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#!/usr/bin/env python3 |
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# -*- coding: utf-8 -*- |
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# File description |
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""" |
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Created on Tue May 24 14:42:05 2022 |
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Plotdatascn.py defines functions to plot to provide a better context of the different parameters part of |
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scenarios eGon2035 and eGon100RE . |
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@author: Alonso |
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""" |
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import logging |
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import os |
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from matplotlib import pyplot as plt |
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import matplotlib.patches as mpatches |
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import matplotlib as mpl |
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import pandas as pd |
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import numpy as np |
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from math import sqrt, log10 |
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from pyproj import Proj, transform |
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import pandas as pd |
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from egon.data import db |
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from egon.data.datasets import Dataset |
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import egon.data.config |
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import geopandas as gpd |
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logger = logging.getLogger(__name__) |
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if 'READTHEDOCS' not in os.environ: |
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from geoalchemy2.shape import to_shape |
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__copyright__ = ("Flensburg University of Applied Sciences, " |
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"Europa-Universität Flensburg, " |
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"Centre for Sustainable Energy Systems, " |
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"DLR-Institute for Networked Energy Systems") |
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__license__ = "" |
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__author__ = "" |
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def plot_generation( |
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carrier,scenario, osm=False |
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): |
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""" |
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Plots color maps according to the capacity of different generators |
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of the two existing scenarios (eGon2035 and eGon100RE) |
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Parameters |
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---------- |
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carrier : generators |
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The list of generators: biomass, central_biomass_CHP, central_biomass_CHP_heat, |
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industrial_biomass_CHP, solar, solar_rooftop, wind_offshore, wind_onshore. |
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scenario: eGon2035, eGon100RE |
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""" |
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con = db.engine() |
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SQLBus = "SELECT bus_id, country FROM grid.egon_etrago_bus WHERE country='DE'" #imports buses of Germany |
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busDE = pd.read_sql(SQLBus,con) |
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busDE = busDE.rename({'bus_id': 'bus'},axis=1) |
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sql = "SELECT bus_id, geom FROM grid.egon_mv_grid_district"#Imports grid districs |
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distr = gpd.GeoDataFrame.from_postgis(sql, con) |
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distr = distr.rename({'bus_id': 'bus'},axis=1) |
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distr = distr.set_index("bus") |
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distr = pd.merge(busDE, distr, on='bus') #merges grid districts with buses |
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sqlCarrier = "SELECT carrier, p_nom, bus FROM grid.egon_etrago_generator" #Imports generator |
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sqlCarrier = "SELECT * FROM grid.egon_etrago_generator" |
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Carriers = pd.read_sql(sqlCarrier,con) |
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Carriers = Carriers.loc[Carriers['scn_name'] == scenario] |
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Carriers = Carriers.set_index("bus") |
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CarrierGen = Carriers.loc[Carriers['carrier'] == carrier] |
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Merge = pd.merge(CarrierGen, distr, on ='bus', how="outer") #merges districts with generators |
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Merge.loc[Merge ['carrier'] != carrier, "p_nom" ] = 0 |
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Merge.loc[Merge ['country'] != "DE", "p_nom" ] = 0 |
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gdf = gpd.GeoDataFrame(Merge , geometry='geom') |
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print(Merge) |
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pnom=gdf['p_nom'] |
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max_pnom=pnom.quantile(0.95) #0.95 quantile is used to filter values that are too high and make noise in the plots. |
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print(max_pnom) |
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fig, ax = plt.subplots(figsize=(10,10)) |
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ax.set_axis_off(); |
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plt.title(f" {carrier} installed capacity in MW , {scenario}") |
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cmap = mpl.cm.coolwarm |
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norm = mpl.colors.Normalize(vmin=0, vmax=max_pnom) |
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gdf.plot(column='p_nom', ax=ax, legend=True, legend_kwds={'label': "p_nom(MW)", |
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'orientation': "vertical"}, cmap=cmap, norm=norm) |
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return 0 |
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plot_generation(carrier, scenario) |
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openego /
eGon-data
