dispatch.optimise_scenario() - Code Metrics - Inspection of "Add examples from oemof-examples" - oemof/oemof-solph - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — dev (#850)

by Uwe

created 2022-11-10 14:13 UTC

dispatch.optimise_scenario() B

↳ Parent: dispatch

Complexity

Conditions

Size

Total Lines	75
Code Lines	42

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	42
dl	0
loc	75
rs	8.872
c	0
b	0
f	0
cc	2
nop	0

How to fix Long Method

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

"""
General description
-------------------
As the csv-reader was removed with version 0.2 this example shows how to create
an excel-reader. The example is equivalent to the old csv-reader example.
Following the example one can customise the excel reader to ones own needs.

The pandas package supports the '.xls' and the '.xlsx' format but one can
create read and adept the files with open source software such as libreoffice,
openoffice, gnumeric,...

Data
----
scenario.xlsx

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

    pip install oemof.solph[examples]

    pip3 install openpyxl


If you want to plot the energy system's graph, you have to install pygraphviz
using:

    pip3 install pygraphviz

For pygraphviz under Windows, some hints are available in the oemof Wiki:
https://github.com/oemof/oemof/wiki/Windows---general

License
-------
Uwe Krien <[email protected]>
Jonathan Amme <[email protected]>

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

"""

import os
import logging
import pandas as pd

from oemof.tools import logger
from oemof import solph

from oemof.network.graph import create_nx_graph
from matplotlib import pyplot as plt
import networkx as nx


def nodes_from_excel(filename):
    """Read node data from Excel sheet

    Parameters
    ----------
    filename : :obj:`str`
        Path to excel file

    Returns
    -------
    :obj:`dict`
        Imported nodes data
    """

    # does Excel file exist?
    if not filename or not os.path.isfile(filename):
        raise FileNotFoundError(
            "Excel data file {} not found.".format(filename)
        )

    xls = pd.ExcelFile(filename)

    nodes_data = {
        "buses": xls.parse("buses"),
        "commodity_sources": xls.parse("commodity_sources"),
        "transformers": xls.parse("transformers"),
        "renewables": xls.parse("renewables"),
        "demand": xls.parse("demand"),
        "storages": xls.parse("storages"),
        "powerlines": xls.parse("powerlines"),
        "timeseries": xls.parse("time_series"),
    }

    # set datetime index
    nodes_data["timeseries"].set_index("timestamp", inplace=True)
    nodes_data["timeseries"].index = pd.to_datetime(
        nodes_data["timeseries"].index
    )

    print("Data from Excel file {} imported.".format(filename))

    return nodes_data


def create_nodes(nd=None):
    """Create nodes (oemof objects) from node dict

    Parameters
    ----------
    nd : :obj:`dict`
        Nodes data

    Returns
    -------
    nodes : `obj`:dict of :class:`nodes <oemof.network.Node>`
    """

    if not nd:
        raise ValueError("No nodes data provided.")

    nodes = []

    # Create Bus objects from buses table
    busd = {}

    for i, b in nd["buses"].iterrows():
        if b["active"]:
            bus = solph.Bus(label=b["label"])
            nodes.append(bus)

            busd[b["label"]] = bus
            if b["excess"]:
                nodes.append(
                    solph.components.Sink(
                        label=b["label"] + "_excess",
                        inputs={
                            busd[b["label"]]: solph.Flow(
                                variable_costs=b["excess costs"]
                            )
                        },
                    )
                )
            if b["shortage"]:
                nodes.append(
                    solph.components.Source(
                        label=b["label"] + "_shortage",
                        outputs={
                            busd[b["label"]]: solph.Flow(
                                variable_costs=b["shortage costs"]
                            )
                        },
                    )
                )

    # Create Source objects from table 'commodity sources'
    for i, cs in nd["commodity_sources"].iterrows():
        if cs["active"]:
            nodes.append(
                solph.components.Source(
                    label=cs["label"],
                    outputs={
                        busd[cs["to"]]: solph.Flow(
                            variable_costs=cs["variable costs"]
                        )
                    },
                )
            )

