A-LEAF plugin

CHANGELOG.md

@@ -16,6 +16,8 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
   ([#107](https://github.com/watts-dev/watts/pull/107))
 * GCMAT plugin via the `PluginGCMAT` class
   ([#114](https://github.com/watts-dev/watts/pull/114))
+* A-LEAF plugin via the `PluginALEAF` class
+  ([#119](https://github.com/watts-dev/watts/pull/119))
 
 ### Changes
 

doc/source/conf.py

@@ -78,22 +78,15 @@
 
 html_theme_options = {
     "github_url": "https://github.com/watts-dev/watts",
-    "favicons": [
-        {
-            "rel": "icon",
-            "sizes": "16x16",
-            "href": "watts_16x16.png",
-        },
-        {
-            "rel": "icon",
-            "sizes": "32x32",
-            "href": "watts_32x32.png",
-        },
-    ],
     "switcher": {
         "json_url": "https://watts.readthedocs.io/en/latest/_static/switcher.json",
         "version_match": version if '-dev' not in version else 'dev',
     },
     "navbar_end": ["version-switcher", "navbar-icon-links"],
     "show_toc_level": 3,
 }
+
+favicons = [
+    "watts_16x16.png",
+    "watts_32x32.png",
+]

doc/source/reference/index.rst

@@ -14,7 +14,8 @@ API Reference
    watts.PluginGeneric
    watts.PluginABCE
    watts.PluginACCERT
-   watts.PluginGCMat
+   watts.PluginALEAF
+   watts.PluginGCMAT
    watts.PluginMCNP
    watts.PluginMOOSE
    watts.PluginOpenMC
@@ -23,6 +24,10 @@ API Reference
    watts.PluginSAS
    watts.PluginSerpent
    watts.Results
+   watts.ResultsABCE
+   watts.ResultsACCERT
+   watts.ResultsALEAF
+   watts.ResultsGCMAT
    watts.ResultsMCNP
    watts.ResultsMOOSE
    watts.ResultsOpenMC

doc/source/user/plugins.rst

@@ -335,6 +335,46 @@ As with other plugins, :class:`~watts.PluginABCE` is easily used by::
 .. note::
     `ABCE` is still under active development.
 
+A-LEAF Plugin
++++++++++++++
+The :class:`~watts.PluginALEAF` class enables simulations with the Argonne
+Low-carbon Electricity Analysis Framework (A-LEAF) code using a text-based input
+file for fuel price modification. The A-LEAF code uses an Excel-based input
+file, and the A-LEAF plugin will only modify the fuel price under the ``Fuel``
+section of the input file. Since the A-LEAF code is still under active
+development, the plugin will be updated as the code is updated.
+
+The A-LEAF fuel price input file can be templated as follows:
+
+.. code-block:: jinja
+
+    Scenario	FUEL	Type	Unit	2022	2023	2024	2025	2026	...
+
+    Reference	Coal	Real	2022 $/MMBtu	1.64	1.61	1.65	1.57	1.52	...
+    ...
+    Base	Nuclear	Real	2020 $/MMBTU	{% for year, price in fuel_price.items() %}
+    {{ price }}{% if not loop.last %}	{% endif %}{% endfor %}
+    Base	Biomass	Real	2020 $/MMBTU	5.00	5.11	5.22	5.34	5.45	...
+    ...
+    Low	OGS	Real	2020 $/MMBTU	3.30	3.00	2.73	2.58	2.56	...
+
+Before running the A-LEAF plugin, you need to specify the directory that the
+executable and the license key are in (they must be in the same directory). This
+can be done by adding the ``ALEAF_DIR`` variable to the environment or by
+explicitly specifying the path in the Python script as::
+
+    aleaf_plugin = watts.PluginALEAF(
+        'aleaf_template',
+        executable="/path/to/A-LEAF",
+        extra_inputs=[]
+    )
+
+As with other plugins, :class:`~watts.PluginALEAF` can be used by calling the
+:meth:`~watts.PluginALEAF` instance directly the same way as other plugins::
+
+    aleaf_plugin = watts.PluginALEAF('aleaf_template')
+    aleaf_result = aleaf_plugin(params)
+
 
 Dakota Plugin
 +++++++++++++
@@ -476,7 +516,7 @@ As with other plugins, :class:`~watts.PluginACCERT` is used by::
 GCMat Plugin
 ++++++++++++
 
