WIP zu reset

examples/scenarios/test_mimo_crop/datapackage.json

@@ -1,7 +1,7 @@
 {
     "profile": "tabular-data-package",
     "name": "test_mimo_crop",
-    "oemof_tabular_version": "0.0.5",
+    "oemof_tabular_version": "0.0.6dev",
     "resources": [
         {
             "path": "data/elements/bus.csv",

examples/scenarios/wefe_pv_panel/data/elements/dispatchable.csv

@@ -1,3 +1,3 @@
 name;type;carrier;tech;capacity;capacity_cost;bus;marginal_cost;carrier_cost;profile;output_parameters;expandable
-solar-radiation;volatile;solar-energy;source;0;0;solar-energy-bus;0.0;0.0;ghi;{};True
+solar-radiation;volatile;solar-energy;source;0;0;solar-energy-bus;0.0;0.0;ghi;{};False
 back-up-elec;dispatchable;electricity;source;0;10;elec-bus;10.0;0.3;1.0;{};True

examples/scenarios/wefe_pv_panel/data/elements/pv_panel.csv

@@ -1,2 +1,2 @@
 name;type;carrier;tech;capacity;capacity_cost;marginal_cost;carrier_cost;capacity_potential;from_bus;to_bus;t_air;ghi;p_rpv;r_ref;n_t;t_c_ref;noct;expandable
-pv-panel;pv-panel;solar-energy;pv;0;0;0;-0.1;100;solar-energy-bus;elec-bus;t-air;ghi;270;1000;-0.0037;25;48;True
+pv-panel;pv-panel;solar-energy;pv;0;0;0;0;100;solar-energy-bus;elec-bus;t-air;ghi;270;1000;-0.0037;25;48;True

examples/scenarios/wefe_pv_panel/datapackage.json

@@ -298,7 +298,7 @@
                     },
                     {
                         "name": "carrier_cost",
-                        "type": "number",
+                        "type": "integer",
                         "format": "default"
                     },
                     {

examples/scripts/compute.py

@@ -67,7 +67,7 @@
     "water_footprint_factor",
 ]
 # set whether the multi-objective optimization should be performed
-moo = True
+moo = False
 
 # -------------- RUNNING THE SCENARIOS --------------
 for scenario in scenarios:
@@ -86,6 +86,7 @@
         moo=moo,
         dash_app=True,
         parameters_units=parameters_units,
+        infer_bus_carrier=False,
     )
     df = calculator.df_results
     print(df)

examples/scripts/results.py

@@ -40,6 +40,7 @@
 # -------------- USER INPUTS --------------
 # list of scenarios to be evaluated
 scenarios = [
+    "test_mimo_crop",
     # "general_add_cost_inputs",
     # "general_basic",
     # "general_constraints",

src/oemof_tabular_plugins/datapackage/post_processing.py

@@ -657,7 +657,8 @@ def construct_dataframe_from_results(
     ]
     # lookup bus-carrier mapping in the dp_path, if existing
     p = Package(dp_path)
-    bus_carrier = infer_busses_carrier(p, infer_from_component=False)
+    # bus_carrier = infer_busses_carrier(p, infer_from_component=False)
+    bus_carrier = None
 
     if bus_carrier is None:
         if infer_bus_carrier is True:

src/oemof_tabular_plugins/general/post_processing/post_processing.py

@@ -286,16 +286,16 @@ def set_unit(carrier, facade_type):
     if kpis is not None:
         kpis.to_csv(results_path + "/kpis.csv", index=True)
 
-        if "mimo" in results_by_flow.index.get_level_values("asset"):
-            kpis.loc["total_water_produced"] = results_by_flow.loc[
-                "permeate-bus", "in"
-            ]["aggregated_flow"].sum()
-            kpis.loc["total_brine_produced"] = results_by_flow.loc["brine-bus", "in"][
-                "aggregated_flow"
-            ].sum()
-            kpis.loc["total_electricity_produced"] = results_by_flow.loc[
-                "ac-elec-bus", "in"
-            ]["aggregated_flow"].sum()
+        # if "mimo" in results_by_flow.index.get_level_values("asset"):
+        #     kpis.loc["total_water_produced"] = results_by_flow.loc[
+        #         "permeate-bus", "in"
+        #     ]["aggregated_flow"].sum()
+        #     kpis.loc["total_brine_produced"] = results_by_flow.loc["brine-bus", "in"][
+        #         "aggregated_flow"
+        #     ].sum()
+        #     kpis.loc["total_electricity_produced"] = results_by_flow.loc[
+        #         "ac-elec-bus", "in"
+        #     ]["aggregated_flow"].sum()
 
         result_tables.update({"kpis": kpis})
 
