State of Charge (slice) *********")
    print(f"{results[(storage, None)]['sequences'][start_time : end_time]}\n")

    # get all variables of a specific component/bus
    custom_storage = views.node(results, STORAGE_LABEL)
    electricity_bus = views.node(results, "electricity")

    # plot the time series (sequences) of a specific component/bus
    plot_figures_for(custom_storage)
    plot_figures_for(electricity_bus)

    # print the solver results
    print("********* Meta results *********")
    pp.pprint(f"{energysystem.results['meta']}\n")

    # print the sums of the flows around the electricity bus
    print("********* Main results *********")
    print(electricity_bus["sequences"].sum(axis=0))


if __name__ == "__main__":
    main()