-The :class:`~watts.PluginGCMat` class enables simulations with Argonne's global
+The :class:`~watts.PluginGCMAT` class enables simulations with Argonne's global
 critical materials agent-based model (GCMat). This code simulates dynamic
 economic markets that are composed of agents who have complex decision-making
 behaviors, and interact with and influence each other, possibly indirectly
@@ -500,15 +540,15 @@ The GCMat plugin requires a template input file that can be templated as follows
 
 The GCMat plugin can be instantiated with the following command line::
 
-    gcmat_plugin = watts.PluginGCMat('gcmat_template')
+    gcmat_plugin = watts.PluginGCMAT('gcmat_template')
 
 Before running the GCMat plugin, the directory that contains the executable
 'run_repast.sh' must be set. This can be done by setting the ``GCMAT_DIR``
 environment variable::
 
     export GCMAT_DIR='/path/to/gcmat/output'
 
-As with other plugins, :class:`~watts.PluginGCMat` is used by::
+As with other plugins, :class:`~watts.PluginGCMAT` is used by::
 
-    gcmat_plugin = watts.PluginGCMat('gcmat_template')
+    gcmat_plugin = watts.PluginGCMAT('gcmat_template')
     gcmat_result = gcmat_plugin(params)

examples/1App_ALAEF_LCGTEP/Fuel.txt

@@ -0,0 +1,19 @@
+Scenario	FUEL	Type	Unit	2022	2023	2024	2025	2026	2027	2028	2029	2030	2031	2032	2033	2034	2035	2036	2037	2038	2039	2040	2041	2042	2043	2044	2045	2046	2047	2048	2049	2050	2051	2052	2053	2054	2055	2056	2057	2058	2059	2060
+Reference	Coal	Real	2021 $/MMBtu	1.64	1.61	1.65	1.57	1.52	1.50	1.49	1.50	1.50	1.50	1.50	1.50	1.51	1.52	1.53	1.54	1.56	1.56	1.58	1.59	1.60	1.60	1.62	1.63	1.64	1.65	1.65	1.66	1.66	1.75	1.85	1.96	2.07	2.19	2.31	2.44	2.58	2.73	2.89
+Reference	NG	Real	2021 $/MMBtu	3.84	3.49	3.17	3.00	2.98	3.08	3.25	3.36	3.46	3.54	3.58	3.65	3.64	3.64	3.65	3.67	3.68	3.69	3.72	3.73	3.70	3.71	3.65	3.61	3.60	3.60	3.62	3.60	3.59	3.80	4.01	4.24	4.48	4.74	5.01	5.29	5.59	5.91	6.25
+Reference	Petroleum	Real	2021 $/MMBtu	12.18	10.50	11.39	11.61	11.89	12.20	12.46	12.58	12.82	13.08	13.27	13.42	13.54	13.67	13.86	14.03	14.20	14.26	14.55	14.70	14.75	15.00	15.28	15.36	15.58	15.65	15.62	15.65	15.59	16.48	17.42	18.41	19.46	20.57	21.74	22.98	24.29	25.68	27.14
+Reference	Nuclear	Real	2021 $/MMBtu	0.72	0.72	0.72	0.72	0.72	0.73	0.73	0.73	0.73	0.73	0.73	0.74	0.74	0.74	0.74	0.74	0.74	0.75	0.75	0.75	0.75	0.76	0.76	0.76	0.76	0.76	0.77	0.77	0.77	0.81	0.86	0.91	0.96	1.02	1.07	1.14	1.20	1.27	1.34
+Reference	Biomass	Real	2020 $/MMBTU	5.00	5.11	5.22	5.34	5.45	5.57	5.70	5.82	5.95	6.08	6.22	6.35	6.49	6.63	6.78	6.93	7.08	7.24	7.40	7.56	7.73	7.90	8.07	8.25	8.43	8.61	8.80	9.00	9.20	9.72	10.27	10.86	11.48	12.13	12.82	13.56	14.33	15.15	16.01