@@ -314,55 +314,55 @@ def set_unit(carrier, facade_type):
         demo_app.run_server(debug=False, port=8060)
 
     # ----- OLD POST-PROCESSING - TO BE DELETED ONCE CERTAIN -----
-    if hasattr(calculator, "scalar_params"):
-        # calculate scalars using functions from clc module
-        aggregated_flows = clc.AggregatedFlows(calculator).result
-        storage_losses = clc.StorageLosses(calculator).result
-        transmission_losses = clc.TransmissionLosses(calculator).result
-        invested_capacity = clc.InvestedCapacity(calculator).result
-        invested_storage_capacity = clc.InvestedStorageCapacity(calculator).result
-        invested_capacity_costs = clc.InvestedCapacityCosts(calculator).result
-        invested_storage_capacity_costs = clc.InvestedStorageCapacityCosts(
-            calculator
-        ).result
-        summed_carrier_costs = clc.SummedCarrierCosts(calculator).result
-        summed_marginal_costs = clc.SummedMarginalCosts(calculator).result
-        total_system_costs = clc.TotalSystemCosts(calculator).result
-
-        # combine all results into a single dataframe
-        all_scalars = [
-            aggregated_flows,
-            storage_losses,
-            transmission_losses,
-            invested_capacity,
-            invested_storage_capacity,
-            invested_capacity_costs,
-            invested_storage_capacity_costs,
-            summed_carrier_costs,
-            summed_marginal_costs,
-        ]
-        # map variable names and add component information
-        all_scalars = pd.concat(all_scalars, axis=0)
-        all_scalars = naming.map_var_names(
-            all_scalars,
-            calculator.scalar_params,
-            calculator.busses,
-            calculator.links,
-        )
-        all_scalars = naming.add_component_info(all_scalars, calculator.scalar_params)
-        print("Total System Cost", total_system_costs)
-        total_system_costs.index.names = ("name", "var_name")
-        all_scalars = pd.concat([all_scalars, total_system_costs], axis=0)
-        all_scalars = all_scalars.sort_values(by=["carrier", "tech", "var_name"])
-        # save all scalar results to a csv file
-        filepath_name_all_scalars = os.path.join(results_path, "all_scalars.csv")
-        all_scalars.to_csv(filepath_name_all_scalars)
-
-        # saves all hourly timeseries as a dataframe (see test_postprocessing.py in oemof-tabular/tests
-        # for example if wanting to filter particular nodes)
-        all_sequences = clc.AggregatedFlows(calculator, resample_mode="H")
-        # save all timeseries (sequence) results to a csv file
-        filepath_name_all_sequences = os.path.join(results_path, "all_sequences.csv")
-        all_sequences.sequences.to_csv(filepath_name_all_sequences)
+    # if hasattr(calculator, "scalar_params"):
+    #     # calculate scalars using functions from clc module
+    #     aggregated_flows = clc.AggregatedFlows(calculator).result
+    #     storage_losses = clc.StorageLosses(calculator).result
+    #     transmission_losses = clc.TransmissionLosses(calculator).result
+    #     invested_capacity = clc.InvestedCapacity(calculator).result
+    #     invested_storage_capacity = clc.InvestedStorageCapacity(calculator).result
+    #     invested_capacity_costs = clc.InvestedCapacityCosts(calculator).result
+    #     invested_storage_capacity_costs = clc.InvestedStorageCapacityCosts(
+    #         calculator
+    #     ).result
+    #     summed_carrier_costs = clc.SummedCarrierCosts(calculator).result
+    #     summed_marginal_costs = clc.SummedMarginalCosts(calculator).result
+    #     total_system_costs = clc.TotalSystemCosts(calculator).result
+    #
+    #     # combine all results into a single dataframe
+    #     all_scalars = [
+    #         aggregated_flows,
+    #         storage_losses,
+    #         transmission_losses,
+    #         invested_capacity,
+    #         invested_storage_capacity,
+    #         invested_capacity_costs,
+    #         invested_storage_capacity_costs,
+    #         summed_carrier_costs,
+    #         summed_marginal_costs,
+    #     ]
+    #     # map variable names and add component information
+    #     all_scalars = pd.concat(all_scalars, axis=0)
+    #     all_scalars = naming.map_var_names(
+    #         all_scalars,
+    #         calculator.scalar_params,
+    #         calculator.busses,
+    #         calculator.links,
+    #     )
+    #     all_scalars = naming.add_component_info(all_scalars, calculator.scalar_params)
+    #     print("Total System Cost", total_system_costs)
+    #     total_system_costs.index.names = ("name", "var_name")
+    #     all_scalars = pd.concat([all_scalars, total_system_costs], axis=0)
+    #     all_scalars = all_scalars.sort_values(by=["carrier", "tech", "var_name"])
+    #     # save all scalar results to a csv file
+    #     filepath_name_all_scalars = os.path.join(results_path, "all_scalars.csv")
+    #     all_scalars.to_csv(filepath_name_all_scalars)
+    #
+    #     # saves all hourly timeseries as a dataframe (see test_postprocessing.py in oemof-tabular/tests
+    #     # for example if wanting to filter particular nodes)
+    #     all_sequences = clc.AggregatedFlows(calculator, resample_mode="H")
+    #     # save all timeseries (sequence) results to a csv file
+    #     filepath_name_all_sequences = os.path.join(results_path, "all_sequences.csv")
+    #     all_sequences.sequences.to_csv(filepath_name_all_sequences)
 
