modified scaling in api calc

ptxboa/api_calc.py

@@ -58,7 +58,7 @@ def calculate(data: CalculateDataType) -> pd.DataFrame:
 
             if not is_transport:
                 flh = step_data["FLH"]
-                liefetime = step_data["LIFETIME"]
+                lifetime = step_data["LIFETIME"]
                 capex_rel = step_data["CAPEX"]
                 opex_f = step_data["OPEX-F"]
 
@@ -72,7 +72,7 @@ def calculate(data: CalculateDataType) -> pd.DataFrame:
                     capacity = main_output_value / flh
 
                 capex = capacity * capex_rel
-                capex_ann = annuity(wacc, liefetime, capex)
+                capex_ann = annuity(wacc, lifetime, capex)
                 opex = opex_f * capacity + opex_o * main_output_value
 
                 results.append((result_process_type, process_code, "CAPEX", capex_ann))
@@ -99,14 +99,14 @@ def calculate(data: CalculateDataType) -> pd.DataFrame:
                     ]
 
                     # no FLH
-                    liefetime = sec_process_data["LIFETIME"]
+                    lifetime = sec_process_data["LIFETIME"]
                     capex = sec_process_data["CAPEX"]
                     opex_f = sec_process_data["OPEX-F"]
                     opex_o = sec_process_data["OPEX-O"]
 
                     capacity = flow_value  # no FLH
                     capex = capacity * capex
-                    capex_ann = annuity(wacc, liefetime, capex)
+                    capex_ann = annuity(wacc, lifetime, capex)
                     opex = opex_f * capacity + opex_o * flow_value
 
                     results.append(
@@ -168,9 +168,16 @@ def calculate(data: CalculateDataType) -> pd.DataFrame:
         results = results.groupby(dim_columns).sum().reset_index()
 
         # normalization:
-        # scale so that we star twith 1 EL input,
+        # scale so that we start with 1 EL input,
         # rescale so that we have 1 unit output
-        norm_factor = sum_el / main_output_value
+        norm_factor = 1 / main_output_value
         results["values"] = results["values"] * norm_factor
 
+        # rescale again ONLY RES to account for additionally needed electricity
+        # sum_el is larger than 1.0
+        norm_factor_el = sum_el
+        idx = results["process_type"] == "Electricity generation"
+        assert idx.any()  # must have at least one entry
+        results.loc[idx, "values"] = results.loc[idx, "values"] * norm_factor_el
+
         return results

tests/test_opt.py

@@ -239,18 +239,17 @@ def network_green_iron(api) -> Tuple[pypsa.Network, dict, dict]:
     return n, metadata, settings
 
 
-@pytest.mark.xfail
 def test_issue_564(network_green_iron, api):
+    # calculate costs from optimization tab:
     n, metadata, settings = network_green_iron
     res_opt = calc_aggregate_statistics(n, include_debugging_output=True)
-    res_costs = api.calculate(**settings)
-    res_costs_agg = (
-        res_costs[0]
-        .pivot_table(
-            index="process_type", columns="cost_type", values="values", aggfunc=sum
-        )
-        .fillna(0)
-    )
+
+    # get costs from costs tab:
+    df_res_costs, _ = api.calculate(**settings)
+
+    res_costs_agg = df_res_costs.pivot_table(
+        index="process_type", columns="cost_type", values="values", aggfunc=sum
+    ).fillna(0)
 
     res_costs_agg["total"] = res_costs_agg.sum(axis=1)
     res_costs_agg.loc["Total"] = res_costs_agg.sum(axis=0)
@@ -282,14 +281,19 @@ def test_issue_564(network_green_iron, api):
         res_costs_agg["total_opt"] - res_costs_agg["total"]
     ).fillna(0)
 
+    # call optimize function directly:
+    res_optimize = optimize(metadata["opt_input_data"])[0]
+
     # write costs data to excel, and metadata to json:
     if not os.path.exists("tests/out"):
         os.makedirs("tests/out")
     res_costs_agg.to_excel("tests/out/test_issue_564.xlsx")
-    with open("tests/out/issue_564_metadata.json", "w") as f:
+    with open("tests/out/issue_564_metadata_optimize_input.json", "w") as f:
         dump(metadata, f)
+    with open("tests/out/issue_564_metadata_optimize_output.json", "w") as f:
+        dump(res_optimize, f)
 
-    # extract input data:
+    # extract DRI input data:
     input_data = api.get_input_data(scenario=settings["scenario"])
     input_data_dri = input_data.loc[
         input_data["process_code"] == "Green iron reduction"
@@ -309,6 +313,11 @@ def test_issue_564(network_green_iron, api):
     # annuized capex should match:
     assert capex_ann_input + opex_fix == pytest.approx(capex_ann_opt)
 
+    # FLH from optimization tab and optimize function output should match:
+    flh_opt_tab = res_opt.at["Derivative production", "Full load hours (h)"]
+    flh_opt_function = res_optimize["DERIV"]["FLH"] * 8760
+    assert flh_opt_tab == pytest.approx(flh_opt_function)
+
     # assert that differences between costs and opt tab are zero:
     # this currently fails
     for i in res_costs_agg["diff"]: