Merge pull request #320 from lodersky/new_env

Update python packages

run_intertemporal.py

@@ -25,54 +25,54 @@
 objective = 'cost'  # set either 'cost' or 'CO2' as objective
 
 # Choose Solver (cplex, glpk, gurobi, ...)
-solver = 'glpk'
+solver = 'gurobi'
 
 # simulation timesteps
-(offset, length) = (0, 8760)  # time step selection
+(offset, length) = (0, 24)  # time step selection
 timesteps = range(offset, offset+length+1)
 dt = 1  # length of each time step (unit: hours)
 
 # detailed reporting commodity/sites
 report_tuples = [
-    (year, 'North', 'Elec'),
-    (year, 'Mid', 'Elec'),
-    (year, 'South', 'Elec'),
-    (year, ['North', 'Mid', 'South'], 'Elec'),
-    (year+5, 'North', 'Elec'),
-    (year+5, 'Mid', 'Elec'),
-    (year+5, 'South', 'Elec'),
-    (year+5, ['North', 'Mid', 'South'], 'Elec'),
-    (year+10, 'North', 'Elec'),
-    (year+10, 'Mid', 'Elec'),
-    (year+10, 'South', 'Elec'),
-    (year+10, ['North', 'Mid', 'South'], 'Elec'),
-    (year+15, 'North', 'Elec'),
-    (year+15, 'Mid', 'Elec'),
-    (year+15, 'South', 'Elec'),
-    (year+15, ['North', 'Mid', 'South'], 'Elec'),    
+    (2019, 'North', 'Elec'),
+    (2019, 'Mid', 'Elec'),
+    (2019, 'South', 'Elec'),
+    (2019, ['North', 'Mid', 'South'], 'Elec'),
+    (2024, 'North', 'Elec'),
+    (2024, 'Mid', 'Elec'),
+    (2024, 'South', 'Elec'),
+    (2024, ['North', 'Mid', 'South'], 'Elec'),
+    (2029, 'North', 'Elec'),
+    (2029, 'Mid', 'Elec'),
+    (2029, 'South', 'Elec'),
+    (2029, ['North', 'Mid', 'South'], 'Elec'),
+    (2034, 'North', 'Elec'),
+    (2034, 'Mid', 'Elec'),
+    (2034, 'South', 'Elec'),
+    (2034, ['North', 'Mid', 'South'], 'Elec'),
     ]
 
 # optional: define names for sites in report_tuples
 report_sites_name = {('North', 'Mid', 'South'): 'All'}
 
 # plotting commodities/sites
 plot_tuples = [
-    (year, 'North', 'Elec'),
-    (year, 'Mid', 'Elec'),
-    (year, 'South', 'Elec'),
-    (year, ['North', 'Mid', 'South'], 'Elec'),
-    (year+5, 'North', 'Elec'),
-    (year+5, 'Mid', 'Elec'),
-    (year+5, 'South', 'Elec'),
-    (year+5, ['North', 'Mid', 'South'], 'Elec'),
-    (year+10, 'North', 'Elec'),
-    (year+10, 'Mid', 'Elec'),
-    (year+10, 'South', 'Elec'),
-    (year+10, ['North', 'Mid', 'South'], 'Elec'),
-    (year+15, 'North', 'Elec'),
-    (year+15, 'Mid', 'Elec'),
-    (year+15, 'South', 'Elec'),
-    (year+15, ['North', 'Mid', 'South'], 'Elec'),    
+    (2019, 'North', 'Elec'),
+    (2019, 'Mid', 'Elec'),
+    (2019, 'South', 'Elec'),
+    (2019, ['North', 'Mid', 'South'], 'Elec'),
+    (2024, 'North', 'Elec'),
+    (2024, 'Mid', 'Elec'),
+    (2024, 'South', 'Elec'),
+    (2024, ['North', 'Mid', 'South'], 'Elec'),
+    (2029, 'North', 'Elec'),
+    (2029, 'Mid', 'Elec'),
+    (2029, 'South', 'Elec'),
+    (2029, ['North', 'Mid', 'South'], 'Elec'),
+    (2034, 'North', 'Elec'),
+    (2034, 'Mid', 'Elec'),
+    (2034, 'South', 'Elec'),
+    (2034, ['North', 'Mid', 'South'], 'Elec'),
     ]
 
 # optional: define names for sites in plot_tuples

run_single_year.py

@@ -25,7 +25,7 @@
 objective = 'cost'  # set either 'cost' or 'CO2' as objective
 
 # Choose Solver (cplex, glpk, gurobi, ...)
-solver = 'glpk'
+solver = 'gurobi'
 
 # simulation timesteps
 (offset, length) = (0, 20)  # time step selection

runme.py

@@ -2,7 +2,6 @@
 import shutil
 import urbs
 
-
 input_files = 'single_year_example.xlsx'  # for single year file name, for intertemporal folder name
 input_dir = 'Input'
 input_path = os.path.join(input_dir, input_files)
@@ -22,7 +21,7 @@
 objective = 'cost'  # set either 'cost' or 'CO2' as objective
 
