Add 2016 historical capacity and generation data

ATTRIBUTION.md

@@ -0,0 +1,101 @@
+### Generation
+#### 2016 Historical Generation in the Texas Interconnection
+##### Source 1
+* Name: Form EIA-923
+* Author: Energy Information Administration (EIA)
+* Description: Electric power data -- monthly and annually -- on electricity generation, fuel consumption, fossil fuel stocks, and receipts at the power plant and prime mover level for 2016
+* Source: https://www.eia.gov
+* Exact source location: https://www.eia.gov/electricity/data/eia923/
+
+##### Source 2
+* Name: 2016 State of the Grid Report
+* Author: Electric Reliability Council of Texas (ERCOT)
+* Description: 2016 report on the state of the electric grid managed by ERCOT. In particular, the 2016 Energy Use bar plot was exploited.
+* Source: https://www.ercot.gov
+* Exact source location: http://www.ercot.com/news/presentations/2016
+
+##### Destination
+* Modifications to source files(s): Aggregation were made in source \#1 to obtain the electricity generated by each generator type in each load zone of the Western interconnection.
+* Location: ***postreise/data/2016_Historical_Texas_Generation_TWh.csv***
+
+##### Legal Note
+* Acknowledge EIA and ERCOT as the source
+
+
+#### 2016 Historical Generation in the Western Interconnection
+##### Source
+* Name: Form EIA-923
+* Author: EIA
+* Description: Electric power data -- monthly and annually -- on electricity generation, fuel consumption, fossil fuel stocks, and receipts at the power plant and prime mover level for 2016
+* Source: https://www.eia.gov
+* Exact source location: https://www.eia.gov/electricity/data/eia923/
+
+##### Destination
+* Modifications to source files(s): Aggregation were made to obtain the electricity generated by each generator type in each load zone of the Western interconnection.
+* Location: ***postreise/data/2016_Historical_Western_Generation_TWh.csv***
+
+##### Legal Note
+* Acknowledge EIA as the source
+
+
+#### 2016 Historical Generation in the U.S. electric Grid
+##### Source 1
+* Name: Form EIA-923
+* Author: EIA
+* Description: Electric power data -- monthly and annually -- on electricity generation, fuel consumption, fossil fuel stocks, and receipts at the power plant and prime mover level for 2016
+* Source: https://www.eia.gov
+* Exact source location: https://www.eia.gov/electricity/data/eia923/
+
+##### Source 2
+* Name: 2016 State of the Grid Report
+* Author: Electric Reliability Council of Texas (ERCOT)
+* Description: 2016 report on the state of the electric grid managed by ERCOT. In particular, the 2016 Energy Use bar plot was exploited.
+* Source: https://www.ercot.gov
+* Exact source location: http://www.ercot.com/news/presentations/2016
+
+##### Destination
+* Modifications to source files(s): Aggregation were made in source \#1 to obtain the electricity generated by each generator type in each load zone of the Western interconnection.
+* Location: ***postreise/data/2016_Historical_USA_TAMU_Generation_GWh.csv***
+
+##### Legal Note
+* Acknowledge EIA and ERCOT as the source
+
+
+---
+### Capacity
+#### 2016 Generation Capacity
+* Name: Form EIA-860
+* Author: EIA
+* Description: 2016 solar technology data for operable units (Schedule 3)
+* Source: https://www.eia.gov
+* Exact source location: https://www.eia.gov/electricity/data/eia860/
+
+##### Destination
+* Modifications to source file(s): Aggregation were made in source to obtain the generation capacity for each generator type in each load zone.
+* Location:
+  * ***2016_Historical_Texas_Capacity_GW.csv***
+  * ***2016_Historical_Western_Capacity_GW.csv***
+  * ***2016_Historical_USA_TAMU_Capacity_MW.csv***
+
+#### Legal Note
+* Acknowledge EIA as the source
+
+
+---
+### NREL Scenarios Results
+#### Open Energy Data Initiative (OEDI)
+* Name: Standard Scenarios Results Viewer
+* Author: NREL
+* Description: Dashboard web application showcasing results from scenarios ran by NREL
+* Source: https://openei.org
+* Exact source location: https://openei.org/apps/reeds/#
+
+##### Destination
+* Modifications to source file(s): None
+* Location:
+  * ***2016_NREL_Low_Demand_Western_Generation_TWh.csv***
+  * ***2016_NREL_Mid_Case_Western_Generation_TWh.csv***
+
+
+#### Legal Note
+* Recommended citation: Cole, Wesley, Nathaniel Gates, Trieu Mai, Daniel Greer, and Paritosh Das. 2019. 2019 Standard Scenarios Report: A U.S. Electricity Sector Outlook, Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-74110. https://www.nrel.gov/docs/fy20osti/74110.pdf.