+Reference	OGS	Real	2020 $/MMBTU	3.84	3.49	3.17	3.00	2.98	3.08	3.25	3.36	3.46	3.54	3.58	3.65	3.64	3.64	3.65	3.67	3.68	3.69	3.72	3.73	3.70	3.71	3.65	3.61	3.60	3.60	3.62	3.60	3.59	3.80	4.01	4.24	4.48	4.74	5.01	5.29	5.59	5.91	6.25
+Base	Coal	Real	2020 $/MMBTU	1.57	1.54	1.57	1.50	1.45	1.43	1.43	1.43	1.43	1.44	1.43	1.43	1.44	1.45	1.46	1.47	1.49	1.49	1.51	1.52	1.52	1.53	1.54	1.56	1.56	1.57	1.57	1.58	1.58	1.67	1.77	1.87	1.98	2.09	2.21	2.33	2.47	2.61	2.76
+Base	NG	Real	2020 $/MMBTU	3.67	3.34	3.03	2.87	2.84	2.94	3.10	3.21	3.30	3.38	3.42	3.48	3.48	3.47	3.48	3.50	3.52	3.52	3.55	3.56	3.54	3.54	3.48	3.45	3.44	3.43	3.46	3.44	3.43	3.62	3.83	4.05	4.28	4.52	4.78	5.05	5.34	5.65	5.97
+Base	Petroleum	Real	2020 $/MMBTU	11.63	10.03	10.88	11.09	11.35	11.65	11.90	12.01	12.24	12.49	12.68	12.82	12.93	13.05	13.24	13.40	13.56	13.61	13.90	14.04	14.09	14.32	14.60	14.67	14.88	14.95	14.92	14.94	14.89	15.74	16.63	17.58	18.58	19.64	20.76	21.95	23.20	24.52	25.92
+Base	Nuclear	Real	2020 $/MMBTU	{% for year, price in fuel_price.items() %}{{ price }}{% if not loop.last %}	{% endif %}{% endfor %}
+Base	Biomass	Real	2020 $/MMBTU	5.00	5.11	5.22	5.34	5.45	5.57	5.70	5.82	5.95	6.08	6.22	6.35	6.49	6.63	6.78	6.93	7.08	7.24	7.40	7.56	7.73	7.90	8.07	8.25	8.43	8.61	8.80	9.00	9.20	9.72	10.27	10.86	11.48	12.13	12.82	13.56	14.33	15.15	16.01
+Base	OGS	Real	2020 $/MMBTU	3.67	3.34	3.03	2.87	2.84	2.94	3.10	3.21	3.30	3.38	3.42	3.48	3.48	3.47	3.48	3.50	3.52	3.52	3.55	3.56	3.54	3.54	3.48	3.45	3.44	3.43	3.46	3.44	3.43	3.62	3.83	4.05	4.28	4.52	4.78	5.05	5.34	5.65	5.97
+Low	Coal	Real	2020 $/MMBTU	1.41	1.38	1.42	1.35	1.31	1.29	1.28	1.29	1.29	1.29	1.29	1.29	1.30	1.30	1.32	1.33	1.34	1.34	1.36	1.37	1.37	1.38	1.39	1.40	1.41	1.42	1.41	1.42	1.43	1.51	1.59	1.68	1.78	1.88	1.99	2.10	2.22	2.35	2.48
+Low	NG	Real	2020 $/MMBTU	3.30	3.00	2.73	2.58	2.56	2.64	2.79	2.89	2.97	3.05	3.07	3.13	3.13	3.13	3.14	3.15	3.17	3.17	3.20	3.20	3.18	3.19	3.13	3.11	3.10	3.09	3.11	3.09	3.09	3.26	3.45	3.64	3.85	4.07	4.30	4.55	4.81	5.08	5.37
+Low	Petroleum	Real	2020 $/MMBTU	10.46	9.03	9.79	9.98	10.22	10.48	10.71	10.81	11.02	11.24	11.41	11.53	11.64	11.75	11.91	12.06	12.21	12.25	12.51	12.63	12.68	12.89	13.14	13.21	13.39	13.45	13.43	13.45	13.40	14.16	14.97	15.82	16.73	17.68	18.69	19.75	20.88	22.07	23.33
+Low	Nuclear	Real	2020 $/MMBTU	0.62	0.62	0.62	0.62	0.62	0.62	0.62	0.63	0.63	0.63	0.63	0.63	0.63	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.41	1.03	1.09	1.15
+Low	Biomass	Real	2020 $/MMBTU	4.50	4.60	4.70	4.80	4.91	5.02	5.13	5.24	5.36	5.47	5.59	5.72	5.84	5.97	6.10	6.24	6.37	6.51	6.66	6.80	6.95	7.11	7.26	7.42	7.59	7.75	7.92	8.10	8.28	8.75	9.25	9.77	10.33	10.92	11.54	12.20	12.90	13.63	14.41
+Low	OGS	Real	2020 $/MMBTU	3.30	3.00	2.73	2.58	2.56	2.64	2.79	2.89	2.97	3.05	3.07	3.13	3.13	3.13	3.14	3.15	3.17	3.17	3.20	3.20	3.18	3.19	3.13	3.11	3.10	3.09	3.11	3.09	3.09	3.26	3.45	3.64	3.85	4.07	4.30	4.55	4.81	5.08	5.37