 # Choose Solver (cplex, glpk, gurobi, ...)
-solver = 'glpk'
+solver = 'gurobi'
 
 # simulation timesteps
 (offset, length) = (3500, 24)  # time step selection

urbs-env.yml

@@ -1,17 +1,16 @@
-name: urbs
+name: urbs-env
 channels:
-  - defaults
   - conda-forge
 dependencies:
-  - python=3.6
-  - numpy=1.17.3
-  - matplotlib=3.1.1
-  - pandas=0.24.2
-  - pandas-datareader=0.8.1
-  - pytables=3.6.1
-  - openpyxl=3.0.1
-  - xlrd=1.2.0
-  - pyomo=5.6.7
+  - python=3.12.2
+  - numpy=1.26.4
+  - matplotlib=3.8.3
+  - pandas=2.2.1
+  - pandas-datareader=0.10.0
+  - pytables=3.9.2
+  - openpyxl=3.1.2
+  - xlrd=2.0.1
+  - pyomo=6.7.1
   - glpk
-  - psutil=5.6.5
-  - pyutilib=5.8.0
+  - psutil=5.9.8
+  - pyutilib=6.0.0

urbs/features/BuySellPrice.py

@@ -130,8 +130,8 @@ def search_sell_buy_tuple(m, stf, sit_in, pro_in, coin):
     Returns:
         a process
     """
-    pro_output_tuples = [x for x in list(m.pro_output_tuples.value) if x[1] == sit_in]
-    pro_input_tuples = [x for x in list(m.pro_input_tuples.value) if x[1] == sit_in]
+    pro_output_tuples = [x for x in list(m.pro_output_tuples.data()) if x[1] == sit_in]
+    pro_input_tuples = [x for x in list(m.pro_input_tuples.data()) if x[1] == sit_in]
     # search the output commodities for the "buy" process
     # buy_out = (stf, site, output_commodity)
     buy_out = set([(x[0], x[1], x[3])

urbs/features/storage.py

@@ -5,9 +5,10 @@
 def add_storage(m):
 
     # storage (e.g. hydrogen, pump storage)
-    indexlist = set()
+    indexlist = list()
     for key in m.storage_dict["eff-in"]:
-        indexlist.add(tuple(key)[2])
+        if key[2] not in indexlist:
+            indexlist.append(key[2])
     m.sto = pyomo.Set(
         initialize=indexlist,
         doc='Set of storage technologies')
@@ -245,14 +246,14 @@ def res_storage_capacity_rule(m, stf, sit, sto, com):
 # forced minimun  storage content in final timestep t[len(m.t)]
 # content[t=1] == storage capacity * fraction <= content[t=final]
 def def_initial_storage_state_rule(m, stf, sit, sto, com):
-    return (m.e_sto_con[m.t[1], stf, sit, sto, com] ==
+    return (m.e_sto_con[m.t.at(1), stf, sit, sto, com] ==
             m.cap_sto_c[stf, sit, sto, com] *
             m.storage_dict['init'][(stf, sit, sto, com)])
 
 
 def res_storage_state_cyclicity_rule(m, stf, sit, sto, com):
-    return (m.e_sto_con[m.t[1], stf, sit, sto, com] <=
-            m.e_sto_con[m.t[len(m.t)], stf, sit, sto, com])
+    return (m.e_sto_con[m.t.at(1), stf, sit, sto, com] <=
+            m.e_sto_con[m.t.at(len(m.t)), stf, sit, sto, com])
 
 
 def def_storage_energy_power_ratio_rule(m, stf, sit, sto, com):

urbs/features/transmission.py

@@ -23,15 +23,16 @@ def remove_duplicate_transmission(transmission_keys):
                 i -= 1
                 break
         i += 1
-    return set(tra_tuple_list)
+    return list(tra_tuple_list)
 
 
 def add_transmission(m):
 
     # tranmission (e.g. hvac, hvdc, pipeline...)
-    indexlist = set()
+    indexlist = list()
     for key in m.transmission_dict["eff"]:
-        indexlist.add(tuple(key)[3])
+        if key[3] not in indexlist:
+            indexlist.append(key[3])
     m.tra = pyomo.Set(
         initialize=indexlist,
         doc='Set of transmission technologies')
@@ -117,20 +118,21 @@ def add_transmission(m):
 # adds the transmission features to model with DCPF model features
 def add_transmission_dc(m):
     # defining transmission tuple sets for transport and DCPF model separately
-    tra_tuples = set()
-    tra_tuples_dc = set()
+    tra_tuples = list()
+    tra_tuples_dc = list()
     for key in m.transmission_dict['reactance']:
-        tra_tuples.add(tuple(key))
+        tra_tuples.append(key)
     for key in m.transmission_dc_dict['reactance']:
-        tra_tuples_dc.add(tuple(key))
+        tra_tuples_dc.append(key)
     tra_tuples_tp = tra_tuples - tra_tuples_dc
     tra_tuples_dc = remove_duplicate_transmission(tra_tuples_dc)
     tra_tuples = tra_tuples_dc | tra_tuples_tp
 
