isodata 0.5.0

API to access energy data

Install — Getting Started — Method Availability — LMP Data — Supported LMP Markets — Feedback

isodata provides standardized API to access energy data from the major Independent System Operators (ISOs) in the United States.

Install

isodata supports python 3.7+. Install with pip

python -m pip install isodata

Getting Started

First, we can see all of the ISOs that are supported

>>> import isodata
>>> isodata.list_isos()

                                    Name     Id
0                         California ISO  caiso
1  Electric Reliability Council of Texas  ercot
2                           New York ISO  nyiso
3                   Southwest Power Pool    spp
4                                    PJM    pjm
5                       Midcontinent ISO   miso
6                        ISO New England  isone

Next, we can select an ISO we want to use

>>> iso = isodata.get_iso('caiso')
>>> caiso = iso()

All ISOs have the same API. Here is how we can get the fuel mix

>>> caiso.get_latest_fuel_mix()

ISO: California ISO
Total Production: 43104 MW
Time: 2022-08-03 18:25:00-07:00
+-------------+-------+-----------+
| Fuel        |    MW |   Percent |
|-------------+-------+-----------|
| Natural Gas | 19868 |      46.1 |
| Solar       |  5388 |      12.5 |
| Imports     |  4997 |      11.6 |
| Wind        |  3887 |       9   |
| Large Hydro |  3312 |       7.7 |
| Nuclear     |  2255 |       5.2 |
| Batteries   |  1709 |       4   |
| Geothermal  |   886 |       2.1 |
| Biomass     |   344 |       0.8 |
| Small hydro |   234 |       0.5 |
| Biogas      |   208 |       0.5 |
| Coal        |    16 |       0   |
| Other       |     0 |       0   |
+-------------+-------+-----------+

or the energy demand throughout the current day as a Pandas DataFrame

>>> iso.get_demand_today()

                         Time   Demand
0   2022-08-03 00:00:00-07:00  30076.0
1   2022-08-03 00:05:00-07:00  29966.0
2   2022-08-03 00:10:00-07:00  29893.0
3   2022-08-03 00:15:00-07:00  29730.0
4   2022-08-03 00:20:00-07:00  29600.0
..                        ...      ...
219 2022-08-03 18:15:00-07:00  41733.0
220 2022-08-03 18:20:00-07:00  41690.0
221 2022-08-03 18:25:00-07:00  41718.0
222 2022-08-03 18:30:00-07:00  41657.0
223 2022-08-03 18:35:00-07:00  41605.0

[224 rows x 2 columns]

we can get today's supply in the same way

>>> iso.get_supply_today()

                         Time  Supply
0   2022-08-03 00:00:00-07:00   31454
1   2022-08-03 00:05:00-07:00   31366
2   2022-08-03 00:10:00-07:00   30985
3   2022-08-03 00:15:00-07:00   30821
4   2022-08-03 00:20:00-07:00   30667
..                        ...     ...
220 2022-08-03 18:20:00-07:00   43096
221 2022-08-03 18:25:00-07:00   43104
222 2022-08-03 18:30:00-07:00   43013
223 2022-08-03 18:35:00-07:00   42885
224 2022-08-03 18:40:00-07:00   42875

[225 rows x 2 columns]

to get data for a specific day, use the historical method calls. For example,

>>> iso.get_historical_demand("Jan 1, 2020")

                         Time  Demand
0   2020-01-01 00:00:00-08:00   21533
1   2020-01-01 00:05:00-08:00   21429
2   2020-01-01 00:10:00-08:00   21320
3   2020-01-01 00:15:00-08:00   21272
4   2020-01-01 00:20:00-08:00   21193
..                        ...     ...
284 2020-01-01 23:40:00-08:00   20383
285 2020-01-01 23:45:00-08:00   20297
286 2020-01-01 23:50:00-08:00   20242
287 2020-01-01 23:55:00-08:00   20128
288 2020-01-01 00:00:00-08:00   20025

[289 rows x 2 columns]

The best part is these APIs work across all the supported ISOs

Method Availability

Here is the current status of availability of each method for each ISO

	New York ISO	California ISO	Electric Reliability Council of Texas	ISO New England	Midcontinent ISO	Southwest Power Pool	PJM
get_latest_status	❌	✅	✅	❌	❌	❌	❌
get_latest_fuel_mix	✅	✅	✅	✅	✅	✅	✅
get_fuel_mix_today	✅	✅	✅	✅	❌	❌	✅
get_fuel_mix_yesterday	✅	✅	❌	✅	❌	❌	✅
get_historical_fuel_mix	✅	✅	❌	✅	❌	❌	✅
get_latest_demand	✅	✅	✅	✅	✅	✅	✅
get_demand_today	✅	✅	✅	✅	✅	✅	✅
get_demand_yesterday	✅	✅	✅	✅	❌	❌	✅
get_historical_demand	✅	✅	❌	✅	❌	❌	✅
get_latest_supply	✅	✅	✅	✅	✅	✅	✅
get_supply_today	✅	✅	✅	✅	❌	❌	✅
get_supply_yesterday	✅	✅	❌	✅	❌	❌	✅
get_historical_supply	✅	✅	❌	✅	❌	❌	✅

