scenario_builder.cop_precalc.get_hp_timeseries() - Code Metrics - Inspection of "Adds new cop precalc functions and help functions" - reegis/scenario_builder - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 783fd3...8a64e3 )

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created 2020-09-21 13:10 UTC

scenario_builder.cop_precalc.get_hp_timeseries() A

↳ Parent: scenario_builder.cop_precalc

Complexity

Conditions

Size

Total Lines	11
Code Lines	8

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	8
dl	0
loc	11
rs	10
c	0
b	0
f	0
cc	3
nop	3

import os
import oemof.thermal.compression_heatpumps_and_chillers as cmpr_hp_chiller
import pandas as pd
import numpy as np
from disaggregator import data
from reegis import coastdat, demand_disaggregator
from reegis import config as cfg


def flow_temperature_dependent_on_ambient(t_max, t_min, t_amb):
    """
    Parameters
    ----------
    t_max : int
        Maximum flow temperature of heating system
    t_min : int
        Minimum flow temperature of heating system
    t_amb : pd.Series
        Ambient temperature

    Returns: list
        List containing flow temperature depending on heatload/ambient temperature
    -------
    """

    delta_t_flow = t_max - t_min
    delta_t_amb = 30
    T_flow = [t_max - (delta_t_flow/delta_t_amb) * i for i in range(31)]
    T_round = np.round(t_amb)

    t_ind = np.zeros(shape=(len(t_amb)))
    tvl = np.zeros(shape=(len(t_amb)))  # Vorlauftemperatur
    for i in range(len(t_amb)):
        if T_round[i] < -14:
            t_ind[i] = 1
        elif T_round[i] >= -14 and T_round[i] < 0:
            t_ind[i] = abs(15 + T_round[i])  # nimm erste 15 Stellen bei -10 bis 0°C
        elif T_round[i] >= 0:
            t_ind[i] = min(31, 16 + T_round[i])
        tvl[i] = int(max(t_min, T_flow[int(t_ind[i] - 1)]))

    tvl = list(tvl)

    return tvl


def calculate_COP(t_flow, t_low, quality_grade):
    """
    Parameters
    ----------
    t_flow : int
        Maximum flow temperature of heating system
    t_low : Series
        Minimum flow temperature of heating system
    quality_grade : float
        Ambient temperature

    Returns: list
        List containing flow temperature depending on heatload/ambient temperature
    -------
    """

    cops = cmpr_hp_chiller.calc_cops(
        temp_high=list([t_flow]),
        temp_low=t_low,
        quality_grade=quality_grade,
        mode='heat_pump',
        temp_threshold_icing=2,
        factor_icing=0.8)

    return cops


def calculate_dynamic_COP(t_flow, t_low, quality_grade):
    r"""
    This function calculates COPs of a heatpump. Both, the flow temperature and the heat source must be given
    as a timeseries and can very for each timestep.

    Parameters
    ----------
    t_flow : Series
        Flow temperature series
    t_low : Series
        Heat source of heatpump
    quality_grade : float
        Carnot efficiency factor of heat pump

    Returns: Series
        Series containing COPs
    -------
    """
    cops = []
    for i in range(0, len(t_flow)):
        tmp_high = list([t_flow[i]])
        tmp_low = list([t_low[i]])

        cop = cmpr_hp_chiller.calc_cops(
            temp_high=tmp_high,
            temp_low=tmp_low,
            quality_grade=quality_grade,
            mode='heat_pump',
            temp_threshold_icing=2,
            factor_icing=0.8)

        cops.append(cop[0])

    cops = pd.Series(cops)

    return cops


def calculate_mixed_COPS_per_region(t_amb, t_ground, quality_grade, share_ashp=0.7, share_gshp=0.3):
    """
    Parameters
    ----------
    t_amb : Series
        Ambient temperature
    t_ground : Series
        Ground temperature for GSHP
    quality_grade : float
        Ambient temperature
    share_ashp: Float
        Share of air sourced heat pumps
    share_gshp: Float
        Share of ground sourced heat pumps

    Returns: DataFrame
        DataFrame containing some COP series
    -------
    """

    t_flow = dict(high = 75, medium=55, low = 40)

    # Calculate flow temperature for different temperature levels
    tvl75 = flow_temperature_dependent_on_ambient(t_flow['high'], 50, t_amb)
    tvl50 = flow_temperature_dependent_on_ambient(t_flow['medium'], 30 , t_amb)
    tvl35 = flow_temperature_dependent_on_ambient(t_flow['low'], 30 , t_amb)

