Merge pull request #307 from jmartin4nrel/meoh-plot

Methanol: Sprucing up plots for white paper

Author: kbrunik

examples/03_methanol/co2_hydrogenation_doc/plot_co2h_methanol.py

@@ -1,15 +1,20 @@
 import pandas as pd
+import matplotlib.dates as mdates
 import matplotlib.pyplot as plt
 
 
+myFmt = mdates.DateFormatter("%m/%d")
+
+
 def plot_methanol(model):
-    plt.clf()
+    fig = plt.figure(figsize=(8, 8))
 
     times = pd.date_range("2013", periods=8760, freq="1h")
 
     # Electricity to H2 using Electrolyzer
     plt.subplot(3, 2, 1)
-    plt.title("Electrolyzer")
+    T = plt.title("Electrolyzer")
+    T.set_position([-0.2, 1.1])
     elyzer_elec_in = (
         model.plant.electrolyzer.eco_pem_electrolyzer_performance.get_val("electricity_in") / 1000
     )
@@ -18,77 +23,115 @@ def plot_methanol(model):
         / 1000
         * 24
     )
-    plt.plot(times, elyzer_elec_in, label="electricity_in [MW]", color=[0.5, 0.5, 1])
+    plt.plot(times, elyzer_elec_in, label="Electricity Available [MW]", color=[0.5, 0.5, 1])
     plt.plot(
         [times[0], times[-1]],
         [160, 160],
         "--",
-        label="electrolyzer_max_input [MW]",
+        label="Electrolyzer Capacity [MW]",
         color=[0.5, 0.5, 1],
     )
-    plt.plot(times, elyzer_h2_out, label="hydrogen_out [t/d]", color=[1, 0.5, 0])
-    plt.legend()
-    plt.xlim(pd.to_datetime("2012-12-15"), pd.to_datetime("2013-01-31"))
+    plt.plot(times, elyzer_h2_out, label="Hydrogen Produced [t/d]", color=[1, 0.5, 0])
+    plt.legend(bbox_to_anchor=(0, 1.02), loc=3)
+    plt.xlim(pd.to_datetime("2012-12-31"), pd.to_datetime("2013-01-31"))
+    plt.xticks(["2013-01-01", "2013-01-08", "2013-01-15", "2013-01-22", "2013-01-29"])
+    ax = plt.gca()
+    ax.xaxis.set_major_formatter(myFmt)
+    plt.xlabel("Day in simulated year")
+    plt.ylabel(
+        "Power\n[MW]\n \nFlow\n[t/d]", rotation="horizontal", va="center", ha="center", labelpad=20
+    )
 
     # Electricity to CO2 using DOC
     plt.subplot(3, 2, 2)
-    plt.title("DOC")
+    T = plt.title("Direct\nOcean\nCapture")
+    T.set_position([-0.2, 1.1])
     doc_elec_in = model.plant.doc.direct_ocean_capture_performance.get_val("electricity_in") / 1e6
     doc_co2_out = model.plant.doc.direct_ocean_capture_performance.get_val("co2_out") / 1000
-    plt.plot(times, doc_elec_in, label="electricity_in [MW]")
+    plt.plot(times, doc_elec_in, label="Electricity Available [MW]", color=[0.5, 0.5, 1])
     plt.plot(
         [times[0], times[-1]],
         [43.32621908, 43.32621908],
         "--",
-        label="doc_max_input [MW]",
+        label="DOC Input Capacity [MW]",
         color=[0.5, 0.5, 1],
     )
-    plt.plot(times, doc_co2_out, label="co2_out [t/hr]", color=[0.5, 0.25, 0])
-    plt.legend()
-    plt.xlim(pd.to_datetime("2012-12-15"), pd.to_datetime("2013-01-31"))
+    plt.plot(times, doc_co2_out, label="CO$_2$ Produced [t/hr]", color=[0.5, 0.25, 0])
+    plt.legend(bbox_to_anchor=(0, 1.02), loc=3)
+    plt.xlim(pd.to_datetime("2012-12-31"), pd.to_datetime("2013-01-31"))
+    plt.xticks(["2013-01-01", "2013-01-08", "2013-01-15", "2013-01-22", "2013-01-29"])
+    ax = plt.gca()
+    ax.xaxis.set_major_formatter(myFmt)
+    plt.xlabel("Day in simulated year")
+    plt.ylabel(
+        "Power\n[MW]\n \nFlow\n[t/hr]", rotation="horizontal", va="center", ha="center", labelpad=20
+    )
 
