wip first try market optimization

etrago/appl.py

@@ -50,20 +50,20 @@
 
 args = {
     # Setup and Configuration:
-    "db": "egon-data",  # database session
+    "db": "local",  # database session
     "gridversion": None,  # None for model_draft or Version number
     "method": {  # Choose method and settings for optimization
-        "type": "lopf",  # type of optimization, currently only 'lopf'
+        "type": "market_grid",  # type of optimization, currently only 'lopf'
         "n_iter": 4,  # abort criterion of iterative optimization, 'n_iter' or 'threshold'
         "pyomo": True,  # set if pyomo is used for model building
     },
     "pf_post_lopf": {
-        "active": True,  # choose if perform a pf after lopf
+        "active": False,  # choose if perform a pf after lopf
         "add_foreign_lopf": True,  # keep results of lopf for foreign DC-links
         "q_allocation": "p_nom",  # allocate reactive power via 'p_nom' or 'p'
     },
     "start_snapshot": 1,
-    "end_snapshot": 10,
+    "end_snapshot": 2,
     "solver": "gurobi",  # glpk, cplex or gurobi
     "solver_options": {
         "BarConvTol": 1.0e-5,
@@ -74,7 +74,7 @@
         "threads": 4,
     },
     "model_formulation": "kirchhoff",  # angles or kirchhoff
-    "scn_name": "eGon2035",  # scenario: eGon2035, eGon100RE or status2019
+    "scn_name": "status2019",  # scenario: eGon2035, eGon100RE or status2019
     # Scenario variations:
     "scn_extension": None,  # None or array of extension scenarios
     "scn_decommissioning": None,  # None or decommissioning scenario
@@ -100,17 +100,17 @@
             "grid_max_abs_foreign": None,  # absolute capacity per voltage level
         },
     },
-    "generator_noise": 789456,  # apply generator noise, False or seed number
+    "generator_noise": 123456,  # apply generator noise, False or seed number
     "extra_functionality": {},  # Choose function name or {}
     # Spatial Complexity:
     "network_clustering_ehv": False,  # clustering of HV buses to EHV buses
     "network_clustering": {
         "active": True,  # choose if clustering is activated
-        "method": "kmedoids-dijkstra",  # choose clustering method: kmeans or kmedoids-dijkstra
-        "n_clusters_AC": 30,  # total number of resulting AC nodes (DE+foreign)
-        "cluster_foreign_AC": False,  # take foreign AC buses into account, True or False
+        "method": "kmeans",  # choose clustering method: kmeans or kmedoids-dijkstra
+        "n_clusters_AC": 50,  # total number of resulting AC nodes (DE+foreign)
+        "cluster_foreign_AC": True,  # take foreign AC buses into account, True or False
         "method_gas": "kmedoids-dijkstra",  # choose clustering method: kmeans or kmedoids-dijkstra
-        "n_clusters_gas": 17,  # total number of resulting CH4 nodes (DE+foreign)
+        "n_clusters_gas": 14,  # total number of resulting CH4 nodes (DE+foreign)
         "cluster_foreign_gas": False,  # take foreign CH4 buses into account, True or False
         "k_elec_busmap": False,  # False or path/to/busmap.csv
         "k_gas_busmap": False,  # False or path/to/ch4_busmap.csv
@@ -122,13 +122,13 @@
         "remove_stubs": False,  # remove stubs bevore kmeans clustering
         "use_reduced_coordinates": False,  # If True, do not average cluster coordinates
         "random_state": 42,  # random state for replicability of clustering results
-        "n_init": 10,  # affects clustering algorithm, only change when neccesary
-        "max_iter": 100,  # affects clustering algorithm, only change when neccesary
+        "n_init": 2,  # affects clustering algorithm, only change when neccesary
+        "max_iter": 10,  # affects clustering algorithm, only change when neccesary
         "tol": 1e-6,  # affects clustering algorithm, only change when neccesary
-        "CPU_cores": 4,  # number of cores used during clustering, "max" for all cores available.
+        "CPU_cores": 2,  # number of cores used during clustering, "max" for all cores available.
     },
     "sector_coupled_clustering": {
-        "active": True,  # choose if clustering is activated
+        "active": False,  # choose if clustering is activated
         "carrier_data": {  # select carriers affected by sector coupling
             "central_heat": {
                 "base": ["CH4", "AC"],
@@ -148,14 +148,14 @@
         "n_clusters": 5,  #  number of periods - only relevant for 'typical_periods'
         "n_segments": 5,  # number of segments - only relevant for segmentation
     },
-    "skip_snapshots": 5,  # False or number of snapshots to skip
+    "skip_snapshots": False,  # False or number of snapshots to skip
     "temporal_disaggregation": {
         "active": False,  # choose if temporally full complex dispatch optimization should be conducted
         "no_slices": 8,  # number of subproblems optimization is divided into
     },
     # Simplifications:
     "branch_capacity_factor": {"HV": 0.5, "eHV": 0.7},  # p.u. branch derating
-    "load_shedding": True,  # meet the demand at value of loss load cost
+    "load_shedding": False,  # meet the demand at value of loss load cost
     "foreign_lines": {
         "carrier": "AC",  # 'DC' for modeling foreign lines as links
         "capacity": "osmTGmod",  # 'osmTGmod', 'tyndp2020', 'ntc_acer' or 'thermal_acer'
@@ -476,17 +476,17 @@ def run_etrago(args, json_path):
     etrago.optimize()
 