    # Calculate COPs for air sourced heat pump
    cop75_ASHP = calculate_dynamic_COP(tvl75, t_amb, quality_grade)
    cop50_ASHP = calculate_dynamic_COP(tvl50, t_amb, quality_grade)
    cop35_ASHP = calculate_dynamic_COP(tvl35, t_amb, quality_grade)
    copwater_ASHP = calculate_dynamic_COP(np.ones(shape=8760)*50, t_amb, quality_grade)

    # Calculate COPs for ground sourced heat pump
    cop75_GSHP = calculate_dynamic_COP(tvl75, t_ground, quality_grade)
    cop50_GSHP = calculate_dynamic_COP(tvl50, t_ground, quality_grade)
    cop35_GSHP = calculate_dynamic_COP(tvl35, t_ground, quality_grade)
    copwater_GSHP = calculate_dynamic_COP(np.ones(shape=8760)*50, t_ground, quality_grade)

    cops_aggregated = pd.DataFrame(columns=['COP35_air', 'COP50_air', 'COP75_air', 'COP35_ground', 'COP50_ground',
                                            'COP75_ground', 'COPwater_air', 'COPwater_ground','COP_mean' ])

    # Write COP-Series to DataFrame
    cops_aggregated['COP35_air'] = cop35_ASHP
    cops_aggregated['COP50_air'] = cop50_ASHP
    cops_aggregated['COP75_air'] = cop75_ASHP
    cops_aggregated['COP35_ground'] = cop35_GSHP
    cops_aggregated['COP50_ground'] = cop50_GSHP
    cops_aggregated['COP75_ground'] = cop75_GSHP
    cops_aggregated['COPwater_air'] = copwater_ASHP
    cops_aggregated['COPwater_ground'] = copwater_GSHP

    # Calculate mean COP and add it to DataFrame
    cop_air_mean = (cop35_ASHP + cop50_ASHP + cop75_ASHP) / 3
    cop_ground_mean = (cop35_GSHP + cop50_GSHP + cop75_GSHP) / 3
    cop_mean = share_ashp * cop_air_mean + share_gshp * cop_ground_mean
    cops_aggregated['COP_mean'] = cop_mean

    # Limit COP to 7
    limit = cops_aggregated >= 7
    cops_aggregated[limit] = 7

    return cops_aggregated


def calculate_mixed_cops_by_nuts3(year, name, share_ashp=0.7, share_gshp=0.3, quality_grade=0.4):
    """
    Parameters
    ----------
    year: int
        Year of interest
    name: string
        Name of the analysed set
    share_ashp: Float
        Share of air sourced heat pumps
    share_gshp: Float
        Share of ground sourced heat pumps
    quality_grade : float
        Ambient temperature

    Returns: 2 DataFrames
        DataFrames containing mean COP series for each German NUTS3 region
    -------
    """

    fn_pattern = "mixed_cops_by_nuts3_{name}_{year}.csv".format(name=name, year=year)
    fn_pattern_water = "cops_by_nuts3_{name}_{year}_water.csv".format(name=name, year=year)
    fn = os.path.join(cfg.get("paths", "cop_precalc"), fn_pattern)
    fn_water = os.path.join(cfg.get("paths", "cop_precalc"), fn_pattern_water)

    if not os.path.isfile(fn):
        share_ashp = share_ashp
        share_gshp = share_gshp
        quality_grade = quality_grade
        t_ground = pd.Series(np.ones(8760)*10)

        outfile = os.path.join(cfg.get("paths", "cop_precalc"), "average_temp_by_nuts3_{year}.csv".format(year=year))

        if not os.path.isfile(outfile):

            # Load NUTS3-geometries
            nuts3 = data.database_shapes()
            nuts3 = nuts3.to_crs(crs=4326)

            # Get average temperature per NUTS3, coastdat only available until 2014
            coastdat.spatial_average_weather(year, nuts3, 'temp_air', 'deTemp', outfile=outfile)
            NUTS3_temp = pd.read_csv(outfile)
            NUTS3_temp.drop('Unnamed: 0', axis='columns', inplace=True)

        else:
            NUTS3_temp = pd.read_csv(outfile)
            NUTS3_temp.drop('Unnamed: 0', axis='columns', inplace=True)

        # Create empty DataFrames
        COP_NUTS3 = pd.DataFrame(index=pd.date_range('1/1/'+str(year), periods=8760, freq='H'),
                                 columns=NUTS3_temp.columns)
        COP_NUTS3_water = pd.DataFrame(index=pd.date_range('1/1/'+str(year), periods=8760, freq='H'),
                                       columns=NUTS3_temp.columns)

