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import os |
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import oemof.thermal.compression_heatpumps_and_chillers as cmpr_hp_chiller |
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import pandas as pd |
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import numpy as np |
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from disaggregator import data |
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from reegis import coastdat |
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from reegis import config as cfg |
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def flow_temperature_dependent_on_ambient(t_max, t_min, t_amb): |
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""" |
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Parameters |
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---------- |
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t_max : int |
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Maximum flow temperature of heating system |
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t_min : int |
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Minimum flow temperature of heating system |
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t_amb : pd.Series |
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Ambient temperature |
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Returns: list |
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List containing flow temperature depending on heatload/ambient temperature |
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------- |
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""" |
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delta_t_flow = t_max - t_min |
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delta_t_amb = 30 |
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T_flow = [t_max - (delta_t_flow/delta_t_amb) * i for i in range(31)] |
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T_round = np.round(t_amb) |
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t_ind = np.zeros(shape=(len(t_amb))) |
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tvl = np.zeros(shape=(len(t_amb))) # Vorlauftemperatur |
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for i in range(len(t_amb)): |
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if T_round[i] < -14: |
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t_ind[i] = 1 |
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elif T_round[i] >= -14 and T_round[i] < 0: |
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t_ind[i] = abs(15 + T_round[i]) # nimm erste 15 Stellen bei -10 bis 0°C |
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elif T_round[i] >= 0: |
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t_ind[i] = min(31, 16 + T_round[i]) |
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tvl[i] = int(max(t_min, T_flow[int(t_ind[i] - 1)])) |
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tvl = list(tvl) |
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return tvl |
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def calculate_COP(t_flow, t_low, quality_grade): |
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""" |
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Parameters |
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---------- |
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t_flow : int |
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Maximum flow temperature of heating system |
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t_low : Series |
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Minimum flow temperature of heating system |
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quality_grade : float |
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Ambient temperature |
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Returns: list |
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List containing flow temperature depending on heatload/ambient temperature |
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------- |
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""" |
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cops = cmpr_hp_chiller.calc_cops( |
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temp_high=list([t_flow]), |
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temp_low=t_low, |
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quality_grade=quality_grade, |
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mode='heat_pump', |
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temp_threshold_icing=2, |
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factor_icing=0.8) |
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return cops |
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def calculate_dynamic_COP(t_flow, t_low, quality_grade): |
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r""" |
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This function calculates COPs of a heatpump. Both, the flow temperature and the heat source must be given |
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as a timeseries and can very for each timestep. |
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Parameters |
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---------- |
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t_flow : Series |
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Flow temperature series |
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t_low : Series |
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Heat source of heatpump |
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quality_grade : float |
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Carnot efficiency factor of heat pump |
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Returns: Series |
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Series containing COPs |
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------- |
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""" |
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cops = [] |
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for i in range(0, len(t_flow)): |
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tmp_high = list([t_flow[i]]) |
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tmp_low = list([t_low[i]]) |
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cop = cmpr_hp_chiller.calc_cops( |
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temp_high=tmp_high, |
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temp_low=tmp_low, |
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quality_grade=quality_grade, |
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mode='heat_pump', |
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temp_threshold_icing=2, |
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factor_icing=0.8) |
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cops.append(cop[0]) |
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cops = pd.Series(cops) |
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return cops |
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def calculate_mixed_COPS_per_region(t_amb, t_ground, quality_grade, share_ashp=0.7, share_gshp=0.3): |
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""" |
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Parameters |
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---------- |
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t_amb : Series |
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Ambient temperature |
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t_ground : Series |
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Ground temperature for GSHP |
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quality_grade : float |
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Ambient temperature |
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share_ashp: Float |
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Share of air sourced heat pumps |
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share_gshp: Float |
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Share of ground sourced heat pumps |
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Returns: DataFrame |
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DataFrame containing some COP series |
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------- |
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""" |
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t_flow = dict(high = 75, medium=55, low = 40) |
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# Calculate flow temperature for different temperature levels |
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tvl75 = flow_temperature_dependent_on_ambient(t_flow['high'], 50, t_amb) |