    # Create Source objects with fixed time series from 'renewables' table
    for i, re in nd["renewables"].iterrows():
        if re["active"]:
            # set static outflow values
            outflow_args = {"nominal_value": re["capacity"]}
            # get time series for node and parameter
            for col in nd["timeseries"].columns.values:
                if col.split(".")[0] == re["label"]:
                    outflow_args[col.split(".")[1]] = nd["timeseries"][col]

            # create
            nodes.append(
                solph.components.Source(
                    label=re["label"],
                    outputs={busd[re["to"]]: solph.Flow(**outflow_args)},
                )
            )

    # Create Sink objects with fixed time series from 'demand' table
    for i, de in nd["demand"].iterrows():
        if de["active"]:
            # set static inflow values
            inflow_args = {"nominal_value": de["nominal value"]}
            # get time series for node and parameter
            for col in nd["timeseries"].columns.values:
                if col.split(".")[0] == de["label"]:
                    inflow_args[col.split(".")[1]] = nd["timeseries"][col]

            # create
            nodes.append(
                solph.components.Sink(
                    label=de["label"],
                    inputs={busd[de["from"]]: solph.Flow(**inflow_args)},
                )
            )

    # Create Transformer objects from 'transformers' table
    for i, t in nd["transformers"].iterrows():
        if t["active"]:
            # set static inflow values
            inflow_args = {"variable_costs": t["variable input costs"]}
            # get time series for inflow of transformer
            for col in nd["timeseries"].columns.values:
                if col.split(".")[0] == t["label"]:
                    inflow_args[col.split(".")[1]] = nd["timeseries"][col]
            # create
            nodes.append(
                solph.components.Transformer(
                    label=t["label"],
                    inputs={busd[t["from"]]: solph.Flow(**inflow_args)},
                    outputs={
                        busd[t["to"]]: solph.Flow(nominal_value=t["capacity"])
                    },
                    conversion_factors={busd[t["to"]]: t["efficiency"]},
                )
            )

    for i, s in nd["storages"].iterrows():
        if s["active"]:
            nodes.append(
                solph.components.GenericStorage(
                    label=s["label"],
                    inputs={
                        busd[s["bus"]]: solph.Flow(
                            nominal_value=s["capacity inflow"],
                            variable_costs=s["variable input costs"],
                        )
                    },
                    outputs={
                        busd[s["bus"]]: solph.Flow(
                            nominal_value=s["capacity outflow"],
                            variable_costs=s["variable output costs"],
                        )
                    },
                    nominal_storage_capacity=s["nominal capacity"],
                    loss_rate=s["capacity loss"],
                    initial_storage_level=s["initial capacity"],
                    max_storage_level=s["capacity max"],
                    min_storage_level=s["capacity min"],
                    inflow_conversion_factor=s["efficiency inflow"],
                    outflow_conversion_factor=s["efficiency outflow"],
                )
            )

    for i, p in nd["powerlines"].iterrows():
        if p["active"]:
            bus1 = busd[p["bus_1"]]
            bus2 = busd[p["bus_2"]]
            nodes.append(
                solph.components.Transformer(
                    label="powerline" + "_" + p["bus_1"] + "_" + p["bus_2"],
                    inputs={bus1: solph.Flow()},
                    outputs={bus2: solph.Flow()},
                    conversion_factors={bus2: p["efficiency"]},
                )
            )
            nodes.append(
                solph.components.Transformer(
                    label="powerline" + "_" + p["bus_2"] + "_" + p["bus_1"],
                    inputs={bus2: solph.Flow()},
                    outputs={bus1: solph.Flow()},
                    conversion_factors={bus1: p["efficiency"]},
                )
            )

    return nodes


def draw_graph(
    grph,
    edge_labels=True,
    node_color="#AFAFAF",
    edge_color="#CFCFCF",
    plot=True,
    node_size=2000,
    with_labels=True,
    arrows=True,
    layout="neato",
):
    """
    Parameters
    ----------
    grph : networkxGraph
        A graph to draw.
    edge_labels : boolean
        Use nominal values of flow as edge label
    node_color : dict or string
        Hex color code oder matplotlib color for each node. If string, all
        colors are the same.