        # Loop through NUTS3-regions and calculate mixed COP for each region
        for r in COP_NUTS3.columns:
            tmp_cop = calculate_mixed_COPS_per_region(NUTS3_temp[r]-273.15, t_ground, quality_grade=quality_grade,
                                                      share_ashp=share_ashp, share_gshp=share_gshp)
            tmp_cop.set_index(COP_NUTS3.index, inplace=True)
            COP_NUTS3[r] = tmp_cop['COP_mean']
            COP_NUTS3_water[r] = tmp_cop['COPwater_air']* share_ashp + tmp_cop['COPwater_ground'] * share_gshp

        COP_NUTS3.to_csv(fn)
        COP_NUTS3_water.to_csv(fn_water)

    else:
        COP_NUTS3 = pd.read_csv(fn)
        COP_NUTS3_water = pd.read_csv(fn_water)

    return COP_NUTS3, COP_NUTS3_water


def aggregate_COP_by_region(regions, COP):
    mean_COP = pd.DataFrame(columns=regions.index)
    #mean_COP_water = pd.DataFrame(columns=regions.index)

    nuts3_list = demand_disaggregator.get_nutslist_for_regions(regions)

    for zone in regions.index:
        idx = nuts3_list.loc[zone]["nuts"]
        mean_COP[zone] = COP[idx].mean(axis=1)
        #mean_COP_water[zone] = COP_water[idx].mean(axis=1)

    return mean_COP


def get_hp_timeseries(heat_profile, cop, E_el):
    cop.index = heat_profile.index

    for Q_rated in range(0,10000000,1000):
        heat_hp = Q_rated * cop * heat_profile
        elc_hp = heat_hp / cop

