|
1
|
|
|
"""The module containing all code dealing with pv rooftop distribution. |
|
2
|
|
|
""" |
|
3
|
|
|
import geopandas as gpd |
|
4
|
|
|
import pandas as pd |
|
5
|
|
|
|
|
6
|
|
|
from egon.data import config, db |
|
7
|
|
|
from egon.data.datasets.scenario_parameters import get_sector_parameters |
|
8
|
|
|
|
|
9
|
|
|
|
|
10
|
|
|
def pv_rooftop_per_mv_grid(): |
|
11
|
|
|
"""Execute pv rooftop distribution method per scenario |
|
12
|
|
|
|
|
13
|
|
|
Returns |
|
14
|
|
|
------- |
|
15
|
|
|
None. |
|
16
|
|
|
|
|
17
|
|
|
""" |
|
18
|
|
|
|
|
19
|
|
|
pv_rooftop_per_mv_grid_and_scenario( |
|
20
|
|
|
scenario="eGon2035", level="federal_state" |
|
21
|
|
|
) |
|
22
|
|
|
|
|
23
|
|
|
pv_rooftop_per_mv_grid_and_scenario(scenario="eGon100RE", level="national") |
|
24
|
|
|
|
|
25
|
|
|
|
|
26
|
|
|
def pv_rooftop_per_mv_grid_and_scenario(scenario, level): |
|
27
|
|
|
"""Intergate solar rooftop per mv grid district |
|
28
|
|
|
|
|
29
|
|
|
The target capacity is distributed to the mv grid districts linear to |
|
30
|
|
|
the residential and service electricity demands. |
|
31
|
|
|
|
|
32
|
|
|
Parameters |
|
33
|
|
|
---------- |
|
34
|
|
|
scenario : str, optional |
|
35
|
|
|
Name of the scenario |
|
36
|
|
|
level : str, optional |
|
37
|
|
|
Choose level of target values. |
|
38
|
|
|
|
|
39
|
|
|
Returns |
|
40
|
|
|
------- |
|
41
|
|
|
None. |
|
42
|
|
|
|
|
43
|
|
|
""" |
|
44
|
|
|
# Select sources and targets from dataset configuration |
|
45
|
|
|
sources = config.datasets()["solar_rooftop"]["sources"] |
|
46
|
|
|
targets = config.datasets()["solar_rooftop"]["targets"] |
|
47
|
|
|
|
|
48
|
|
|
# Delete existing rows |
|
49
|
|
|
db.execute_sql( |
|
50
|
|
|
f""" |
|
51
|
|
|
DELETE FROM {targets['generators']['schema']}. |
|
52
|
|
|
{targets['generators']['table']} |
|
53
|
|
|
WHERE carrier IN ('solar_rooftop') |
|
54
|
|
|
AND scn_name = '{scenario}' |
|
55
|
|
|
AND bus IN (SELECT bus_id FROM |
|
56
|
|
|
{sources['egon_mv_grid_district']['schema']}. |
|
57
|
|
|
{sources['egon_mv_grid_district']['table']} ) |
|
58
|
|
|
""" |
|
59
|
|
|
) |
|
60
|
|
|
|
|
61
|
|
|
db.execute_sql( |
|
62
|
|
|
f""" |
|
63
|
|
|
DELETE FROM {targets['generator_timeseries']['schema']}. |
|
64
|
|
|
{targets['generator_timeseries']['table']} |
|
65
|
|
|
WHERE scn_name = '{scenario}' |
|
66
|
|
|
AND generator_id NOT IN ( |
|
67
|
|
|
SELECT generator_id FROM |
|
68
|
|
|
grid.egon_etrago_generator |
|
69
|
|
|
WHERE scn_name = '{scenario}') |
|
70
|
|
|
""" |
|
71
|
|
|
) |
|
72
|
|
|
|
|
73
|
|
|
# Select demand per mv grid district |
|
74
|
|
|
demand = db.select_dataframe( |
|
75
|
|
|
f""" |
|
76
|
|
|
SELECT SUM(demand) as demand, |