examples/1App_ALAEF_LCGTEP/README.md

@@ -0,0 +1,21 @@
+# 1App_ALEAF_LCGTEP
+
+## Purpose
+
+This example provides a demonstration for using WATTS to run and analyze simulations with A-LEAF (Argonne Low-carbon Electricity Analysis Framework) under various supply chain disruption scenarios. The example explores different fuel price settings to understand the impact on US nuclear capacity expansion result.
+
+
+## Code(s)
+
+- A-LEAF
+
+## Keywords
+
+- Energy Market
+- Capacity Expansion
+- Economic Modeling 
+
+## File descriptions
+
+- [__watts_exec.py__](watts_exec.py): WATTS workflow for this example. This is the file to execute to run the problem described above.
+- [__A-LEAF_fuel_template__](Fuel.txt): Fuel price for this example

examples/1App_ALAEF_LCGTEP/watts_exec.py

@@ -0,0 +1,67 @@
+# SPDX-FileCopyrightText: 2022-2025 UChicago Argonne, LLC
+# SPDX-License-Identifier: MIT
+
+"""
+This example demonstrates how to use WATTS to run an A-LEAF calculation.
+"""
+
+import watts
+from pathlib import Path
+import pandas as pd
+
+params = watts.Parameters()
+
+
+fuel_price = {
+    2022: 0.62, 2023: 0.62, 2024: 0.62, 2025: 0.62, 2026: 0.62, 2027: 0.62,
+    2028: 0.63, 2029: 0.63, 2030: 0.63, 2031: 0.76, 2032: 0.76, 2033: 0.76,
+    2034: 0.76, 2035: 0.76, 2036: 0.77, 2037: 0.77, 2038: 0.77, 2039: 0.77,
+    2040: 0.77, 2041: 0.78, 2042: 0.78, 2043: 0.78, 2044: 0.78, 2045: 0.78,
+    2046: 0.79, 2047: 0.79, 2048: 0.79, 2049: 0.79, 2050: 0.79, 2051: 0.80,
+    2052: 0.80, 2053: 0.80, 2054: 0.80, 2055: 0.80, 2056: 0.81, 2057: 0.81,
+    2058: 0.81, 2059: 0.81, 2060: 0.81
+}
+
+
+# Set nuclear prices from reference and calculated growth
+starting_year = 2031
+starting_price = 0.76
+
+# Compute prices dynamically for years after the starting year
+growth_rates = {year: 1.0028 if year <= 2050 else 1.057 for year in range(starting_year + 1, 2061)}
+computed_prices = {starting_year: starting_price}
+for year in range(starting_year + 1, 2061):
+    prev_price = computed_prices[year - 1]
+    computed_prices[year] = round(prev_price * growth_rates[year], 3)
+
+fuel_price.update(computed_prices)
+
+# Initialize parameters and pass the fuel price dictionary
+params = watts.Parameters()
+params['fuel_price'] = fuel_price
+# Display parameter summary
+params.show_summary(show_metadata=True, sort_by='key')
+
+# Set default path for results
+results_path = Path.cwd() / 'results'
+results_path.mkdir(exist_ok=True, parents=True)
+watts.Database.set_default_path(results_path)
+
+# Create ALEAF plugin
+# aleaf_plugin = watts.PluginALEAF('Fuel.txt', extra_templates={'Simulation Configuration': 'Simulation Configuration.txt'})
+aleaf_plugin = watts.PluginALEAF('Fuel.txt')
+# Run ALEAF
+aleaf_result = aleaf_plugin(params)
+print('ALEAF simulation completed.')
+
+# Get the technology summary
+techsummary = aleaf_result.csv_data
+# keep only the capacity columns 
+techsummary = techsummary[['Year', 'UnitGroup', 'Unit_Type', 'Fuel', 'ICAP', 'ICap_New', 'ICap_Ret']]
+
+# group the technology summary by year and fuel
+grouped_df = techsummary.groupby(['Year','Fuel']).sum()
+# print out the grouped dataframe
+print(grouped_df)
+
+

pyproject.toml

@@ -49,6 +49,7 @@ docs = [
     "pydata_sphinx_theme",
     "sphinx_design",
     "sphinx-autodoc-typehints",
+    "sphinx-favicon",
 ]
 
 [project.urls]

src/watts/__init__.py

@@ -2,6 +2,7 @@
 # SPDX-License-Identifier: MIT
 
 from .plugin import *
+from .plugin_aleaf import *
 from .plugin_accert import *
 from .plugin_openmc import *
 from .plugin_moose import *