diff --git a/dhnx/gistools/geometry_operations.py b/dhnx/gistools/geometry_operations.py index a8efab39..984d00c8 100644 --- a/dhnx/gistools/geometry_operations.py +++ b/dhnx/gistools/geometry_operations.py @@ -27,13 +27,17 @@ from shapely.ops import linemerge from shapely.ops import nearest_points from shapely.ops import unary_union + except ImportError: print("Need to install shapely to process geometry.") import logging import matplotlib.pyplot as plt +# from shapely import geometry, ops import pandas as pd +from shapely import geometry +from shapely import ops def create_forks(lines): @@ -511,3 +515,304 @@ def check_crs(gdf, crs=4647): logging.info('CRS of GeoDataFrame converted to EPSG:{0}'.format(crs)) return gdf + + +def aggregation(forks, pipes, consumers, producers): + """ + This function forms a new aggregated network consisting of super forks, super pipes and super produsers + Super forks are forks with less or more than two connections to DL or are connected to GL. + Super pipes are all pipes between those super forks + + Parameters + ---------- + forks : geopandas.GeoDataFrame + Location of forks. Expected geometry: Polygons or Points. + pipes : geopandas.GeoDataFrame + Routes for the DHS. Expected geometry Linestrings or MultilineStrings. + The graph of this line network should be connected. + consumers : geopandas.GeoDataFrame + Location of demand/consumers. Expected geometry: Polygons or Points. + producers : geopandas.GeoDataFrame + Location of supply sites. Expected geometry: Polygons or Points. + + Returns + ------- + dict : Results of aggregation. Contains: + - 'super_forks' : geopandas.GeoDataFrame + forks identifyed as super forks + - 'super_pipes' : geopandas.GeoDataFrame + pipes aggregated to super pipes + - 'super_producer' : geopandas.GeoDataFrame + unchanged producers + - 'super_consumer' : geopandas.GeoDataFrame + unchanged consumers + + Example: + network_aggregation + """ + + # # # 1. identify super forks + # DL and GL pipes copied from pipes + DLpipes = pipes.loc[pipes['type'].isin(['DL'])] + GLpipes = pipes.loc[pipes['type'].isin(['GL'])] + + # count connections of forks to DL pipes + count_forks = DLpipes['from_node'].value_counts() + DLpipes['to_node'].value_counts() + + # identify super forks with less or more than two connections + count_superforks = count_forks[~count_forks.isin([2])] + + # add producer super forks + producers_superforks = GLpipes['from_node'].tolist() + GLpipes['to_node'].tolist() + + # save indexes of super forks + superforks_indexlist = count_superforks.index.tolist() + producers_superforks + + # copy super forks out of forks, if they are identified as super fork + super_forks = forks.copy() + super_forks = super_forks[super_forks['id_full'].isin(superforks_indexlist)] + super_forks = super_forks.reset_index(drop=True) + + # # # 2. identify and merge super pipes + + # initialize super pipes + super_pipes = pipes.copy() + super_pipes = super_pipes.drop(range(0, len(pipes))) + # initialize lists for aggregated forks and pipes + aggregated_forks = [] + aggregated_pipes = [] + + # initialize HL pipes for aggregated_consumers_segment_i_a + HLpipes = pipes.loc[pipes['type'].isin(['HL'])] + # DL and GL pipes + DLGLpipes = pipes.loc[pipes['type'].isin(['DL', 'GL'])] + + # # # first loop: go throught super forks + # the length is absolute and id starts at 0 + i = -1 + while i <= len(super_forks) - 2: + i += 1 + # select the current super fork (i)... + superfork_i_id_full = super_forks.loc[i]['id_full'] + + # search for consumer which are connected with super fork i + aggregated_consumer_super_fork_i = [] + aggregated_consumer_super_fork_i = HLpipes.loc[HLpipes['from_node'] == superfork_i_id_full]['to_node'].tolist() + str_aggregated_consumer_super_forks = ', '.join(aggregated_consumer_super_fork_i) + index = super_forks.loc[super_forks['id_full'] == superfork_i_id_full].index[0] + super_forks.at[index, 'aggregated_consumers'] = str_aggregated_consumer_super_forks + + # add the max_heat off all connected consumers to super fork i + aggregated_P_heat_super_fork_i = [] + aggregated_P_heat_super_fork_i = consumers.loc[consumers['id_full'].isin(aggregated_consumer_super_fork_i)][ + 'P_heat_max'].tolist() + sum_aggregated_P_heat_max_super_fork_i = sum(aggregated_P_heat_super_fork_i) + super_forks.at[index, 'P_heat_max'] = sum_aggregated_P_heat_max_super_fork_i + + # Find pipes connected to the super fork i and save them in a GeoDataFrame. + pipes_superfork_i_from_node = DLGLpipes[DLGLpipes['from_node'] == superfork_i_id_full] + pipes_superfork_i_to_node = DLGLpipes[DLGLpipes['to_node'] == superfork_i_id_full] + pipes_superfork_i = pipes_superfork_i_from_node.append(pipes_superfork_i_to_node) + pipes_superfork_i = pipes_superfork_i.reset_index(drop=True) + + # # # second loop: go through super pipes a from super fork i + # the length is absolute and id starts at 0 + a = - 1 + while a <= len(pipes_superfork_i) - 2: + a += 1 + # If the pipe has already been aggregated, the next pipe should be checked + if pipes_superfork_i.loc[a]['id'] in aggregated_pipes: + continue # evtl. a += 1 vorher break oder continue + # A segment is the accumulation of pipes before they are merged + # The segment is initialized and the pipe a is the first pipe of the segment + segment_i_a = pipes_superfork_i.copy() + segment_i_a = segment_i_a[segment_i_a.index == a] + segment_i_a = segment_i_a.reset_index(drop=True) + aggregated_forks_segment_i_a = [] + # # # thrid loop: Go through the elements b (pipes and forks) of super pipe a + aggregation_segment = False + b = 0 + while aggregation_segment == False: + + # Searching for the next fork connected to pipe b + fork_from_pipe_b_segment_i_a = segment_i_a.at[b, 'from_node'] + fork_to_pipe_b_segment_i_a = segment_i_a.at[b, 'to_node'] + count_type_forks_pipe_b_segment_i_a = 0 + + # Initialize the next fork + fork_next_segment_i_a = 0 + + # Check if the fork 'from' is already aggregated or is a superfork or is a producer + # If yes the number of count_type_forks_pipe_b_segment_i_a is increased ... + # ... if not the fork to the next segment is the fork 'from'. + if fork_from_pipe_b_segment_i_a in super_forks[ + 'id_full'].unique() or fork_from_pipe_b_segment_i_a in aggregated_forks or fork_from_pipe_b_segment_i_a in \ + producers['id_full'].unique(): + count_type_forks_pipe_b_segment_i_a += 1 + # The next fork must not be aggregated yet + else: + fork_next_segment_i_a = fork_from_pipe_b_segment_i_a + + # Check if the fork 'to' is already aggregated or is a superfork or is a producer + # If yes the number of count_type_forks_pipe_b_segment_i_a is increased + # ... if not the fork to the next segment is the fork 'to'. + if fork_to_pipe_b_segment_i_a in super_forks[ + 'id_full'].unique() or fork_to_pipe_b_segment_i_a in aggregated_forks or fork_to_pipe_b_segment_i_a in \ + producers['id_full'].unique(): + count_type_forks_pipe_b_segment_i_a += 1 + # The next fork must not be aggregated yet + else: + fork_next_segment_i_a = fork_to_pipe_b_segment_i_a + + # If both forks are aggregated or are super forks the segment is merged + if count_type_forks_pipe_b_segment_i_a == 2: + # merge new geometry + geom_multi_segment_i_a = geometry.MultiLineString(segment_i_a['geometry'].unique()) + geom_line_segment_i_a = ops.linemerge(geom_multi_segment_i_a) + # create new pipe + merged_segment_i_a = segment_i_a[segment_i_a.index == 0] + merged_segment_i_a['geometry'] = geom_line_segment_i_a + + # # set from_node and to_node of the merged segment + # from_node is the super fork i + merged_segment_i_a.at[0, 'from_node'] = superfork_i_id_full + # to_node is not the super fork i and ether a super fork or a producer + last_fork_segment_i_a = 0 + if fork_to_pipe_b_segment_i_a != superfork_i_id_full and (fork_to_pipe_b_segment_i_a in super_forks[ + 'id_full'].unique() or fork_to_pipe_b_segment_i_a in \ + producers['id_full'].unique()): + + last_fork_segment_i_a = fork_to_pipe_b_segment_i_a + + elif fork_from_pipe_b_segment_i_a != superfork_i_id_full and ( + fork_from_pipe_b_segment_i_a in super_forks[ + 'id_full'].unique() or fork_from_pipe_b_segment_i_a in \ + producers['id_full'].unique()): + + last_fork_segment_i_a = fork_from_pipe_b_segment_i_a + + else: + print('error: last fork is not a super fork') + + merged_segment_i_a.at[0, 'to_node'] = last_fork_segment_i_a + + # add last and first fork, if they are not aggregated yet + if superfork_i_id_full not in aggregated_forks: + aggregated_forks.append(superfork_i_id_full) + aggregated_forks_segment_i_a.append(superfork_i_id_full) + + if last_fork_segment_i_a not in aggregated_forks: + aggregated_forks.append(last_fork_segment_i_a) + aggregated_forks_segment_i_a.append(last_fork_segment_i_a) + + # # add column 'aggregated_forks' + str_aggregated_forks_segment_i_a = ', '.join(aggregated_forks_segment_i_a) + merged_segment_i_a.at[0, 'aggregated_forks'] = str_aggregated_forks_segment_i_a + + # # add column 'aggregated_pipes' + str_aggregated_pipes_segment_i_a = ', '.join(str(x) for x in segment_i_a['id'].tolist()) + merged_segment_i_a.at[0, 'aggregated_pipes'] = str_aggregated_pipes_segment_i_a + + # # add column 'aggregated_consumers' + aggregated_consumer_segment_i_a = [] + aggregated_consumer_segment_i_a = \ + HLpipes.loc[HLpipes['from_node'].isin(aggregated_forks_segment_i_a)][ + 'to_node'].tolist() + str_aggregated_consumer_segment_i_a = ', '.join(aggregated_consumer_segment_i_a) + merged_segment_i_a.at[0, 'aggregated_consumer'] = str_aggregated_consumer_segment_i_a + + # # add column 'aggregated_P_heat_max' + aggregated_P_heat_max_segment_i_a = [] + aggregated_P_heat_max_segment_i_a = \ + consumers.loc[consumers['id_full'].isin(aggregated_consumer_segment_i_a)]['P_heat_max'].tolist() + sum_aggregated_P_heat_max_segment_i_a = sum(aggregated_P_heat_max_segment_i_a) + merged_segment_i_a.at[0, 'P_heat_max'] = sum_aggregated_P_heat_max_segment_i_a + + # # add column 'simultaneity factor' + number_aggregated_consumer_segment_i_a = len(aggregated_consumer_segment_i_a) + simultaneity_factor_segment_i_a = pow(1.05, + -number_aggregated_consumer_segment_i_a) # Note: this is just a placeholder formula + merged_segment_i_a.at[0, 'simultaneity factor'] = simultaneity_factor_segment_i_a + + # add new pipe to super pipes + super_pipes = super_pipes.append(merged_segment_i_a) + # add pipe_ids to aggregated pipes + aggregated_pipes = aggregated_pipes + segment_i_a['id'].tolist() + + aggregation_segment == True + + break + + # Identify which pipes are connected to fork_next_segment_i_a + elif count_type_forks_pipe_b_segment_i_a == 1: # + + pipe_next_segment_i_a = 0 + + # Next pipe must not be the current one + list_of_connected_pipes_to_next_fork = [] + list_of_connected_pipes_to_next_fork = list_of_connected_pipes_to_next_fork + DLGLpipes.loc[ + DLGLpipes['from_node'].isin([fork_next_segment_i_a])]['id'].tolist() + DLGLpipes.loc[ + DLGLpipes['to_node'].isin([fork_next_segment_i_a])][ + 'id'].tolist() + + if segment_i_a.at[b, 'id'] == list_of_connected_pipes_to_next_fork[0]: + pipe_next_segment_i_a = DLGLpipes.loc[ + DLGLpipes['id'].isin([list_of_connected_pipes_to_next_fork[1]])] + + elif segment_i_a.at[b, 'id'] == list_of_connected_pipes_to_next_fork[1]: + pipe_next_segment_i_a = DLGLpipes.loc[ + DLGLpipes['id'].isin([list_of_connected_pipes_to_next_fork[0]])] + + else: + print('error: next fork doesnt connect to any new pipe') + + # count b up + b += 1 + + # add to segment i_a + segment_i_a = segment_i_a.append(pipe_next_segment_i_a) + + # reset index + segment_i_a = segment_i_a.reset_index(drop=True) + + # add fork to aggregated forks + aggregated_forks.append(fork_next_segment_i_a) + + # add fork to aggregated forks segment i a + aggregated_forks_segment_i_a.append(fork_next_segment_i_a) + else: + print('error: pipe is not connected to any aggregated fork or super fork') + + # Calculate length of super_pipes + super_pipes['length'] = super_pipes.length + super_pipes = super_pipes.reset_index(drop=True) + # return + return { + 'super_forks': super_forks, + 'super_consumers': consumers, # not yet defined + 'super_producers': producers, # not yet defined + 'super_pipes': super_pipes, + } + + +def remove_con_prod_forks(forks, consumers, producers): + """ + Parameters + ---------- + forks + consumers + producers + Returns + ------- + """ + consumers["geometry_wkt"] = \ + consumers["geometry"].apply(lambda geom: geom.wkt) + + producers["geometry_wkt"] = \ + producers["geometry"].apply(lambda geom: geom.wkt) + + con_prod = list(producers["geometry_wkt"]) + list(consumers["geometry_wkt"]) + + forks_only = forks[forks.geometry_wkt.isin(con_prod) == False] + + return forks_only diff --git a/dhnx/network.py b/dhnx/network.py index 801692e0..e913663a 100644 --- a/dhnx/network.py +++ b/dhnx/network.py @@ -14,6 +14,7 @@ import os import pandas as pd +import geopandas as gpd from .graph import thermal_network_to_nx_graph from .helpers import Dict @@ -24,6 +25,7 @@ from .optimization.optimization_models import setup_optimise_investment from .optimization.optimization_models import solve_optimisation_investment from .simulation import simulate +from .gistools.geometry_operations import aggregation dir_name = os.path.dirname(__file__) @@ -316,3 +318,13 @@ def optimize_investment(self, invest_options, **kwargs): def simulate(self, *args, **kwargs): self.results.simulation = simulate(self, *args, **kwargs) + + def aggregate(self): + + self.aggregatednetwork = dict() + forks = self.components["forks"] + pipes = self.components["pipes"] + consumers = self.components["consumers"] + producers = self.components["producers"] + self.aggregatednetwork = aggregation(forks, pipes, consumers, producers) + diff --git a/dhnx/optimization/dhs_nodes.py b/dhnx/optimization/dhs_nodes.py index fa158ef8..f8afe2a1 100644 --- a/dhnx/optimization/dhs_nodes.py +++ b/dhnx/optimization/dhs_nodes.py @@ -279,3 +279,4 @@ def add_nodes_houses(opti_network, nodes, busd, label_1): nodes = ac.add_storage(item, d_labels, nodes, busd) return nodes, busd + diff --git a/dhnx/optimization/optimization_models.py b/dhnx/optimization/optimization_models.py index e5993c9a..aa75dc63 100644 --- a/dhnx/optimization/optimization_models.py +++ b/dhnx/optimization/optimization_models.py @@ -704,7 +704,6 @@ def setup_optimise_investment( Additional logging info is printed. write_lp_file : bool Linear program file is stored (‘User/.oemof/lp_files/DHNx.lp’). - Returns ------- oemof.solph.Model : The oemof.solph.Model is build. diff --git a/examples/optimisation/network_aggregation/network_aggregation.py b/examples/optimisation/network_aggregation/network_aggregation.py new file mode 100644 index 00000000..bd6a2502 --- /dev/null +++ b/examples/optimisation/network_aggregation/network_aggregation.py @@ -0,0 +1,59 @@ + +""" +Using the aggregation function to aggregate a network to a super network + +""" + +from dhnx.network import ThermalNetwork +from dhnx.gistools.geometry_operations import aggregation +import geopandas as gpd +import matplotlib.pyplot as plt + +# Part I: Create a new network out of components using aggregation(forks, pipes, consumers, producers) + +# import the components +pipes = gpd.read_file('network_data/pipes.geojson') +forks = gpd.read_file('network_data/forks.geojson') +consumers = gpd.read_file('network_data/consumers.geojson') +producers = gpd.read_file('network_data/producers.geojson') + +# a new dict with the components of the super network is created +super_network = aggregation(forks, pipes, consumers, producers) + +# plot of the super network +_, ax = plt.subplots() +super_network['super_pipes'].plot(ax=ax, color='red') +super_network['super_producers'].plot(ax=ax, color='blue') +super_network['super_forks'].plot(ax=ax, color='grey') +plt.title('Geometry after aggregation of network') +plt.show() + +# export as geojson +super_network['super_forks'].to_file('super_network/super_forks.geojson', driver='GeoJSON') +super_network['super_pipes'].to_file('super_network/super_pipes.geojson', driver='GeoJSON') + +# Part II: Create a new network out an existing network using aggregate() +# save the imported components in a dict +tn_input = { + 'forks': forks, + 'consumers': consumers, + 'producers': producers, + 'pipes': pipes, +} +# initialize a ThermalNetwork +network = ThermalNetwork() + +# add the