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tvl50 = flow_temperature_dependent_on_ambient(t_flow['medium'], 30 , t_amb) |
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tvl35 = flow_temperature_dependent_on_ambient(t_flow['low'], 30 , t_amb) |
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# Calculate COPs for air sourced heat pump |
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cop75_ASHP = calculate_dynamic_COP(tvl75, t_amb, quality_grade) |
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cop50_ASHP = calculate_dynamic_COP(tvl50, t_amb, quality_grade) |
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cop35_ASHP = calculate_dynamic_COP(tvl35, t_amb, quality_grade) |
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copwater_ASHP = calculate_dynamic_COP(np.ones(shape=8760)*50, t_amb, quality_grade) |
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# Calculate COPs for ground sourced heat pump |
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cop75_GSHP = calculate_dynamic_COP(tvl75, t_ground, quality_grade) |
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cop50_GSHP = calculate_dynamic_COP(tvl50, t_ground, quality_grade) |
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cop35_GSHP = calculate_dynamic_COP(tvl35, t_ground, quality_grade) |
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copwater_GSHP = calculate_dynamic_COP(np.ones(shape=8760)*50, t_ground, quality_grade) |
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cops_aggregated = pd.DataFrame(columns=['COP35_air', 'COP50_air', 'COP75_air', 'COP35_ground', 'COP50_ground', |
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'COP75_ground', 'COPwater_air', 'COPwater_ground','COP_mean' ]) |
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# Write COP-Series to DataFrame |
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cops_aggregated['COP35_air'] = cop35_ASHP |
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cops_aggregated['COP50_air'] = cop50_ASHP |
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cops_aggregated['COP75_air'] = cop75_ASHP |
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cops_aggregated['COP35_ground'] = cop35_GSHP |
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cops_aggregated['COP50_ground'] = cop50_GSHP |
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cops_aggregated['COP75_ground'] = cop75_GSHP |
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cops_aggregated['COPwater_air'] = copwater_ASHP |
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cops_aggregated['COPwater_ground'] = copwater_GSHP |
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# Calculate mean COP and add it to DataFrame |
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cop_air_mean = (cop35_ASHP + cop50_ASHP + cop75_ASHP) / 3 |
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cop_ground_mean = (cop35_GSHP + cop50_GSHP + cop75_GSHP) / 3 |
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cop_mean = share_ashp * cop_air_mean + share_gshp * cop_ground_mean |
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cops_aggregated['COP_mean'] = cop_mean |
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# Limit COP to 7 |
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limit = cops_aggregated >= 7 |
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cops_aggregated[limit] = 7 |
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return cops_aggregated |
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def calculate_mixed_cops_by_nuts3(year, name, share_ashp=0.7, share_gshp=0.3, quality_grade=0.4): |
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""" |
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Parameters |
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---------- |
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year: int |
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Year of interest |
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name: string |
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Name of the analysed set |
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share_ashp: Float |
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Share of air sourced heat pumps |
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share_gshp: Float |
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Share of ground sourced heat pumps |
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quality_grade : float |
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Ambient temperature |
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Returns: 2 DataFrames |
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DataFrames containing mean COP series for each German NUTS3 region |
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------- |
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""" |
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fn_pattern = "mixed_cops_by_nuts3_{name}_{year}.csv".format(name=name, year=year) |
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fn_pattern_water = "cops_by_nuts3_{name}_{year}_water.csv".format(name=name, year=year) |
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fn = os.path.join(cfg.get("paths", "cop_precalc"), fn_pattern) |
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fn_water = os.path.join(cfg.get("paths", "cop_precalc"), fn_pattern_water) |
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if not os.path.isfile(fn): |
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share_ashp = share_ashp |
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share_gshp = share_gshp |
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quality_grade = quality_grade |
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t_ground = pd.Series(np.ones(8760)*10) |
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outfile = os.path.join(cfg.get("paths", "cop_precalc"), "average_temp_by_nuts3_{year}.csv".format(year=year)) |
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if not os.path.isfile(outfile): |
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# Load NUTS3-geometries |
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nuts3 = data.database_shapes() |
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nuts3 = nuts3.to_crs(crs=4326) |
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# Get average temperature per NUTS3, coastdat only available until 2014 |
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coastdat.spatial_average_weather(year, nuts3, 'temp_air', 'deTemp', outfile=outfile) |
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NUTS3_temp = pd.read_csv(outfile) |
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NUTS3_temp.drop('Unnamed: 0', axis='columns', inplace=True) |
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else: |
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NUTS3_temp = pd.read_csv(outfile) |
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NUTS3_temp.drop('Unnamed: 0', axis='columns', inplace=True) |
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# Create empty DataFrames |
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COP_NUTS3 = pd.DataFrame(index=pd.date_range('1/1/'+str(year), periods=8760, freq='H'), |
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columns=NUTS3_temp.columns) |
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COP_NUTS3_water = pd.DataFrame(index=pd.date_range('1/1/'+str(year), periods=8760, freq='H'), |
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columns=NUTS3_temp.columns) |
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# Loop through NUTS3-regions and calculate mixed COP for each region |
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for r in COP_NUTS3.columns: |
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tmp_cop = calculate_mixed_COPS_per_region(NUTS3_temp[r]-273.15, t_ground, quality_grade=quality_grade, |
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share_ashp=share_ashp, share_gshp=share_gshp) |
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tmp_cop.set_index(COP_NUTS3.index, inplace=True) |
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COP_NUTS3[r] = tmp_cop['COP_mean'] |
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COP_NUTS3_water[r] = tmp_cop['COPwater_air']* share_ashp + tmp_cop['COPwater_ground'] * share_gshp |
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COP_NUTS3.to_csv(fn) |
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COP_NUTS3_water.to_csv(fn_water) |
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else: |
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COP_NUTS3 = pd.read_csv(fn) |
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COP_NUTS3_water = pd.read_csv(fn_water) |
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return COP_NUTS3, COP_NUTS3_water |
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reegis /
scenario_builder