     # conduct lopf with full complex timeseries for dispatch disaggregation
-    etrago.dispatch_disaggregation()
+    #etrago.dispatch_disaggregation()
 
     # start power flow based on lopf results
-    etrago.pf_post_lopf()
+    #etrago.pf_post_lopf()
 
     # spatial disaggregation
     # needs to be adjusted for new sectors
-    etrago.disaggregation()
+    #etrago.disaggregation()
 
     # calculate central etrago results
-    etrago.calc_results()
+    #etrago.calc_results()
 
     return etrago
 

etrago/cluster/electrical.py

@@ -881,7 +881,6 @@ def postprocessing(etrago, busmap, busmap_foreign, medoid_idx=None):
     network.generators["weight"] = network.generators["p_nom"]
     aggregate_one_ports = network.one_port_components.copy()
     aggregate_one_ports.discard("Generator")
-
     clustering = get_clustering_from_busmap(
         network,
         busmap,

etrago/execute/__init__.py

@@ -415,9 +415,9 @@ def optimize(self):
 
         self.market_optimization()
 
-        self.market_results_to_grid()
+        #self.market_results_to_grid()
 
-        self.grid_optimization()
+        #self.grid_optimization()
 
     else:
         print("Method not defined")

etrago/execute/market_optimization.py

@@ -29,6 +29,15 @@
 
     import numpy as np
     import pandas as pd
+
+    from etrago.cluster.electrical import preprocessing    
+    from etrago.cluster.spatial import (
+        strategies_one_ports,
+        strategies_generators,)
+
+    from pypsa.networkclustering import get_clustering_from_busmap
+
+
 
 
     logger = logging.getLogger(__name__)
@@ -47,9 +56,10 @@
 
 def market_optimization(self):
 
-    build_market_model()
+    logger.info("Start building market model")
+    build_market_model(self)
 
-    self.market_model.lopf(
+    self.network.lopf(
             solver_name=self.args["solver"],
             solver_options=self.args["solver_options"],
             pyomo=True,
@@ -59,7 +69,7 @@ def market_optimization(self):
         )
 
 
-def build_market_model():
+def build_market_model(self):
     """Builds market model based on imported network from eTraGo
     
     
@@ -73,9 +83,47 @@ def build_market_model():
     None.
 
     """
-    # Not useful, just for demonstration
-    self.market_model = self.network.copy()
 
+    # use existing preprocessing to get only the electricity system
+
+    net, weight, n_clusters, busmap_foreign = preprocessing(self)
+
+    df = pd.DataFrame({'country': net.buses.country.unique(), 
+                       'marketzone': net.buses.country.unique()}, 
+                      columns = ["country", "marketzone"])
+
+    df.loc[(df.country == 'DE')| (df.country == 'LU'), 'marketzone'] = 'DE/LU' 
+
+    df['cluster'] = df.groupby(df.marketzone).grouper.group_info[0]
+
+
+    for i in net.buses.country.unique():
+
+        net.buses.loc[net.buses.country== i , 'cluster'] = df.loc[df.country == i, 'cluster'].values[0]
+
+
+    busmap = pd.Series(net.buses.cluster.astype(int).astype(str), net.buses.index)
+
+    aggregate_one_ports = net.one_port_components.copy()
+    aggregate_one_ports.discard("Generator")
+
+    net.generators.control = "PV"
+
+    #net.lines.sub_network.fillna('', inplace=True)
+
+
+    clustering = get_clustering_from_busmap(
+        net,
+        busmap,
+        aggregate_generators_weighted=False,
+        one_port_strategies=strategies_one_ports(),
+        generator_strategies=strategies_generators(),
+        aggregate_one_ports=aggregate_one_ports,
+        line_length_factor=1,
+    )
+    net = clustering.network
+    self.network = net
+
 
 
 def extra_functionality():