pipes, forks, consumer, and producers to the ThermalNetwork +for k, v in tn_input.items(): + network.components[k] = v + +# create a new dict in network with the components of the super network +network.aggregate() + +# plot the supernetwork +_, ax = plt.subplots() +network.aggregatednetwork['super_pipes'].plot(ax=ax, color='red') +network.aggregatednetwork['super_producers'].plot(ax=ax, color='blue') +network.aggregatednetwork['super_forks'].plot(ax=ax, color='grey') +plt.title('Geometry after aggregation of network') +plt.show() diff --git a/examples/optimisation/network_aggregation/network_data/consumers.geojson b/examples/optimisation/network_aggregation/network_data/consumers.geojson new file mode 100644 index 00000000..e57799fa --- /dev/null +++ b/examples/optimisation/network_aggregation/network_data/consumers.geojson @@ -0,0 +1,164 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::4647" } }, +"features": [ +{ "type": "Feature", "properties": { "id": 0, "P_heat_max": 23, "id_full": "consumers-0", "type": "H" }, "geometry": { "type": "Point", "coordinates": [ 32506955.09166768565774, 6005014.398159652017057 ] } }, +{ "type": "Feature", "properties": { "id": 1, "P_heat_max": 13, "id_full": "consumers-1", "type": "H" }, "geometry": { "type": "Point", "coordinates": [ 32506801.195647560060024, 6005039.200124303810298 ] } }, +{ "type": "Feature", "properties": { "id": 2, "P_heat_max": 31, "id_full": "consumers-2", "type": "H" }, "geometry": { "type": "Point", "coordinates": [ 32506947.681407205760479, 6005117.471685512922704 ] } }, +{ "type": "Feature", "properties": { "id": 3, "P_heat_max": 31, "id_full": "consumers-3", "type": "H" }, "geometry": { "type": "Point", "coordinates": [ 32506936.092872586101294, 6004965.141892026178539 ] } }, +{ "type": "Feature", "properties": { "id": 4, "P_heat_max": 10, "id_full": "consumers-4", "type": "H" }, "geometry": { "type": "Point", "coordinates": [ 32506912.159830294549465, 6005020.755500017665327 ] } }, +{ "type": "Feature", "properties": { "id": 5, "P_heat_max": 31, "id_full": "consumers-5", "type": "H" }, "geometry": { "type": "Point", "coordinates": [ 32506905.141416907310486, 6005069.070721743628383 ] } }, +{ "type": "Feature", "properties": { "id": 6, "P_heat_max": 29, "id_full": "consumers-6", "type": "H" }, "geometry": { "type": "Point", "coordinates": [ 32506860.149385698139668, 6005157.808408037759364 ] } }, +{ "type": "Feature", "properties": { "id": 7, "P_heat_max": 19, "id_full": "consumers-7", "type": "H" }, "geometry": { "type": "Point", "coordinates": [ 32506980.289583649486303, 6004946.575534526258707 ] } }, +{ "type": "Feature", "properties": { "id": 8, "P_heat_max": 37, "id_full": "consumers-8", "type": "H" }, "geometry": { "type": "Point", "coordinates": [ 32506907.347752008587122, 6004969.531430990435183 ] } }, +{ "type": "Feature", "properties": { "id": 9, "P_heat_max": 43, "id_full": "consumers-9", "type": "H" }, "geometry": { "type": "Point", "coordinates": [ 32506831.70174864679575, 6005105.872498782351613 ] } }, +{ "type": "Feature", "properties": { "id": 10, "P_heat_max": 21, "id_full": "consumers-10", "type": "H" }, "geometry": { "type": "Point", "coordinates": [ 32506807.097907729446888, 6005102.670010705478489 ] } }, +{ "type": "Feature", "properties": { "id": 11, "P_heat_max": 24, "id_full": "consumers-11", "type": "H" }, "geometry": { "type": "Point", "coordinates": [ 32506912.889645788818598, 6005003.684148876927793 ] } }, +{ "type": "Feature", "properties": { "id": 12, "P_heat_max": 43, "id_full": "consumers-12", "type": "H" }, "geometry": { "type": "Point", "coordinates": [ 32506805.105399034917355, 6005124.209070673212409 ] } }, +{ "type": "Feature", "properties": { "id": 13, "P_heat_max": 18, "id_full": "consumers-13", "type": "H" }, "geometry": { "type": "Point", "coordinates": [ 32506969.710390973836184, 6004967.766494301147759 ] } }, +{ "type": "Feature", "properties": { "id": 14, "P_heat_max": 47, "id_full": "consumers-14", "type": "H" }, "geometry": { "type": "Point", 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