|
77
|
|
|
b.bus_id, vg250_lan |
|
78
|
|
|
FROM {sources['electricity_demand']['schema']}. |
|
79
|
|
|
{sources['electricity_demand']['table']} a |
|
80
|
|
|
JOIN {sources['map_zensus_grid_districts']['schema']}. |
|
81
|
|
|
{sources['map_zensus_grid_districts']['table']} b |
|
82
|
|
|
ON a.zensus_population_id = b.zensus_population_id |
|
83
|
|
|
JOIN {sources['map_grid_boundaries']['schema']}. |
|
84
|
|
|
{sources['map_grid_boundaries']['table']} c |
|
85
|
|
|
ON c.bus_id = b.bus_id |
|
86
|
|
|
WHERE scenario = '{scenario}' |
|
87
|
|
|
GROUP BY (b.bus_id, vg250_lan) |
|
88
|
|
|
""" |
|
89
|
|
|
) |
|
90
|
|
|
|
|
91
|
|
|
# Distribute to mv grids per federal state or Germany |
|
92
|
|
|
if level == "federal_state": |
|
93
|
|
|
targets_per_federal_state = db.select_dataframe( |
|
94
|
|
|
f""" |
|
95
|
|
|
SELECT DISTINCT ON (gen) capacity, gen |
|
96
|
|
|
FROM {sources['scenario_capacities']['schema']}. |
|
97
|
|
|
{sources['scenario_capacities']['table']} a |
|
98
|
|
|
JOIN {sources['federal_states']['schema']}. |
|
99
|
|
|
{sources['federal_states']['table']} b |
|
100
|
|
|
ON a.nuts = b.nuts |
|
101
|
|
|
WHERE carrier = 'solar_rooftop' |
|
102
|
|
|
AND scenario_name = '{scenario}' |
|
103
|
|
|
""", |
|
104
|
|
|
index_col="gen", |
|
105
|
|
|
) |
|
106
|
|
|
|
|
107
|
|
|
demand["share_federal_state"] = demand.groupby( |
|
108
|
|
|
"vg250_lan" |
|
109
|
|
|
).demand.apply(lambda grp: grp / grp.sum()) |
|
110
|
|
|
|
|
111
|
|
|
demand["target_federal_state"] = targets_per_federal_state.capacity[ |
|
112
|
|
|
demand.vg250_lan |
|
113
|
|
|
].values |
|
114
|
|
|
|
|
115
|
|
|
demand.set_index("bus_id", inplace=True) |
|
116
|
|
|
|
|
117
|
|
|
capacities = demand["share_federal_state"].mul( |
|
118
|
|
|
demand["target_federal_state"] |
|
119
|
|
|
) |
|
120
|
|
|
else: |
|
121
|
|
|
|
|
122
|
|
|
target = db.select_dataframe( |
|
123
|
|
|
f""" |
|
124
|
|
|
SELECT capacity |
|
125
|
|
|
FROM {sources['scenario_capacities']['schema']}. |
|
126
|
|
|
{sources['scenario_capacities']['table']} a |
|
127
|
|
|
WHERE carrier = 'solar_rooftop' |
|
128
|
|
|
AND scenario_name = '{scenario}' |
|
129
|
|
|
""" |
|
130
|
|
|
).capacity[0] |
|
131
|
|
|
|
|
132
|
|
|
demand["share_country"] = demand.demand / demand.demand.sum() |
|
133
|
|
|
|
|
134
|
|
|
demand.set_index("bus_id", inplace=True) |
|
135
|
|
|
|
|
136
|
|
|
capacities = demand["share_country"].mul(target) |
|
137
|
|
|
|
|
138
|
|
|
# Select next id value |
|
139
|
|
|
new_id = db.next_etrago_id("generator") |
|
140
|
|
|
|
|
141
|
|
|
# Store data in dataframe |
|
142
|
|
|
pv_rooftop = pd.DataFrame( |
|
143
|
|
|
data={ |
|
144
|
|
|