     return calculator

src/oemof_tabular_plugins/script/compute.py

@@ -95,9 +95,29 @@ def compute_scenario(
 
     logger.info("Energy system created from datapackage")
 
+    from oemof_visio import ESGraphRenderer
+
+    gr = ESGraphRenderer(
+        energy_system=es, filepath=os.path.join(results_path, scenario_name)
+    )
+    gr.render()
+
     # create model from energy system (this is just oemof.solph)
     m = Model(es)
     logger.info("Model created from energy system")
+    # mimo = [n for n in es.nodes if "mimo" in n.label]
+    # print([l.label for l in mimo])
+    #
+    # crop = mimo[0]
+    # print("Investments on inputs")
+    # print([f"{i.label}({str(f.investment)})" for i,f in crop.inputs.items()])
+    # print("Nominal values on inputs")
+    # print([f"{i.label}({str(f.nominal_value)})" for i,f in crop.inputs.items()])
+    #
+    # print("Investments on outputs")
+    # print([f"{i.label}({str(f.investment)})" for i,f in crop.outputs.items()])
+    # print("Nominal values on outputs")
+    # print([f"{i.label}({str(f.nominal_value)})" for i,f in crop.outputs.items()])
 
     # add constraints from datapackage to the model
     m.add_constraints_from_datapackage(

src/oemof_tabular_plugins/wefe/facades/water.py

@@ -1,4 +1,5 @@
-from dataclasses import field
+from dataclasses import field, dataclass
+
 from typing import Sequence, Union
 