    edge_color : string
        Hex color code oder matplotlib color for edge color.

    plot : boolean
        Show matplotlib plot.

    node_size : integer
        Size of nodes.

    with_labels : boolean
        Draw node labels.

    arrows : boolean
        Draw arrows on directed edges. Works only if an optimization_model has
        been passed.
    layout : string
        networkx graph layout, one of: neato, dot, twopi, circo, fdp, sfdp.
    """
    if isinstance(node_color, dict):
        node_color = [node_color.get(g, "#AFAFAF") for g in grph.nodes()]

    # set drawing options
    options = {
        "with_labels": with_labels,
        "node_color": node_color,
        "edge_color": edge_color,
        "node_size": node_size,
        "arrows": arrows,
    }

    # try to use pygraphviz for graph layout
    try:
        import pygraphviz

        pos = nx.drawing.nx_agraph.graphviz_layout(grph, prog=layout)
    except ImportError:
        logging.error("Module pygraphviz not found, I won't plot the graph.")
        return

    # draw graph
    nx.draw(grph, pos=pos, **options)

    # add edge labels for all edges
    if edge_labels is True and plt:
        labels = nx.get_edge_attributes(grph, "weight")
        nx.draw_networkx_edge_labels(grph, pos=pos, edge_labels=labels)

    # show output
    if plot is True:
        plt.show()


def optimise_scenario():
    logger.define_logging()
    datetime_index = pd.date_range(
        "2016-01-01 00:00:00", "2016-01-01 23:00:00", freq="60min"
    )

    # model creation and solving
    logging.info("Starting optimization")

    # initialisation of the energy system
    esys = solph.EnergySystem(
        timeindex=datetime_index, infer_last_interval=False
    )

    # read node data from Excel sheet
    excel_nodes = nodes_from_excel(
        os.path.join(
            os.getcwd(),
            "scenario.xlsx",
        )
    )

    # create nodes from Excel sheet data
    my_nodes = create_nodes(nd=excel_nodes)

    # add nodes and flows to energy system
    esys.add(*my_nodes)

    print("*********************************************************")
    print("The following objects have been created from excel sheet:")
    for n in esys.nodes:
        oobj = (
            str(type(n)).replace("<class 'oemof.solph.", "").replace("'>", "")
        )
        print(oobj + ":", n.label)
    print("*********************************************************")

    # creation of a least cost model from the energy system
    om = solph.Model(esys)
    om.receive_duals()

    # solving the linear problem using the given solver
    om.solve(solver="cbc")

    # create graph of esys
    # You can use argument filename='/home/somebody/my_graph.graphml'
    # to dump your graph to disc. You can open it using e.g. yEd or gephi
    graph = create_nx_graph(esys)

    # plot esys graph
    draw_graph(
        grph=graph,
        plot=True,
        layout="neato",
        node_size=1000,
        node_color={"R1_bus_el": "#cd3333", "R2_bus_el": "#cd3333"},
    )

    # print and plot some results
    results = solph.processing.results(om)

    region2 = solph.views.node(results, "R2_bus_el")
    region1 = solph.views.node(results, "R1_bus_el")

    print(region2["sequences"].sum())
    print(region1["sequences"].sum())

    fig, ax = plt.subplots(figsize=(10, 5))
    region1["sequences"].plot(ax=ax)
    ax.legend(
        loc="upper center", prop={"size": 8}, bbox_to_anchor=(0.5, 1.4), ncol=3
    )
    fig.subplots_adjust(top=0.7)
    plt.show()
    logging.info("Done!")


if __name__ == "__main__":
    optimise_scenario()