        if elc_hp.sum() > E_el:
            break

    return elc_hp, heat_hp



1			import os
2			import oemof.thermal.compression_heatpumps_and_chillers as cmpr_hp_chiller
3			import pandas as pd
4			import numpy as np
5			from disaggregator import data
6			from reegis import coastdat, demand_disaggregator
7			from reegis import config as cfg
8
9
10			def flow_temperature_dependent_on_ambient(t_max, t_min, t_amb):
11			"""
12			Parameters
13			----------
14			t_max : int
15			Maximum flow temperature of heating system
16			t_min : int
17			Minimum flow temperature of heating system
18			t_amb : pd.Series
19			Ambient temperature
20
21			Returns: list
22			List containing flow temperature depending on heatload/ambient temperature
23			-------
24			"""
25
26			delta_t_flow = t_max - t_min
27			delta_t_amb = 30
28			T_flow = [t_max - (delta_t_flow/delta_t_amb) * i for i in range(31)]
29			T_round = np.round(t_amb)
30
31			t_ind = np.zeros(shape=(len(t_amb)))
32			tvl = np.zeros(shape=(len(t_amb))) # Vorlauftemperatur
33			for i in range(len(t_amb)):
34			if T_round[i] < -14:
35			t_ind[i] = 1
36			elif T_round[i] >= -14 and T_round[i] < 0:
37			t_ind[i] = abs(15 + T_round[i]) # nimm erste 15 Stellen bei -10 bis 0°C
38			elif T_round[i] >= 0:
39			t_ind[i] = min(31, 16 + T_round[i])
40			tvl[i] = int(max(t_min, T_flow[int(t_ind[i] - 1)]))
41
42			tvl = list(tvl)
43
44			return tvl
45
46
47			def calculate_COP(t_flow, t_low, quality_grade):
48			"""
49			Parameters
50			----------
51			t_flow : int
52			Maximum flow temperature of heating system
53			t_low : Series
54			Minimum flow temperature of heating system
55			quality_grade : float
56			Ambient temperature
57
58			Returns: list
59			List containing flow temperature depending on heatload/ambient temperature
60			-------
61			"""
62
63			cops = cmpr_hp_chiller.calc_cops(
64			temp_high=list([t_flow]),
65			temp_low=t_low,
66			quality_grade=quality_grade,
67			mode='heat_pump',
68			temp_threshold_icing=2,
69			factor_icing=0.8)
70
71			return cops
72
73
74			def calculate_dynamic_COP(t_flow, t_low, quality_grade):
75			r"""
76			This function calculates COPs of a heatpump. Both, the flow temperature and the heat source must be given
77			as a timeseries and can very for each timestep.
78
79			Parameters
80			----------
81			t_flow : Series
82			Flow temperature series
83			t_low : Series
84			Heat source of heatpump
85			quality_grade : float
86			Carnot efficiency factor of heat pump
87
88			Returns: Series
89			Series containing COPs
90			-------
91			"""
92			cops = []
93			for i in range(0, len(t_flow)):
94			tmp_high = list([t_flow[i]])
95			tmp_low = list([t_low[i]])
96
97			cop = cmpr_hp_chiller.calc_cops(
98			temp_high=tmp_high,
99			temp_low=tmp_low,
100			quality_grade=quality_grade,
101			mode='heat_pump',
102			temp_threshold_icing=2,
103			factor_icing=0.8)
104
105			cops.append(cop[0])
106
107			cops = pd.Series(cops)
108
109			return cops
110
111
112			def calculate_mixed_COPS_per_region(t_amb, t_ground, quality_grade, share_ashp=0.7, share_gshp=0.3):
113			"""
114			Parameters
115			----------
116			t_amb : Series
117			Ambient temperature
118			t_ground : Series
119			Ground temperature for GSHP
120			quality_grade : float
121			Ambient temperature
122			share_ashp: Float
123			Share of air sourced heat pumps
124			share_gshp: Float
125			Share of ground sourced heat pumps
126
127			Returns: DataFrame
128			DataFrame containing some COP series
129			-------
130			"""
131
132			t_flow = dict(high = 75, medium=55, low = 40)
133
134			# Calculate flow temperature for different temperature levels
135			tvl75 = flow_temperature_dependent_on_ambient(t_flow['high'], 50, t_amb)
136			tvl50 = flow_temperature_dependent_on_ambient(t_flow['medium'], 30 , t_amb)
137			tvl35 = flow_temperature_dependent_on_ambient(t_flow['low'], 30 , t_amb)
138
139			# Calculate COPs for air sourced heat pump
140			cop75_ASHP = calculate_dynamic_COP(tvl75, t_amb, quality_grade)
141			cop50_ASHP = calculate_dynamic_COP(tvl50, t_amb, quality_grade)
142			cop35_ASHP = calculate_dynamic_COP(tvl35, t_amb, quality_grade)
143			copwater_ASHP = calculate_dynamic_COP(np.ones(shape=8760)*50, t_amb, quality_grade)
144
145			# Calculate COPs for ground sourced heat pump
146			cop75_GSHP = calculate_dynamic_COP(tvl75, t_ground, quality_grade)
147			cop50_GSHP = calculate_dynamic_COP(tvl50, t_ground, quality_grade)
148			cop35_GSHP = calculate_dynamic_COP(tvl35, t_ground, quality_grade)
149			copwater_GSHP = calculate_dynamic_COP(np.ones(shape=8760)*50, t_ground, quality_grade)
150
151			cops_aggregated = pd.DataFrame(columns=['COP35_air', 'COP50_air', 'COP75_air', 'COP35_ground', 'COP50_ground',
152			'COP75_ground', 'COPwater_air', 'COPwater_ground','COP_mean' ])
153
154			# Write COP-Series to DataFrame
155			cops_aggregated['COP35_air'] = cop35_ASHP
