import json
import pandas as pd
import requests
from gridstatus import utils
from gridstatus.base import ISOBase, Markets, NotSupported
from gridstatus.lmp_config import lmp_config
from gridstatus.logging import log
[docs]class MISO(ISOBase):
"""Midcontinent Independent System Operator (MISO)"""
BASE = "https://api.misoenergy.org/MISORTWDDataBroker/DataBrokerServices.asmx"
interconnection_homepage = (
"https://www.misoenergy.org/planning/generator-interconnection/GI_Queue/"
)
name = "Midcontinent ISO"
iso_id = "miso"
# miso spans multiple timezones, so picking central
# all parsing is done in EST since that is what api returns
default_timezone = "US/Eastern"
markets = [Markets.REAL_TIME_5_MIN, Markets.DAY_AHEAD_HOURLY]
hubs = [
"ILLINOIS.HUB",
"INDIANA.HUB",
"LOUISIANA.HUB",
"MICHIGAN.HUB",
"MINN.HUB",
"MS.HUB",
"TEXAS.HUB",
"ARKANSAS.HUB",
]
[docs] def get_fuel_mix(self, date, verbose=False):
"""Get the fuel mix for a given day for a provided MISO.
Arguments:
date (datetime.date, str): "latest", "today", or an object
that can be parsed as a datetime for the day to return data.
verbose (bool, optional): print verbose output. Defaults to False.
Returns:
pandas.DataFrame: DataFrame with columns "Time", "Load", "Fuel Mix"
"""
if date != "latest":
raise NotSupported()
url = self.BASE + "?messageType=getfuelmix&returnType=json"
r = self._get_json(url, verbose=verbose)
time = (
pd.to_datetime(r["Fuel"]["Type"][0]["INTERVALEST"])
.tz_localize(
"EST",
)
.tz_convert(
self.default_timezone,
)
)
mix = {}
for fuel in r["Fuel"]["Type"]:
amount = int(fuel["ACT"])
if amount == -1:
amount = 0
mix[fuel["CATEGORY"]] = amount
df = pd.DataFrame(mix, index=[time])
df.index.name = "Interval Start"
df = df.reset_index()
df = add_interval_end(df, 5)
return df
[docs] def get_load(self, date, verbose=False):
if date == "latest":
return self._latest_from_today(self.get_load, verbose=verbose)
elif utils.is_today(date, tz=self.default_timezone):
r = self._get_load_and_forecast_data(verbose=verbose)
date = pd.to_datetime(r["LoadInfo"]["RefId"].split(" ")[0])
df = pd.DataFrame([x["Load"] for x in r["LoadInfo"]["FiveMinTotalLoad"]])
df["Interval Start"] = df["Time"].apply(
lambda x, date=date: date
+ pd.Timedelta(
hours=int(
x.split(":")[0],
),
minutes=int(x.split(":")[1]),
),
)
df["Interval Start"] = (
df["Interval Start"]
.dt.tz_localize("EST")
.dt.tz_convert(
self.default_timezone,
)
)
df = df.rename(columns={"Value": "Load"})
df["Load"] = pd.to_numeric(df["Load"])
df = add_interval_end(df, 5)
return df
else:
raise NotSupported
[docs] def get_load_forecast(self, date, verbose=False):
if not utils.is_today(date, self.default_timezone):
raise NotSupported()
r = self._get_load_and_forecast_data(verbose=verbose)
date = (
pd.to_datetime(r["LoadInfo"]["RefId"].split(" ")[0])
.tz_localize(
tz="EST",
)
.tz_convert(
self.default_timezone,
)
)
df = pd.DataFrame(
[x["Forecast"] for x in r["LoadInfo"]["MediumTermLoadForecast"]],
)
df["Interval Start"] = date + pd.to_timedelta(
df["HourEnding"].astype(int) - 1,
"h",
)
df["Forecast Time"] = date
df = add_interval_end(df, 60)
df = df[
["Time", "Interval Start", "Interval End", "Forecast Time", "LoadForecast"]
].rename(
columns={"LoadForecast": "Load Forecast"},
)
return df
def _get_load_and_forecast_data(self, verbose=False):
url = "https://api.misoenergy.org/MISORTWDDataBroker/DataBrokerServices.asmx?messageType=gettotalload&returnType=json" # noqa
r = self._get_json(url, verbose=verbose)
return r
@lmp_config(
supports={
Markets.REAL_TIME_5_MIN: ["latest"],
Markets.DAY_AHEAD_HOURLY: ["latest"],
},
)
[docs] def get_lmp(self, date, market: str, locations: list = None, verbose=False):
"""
Supported Markets:
- ``REAL_TIME_5_MIN`` - (FiveMinLMP)
- ``DAY_AHEAD_HOURLY`` - (DayAheadExPostLMP)
"""
if locations is None:
locations = "ALL"
url = "https://api.misoenergy.org/MISORTWDDataBroker/DataBrokerServices.asmx?messageType=getLMPConsolidatedTable&returnType=json" # noqa
r = self._get_json(url, verbose=verbose)
time = r["LMPData"]["RefId"]
time_str = time[:11] + " " + time[-9:-4]
