from datascience import *
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import pandas as pd
prices = Table.read_table('F2_bids_5.csv').sort('PRICE1')
prices
TEAM | TEAM_ID | PORTFOLIO | PORTFOLIO_ID | PLANT | PLANT_ID | PERIOD | PRICE1 | PRICE2 | PRICE3 | PRICE4 |
---|---|---|---|---|---|---|---|---|---|---|
Coase | 3 | Old_Timers | 6 | BIG_CREEK | 61 | 5 | 0 | 0 | 0 | 0 |
Arrow | 1 | Low_Fossil | 7 | HELMS | 72 | 5 | 0.5 | 0.5 | 0.5 | 0.5 |
Arrow | 1 | Low_Fossil | 7 | DIABLO_CANYON_1 | 75 | 5 | 11.5 | 11.5 | 11.5 | 11.5 |
Coase | 3 | Old_Timers | 6 | MOHAVE_1 | 62 | 5 | 34.5 | 34.5 | 34.5 | 34.5 |
Friedman | 5 | Bay_Views | 3 | MOSS_LANDING_6 | 33 | 5 | 40 | 40 | 40 | 40 |
Friedman | 5 | Bay_Views | 3 | MOSS_LANDING_7 | 34 | 5 | 40 | 40 | 40 | 40 |
Krugman | 7 | Big_Coal | 1 | HUNTINGTON_BEACH_1-2 | 13 | 5 | 40.5 | 40.5 | 40.5 | 40.5 |
Becker | 2 | Big_Gas | 2 | EL_SEGUNDO_3-4 | 22 | 5 | 41.97 | 44.97 | 61.97 | 44.97 |
Becker | 2 | Big_Gas | 2 | ENCINA | 25 | 5 | 41.97 | 44.97 | 61.97 | 44.97 |
Krugman | 7 | Big_Coal | 1 | REDONDO_5-6 | 15 | 5 | 42.3 | 41.94 | 41.94 | 42.3 |
... (32 rows omitted)
ESG = Table.read_table('ESGPorfolios_.csv').sort("Total_Var_Cost_USDperMWH")
ESG
Group | Group_num | UNIT NAME | Capacity_MW | Heat_Rate_MMBTUperMWh | Fuel_Price_USDperMMBTU | Fuel_Cost_USDperMWH | Var_OandM_USDperMWH | Total_Var_Cost_USDperMWH | Carbon_tonsperMWH | FixedCst_OandM_perDay |
---|---|---|---|---|---|---|---|---|---|---|
Old_Timers | 7 | BIG CREEK | 1000 | nan | 0 | 0 | 0 | 0 | 0 | $15,000 |
Fossil_Light | 8 | HELMS | 800 | nan | 0 | 0 | 0.5 | 0.5 | 0 | $15,000 |
Fossil_Light | 8 | DIABLO CANYON 1 | 1000 | 1 | 7.5 | 7.5 | 4 | 11.5 | 0 | $20,000 |
Bay_Views | 4 | MOSS LANDING 6 | 750 | 6.9 | 4.5 | 31.06 | 1.5 | 32.56 | 0.37 | $8,000 |
Bay_Views | 4 | MOSS LANDING 7 | 750 | 6.9 | 4.5 | 31.06 | 1.5 | 32.56 | 0.37 | $8,000 |
Old_Timers | 7 | MOHAVE 1 | 750 | 10 | 3 | 30 | 4.5 | 34.5 | 0.94 | $15,000 |
Old_Timers | 7 | MOHAVE 2 | 750 | 10 | 3 | 30 | 4.5 | 34.5 | 0.94 | $15,000 |
Big_Coal | 1 | FOUR CORNERS | 1900 | 11.67 | 3 | 35 | 1.5 | 36.5 | 1.1 | $8,000 |
Bay_Views | 4 | MORRO BAY 3&4 | 665 | 8.02 | 4.5 | 36.11 | 0.5 | 36.61 | 0.43 | $4,000 |
East_Bay | 6 | PITTSBURGH 5&6 | 650 | 8.02 | 4.5 | 36.11 | 0.5 | 36.61 | 0.43 | $2,500 |
... (32 rows omitted)
big_coal = prices.where("PORTFOLIO","Big_Coal")
capacities = ESG.where("Group","Big Coal").column('Capacity_MW')