1			# -- coding: utf-8 --
2
3			"""
4			General description
5			-------------------
6			As the csv-reader was removed with version 0.2 this example shows how to create
7			an excel-reader. The example is equivalent to the old csv-reader example.
8			Following the example one can customise the excel reader to ones own needs.
9
10			The pandas package supports the '.xls' and the '.xlsx' format but one can
11			create read and adept the files with open source software such as libreoffice,
12			openoffice, gnumeric,...
13
14			Data
15			----
16			scenario.xlsx
17
18			Installation requirements
19			-------------------------
20			This example requires oemof.solph (v0.5.x), install by:
21
22			pip install oemof.solph[examples]
23
24			pip3 install openpyxl
25
26
27			If you want to plot the energy system's graph, you have to install pygraphviz
28			using:
29
30			pip3 install pygraphviz
31
32			For pygraphviz under Windows, some hints are available in the oemof Wiki:
33			https://github.com/oemof/oemof/wiki/Windows---general
34
35			License
36			-------
37			Uwe Krien <[email protected]>
38			Jonathan Amme <[email protected]>
39
40			`MIT license <https://github.com/oemof/oemof-solph/blob/dev/LICENSE>`_
41
42			"""
43
44			import os
45			import logging
46			import pandas as pd
47
48			from oemof.tools import logger
49			from oemof import solph
50
51			from oemof.network.graph import create_nx_graph
52			from matplotlib import pyplot as plt
53			import networkx as nx
54
55
56			def nodes_from_excel(filename):
57			"""Read node data from Excel sheet
58
59			Parameters
60			----------
61			filename : :obj:`str`
62			Path to excel file
63
64			Returns
65			-------
66			:obj:`dict`
67			Imported nodes data
68			"""
69
70			# does Excel file exist?
71			if not filename or not os.path.isfile(filename):
72			raise FileNotFoundError(
73			"Excel data file {} not found.".format(filename)
74			)
75
76			xls = pd.ExcelFile(filename)
77
78			nodes_data = {
79			"buses": xls.parse("buses"),
80			"commodity_sources": xls.parse("commodity_sources"),
81			"transformers": xls.parse("transformers"),
82			"renewables": xls.parse("renewables"),
83			"demand": xls.parse("demand"),
84			"storages": xls.parse("storages"),
85			"powerlines": xls.parse("powerlines"),
86			"timeseries": xls.parse("time_series"),
87			}
88
89			# set datetime index
90			nodes_data["timeseries"].set_index("timestamp", inplace=True)
91			nodes_data["timeseries"].index = pd.to_datetime(
92			nodes_data["timeseries"].index
93			)
94
95			print("Data from Excel file {} imported.".format(filename))
96
97			return nodes_data
98
99
100			def create_nodes(nd=None):
101			"""Create nodes (oemof objects) from node dict
102
103			Parameters
104			----------
105			nd : :obj:`dict`
106			Nodes data
107
108			Returns
109			-------
110			nodes : `obj`:dict of :class:`nodes <oemof.network.Node>`
111			"""
112
113			if not nd:
114			raise ValueError("No nodes data provided.")
115
116			nodes = []
117
118			# Create Bus objects from buses table
119			busd = {}
120
121			for i, b in nd["buses"].iterrows():
122			if b["active"]:
123			bus = solph.Bus(label=b["label"])
124			nodes.append(bus)
125
126			busd[b["label"]] = bus
127			if b["excess"]:
128			nodes.append(
129			solph.components.Sink(
130			label=b["label"] + "_excess",
131			inputs={
132			busd[b["label"]]: solph.Flow(
133			variable_costs=b["excess costs"]
134			)
135			},
136			)
137			)
138			if b["shortage"]:
139			nodes.append(
140			solph.components.Source(
141			label=b["label"] + "_shortage",
142			outputs={
143			busd[b["label"]]: solph.Flow(
144			variable_costs=b["shortage costs"]
145			)
146			},
147			)
148			)
149
150			# Create Source objects from table 'commodity sources'
151			for i, cs in nd["commodity_sources"].iterrows():
152			if cs["active"]:
153			nodes.append(
154			solph.components.Source(
155			label=cs["label"],
156			outputs={
157			busd[cs["to"]]: solph.Flow(
158			variable_costs=cs["variable costs"]
159			)