 from oemof.solph._plumbing import sequence
@@ -8,6 +9,8 @@
 
 from oemof.tabular._facade import dataclass_facade, Facade
 
+from oemof_tabular_plugins.wefe.facades import MIMO
+
 
 @dataclass_facade
 class WaterFiltration(Converter, Facade):
@@ -225,3 +228,156 @@ def build_solph_components(self):
                 )
             }
         )
+
+
+@dataclass(unsafe_hash=False, frozen=False, eq=False)
+class ReverseOsmosis(MIMO):
+    r"""
+    Crop Model Converter with 3 inputs (irradiation, precipitation, irrigation)
+    and 2 outputs (crop harvest, remaining biomass), based on MultipleInputMultipleOutputConverter.
+
+    Functions designed according to 3 publications, please note the abbreviations in the function descriptions:
+    FAO56       ISBN: 978-92-5-104219-9
+    SIMPLE      https://doi.org/10.1016/j.eja.2019.01.009
+    ARID        https://doi.org/10.2134/agronj2011.0286
+
+    Parameters
+    ----------
+    type: str
+        will link MimoCrop to TYPEMAP, currently named 'mimo-crop'
+    name: str
+        a remarkable name for your component
+    tech: str
+        whatever, I just specified it as 'mimo'
+    carrier: str
+        carrier of the primary bus, should be overwritten by carriers directly assigned to buses
+    primary: str
+        primary bus object to which investments etc. are assigned
+    expandable: boolean or numeric (binary)
+        True, if capacity can be expanded within optimization. Default: False.
+    capacity: numeric
+        The capacity of crop. It is expressed in cultivated area [m²]
+    capacity_minimum: numeric
+        Minimum invest capacity in unit of output capacity.
+    capacity_potential: numeric   (seems not to work as it should)
+        Maximum invest capacity in unit of output capacity.
+    capacity_cost: numeric
+        Investment costs per unit of output capacity. If capacity is not set,
+        this value will be used for optimizing the conversion output capacity.
+    crop_type: str
+        the name of crop as defined in global_specs/crop_specs.py
+    solar_energy_bus: oemof.solph.Bus
+        An oemof bus instance where the MimoCrop unit is connected to with
+        its input, it is expected to provide W/m² irradiance.
+    precipitation_bus: oemof.solph.Bus
+        An oemof bus instance where the MimoCrop unit is connected to with
+        its input, it is expected to provide mm precipitation.
+    irrigation_bus: oemof.solph.Bus
+        An oemof bus instance where the MimoCrop unit is connected to with
+        its input, it is expected to provide mm irrigation.
+    crop_bus: oemof.solph.Bus
+        An oemof bus instance where the MimoCrop unit is connected to with
+        its crop-harvest output. The unit is kg.
+    biomass_bus: oemof.solph.Bus
+        An oemof bus instance where the MimoCrop is connected with its
+        non-edible, remaining (residual) biomass output. The unit is kg.
+    time_profile: time series
+        time profile separate from time index
+    ghi_profile: time series
+        global horizontal solar irradiance in W/m²
+    tp_profile: time series
+        total precipitation in mm
+    t_air_profile: time series
+        ambient air tempearture in °C
+    t_dp_profile: time series
+        dew point temperature in °C
+    windspeed_profile: time series
+        windspeed in m/s
+    elevation: float
+        elevation above sea level of the location
+    crop_type: str
+        the name of crop as defined in global_specs/crop_specs.py
+    sowing_date: str
+        date when cultivation period starts, MM-DD format
+    harvest_date:
+        date when cultivation period ends, MM-DD format
+    has_irrigation: bool
+        if set to False, irrigation profile will be set to 0
+
+    Other optional parameters that could be added:
+    ---------------------------------------------
+    marginal_cost: numeric
+        Marginal cost for one unit of produced output. Default: 0
+    carrier_cost: numeric
+        Carrier cost for one unit of used input. Default: 0
+    input_parameters: dict (optional)
+        Set parameters on the input edge of the conversion unit
+        (see oemof.solph for more information on possible parameters)
+    output_parameters: dict (optional)
+        Set parameters on the output edge of the conversion unit
+        (see oemof.solph for more information on possible parameters)
+
+    Notes:
+    ---------------------------------------------
+    One input (eg. ghi) has to be volatile with a fixed capacity, other 2 dispatchable.
+    MimoCrop capacity_potential seems to not work correctly.
+    Two options to successfully run MimoCrop:
+    1) Expandable = True and ignore capacity, use capacity of volatile source instead.
+    2) Expandable = False, set capacity equal to that of the volatile source.
+
+    In case you experience a weird error, for example "TypeError: can't multiply sequence by non-int of type 'float'",
+    this is likely due to empty rows at the end of your input csv files.
+    Please remove them and try again.
+    """
+
+    type: str = "mimo-crop"
+
+    name: str = ""
+
+    tech: str = "mimo"
+
+    carrier: str = ""
+
+    primary: str = ""
+
+    expandable: bool = False
+
+    capacity: float = 0
+
+    capacity_minimum: float = None
+
+    capacity_potential: float = None
+
+    capacity_cost: float = 0
+
+    solar_energy_bus: Bus = None
+
+    precipitation_bus: Bus = None
+
+    irrigation_bus: Bus = None
+
+    crop_bus: Bus = None
+
+    biomass_bus: Bus = None
+
+    time_profile: Union[float, Sequence[float]] = None
+
+    ghi_profile: Union[float, Sequence[float]] = None
+
+    tp_profile: Union[float, Sequence[float]] = None
+
+    t_air_profile: Union[float, Sequence[float]] = None
+
+    t_dp_profile: Union[float, Sequence[float]] = None
+
+    windspeed_profile: Union[float, Sequence[float]] = None
+
+    elevation: float = 0
+
+    crop_type: str = ""  # according to crop_dict
+
+    sowing_date: str = ""  # MM-DD format
+
+    harvest_date: str = ""  # MM-DD format
+
+    has_irrigation: bool = False