time_zone = time[-3:]
interval_start = (
pd.to_datetime(time_str)
.tz_localize(
time_zone,
)
.tz_convert(self.default_timezone)
)
if market == Markets.REAL_TIME_5_MIN:
data = pd.DataFrame(r["LMPData"]["FiveMinLMP"]["PricingNode"])
interval_duration = 5
elif market == Markets.DAY_AHEAD_HOURLY:
data = pd.DataFrame(
r["LMPData"]["DayAheadExPostLMP"]["PricingNode"],
)
interval_start = interval_start.floor("H")
interval_duration = 60
rename = {
"name": "Location",
"LMP": "LMP",
"MLC": "Loss",
"MCC": "Congestion",
}
data.rename(columns=rename, inplace=True)
data[["LMP", "Loss", "Congestion"]] = data[["LMP", "Loss", "Congestion"]].apply(
pd.to_numeric,
errors="coerce",
)
data["Energy"] = data["LMP"] - data["Loss"] - data["Congestion"]
data["Interval Start"] = interval_start
data = add_interval_end(data, interval_duration)
data["Market"] = market.value
data["Location Type"] = "Pricing Node"
data.loc[
data["Location"].str.endswith(
".HUB",
),
"Location Type",
] = "Hub"
data = data[
[
"Time",
"Interval Start",
"Interval End",
"Market",
"Location",
"Location Type",
"LMP",
"Energy",
"Congestion",
"Loss",
]
]
data = utils.filter_lmp_locations(data, locations)
return data
[docs] def get_interconnection_queue(self, verbose=False):
"""Get the interconnection queue
Returns:
pandas.DataFrame: Interconnection queue
"""
url = "https://www.misoenergy.org/api/giqueue/getprojects"
msg = f"Downloading interconnection queue from {url}"
log(msg, verbose)
json_str = requests.get(url).text
data = json.loads(json_str)
# todo there are also study documents available: https://www.misoenergy.org/planning/generator-interconnection/GI_Queue/gi-interactive-queue/
# there is also a map that plots the locations of these projects:
queue = pd.DataFrame(data)
queue = queue.rename(
columns={
"postGIAStatus": "Post Generator Interconnection Agreement Status",
"doneDate": "Interconnection Approval Date",
},
)
queue["Capacity (MW)"] = queue[
[
"summerNetMW",
"winterNetMW",
]
].max(axis=1)
rename = {
"projectNumber": "Queue ID",
"county": "County",
"state": "State",
"transmissionOwner": "Transmission Owner",
"poiName": "Interconnection Location",
"queueDate": "Queue Date",
"withdrawnDate": "Withdrawn Date",
"applicationStatus": "Status",
"Capacity (MW)": "Capacity (MW)",
"summerNetMW": "Summer Capacity (MW)",
"winterNetMW": "Winter Capacity (MW)",
"negInService": "Proposed Completion Date",
"fuelType": "Generation Type",
}
extra_columns = [
"facilityType",
"Post Generator Interconnection Agreement Status",
"Interconnection Approval Date",
"inService",
"giaToExec",
"studyCycle",
"studyGroup",
"studyPhase",
"svcType",
"dp1ErisMw",
"dp1NrisMw",
"dp2ErisMw",
"dp2NrisMw",
"sisPhase1",
]
missing = [
# todo the actual complettion date
# can be calculated by looking at status and other date columns
"Actual Completion Date",
"Withdrawal Comment",
"Project Name",
"Interconnecting Entity",
]
queue = utils.format_interconnection_df(
queue=queue,
rename=rename,
extra=extra_columns,
missing=missing,
)
return queue
[docs]def add_interval_end(df, duration_min):
"""Add an interval end column to a dataframe
Args:
df (pandas.DataFrame): Dataframe with a time column
duration_min (int): Interval duration in minutes
Returns:
pandas.DataFrame: Dataframe with an interval end column
"""
df["Interval End"] = df["Interval Start"] + pd.Timedelta(minutes=duration_min)
df["Time"] = df["Interval Start"]
df = utils.move_cols_to_front(
df,
["Time", "Interval Start", "Interval End"],
)
return df
"""
Notes
- Real-time 5-minute LMP data for current day, previous day available
https://api.misoenergy.org/MISORTWDBIReporter/Reporter.asmx?messageType=rollingmarketday&returnType=json
- market reports https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/#nt=
historical fuel mix: https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/#nt=%2FMarketReportType%3ASummary%2FMarketReportName%3AHistorical%20Generation%20Fuel%20Mix%20(xlsx)&t=10&p=0&s=MarketReportPublished&sd=desc
- ancillary services available in consolidate api
""" # noqa