160			},
161			)
162			)
163
164			# Create Source objects with fixed time series from 'renewables' table
165			for i, re in nd["renewables"].iterrows():
166			if re["active"]:
167			# set static outflow values
168			outflow_args = {"nominal_value": re["capacity"]}
169			# get time series for node and parameter
170			for col in nd["timeseries"].columns.values:
171			if col.split(".")[0] == re["label"]:
172			outflow_args[col.split(".")[1]] = nd["timeseries"][col]
173
174			# create
175			nodes.append(
176			solph.components.Source(
177			label=re["label"],
178			outputs={busd[re["to"]]: solph.Flow(**outflow_args)},
179			)
180			)
181
182			# Create Sink objects with fixed time series from 'demand' table
183			for i, de in nd["demand"].iterrows():
184			if de["active"]:
185			# set static inflow values
186			inflow_args = {"nominal_value": de["nominal value"]}
187			# get time series for node and parameter
188			for col in nd["timeseries"].columns.values:
189			if col.split(".")[0] == de["label"]:
190			inflow_args[col.split(".")[1]] = nd["timeseries"][col]
191
192			# create
193			nodes.append(
194			solph.components.Sink(
195			label=de["label"],
196			inputs={busd[de["from"]]: solph.Flow(**inflow_args)},
197			)
198			)
199
200			# Create Transformer objects from 'transformers' table
201			for i, t in nd["transformers"].iterrows():
202			if t["active"]:
203			# set static inflow values
204			inflow_args = {"variable_costs": t["variable input costs"]}
205			# get time series for inflow of transformer
206			for col in nd["timeseries"].columns.values:
207			if col.split(".")[0] == t["label"]:
208			inflow_args[col.split(".")[1]] = nd["timeseries"][col]
209			# create
210			nodes.append(
211			solph.components.Transformer(
212			label=t["label"],
213			inputs={busd[t["from"]]: solph.Flow(**inflow_args)},
214			outputs={
215			busd[t["to"]]: solph.Flow(nominal_value=t["capacity"])
216			},
217			conversion_factors={busd[t["to"]]: t["efficiency"]},
218			)
219			)
220
221			for i, s in nd["storages"].iterrows():
222			if s["active"]:
223			nodes.append(
224			solph.components.GenericStorage(
225			label=s["label"],
226			inputs={
227			busd[s["bus"]]: solph.Flow(
228			nominal_value=s["capacity inflow"],
229			variable_costs=s["variable input costs"],
230			)
231			},
232			outputs={
233			busd[s["bus"]]: solph.Flow(
234			nominal_value=s["capacity outflow"],
235			variable_costs=s["variable output costs"],
236			)
237			},
238			nominal_storage_capacity=s["nominal capacity"],
239			loss_rate=s["capacity loss"],
240			initial_storage_level=s["initial capacity"],
241			max_storage_level=s["capacity max"],
242			min_storage_level=s["capacity min"],
243			inflow_conversion_factor=s["efficiency inflow"],
244			outflow_conversion_factor=s["efficiency outflow"],
245			)
246			)
247
248			for i, p in nd["powerlines"].iterrows():
249			if p["active"]:
250			bus1 = busd[p["bus_1"]]
251			bus2 = busd[p["bus_2"]]
252			nodes.append(
253			solph.components.Transformer(
254			label="powerline" + "_" + p["bus_1"] + "_" + p["bus_2"],
255			inputs={bus1: solph.Flow()},
256			outputs={bus2: solph.Flow()},
257			conversion_factors={bus2: p["efficiency"]},
258			)
259			)
260			nodes.append(
261			solph.components.Transformer(
262			label="powerline" + "_" + p["bus_2"] + "_" + p["bus_1"],
263			inputs={bus2: solph.Flow()},
264			outputs={bus1: solph.Flow()},
265			conversion_factors={bus1: p["efficiency"]},
266			)
267			)
268
269			return nodes
270
271
272			def draw_graph(
273			grph,
274			edge_labels=True,
275			node_color="#AFAFAF",
276			edge_color="#CFCFCF",
277			plot=True,
278			node_size=2000,
279			with_labels=True,
280			arrows=True,
281			layout="neato",
282			):
283			"""
284			Parameters
285			----------
286			grph : networkxGraph
287			A graph to draw.
288			edge_labels : boolean
289			Use nominal values of flow as edge label
290			node_color : dict or string
291			Hex color code oder matplotlib color for each node. If string, all