156			cops_aggregated['COP50_air'] = cop50_ASHP
157			cops_aggregated['COP75_air'] = cop75_ASHP
158			cops_aggregated['COP35_ground'] = cop35_GSHP
159			cops_aggregated['COP50_ground'] = cop50_GSHP
160			cops_aggregated['COP75_ground'] = cop75_GSHP
161			cops_aggregated['COPwater_air'] = copwater_ASHP
162			cops_aggregated['COPwater_ground'] = copwater_GSHP
163
164			# Calculate mean COP and add it to DataFrame
165			cop_air_mean = (cop35_ASHP + cop50_ASHP + cop75_ASHP) / 3
166			cop_ground_mean = (cop35_GSHP + cop50_GSHP + cop75_GSHP) / 3
167			cop_mean = share_ashp * cop_air_mean + share_gshp * cop_ground_mean
168			cops_aggregated['COP_mean'] = cop_mean
169
170			# Limit COP to 7
171			limit = cops_aggregated >= 7
172			cops_aggregated[limit] = 7
173
174			return cops_aggregated
175
176
177			def calculate_mixed_cops_by_nuts3(year, name, share_ashp=0.7, share_gshp=0.3, quality_grade=0.4):
178			"""
179			Parameters
180			----------
181			year: int
182			Year of interest
183			name: string
184			Name of the analysed set
185			share_ashp: Float
186			Share of air sourced heat pumps
187			share_gshp: Float
188			Share of ground sourced heat pumps
189			quality_grade : float
190			Ambient temperature
191
192			Returns: 2 DataFrames
193			DataFrames containing mean COP series for each German NUTS3 region
194			-------
195			"""
196
197			fn_pattern = "mixed_cops_by_nuts3_{name}_{year}.csv".format(name=name, year=year)
198			fn_pattern_water = "cops_by_nuts3_{name}_{year}_water.csv".format(name=name, year=year)
199			fn = os.path.join(cfg.get("paths", "cop_precalc"), fn_pattern)
200			fn_water = os.path.join(cfg.get("paths", "cop_precalc"), fn_pattern_water)
201
202			if not os.path.isfile(fn):
203			share_ashp = share_ashp
204			share_gshp = share_gshp
205			quality_grade = quality_grade
206			t_ground = pd.Series(np.ones(8760)*10)
207
208			outfile = os.path.join(cfg.get("paths", "cop_precalc"), "average_temp_by_nuts3_{year}.csv".format(year=year))
209
210			if not os.path.isfile(outfile):
211
212			# Load NUTS3-geometries
213			nuts3 = data.database_shapes()
214			nuts3 = nuts3.to_crs(crs=4326)
215
216			# Get average temperature per NUTS3, coastdat only available until 2014
217			coastdat.spatial_average_weather(year, nuts3, 'temp_air', 'deTemp', outfile=outfile)
218			NUTS3_temp = pd.read_csv(outfile)
219			NUTS3_temp.drop('Unnamed: 0', axis='columns', inplace=True)
220
221			else:
222			NUTS3_temp = pd.read_csv(outfile)
223			NUTS3_temp.drop('Unnamed: 0', axis='columns', inplace=True)
224
225			# Create empty DataFrames
226			COP_NUTS3 = pd.DataFrame(index=pd.date_range('1/1/'+str(year), periods=8760, freq='H'),
227			columns=NUTS3_temp.columns)
228			COP_NUTS3_water = pd.DataFrame(index=pd.date_range('1/1/'+str(year), periods=8760, freq='H'),
229			columns=NUTS3_temp.columns)
230
231			# Loop through NUTS3-regions and calculate mixed COP for each region
232			for r in COP_NUTS3.columns:
233			tmp_cop = calculate_mixed_COPS_per_region(NUTS3_temp[r]-273.15, t_ground, quality_grade=quality_grade,
234			share_ashp=share_ashp, share_gshp=share_gshp)
235			tmp_cop.set_index(COP_NUTS3.index, inplace=True)
236			COP_NUTS3[r] = tmp_cop['COP_mean']
237			COP_NUTS3_water[r] = tmp_cop['COPwater_air']* share_ashp + tmp_cop['COPwater_ground'] * share_gshp
238
239			COP_NUTS3.to_csv(fn)
240			COP_NUTS3_water.to_csv(fn_water)
241
242			else:
243			COP_NUTS3 = pd.read_csv(fn)
244			COP_NUTS3_water = pd.read_csv(fn_water)
245
246			return COP_NUTS3, COP_NUTS3_water
247
248
249			def aggregate_COP_by_region(regions, COP):
250			mean_COP = pd.DataFrame(columns=regions.index)
251			#mean_COP_water = pd.DataFrame(columns=regions.index)
252
253			nuts3_list = demand_disaggregator.get_nutslist_for_regions(regions)
254
255			for zone in regions.index:
256			idx = nuts3_list.loc[zone]["nuts"]
257			mean_COP[zone] = COP[idx].mean(axis=1)
258			#mean_COP_water[zone] = COP_water[idx].mean(axis=1)
259
260			return mean_COP
261
262
263			def get_hp_timeseries(heat_profile, cop, E_el):
264			cop.index = heat_profile.index
265
266			for Q_rated in range(0,10000000,1000):
267			heat_hp = Q_rated * cop * heat_profile
268			elc_hp = heat_hp / cop
269
270			if elc_hp.sum() > E_el:
271			break
272
273			return elc_hp, heat_hp
			0 ignored issues – show introduced 2020-09-21 13:31 UTC by Report Bug Copy Issue Report The variable `heat_hp` does not seem to be defined in case the `for` loop on line `266` is not entered. Are you sure this can never be the case? Loading history... introduced 2020-09-21 13:31 UTC by Report Bug Copy Issue Report The variable `elc_hp` does not seem to be defined in case the `for` loop on line `266` is not entered. Are you sure this can never be the case? Loading history...
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scenario_builder.cop_precalc.get_hp_timeseries() A

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