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# -*- coding: utf-8 -*- |
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""" |
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Example that shows how to use "Offset-Invest". |
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Installation requirements |
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------------------------- |
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This example requires oemof.solph (v0.5.x), install by: |
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pip install oemof.solph[examples] |
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License |
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------- |
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Johannes Röder <https://www.uni-bremen.de/en/res/team/johannes-roeder-m-sc> |
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`MIT license <https://github.com/oemof/oemof-solph/blob/dev/LICENSE>`_ |
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""" |
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import logging |
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import os |
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import pandas as pd |
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from matplotlib import pyplot as plt |
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from oemof import solph |
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data = [0, 15, 30, 35, 20, 25, 27, 10, 5, 2, 15, 40, 20, 0, 0] |
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# create an energy system |
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idx = solph.create_time_index(2017, number=len(data)) |
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es = solph.EnergySystem(timeindex=idx, infer_last_interval=False) |
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bus_0 = solph.Bus(label="bus_0") |
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bus_1 = solph.Bus(label="bus_1") |
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es.add(bus_0, bus_1) |
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c_0 = 10 |
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c_1 = 100 |
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es.add( |
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solph.components.Source( |
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label="source_0", outputs={bus_0: solph.Flow(variable_costs=c_0)} |
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) |
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) |
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es.add( |
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solph.components.Source( |
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label="source_1", outputs={bus_1: solph.Flow(variable_costs=c_1)} |
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) |
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) |
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es.add( |
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solph.components.Sink( |
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label="demand", inputs={bus_1: solph.Flow(fix=data, nominal_value=1)} |
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) |
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) |
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# solph.Sink(label='excess_1', inputs={ |
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# bus_1: solph.Flow()}) |
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# parameter |
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p_install_min = 20 |
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p_install_max = 35000 |
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c_fix = 2000 |
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c_var = 180 |
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eta = 0.8 |
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# non offset invest |
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trafo = solph.components.Transformer( |
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label="transformer", |
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inputs={bus_0: solph.Flow()}, |
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outputs={ |
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bus_1: solph.Flow( |
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nominal_value=None, |
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investment=solph.Investment( |
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ep_costs=c_var, |
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maximum=p_install_max, |
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minimum=p_install_min, |
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# existing=10, |
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nonconvex=True, |
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offset=c_fix, |
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), |
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# min=0.1, |
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# fixed=True, |
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# actual_value=[0.5, 0.5, 0.5, 0.5, 0.5], |
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) |
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}, |
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conversion_factors={bus_1: eta}, |
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) |
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es.add(trafo) |
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# create an optimization problem and solve it |
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om = solph.Model(es) |
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# export lp file |
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filename = os.path.join( |
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solph.helpers.extend_basic_path("lp_files"), "OffsetInvestor.lp" |
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) |
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logging.info("Store lp-file in {0}.".format(filename)) |
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om.write(filename, io_options={"symbolic_solver_labels": True}) |
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# solve model |
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om.solve(solver="cbc", solve_kwargs={"tee": True}) |
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# create result object |
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results = solph.processing.results(om) |
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bus1 = solph.views.node(results, "bus_1")["sequences"] |
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# plot the time series (sequences) of a specific component/bus |
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if plt is not None: |
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bus1.plot(kind="line", drawstyle="steps-mid") |
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plt.legend() |
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plt.show() |
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# Nachvollziehen der Berechnung |
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# Kosten Invest |
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p_invest = solph.views.node(results, "transformer")["scalars"][ |
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(("transformer", "bus_1"), "invest") |
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] |
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invest_binary = solph.views.node(results, "transformer")["scalars"][ |
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(("transformer", "bus_1"), "invest_status") |
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] |
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c_invest = p_invest * c_var + c_fix * invest_binary |
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# costs analysis |
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e_source_0 = solph.views.node(results, "source_0")["sequences"][ |
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(("source_0", "bus_0"), "flow") |
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].sum() |
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c_source_0 = c_0 * e_source_0 |
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e_source_1 = solph.views.node(results, "source_1")["sequences"][ |
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(("source_1", "bus_1"), "flow") |
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].sum() |
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c_source_1 = c_1 * e_source_1 |
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c_total = c_invest + c_source_0 + c_source_1 |
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es.results["meta"] = solph.processing.meta_results(om) |
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objective = pd.DataFrame.from_dict(es.results["meta"]).at[ |
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"Lower bound", "objective" |
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] |
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print(" objective:", objective) |
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print(" berechnet:", c_total) |
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print("") |
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print("Max. zulässige Investleistung", p_install_max) |
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print("Erforderlicher Mindest-Invest", p_install_min) |
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print("Installierte Leistung:", p_invest) |
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print( |
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"Maximale Leistung - demand:", |
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solph.views.node(results, "bus_1")["sequences"][ |
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(("bus_1", "demand"), "flow") |
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].max(), |
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) |
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print( |
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"Maximale Leistung im Einsatz", |
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solph.views.node(results, "transformer")["sequences"][ |
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(("transformer", "bus_1"), "flow") |
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].max(), |
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) |
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if p_invest > max(data): |
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print("Anlage wurde überdimensioniert") |
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oemof /
oemof-solph