292			colors are the same.
293
294			edge_color : string
295			Hex color code oder matplotlib color for edge color.
296
297			plot : boolean
298			Show matplotlib plot.
299
300			node_size : integer
301			Size of nodes.
302
303			with_labels : boolean
304			Draw node labels.
305
306			arrows : boolean
307			Draw arrows on directed edges. Works only if an optimization_model has
308			been passed.
309			layout : string
310			networkx graph layout, one of: neato, dot, twopi, circo, fdp, sfdp.
311			"""
312			if isinstance(node_color, dict):
313			node_color = [node_color.get(g, "#AFAFAF") for g in grph.nodes()]
314
315			# set drawing options
316			options = {
317			"with_labels": with_labels,
318			"node_color": node_color,
319			"edge_color": edge_color,
320			"node_size": node_size,
321			"arrows": arrows,
322			}
323
324			# try to use pygraphviz for graph layout
325			try:
326			import pygraphviz
327
328			pos = nx.drawing.nx_agraph.graphviz_layout(grph, prog=layout)
329			except ImportError:
330			logging.error("Module pygraphviz not found, I won't plot the graph.")
331			return
332
333			# draw graph
334			nx.draw(grph, pos=pos, **options)
335
336			# add edge labels for all edges
337			if edge_labels is True and plt:
338			labels = nx.get_edge_attributes(grph, "weight")
339			nx.draw_networkx_edge_labels(grph, pos=pos, edge_labels=labels)
340
341			# show output
342			if plot is True:
343			plt.show()
344
345
346			def optimise_scenario():
347			logger.define_logging()
348			datetime_index = pd.date_range(
349			"2016-01-01 00:00:00", "2016-01-01 23:00:00", freq="60min"
350			)
351
352			# model creation and solving
353			logging.info("Starting optimization")
354
355			# initialisation of the energy system
356			esys = solph.EnergySystem(
357			timeindex=datetime_index, infer_last_interval=False
358			)
359
360			# read node data from Excel sheet
361			excel_nodes = nodes_from_excel(
362			os.path.join(
363			os.getcwd(),
364			"scenario.xlsx",
365			)
366			)
367
368			# create nodes from Excel sheet data
369			my_nodes = create_nodes(nd=excel_nodes)
370
371			# add nodes and flows to energy system
372			esys.add(*my_nodes)
373
374			print("*********************************************************")
375			print("The following objects have been created from excel sheet:")
376			for n in esys.nodes:
377			oobj = (
378			str(type(n)).replace("<class 'oemof.solph.", "").replace("'>", "")
379			)
380			print(oobj + ":", n.label)
381			print("*********************************************************")
382
383			# creation of a least cost model from the energy system
384			om = solph.Model(esys)
385			om.receive_duals()
386
387			# solving the linear problem using the given solver
388			om.solve(solver="cbc")
389
390			# create graph of esys
391			# You can use argument filename='/home/somebody/my_graph.graphml'
392			# to dump your graph to disc. You can open it using e.g. yEd or gephi
393			graph = create_nx_graph(esys)
394
395			# plot esys graph
396			draw_graph(
397			grph=graph,
398			plot=True,
399			layout="neato",
400			node_size=1000,
401			node_color={"R1_bus_el": "#cd3333", "R2_bus_el": "#cd3333"},
402			)
403
404			# print and plot some results
405			results = solph.processing.results(om)
406
407			region2 = solph.views.node(results, "R2_bus_el")
408			region1 = solph.views.node(results, "R1_bus_el")
409
410			print(region2["sequences"].sum())
411			print(region1["sequences"].sum())
412
413			fig, ax = plt.subplots(figsize=(10, 5))
414			region1["sequences"].plot(ax=ax)
415			ax.legend(
416			loc="upper center", prop={"size": 8}, bbox_to_anchor=(0.5, 1.4), ncol=3
417			)
418			fig.subplots_adjust(top=0.7)
419			plt.show()
420			logging.info("Done!")
421
422
423			if __name__ == "__main__":
424			optimise_scenario()
425

oemof / oemof-solph

Pull Request — dev (#850)

dispatch.optimise_scenario() B

Complexity

Size

Duplication

Importance

How to fix Long Method

Long Method

Duplication Side-by-Side

Filter issues like