     # tranmission (e.g. hvac, hvdc, pipeline...)
-    indexlist = set()
+    indexlist = list()
     for key in m.transmission_dict["eff"]:
-        indexlist.add(tuple(key)[3])
+        if key[3] not in indexlist:
+            indexlist.append(key[3])
     m.tra = pyomo.Set(
         initialize=indexlist,
         doc='Set of transmission technologies')

urbs/input.py

@@ -47,7 +47,7 @@ def read_input(input_files, year):
             global_prop = xls.parse('Global').set_index(['Property'])
             # create support timeframe index
             if ('Support timeframe' in
-                    xls.parse('Global').set_index('Property').value):
+                    global_prop.value):
                 support_timeframe = (
                     global_prop.loc['Support timeframe']['value'])
                 global_prop = (
@@ -174,7 +174,7 @@ def read_input(input_files, year):
 
     # sort nested indexes to make direct assignments work
     for key in data:
-        if isinstance(data[key].index, pd.core.index.MultiIndex):
+        if isinstance(data[key].index, pd.MultiIndex):
             data[key].sort_index(inplace=True)
     return data
 

urbs/model.py

@@ -38,18 +38,21 @@ def create_model(data, dt=1, timesteps=None, objective='cost',
     # costs are annual by default, variable costs are scaled by weight) and
     # among different simulation durations meaningful.
     m.weight = pyomo.Param(
+        within=pyomo.Reals,
         initialize=float(8760) / ((len(m.timesteps) - 1) * dt),
         doc='Pre-factor for variable costs and emissions for an annual result')
 
     # dt = spacing between timesteps. Required for storage equation that
     # converts between energy (storage content, e_sto_con) and power (all other
     # quantities that start with "e_")
     m.dt = pyomo.Param(
+        within=pyomo.Reals,
         initialize=dt,
         doc='Time step duration (in hours), default: 1')
 
     # import objective function information
     m.obj = pyomo.Param(
+        within=pyomo.Any,
         initialize=objective,
         doc='Specification of minimized quantity, default: "cost"')
 
@@ -62,6 +65,7 @@ def create_model(data, dt=1, timesteps=None, objective='cost',
 
     # generate ordered time step sets
     m.t = pyomo.Set(
+        within=pyomo.Reals,
         initialize=m.timesteps,
         ordered=True,
         doc='Set of timesteps')
@@ -74,41 +78,47 @@ def create_model(data, dt=1, timesteps=None, objective='cost',
         doc='Set of modelled timesteps')
 
     # support timeframes (e.g. 2020, 2030...)
-    indexlist = set()
+    indexlist = list()
     for key in m.commodity_dict["price"]:
-        indexlist.add(tuple(key)[0])
+        if key[0] not in indexlist:
+            indexlist.append(key[0])
     m.stf = pyomo.Set(
+        within=pyomo.Reals,
         initialize=indexlist,
         doc='Set of modeled support timeframes (e.g. years)')
 
     # site (e.g. north, middle, south...)
-    indexlist = set()
+    indexlist = list()
     for key in m.commodity_dict["price"]:
-        indexlist.add(tuple(key)[1])
+        if key[1] not in indexlist:
+            indexlist.append(key[1])
     m.sit = pyomo.Set(
         initialize=indexlist,
         doc='Set of sites')
 
     # commodity (e.g. solar, wind, coal...)
-    indexlist = set()
+    indexlist = list()
     for key in m.commodity_dict["price"]:
-        indexlist.add(tuple(key)[2])
+        if key[2] not in indexlist:
+            indexlist.append(key[2])
     m.com = pyomo.Set(
         initialize=indexlist,
         doc='Set of commodities')
 
     # commodity type (i.e. SupIm, Demand, Stock, Env)
-    indexlist = set()
+    indexlist = list()
     for key in m.commodity_dict["price"]:
-        indexlist.add(tuple(key)[3])
+        if key[3] not in indexlist:
+            indexlist.append(key[3])
     m.com_type = pyomo.Set(
         initialize=indexlist,
         doc='Set of commodity types')
 
     # process (e.g. Wind turbine, Gas plant, Photovoltaics...)
-    indexlist = set()
+    indexlist = list()
     for key in m.process_dict["inv-cost"]:
-        indexlist.add(tuple(key)[2])
+        if key[2] not in indexlist:
+            indexlist.append(key[2])
     m.pro = pyomo.Set(
         initialize=indexlist,
         doc='Set of conversion processes')