     # H2 and Storage
     plt.subplot(3, 2, 3)
-    plt.title("H2 Storage")
+    T = plt.title("Hydrogen\nStorage")
+    T.set_position([-0.2, 1.1])
     h2_storage_in = model.plant.electrolyzer_to_h2_storage_pipe.get_val("hydrogen_in") * 3600
     h2_storage_out = model.plant.h2_storage_to_methanol_pipe.get_val("hydrogen_out") * 3600
-    plt.plot(times, h2_storage_in, label="hydrogen_in [kg/hr]", color=[1, 0.5, 0])
-    plt.plot(times, h2_storage_out, label="hydrogen_out [kg/hr]", color=[0, 0.5, 0])
-    plt.legend()
-    plt.xlim(pd.to_datetime("2012-12-15"), pd.to_datetime("2013-01-31"))
+    plt.plot(times, h2_storage_in, label="Hydrogen In [kg/hr]", color=[1, 0.5, 0])
+    plt.plot(times, h2_storage_out, label="Hydrogen Out [kg/hr]", color=[0, 0.5, 0])
+    plt.legend(bbox_to_anchor=(0, 1.02), loc=3)
+    plt.xlim(pd.to_datetime("2012-12-31"), pd.to_datetime("2013-01-31"))
+    plt.xticks(["2013-01-01", "2013-01-08", "2013-01-15", "2013-01-22", "2013-01-29"])
+    ax = plt.gca()
+    ax.xaxis.set_major_formatter(myFmt)
+    plt.xlabel("Day in simulated year")
+    plt.ylabel("Flow\n[kg/hr]", rotation="horizontal", va="center", ha="center", labelpad=20)
 
     # H2 and Storage
     plt.subplot(3, 2, 4)
-    plt.title("CO2 Storage")
+    T = plt.title("CO$_2$\nStorage")
+    T.set_position([-0.2, 1.1])
     co2_storage_in = model.plant.doc_to_co2_storage_pipe.get_val("co2_in")
     co2_storage_out = model.plant.co2_storage_to_methanol_pipe.get_val("co2_out")
-    plt.plot(times, co2_storage_in, label="co2_in [kg/hr]", color=[0.5, 0.25, 0])
-    plt.plot(times, co2_storage_out, label="co2_out [kg/hr]", color=[0, 0.25, 0.5])
-    plt.legend()
-    plt.xlim(pd.to_datetime("2012-12-15"), pd.to_datetime("2013-01-31"))
+    plt.plot(times, co2_storage_in, label="CO$_2$ In [kg/hr]", color=[0.5, 0.25, 0])
+    plt.plot(times, co2_storage_out, label="CO$_2$ Out [kg/hr]", color=[0, 0.25, 0.5])
+    plt.legend(bbox_to_anchor=(0, 1.02), loc=3)
+    plt.xlim(pd.to_datetime("2012-12-31"), pd.to_datetime("2013-01-31"))
+    plt.xticks(["2013-01-01", "2013-01-08", "2013-01-15", "2013-01-22", "2013-01-29"])
+    ax = plt.gca()
+    ax.xaxis.set_major_formatter(myFmt)
+    plt.xlabel("Day in simulated year")
+    plt.ylabel("Flow\n[kg/hr]", rotation="horizontal", va="center", ha="center", labelpad=20)
 