LMP Pricing Data

We are currently adding Locational Marginal Price (LMP). Even though each BA offers different markets, but you can query them with a standardized API

>>> import isodata
>>> iso = isodata.NYISO()
>>> iso.get_lmp_today(iso.REAL_TIME_5_MIN, nodes="ALL")

                          Time           Market    Zone     LMP  Energy  Congestion  Losses
0    2022-08-08 00:05:00-04:00  REAL_TIME_5_MIN  CAPITL  125.15   90.63      -26.64    7.88
1    2022-08-08 00:05:00-04:00  REAL_TIME_5_MIN  CENTRL   92.17   90.63        0.00    1.54
2    2022-08-08 00:05:00-04:00  REAL_TIME_5_MIN  DUNWOD   99.52   90.63        0.00    8.89
3    2022-08-08 00:05:00-04:00  REAL_TIME_5_MIN  GENESE   92.53   90.62        0.00    1.91
4    2022-08-08 00:05:00-04:00  REAL_TIME_5_MIN     H Q   88.09   90.63        0.00   -2.54
...                        ...              ...     ...     ...     ...         ...     ...
3970 2022-08-08 22:00:00-04:00  REAL_TIME_5_MIN   NORTH  110.17  120.71        7.04   -3.50
3971 2022-08-08 22:00:00-04:00  REAL_TIME_5_MIN     NPX  236.60  120.72     -107.18    8.70
3972 2022-08-08 22:00:00-04:00  REAL_TIME_5_MIN     O H  121.23  120.72       -4.49   -3.98
3973 2022-08-08 22:00:00-04:00  REAL_TIME_5_MIN     PJM  146.13  120.71      -20.23    5.19
3974 2022-08-08 22:00:00-04:00  REAL_TIME_5_MIN    WEST  125.26  120.72       -5.02   -0.48

[3975 rows x 7 columns]

And here is querying CAISO

>>> import isodata
>>> iso = isodata.CAISO()
>>> iso.get_lmp_today(iso.DAY_AHEAD_HOURLY, nodes=["TH_NP15_GEN-APND", "TH_SP15_GEN-APND", "TH_ZP26_GEN-APND"])

LMP_TYPE                      Time                    Market              Node        LMP     Energy  Congestion     Loss
0        2022-08-12 00:00:00-07:00  Markets.DAY_AHEAD_HOURLY  TH_NP15_GEN-APND   90.17747   97.01718         0.0 -6.83971
1        2022-08-12 00:00:00-07:00  Markets.DAY_AHEAD_HOURLY  TH_SP15_GEN-APND   95.57163   97.01718         0.0 -1.44556
2        2022-08-12 00:00:00-07:00  Markets.DAY_AHEAD_HOURLY  TH_ZP26_GEN-APND   92.04020   97.01718         0.0 -4.97698
3        2022-08-12 01:00:00-07:00  Markets.DAY_AHEAD_HOURLY  TH_NP15_GEN-APND   84.90745   90.76157         0.0 -5.85412
4        2022-08-12 01:00:00-07:00  Markets.DAY_AHEAD_HOURLY  TH_SP15_GEN-APND   89.18232   90.76157         0.0 -1.57925
..                             ...                       ...               ...        ...        ...         ...      ...
67       2022-08-12 22:00:00-07:00  Markets.DAY_AHEAD_HOURLY  TH_SP15_GEN-APND  121.11000  122.06208         0.0 -0.95208
68       2022-08-12 22:00:00-07:00  Markets.DAY_AHEAD_HOURLY  TH_ZP26_GEN-APND  114.20129  122.06208         0.0 -7.86080
69       2022-08-12 23:00:00-07:00  Markets.DAY_AHEAD_HOURLY  TH_NP15_GEN-APND  100.50102  109.72925         0.0 -9.22823
70       2022-08-12 23:00:00-07:00  Markets.DAY_AHEAD_HOURLY  TH_SP15_GEN-APND  108.69780  109.72925         0.0 -1.03145
71       2022-08-12 23:00:00-07:00  Markets.DAY_AHEAD_HOURLY  TH_ZP26_GEN-APND  103.18939  109.72925         0.0 -6.53986

[72 rows x 7 columns]

The possible lmp query methods are ISO.get_latest_lmp, ISO.get_lmp_today, ISO.get_lmp_yesterday, and ISO.get_historical_lmp.

Supported LMP Markets

	Markets
Midcontinent ISO	REAL_TIME_5_MIN, DAY_AHEAD_HOURLY
California ISO	REAL_TIME_15_MIN, REAL_TIME_HOURLY, DAY_AHEAD_HOURLY
PJM
Electric Reliability Council of Texas
Southwest Power Pool
New York ISO	REAL_TIME_5_MIN, DAY_AHEAD_5_MIN
ISO New England	REAL_TIME_5_MIN, REAL_TIME_HOURLY, DAY_AHEAD_HOURLY

Feedback Welcome

isodata is under active development. If there is any particular data you would like access to, let us know by posting an issue or emailing [e-mail].

Related projects

• pyiso