Basic example¶
Using standard oemof-solph components¶
General description¶
A basic example to show how to model a simple energy system with oemof.solph.
The following energy system is modeled:
input/output bgas bel
| | |
| | |
wind(FixedSource) |------------------>|
| | |
pv(FixedSource) |------------------>|
| | |
rgas(Commodity) |--------->| |
| | |
demand(Sink) |<------------------|
| | |
| | |
pp_gas(Converter) |<---------| |
|------------------>|
| | |
storage(Storage) |<------------------|
|------------------>|
Code¶
Download source code: basic_example.py
Click to display code
import logging
import os
import pprint as pp
import warnings
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:
warn_msg = f"Data file not found: {file_path}."
"Values for one timestep created!"
warnings.warn(warn_msg, UserWarning)
data = pd.DataFrame({"pv": [0.3], "wind": [0.6], "demand_el": [500]})
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):
# 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"
file_path = os.path.join(os.getcwd(), file_name)
data = get_data_from_file_path(file_path)
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(
solver_components.Sink(
label="excess_bus_electricity",
inputs={bus_electricity: flows.Flow()},
)
)
# create source object representing the gas commodity
energysystem.add(
solver_components.Source(
label="rgas",
outputs={bus_gas: flows.Flow()},
)
)
# create fixed source object representing wind power plants
energysystem.add(
solver_components.Source(
label="wind",
outputs={
bus_electricity: flows.Flow(
fix=data["wind"], nominal_value=1000000
)
},
)
)
# create fixed source object representing pv power plants
energysystem.add(
solver_components.Source(
label="pv",
outputs={
bus_electricity: flows.Flow(
fix=data["pv"], nominal_value=582000
)
},
)
)
# create simple sink object representing the electrical demand
# nominal_value is set to 1 because demand_el is not a normalised series
energysystem.add(
solver_components.Sink(
label="demand",
inputs={
bus_electricity: flows.Flow(
fix=data["demand_el"], nominal_value=1
)
},
)
)
# create simple converter object representing a gas power plant
energysystem.add(
solver_components.Converter(
label="pp_gas",
inputs={bus_gas: flows.Flow()},
outputs={
bus_electricity: flows.Flow(
nominal_value=10e10, variable_costs=50
)
},
conversion_factors={bus_electricity: 0.58},
)
)
# create storage object representing a battery
nominal_capacity = 10077997
nominal_value = nominal_capacity / 6
battery_storage = solver_components.GenericStorage(
nominal_storage_capacity=nominal_capacity,
label=BATTERY_STORAGE,
inputs={bus_electricity: flows.Flow(nominal_value=nominal_value)},
outputs={
bus_electricity: flows.Flow(
nominal_value=nominal_value, 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
##########################################################################
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[BATTERY_STORAGE]
# 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, BATTERY_STORAGE)
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()
Data¶
Download data: basic_example.csv
Installation requirements¶
This example requires oemof.solph (v0.5.x), install by:
pip install oemof.solph[examples]