     # H2 and CO2 to Methanol
     plt.subplot(3, 2, 5)
-    plt.title("Methanol")
+    T = plt.title("Methanol")
+    T.set_position([-0.2, 1.1])
     meoh_h2_in = model.plant.methanol.co2h_methanol_plant_performance.get_val("hydrogen_in")
     meoh_co2_in = model.plant.methanol.co2h_methanol_plant_performance.get_val("co2_in")
     meoh_meoh_out = model.plant.methanol.co2h_methanol_plant_performance.get_val("methanol_out")
-    plt.plot(times, meoh_h2_in, label="hydrogen_in [kg/hr]", color=[0, 0.5, 0])
-    plt.plot(times, meoh_co2_in, label="co2_in [kg/hr]", color=[0, 0.25, 0.5])
-    plt.plot(times, meoh_meoh_out, label="methanol_out [kg/hr]", color=[1, 0, 0.5])
-    plt.legend()
-    plt.xlim(pd.to_datetime("2012-12-15"), pd.to_datetime("2013-01-31"))
+    plt.plot(times, meoh_h2_in, label="Hydrogen In [kg/hr]", color=[0, 0.5, 0])
+    plt.plot(times, meoh_co2_in, label="CO$_2$ In [kg/hr]", color=[0, 0.25, 0.5])
+    plt.plot(times, meoh_meoh_out, label="Methanol Out [kg/hr]", color=[1, 0, 0.5])
+    plt.legend(bbox_to_anchor=(0, 1.02), loc=3)
+    plt.xlim(pd.to_datetime("2012-12-31"), pd.to_datetime("2013-01-31"))
+    plt.xticks(["2013-01-01", "2013-01-08", "2013-01-15", "2013-01-22", "2013-01-29"])
+    ax = plt.gca()
+    ax.xaxis.set_major_formatter(myFmt)
+    plt.xlabel("Day in simulated year")
+    plt.ylabel("Flow\n[kg/hr]", rotation="horizontal", va="center", ha="center", labelpad=20)
 
     # H2 and CO2 storage SOC
     plt.subplot(3, 2, 6)
-    plt.title("Storage SOC")
+    T = plt.title("State of\nCharge\n(SOC)")
+    T.set_position([-0.2, 1.1])
     h2_soc = model.plant.h2_storage.get_val("hydrogen_soc") * 100
     co2_soc = model.plant.co2_storage.get_val("co2_soc") * 100
-    plt.plot(times, h2_soc, label="hydrogen_soc [%]", color=[1, 0.5, 0])
-    plt.plot(times, co2_soc, label="co2_soc [%]", color=[0.5, 0.25, 0])
-    plt.legend()
-    plt.xlim(pd.to_datetime("2012-12-15"), pd.to_datetime("2013-01-31"))
+    plt.plot(times, h2_soc, label="Hydrogen SOC [%]", color=[1, 0.5, 0])
+    plt.plot(times, co2_soc, label="CO$_2$ SOC [%]", color=[0.5, 0.25, 0])
+    plt.legend(bbox_to_anchor=(0, 1.02), loc=3)
+    plt.xlim(pd.to_datetime("2012-12-31"), pd.to_datetime("2013-01-31"))
+    plt.xticks(["2013-01-01", "2013-01-08", "2013-01-15", "2013-01-22", "2013-01-29"])
+    ax = plt.gca()
+    ax.xaxis.set_major_formatter(myFmt)
+    plt.xlabel("Day in simulated year")
+    plt.ylabel("SOC\n[kg]", rotation="horizontal", va="center", ha="center", labelpad=20)
 
-    fig = plt.gcf()
     fig.tight_layout()
     plt.show()

examples/03_methanol/co2_hydrogenation_doc/tech_config_co2h.yaml

@@ -68,7 +68,7 @@ technologies:
         demand_profile: 2108.73919484047
       cost_parameters:
         cost_year: 2022
-        energy_capex: 383 # $/kg
+        energy_capex: 383 # $/kg - based on https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/review22/st235_houchins_2022_p-pdf.pdf?Status=Master
         power_capex: .000001 # $/kg/h
         opex_fraction: .000001 # percent of capex
   doc:
@@ -120,7 +120,7 @@ technologies:
         demand_profile: 15388.3891
       cost_parameters:
         cost_year: 2022
-        energy_capex: 5 # $/kg
+        energy_capex: 5 # $/kg - based on https://www.maritime.dot.gov/sites/marad.dot.gov/files/2024-11/LCE%20MARAD%20CCS%20TEA%20Final%20Report%20-%2031%20May%202024%20%283%29.pdf
         power_capex: .000001 # $/kg/h
         opex_fraction: .000001 # percent of capex
   methanol: