Heatpipeline example

oemof_examples/oemof.solph/v0.3.x/heatpipeline/Heatpipeline_example.py

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+# -*- coding: utf-8 -*-
+"""
+Example that shows how to use custom component `HeatPipeline`.
+
+This file is part of project oemof (github.com/oemof/oemof). It's copyrighted
+by the contributors recorded in the version control history of the file,
+available from its original location
+
+SPDX-License-Identifier: GPL-3.0-or-later
+"""
+
+import os
+import pandas as pd
+import oemof.solph as solph
+import matplotlib.pyplot as plt
+from oemof.network import Node
+from oemof.outputlib import processing, views
+import numpy as np
+from oemof.tools import helpers
+
+
+# date for heat sink
+data = pd.Series(np.arange(1, 100, 1))
+
+# select periods
+periods = len(data)-1
+
+# create an energy system
+idx = pd.date_range('1/1/2017', periods=periods, freq='H')
+es = solph.EnergySystem(timeindex=idx)
+Node.registry = es
+
+# heat bus start (source)
+b_heat_0 = solph.Bus(label='b_heat_0')
+# heat bus end (sink)
+b_heat_1 = solph.Bus(label='b_heat_1')
+
+solph.Source(label='source_th',
+             outputs={b_heat_1: solph.Flow(variable_costs=10000)})
+
+solph.Sink(label='demand_th', inputs={
+    b_heat_1: solph.Flow(
+        fixed=True, actual_value=data, nominal_value=4)})
+
+solph.Source(label='source_el', outputs={
+    b_heat_0: solph.Flow(variable_costs=1)})
+
+# calculate coeffiencies and parameters
+P_input_max = 100   # nominal power of input flow
+P_input_min = 0    # minimal power of input flow
+
+# generic heatpipe
+hp = solph.custom.HeatPipeline(
+    label='heat_pipe',
+    inputs={b_heat_0: solph.Flow()},
+    outputs={b_heat_1: solph.Flow(
+        # nominal_value=250,
+        nominal_value=None,
+        investment=solph.Investment(ep_costs=100,
+                                    maximum=250),
+                                  )},
+    heat_loss_factor=0.0002,
+    length=200.0,
+    conversion_factors={b_heat_1: 0.8}
+    )
+
+# create an optimization problem and solve it
+om = solph.Model(es)
+
+filename = os.path.join(
+    helpers.extend_basic_path('lp_files'), 'basic_example.lp')
+# logging.info('Store lp-file in {0}.'.format(filename))
+om.write(filename, io_options={'symbolic_solver_labels': True})
+
+# solve model
+om.solve(solver='cbc', solve_kwargs={'tee': True})
+
+# create result object
+results = processing.results(om)
+
+data = views.node(results, 'b_heat_1')['sequences'].sum(axis=0).to_dict()
+
+electricity_bus = views.node(results, 'b_heat_0')["sequences"]
+heat_bus = views.node(results, 'b_heat_1')["sequences"]
+heat_pipe_eval = views.node(results, 'heat_pipe')["sequences"]
+
+# plot the time series (sequences) of a specific component/bus
+if plt is not None:
+    electricity_bus.plot(kind='line', drawstyle='steps-post', legend='right')
+    plt.legend()
+    plt.show()
+    heat_bus.plot(kind='line', drawstyle='steps-post')
+    plt.legend()
+    heat_pipe_eval.plot(kind='line', drawstyle='steps-post')
+    plt.legend()
+    plt.show()
+
+df_ges = pd.concat([electricity_bus, heat_bus], axis=1)
+
+elec_array = views.node(results, 'b_heat_0')['sequences'].values[:, 0]
+heat_array = views.node(results, 'b_heat_1')['sequences'].values[:, 1]
+
+cop = np.divide(heat_array, elec_array, out=np.zeros_like(heat_array),
+                where=elec_array != 0)
+plt.plot(idx, cop)
+plt.show()