"scn_name": scenario, |
|
145
|
|
|
"carrier": "solar_rooftop", |
|
146
|
|
|
"bus": demand.index, |
|
147
|
|
|
"p_nom": capacities, |
|
148
|
|
|
"generator_id": range(new_id, new_id + len(demand)), |
|
149
|
|
|
} |
|
150
|
|
|
) |
|
151
|
|
|
|
|
152
|
|
|
# Select feedin timeseries |
|
153
|
|
|
weather_cells = db.select_geodataframe( |
|
154
|
|
|
f""" |
|
155
|
|
|
SELECT w_id, geom |
|
156
|
|
|
FROM {sources['weather_cells']['schema']}. |
|
157
|
|
|
{sources['weather_cells']['table']} |
|
158
|
|
|
""", |
|
159
|
|
|
index_col="w_id", |
|
160
|
|
|
) |
|
161
|
|
|
|
|
162
|
|
|
mv_grid_districts = db.select_geodataframe( |
|
163
|
|
|
f""" |
|
164
|
|
|
SELECT bus_id as bus_id, ST_Centroid(geom) as geom |
|
165
|
|
|
FROM {sources['egon_mv_grid_district']['schema']}. |
|
166
|
|
|
{sources['egon_mv_grid_district']['table']} |
|
167
|
|
|
""", |
|
168
|
|
|
index_col="bus_id", |
|
169
|
|
|
) |
|
170
|
|
|
|
|
171
|
|
|
# Map centroid of mv grids to weather cells |
|
172
|
|
|
join = gpd.sjoin(weather_cells, mv_grid_districts)[["index_right"]] |
|
173
|
|
|
|
|
174
|
|
|
feedin = db.select_dataframe( |
|
175
|
|
|
f""" |
|
176
|
|
|
SELECT w_id, feedin |
|
177
|
|
|
FROM {sources['solar_feedin']['schema']}. |
|
178
|
|
|
{sources['solar_feedin']['table']} |
|
179
|
|
|
WHERE carrier = 'pv' |
|
180
|
|
|
AND weather_year = 2011 |
|
181
|
|
|
""", |
|
182
|
|
|
index_col="w_id", |
|
183
|
|
|
) |
|
184
|
|
|
|
|
185
|
|
|
# Create timeseries only for mv grid districts with pv rooftop |
|
186
|
|
|
join = join[join.index_right.isin(pv_rooftop.bus)] |
|
187
|
|
|
|
|
188
|
|
|
timeseries = pd.DataFrame( |
|
189
|
|
|
data={ |
|
190
|
|
|
"scn_name": scenario, |
|
191
|
|
|
"temp_id": 1, |
|
192
|
|
|
"p_max_pu": feedin.feedin[join.index].values, |
|
193
|
|
|
"generator_id": pv_rooftop.generator_id[join.index_right].values, |
|
194
|
|
|
} |
|
195
|
|
|
).set_index("generator_id") |
|
196
|
|
|
|
|
197
|
|
|
pv_rooftop = pv_rooftop.set_index("generator_id") |
|
198
|
|
|
pv_rooftop["marginal_cost"] = get_sector_parameters( |
|
199
|
|
|
"electricity", scenario |
|
200
|
|
|
)["marginal_cost"]["solar"] |
|
201
|
|
|
|
|
202
|
|
|
# Insert data to database |
|
203
|
|
|
pv_rooftop.to_sql( |
|
204
|
|
|
targets["generators"]["table"], |
|
205
|
|
|
schema=targets["generators"]["schema"], |
|
206
|
|
|
if_exists="append", |
|
207
|
|
|
con=db.engine(), |
|
208
|
|
|
) |
|
209
|
|
|
|
|
210
|
|
|
timeseries.to_sql( |
|
211
|
|
|
targets["generator_timeseries"]["table"], |
|
212
|
|
|
schema=targets["generator_timeseries"]["schema"], |
|
213
|
|
|
if_exists="append", |
|
214
|
|
|
con=db.engine(), |
|
215
|
|
|
) |
|
216
|
|
|
|
openego /
eGon-data