postreise/analyze/generation/summarize.py

@@ -2,17 +2,23 @@
 
 import numpy as np
 import pandas as pd
-from powersimdata.network.usa_tamu.constants.plants import label2type
+from powersimdata.network.usa_tamu.constants.plants import type2label
 from powersimdata.network.usa_tamu.constants.zones import (
     abv2state,
     interconnect2abv,
+    interconnect2loadzone,
     loadzone2interconnect,
     loadzone2state,
+    state2loadzone,
 )
 from powersimdata.scenario.analyze import Analyze
 from powersimdata.scenario.scenario import Scenario
 
-from postreise.analyze.check import _check_scenario_is_in_analyze_state
+from postreise.analyze.check import (
+    _check_data_frame,
+    _check_resources_and_format,
+    _check_scenario_is_in_analyze_state,
+)
 
 
 def sum_generation_by_type_zone(scenario: Scenario) -> pd.DataFrame:
@@ -71,11 +77,12 @@ def _groupby_state(index: str) -> str:
     """Use state as a dict key if index is a smaller region (e.g. Texas East),
     otherwise use the given index.
 
-    :param str index: either a state name or region within a state
-    :return: (*str*) the corresponding state name
+    :param str index: either a state name or region within a state.
+    :return: (*str*) -- the corresponding state name.
     """
-    for state in ("Texas", "New Mexico", "Montana"):
-        if state in index:
+    interconnect_spanning_states = ("Texas", "New Mexico", "Montana")
+    for state in interconnect_spanning_states:
+        if index in state2loadzone[state]:
             return state
     return index
 
@@ -84,19 +91,44 @@ def summarize_hist_gen(hist_gen_raw: pd.DataFrame, all_resources: list) -> pd.Da
     """Sum generation by state for the given resources from a scenario, adding
     totals for interconnects and for all states.
 
-    :param pandas.DataFrame hist_gen_raw: historical generation data frame
-    :param list all_resources: list of resources from the scenario
-    :return: (*pandas.DataFrame*) historical generation per resource
+    :param pandas.DataFrame hist_gen_raw: historical generation data frame. Columns
+        are resources and indices are either state or load zone.
+    :param list all_resources: list of resources.
+    :return: (*pandas.DataFrame*) -- historical generation per resource.
     """
-    western = [abv2state[s] for s in interconnect2abv["Western"]]
-    eastern = [abv2state[s] for s in interconnect2abv["Eastern"]]
+    _check_data_frame(hist_gen_raw, "PG")
+    filtered_colnames = _check_resources_and_format(all_resources)
 
-    filtered_colnames = [k for k in label2type.keys() if label2type[k] in all_resources]
     result = hist_gen_raw.copy()
-    result = result.loc[:, filtered_colnames]
-    result.rename(columns=label2type, inplace=True)
+
+    # Interconnection
+    eastern_areas = (
+        set([abv2state[s] for s in interconnect2abv["Eastern"]])
+        | interconnect2loadzone["Eastern"]
+    )
+    eastern = result.loc[result.index.isin(eastern_areas)].sum()
+
+    ercot_areas = interconnect2loadzone["Texas"]
+    ercot = result.loc[result.index.isin(ercot_areas)].sum()
+
+    western_areas = (
+        set([abv2state[s] for s in interconnect2abv["Western"]])
+        | interconnect2loadzone["Western"]
+    )
+    western = result.loc[result.index.isin(western_areas)].sum()
+
+    # State
     result = result.groupby(by=_groupby_state).aggregate(np.sum)
-    result.loc["Western"] = result[result.index.isin(western)].sum()
-    result.loc["Eastern"] = result[result.index.isin(eastern)].sum()
-    result.loc["all"] = result[~result.index.isin(["Eastern", "Western"])].sum()
+
+    # Summary
+    all = result.sum()
+
+    result.loc["Eastern interconnection"] = eastern
+    result.loc["Western interconnection"] = western
+    result.loc["Texas interconnection"] = ercot
+    result.loc["All"] = all
+
+    result = result.loc[:, filtered_colnames]
+    result.rename(columns=type2label, inplace=True)
+
     return result

