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Merge pull request #412 from PyPSA/fix-simplify-alternative

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Fix line voltages alterantive
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Martha Frysztacki 2022-09-05 13:38:11 +02:00 committed by GitHub
commit ef2633ae05
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2 changed files with 22 additions and 16 deletions
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2
doc/release_notes.rst
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@ -12,6 +12,8 @@ Upcoming Release
* Add functionality to consider shipping routes when calculating the available area for offshore technologies. Data for the shipping density comes from the `Global Shipping Traffic Density dataset <https://datacatalog.worldbank.org/search/dataset/0037580/Global-Shipping-Traffic-Density>` * Add functionality to consider shipping routes when calculating the available area for offshore technologies. Data for the shipping density comes from the `Global Shipping Traffic Density dataset <https://datacatalog.worldbank.org/search/dataset/0037580/Global-Shipping-Traffic-Density>`
* When fixing line voltages to 380kV, the workflow now preserves the transmission capacity instead of the electrical impedance and reactance.
PyPSA-Eur 0.5.0 (27th July 2022) PyPSA-Eur 0.5.0 (27th July 2022)
===================================== =====================================

36
scripts/simplify_network.py
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@ -105,26 +105,30 @@ logger = logging.getLogger(__name__)
def simplify_network_to_380(n): def simplify_network_to_380(n):
## All goes to v_nom == 380 """
Fix all lines to a voltage level of 380 kV and remove all transformers.
The function preserves the transmission capacity for each line while updating
its voltage level, line type and number of parallel bundles (num_parallel).
Transformers are removed and connected components are moved from their
starting bus to their ending bus. The corresponing starting buses are
removed as well.
"""
logger.info("Mapping all network lines onto a single 380kV layer") logger.info("Mapping all network lines onto a single 380kV layer")
n.buses['v_nom'] = 380. n.buses['v_nom'] = 380.
linetype_380, = n.lines.loc[n.lines.v_nom == 380., 'type'].unique() linetype_380, = n.lines.loc[n.lines.v_nom == 380., 'type'].unique()
lines_v_nom_b = n.lines.v_nom != 380. n.lines['type'] = linetype_380
n.lines.loc[lines_v_nom_b, 'num_parallel'] *= (n.lines.loc[lines_v_nom_b, 'v_nom'] / 380.)**2 n.lines["v_nom"] = 380
n.lines.loc[lines_v_nom_b, 'v_nom'] = 380. n.lines["i_nom"] = n.line_types.i_nom[linetype_380]
n.lines.loc[lines_v_nom_b, 'type'] = linetype_380 n.lines['num_parallel'] = n.lines.eval("s_nom / (sqrt(3) * v_nom * i_nom)")
n.lines.loc[lines_v_nom_b, 's_nom'] = (
np.sqrt(3) * n.lines['type'].map(n.line_types.i_nom) *
n.lines.bus0.map(n.buses.v_nom) * n.lines.num_parallel
)
# Replace transformers by lines trafo_map = pd.Series(n.transformers.bus1.values, n.transformers.bus0.values)
trafo_map = pd.Series(n.transformers.bus1.values, index=n.transformers.bus0.values)
trafo_map = trafo_map[~trafo_map.index.duplicated(keep='first')] trafo_map = trafo_map[~trafo_map.index.duplicated(keep='first')]
several_trafo_b = trafo_map.isin(trafo_map.index) several_trafo_b = trafo_map.isin(trafo_map.index)
trafo_map.loc[several_trafo_b] = trafo_map.loc[several_trafo_b].map(trafo_map) trafo_map[several_trafo_b] = trafo_map[several_trafo_b].map(trafo_map)
missing_buses_i = n.buses.index.difference(trafo_map.index) missing_buses_i = n.buses.index.difference(trafo_map.index)
missing = pd.Series(missing_buses_i, missing_buses_i) missing = pd.Series(missing_buses_i, missing_buses_i)
trafo_map = pd.concat([trafo_map, missing]) trafo_map = pd.concat([trafo_map, missing])
@ -187,8 +191,8 @@ def _adjust_capital_costs_using_connection_costs(n, connection_costs_to_bus, out
logger.info("Displacing {} generator(s) and adding connection costs to capital_costs: {} " logger.info("Displacing {} generator(s) and adding connection costs to capital_costs: {} "
.format(tech, ", ".join("{:.0f} Eur/MW/a for `{}`".format(d, b) for b, d in costs.iteritems()))) .format(tech, ", ".join("{:.0f} Eur/MW/a for `{}`".format(d, b) for b, d in costs.iteritems())))
connection_costs[tech] = costs connection_costs[tech] = costs
pd.DataFrame(connection_costs).to_csv(output.connection_costs) pd.DataFrame(connection_costs).to_csv(output.connection_costs)
def _aggregate_and_move_components(n, busmap, connection_costs_to_bus, output, def _aggregate_and_move_components(n, busmap, connection_costs_to_bus, output,
@ -335,7 +339,7 @@ def remove_stubs(n, costs, config, output, aggregation_strategies=dict()):
def aggregate_to_substations(n, aggregation_strategies=dict(), buses_i=None): def aggregate_to_substations(n, aggregation_strategies=dict(), buses_i=None):
# can be used to aggregate a selection of buses to electrically closest neighbors # can be used to aggregate a selection of buses to electrically closest neighbors
# if no buses are given, nodes that are no substations or without offshore connection are aggregated # if no buses are given, nodes that are no substations or without offshore connection are aggregated
if buses_i is None: if buses_i is None:
logger.info("Aggregating buses that are no substations or have no valid offshore connection") logger.info("Aggregating buses that are no substations or have no valid offshore connection")
buses_i = list(set(n.buses.index)-set(n.generators.bus)-set(n.loads.bus)) buses_i = list(set(n.buses.index)-set(n.generators.bus)-set(n.loads.bus))
@ -374,7 +378,7 @@ def cluster(n, n_clusters, config, algorithm="hac", feature=None, aggregation_st
logger.info(f"Clustering to {n_clusters} buses") logger.info(f"Clustering to {n_clusters} buses")
focus_weights = config.get('focus_weights', None) focus_weights = config.get('focus_weights', None)
renewable_carriers = pd.Index([tech renewable_carriers = pd.Index([tech
for tech in n.generators.carrier.unique() for tech in n.generators.carrier.unique()
if tech.split('-', 2)[0] in config['renewable']]) if tech.split('-', 2)[0] in config['renewable']])