postreise/analyze/generation/tests/test_summarize.py

@@ -1,3 +1,4 @@
+import os
 import pathlib
 import unittest
 
@@ -71,9 +72,14 @@ def sim_gen_result():
 
 @pytest.fixture
 def hist_gen_raw():
-    raw_hist_gen_csv = "usa_hist_gen.csv"
-    path_to_csv = pathlib.Path(__file__).parent.joinpath(raw_hist_gen_csv)
-    hist_gen_raw = pd.read_csv(path_to_csv, index_col=0)
+    # raw_hist_gen_csv = "usa_hist_gen.csv"
+    # path_to_csv = pathlib.Path(__file__).parent.joinpath(raw_hist_gen_csv)
+
+    path_to_csv = pathlib.Path(__file__).parent.joinpath(
+        "..", "..", "..", "data", "2016_Historical_USA_TAMU_Generation_GWh.csv"
+    )
+
+    hist_gen_raw = pd.read_csv(path_to_csv, index_col=0).T
     return hist_gen_raw
 
 
@@ -98,11 +104,19 @@ def test_sum_generation_by_state_values_scaled(sim_gen_result):
 def test_summarize_hist_gen_include_areas(hist_gen_raw):
     all_resources = ["wind", "hydro", "coal"]
     actual_hist_gen = summarize_hist_gen(hist_gen_raw, all_resources)
-    for area in ("Western", "Eastern", "Texas", "Montana", "all"):
+    for area in (
+        "Western interconnection",
+        "Eastern interconnection",
+        "Texas interconnection",
+        "Montana",
+        "New Mexico",
+        "All",
+    ):
         assert area in actual_hist_gen.index
 
 
 def test_summarize_hist_gen_shape(hist_gen_raw):
     all_resources = ["wind", "hydro", "coal"]
     actual_hist_gen = summarize_hist_gen(hist_gen_raw, all_resources)
-    assert (8, 3) == actual_hist_gen.shape
+    # 48 contiguous, 3 interconnections and total
+    assert (52, 3) == actual_hist_gen.shape

postreise/data/2016_Historical_USA_TAMU_Capacity_MW.csv

@@ -0,0 +1,10 @@
+Resource Type,Arizona,California,Colorado,Idaho,Montana Western,Nevada,New Mexico Western,Oregon,Utah,Washington,Wyoming,El Paso,Coast,East,Far West,North,North Central,South,South Central,West,East Texas,Texas Panhandle,Alabama,Arkansas,Connecticut,Delaware,Florida,Georgia,Iowa,Illinois,Indiana,Kansas,Kentucky,Louisiana,Massachusetts,Maryland,Maine,Michigan,Minnesota,Missouri,Mississippi,Montana Eastern,North Carolina,North Dakota,Nebraska,New Hampshire,New Jersey,New Mexico Eastern,New York,Ohio,Oklahoma,Pennsylvania,Rhode Island,South Carolina,South Dakota,Tennessee,Virginia,Vermont,Wisconsin,West Virginia
+coal,6245.000,63.000,5525.000,10.000,2434.000,1104.000,3741.000,642.000,4894.000,1460.000,7254.000,0.000,2737.000,8145.000,0.000,720.000,2858.000,1032.000,5318.000,0.000,1837.000,2215.800,6671.200,5487.000,400.000,445.500,11090.300,10109.700,6034.500,15662.700,17585.400,4946.700,15601.700,3170.200,1124.600,5126.800,0.000,10354.200,4655.700,12490.600,2010.300,54.000,11494.200,4350.100,3982.900,559.200,1343.300,0.000,1849.700,16273.800,5371.000,14155.000,0.000,5526.900,450.000,8126.500,4054.900,0.000,7744.700,13510.900
+dfo,128.300,512.500,202.700,5.000,0.000,6.000,76.800,0.000,29.300,15.500,5.800,0.000,3.200,14.000,0.000,6.000,16.500,11.800,1.500,0.000,0.000,0.000,47.800,12.300,2816.800,121.600,5663.300,1072.600,1134.100,808.200,285.700,625.900,11.500,55.200,3331.500,1389.000,917.600,560.500,921.200,1316.600,48.000,0.000,490.800,68.500,360.700,110.000,258.400,0.000,3882.100,713.900,80.300,2093.100,16.400,704.200,282.400,47.600,2755.900,132.900,732.000,18.500
+geothermal,0.000,1322.000,0.000,0.000,0.000,383.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000
+hydro,2912.000,13801.000,1178.000,2543.000,2665.000,1052.000,82.000,8451.000,262.000,21437.000,303.000,0.000,60.000,0.000,0.000,102.000,46.000,41.000,258.000,121.000,81.000,0.000,3319.300,1348.900,149.500,0.000,55.700,3597.800,129.200,39.700,92.100,7.000,1016.600,192.000,1808.900,550.800,714.800,2340.400,215.300,1106.400,0.000,0.000,1985.400,614.000,332.300,424.800,467.700,0.000,5917.700,128.600,1068.500,2460.500,2.800,4054.400,1602.100,4213.000,3931.700,327.400,536.900,370.600
+ng,16265.000,45228.000,7442.000,1275.000,260.000,8616.000,2304.000,4331.000,3229.000,4116.000,418.000,1540.000,21128.000,3194.000,2751.000,2257.000,14883.000,5731.000,9757.000,1257.000,9644.500,4167.800,15383.000,7229.400,3806.600,3016.200,43830.100,18652.300,3488.500,17599.000,8344.200,5197.100,7326.900,20853.700,6896.100,4316.900,1758.200,12031.000,5785.100,6905.600,14275.000,216.600,12610.400,458.500,2358.500,1814.500,13573.300,1459.600,23357.000,11282.400,15053.300,16833.200,1973.100,6974.700,1067.000,6453.000,12794.600,0.000,7621.000,1339.400
+nuclear,3689.650,2154.657,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1096.910,0.000,0.000,2469.752,0.000,0.000,0.000,2320.713,0.000,0.000,0.000,0.000,0.000,4547.111,1529.424,1888.898,0.000,3341.231,3929.323,535.903,11236.993,0.000,939.697,0.000,1954.576,617.001,1682.030,0.000,3595.548,1579.541,1074.638,672.037,0.000,4875.787,0.000,1065.568,1226.313,3405.635,0.000,4737.318,1916.464,0.000,9449.797,0.000,6361.771,0.000,3370.639,3388.167,0.000,1156.826,0.000
+solar,1890.000,9862.000,419.000,120.000,0.000,1598.000,312.000,100.000,856.000,1.000,1.000,10.000,1.000,1.000,200.000,100.000,7.000,1.000,128.000,140.000,1.000,1.000,75.000,13.000,25.200,30.800,331.600,978.100,2.600,34.100,160.600,1.000,10.000,1.000,482.300,144.400,1.000,12.500,256.200,26.700,3.100,1.000,2431.400,1.000,6.300,1.000,613.900,165.000,110.300,48.800,2.500,53.700,10.200,19.300,1.000,72.600,139.000,66.000,3.100,1.000
+wind,267.000,5671.000,3027.000,974.000,657.000,150.000,629.000,3161.000,388.000,3074.000,1487.000,1.000,1.000,1.000,3189.000,2516.000,1831.000,4206.000,1.000,5605.000,1.000,1247.600,1.000,1.000,5.000,2.000,3.000,2.000,6935.300,4008.500,1889.700,4469.800,1.000,1.000,96.400,190.000,898.800,1609.900,3517.000,660.400,1.000,30.000,209.000,2828.400,1318.900,185.300,9.000,483.600,1829.800,533.900,6654.800,1373.400,21.000,1.000,627.800,28.800,2.000,121.000,637.500,686.300
+other,24.000,768.000,25.000,68.000,53.000,9.000,2.000,119.000,34.000,236.000,8.000,1.000,133.000,11.000,1.000,1.000,31.000,11.000,24.000,1.000,69.000,1.000,381.000,157.000,166.000,10.000,887.000,546.000,34.000,84.000,91.000,7.000,59.000,957.000,235.000,105.000,361.000,319.000,262.000,17.000,174.000,37.000,366.000,6.000,11.000,201.000,194.000,1.000,366.000,186.000,44.000,376.000,24.000,279.000,1.000,108.000,518.000,55.000,187.000,1.000

postreise/data/2016_Historical_USA_TAMU_Generation_GWh.csv

@@ -0,0 +1,11 @@
+Resource Type,Arizona,California,Colorado,Idaho,Montana Western,Nevada,New Mexico Western,Oregon,Utah,Washington,Wyoming,El Paso,Coast,East,Far West,North,North Central,South,South Central,West,East Texas,Texas Panhandle,Alabama,Arkansas,Connecticut,Delaware,Florida,Georgia,Iowa,Illinois,Indiana,Kansas,Kentucky,Louisiana,Massachusetts,Maryland,Maine,Michigan,Minnesota,Missouri,Mississippi,Montana Eastern,North Carolina,North Dakota,Nebraska,New Hampshire,New Jersey,New Mexico Eastern,New York,Ohio,Oklahoma,Pennsylvania,Rhode Island,South Carolina,South Dakota,Tennessee,Virginia,Vermont,Wisconsin,West Virginia
+coal,30403.392,318.876,29948.675,29.188,14003.160,2166.831,18365.269,1898.202,25939.443,4601.726,40026.874,0.000,12244.469,41248.699,0.000,2485.825,13265.643,5468.957,26293.020,0.000,9555.977,10668.343,34258.405,23800.070,177.370,479.350,41477.954,37890.475,25198.347,59337.802,72533.080,23096.064,67952.425,12013.649,1874.932,13826.213,69.823,41225.684,23206.289,60321.889,5342.026,266.062,37435.780,26580.350,21897.715,422.017,1314.541,0.000,1770.238,68775.164,19158.044,54672.030,0.000,21002.913,2082.680,31167.634,16499.412,0.000,33362.841,71512.961
+dfo,51.595,34.643,6.311,0.183,10.409,10.985,51.683,4.850,31.552,13.529,51.961,1.089,0.245,20.664,2.228,5.586,1.592,6.663,26.488,0.189,14.731,0.470,34.353,39.008,63.780,39.854,770.497,90.103,238.770,69.449,102.006,28.324,88.047,14.478,420.319,159.672,109.206,101.667,30.513,73.671,17.785,0.275,248.855,29.782,0.000,9.129,34.263,0.000,195.594,217.294,17.261,270.955,24.163,83.818,2.931,121.581,535.361,2.347,26.309,122.369
+geothermal,0.000,11457.299,0.000,71.995,0.000,3353.399,14.218,183.700,485.094,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000
+hydro,7401.466,30683.589,2444.138,9033.272,10082.529,1789.219,147.972,34549.366,759.542,78348.633,973.488,0.000,122.742,0.000,0.000,336.447,143.680,88.636,387.926,93.604,168.970,0.000,7462.945,3608.810,234.988,0.000,174.551,5173.998,916.789,132.834,426.265,30.538,3477.930,1103.023,2161.236,1392.187,2999.688,3446.973,1208.502,1982.838,0.000,0.000,4489.092,1912.005,1529.430,1145.386,537.035,0.000,27784.974,500.320,2664.281,4202.499,2.128,6160.029,4132.504,9532.675,4872.869,1077.517,2795.258,1638.247
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