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IEMOP.py
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import logging
from datetime import datetime, timedelta
from logging import Logger, getLogger
from typing import NamedTuple
import pandas as pd
from requests import Response, Session
from electricitymap.contrib.config.constants import PRODUCTION_MODES
from electricitymap.contrib.lib.models.event_lists import (
ExchangeList,
ProductionBreakdownList,
)
from electricitymap.contrib.lib.models.events import (
EventSourceType,
ProductionMix,
StorageMix,
)
from electricitymap.contrib.lib.types import ZoneKey
from parsers.lib.config import refetch_frequency
from parsers.lib.exceptions import ParserException
REPORTS_ADMIN_URL = "https://www.iemop.ph/wp-admin/admin-ajax.php"
DIPC_URL = "https://www.iemop.ph/market-data/dipc-energy-results-raw/"
RTDHS_URL = "https://www.iemop.ph/market-data/rtd-hvdc-schedules/"
TIMEZONE = "Asia/Manila"
SOURCE = "iemop.ph"
REGION_TO_ZONE_KEY = {
"LUZON": "PH-LU",
"VISAYAS": "PH-VI",
"MINDANAO": "PH-MI",
}
RESOURCE_NAME_TO_MODE = {
"1ACNPC_G01": "biomass",
"1AMBUK_U01": "hydro",
"1AMBUK_U02": "hydro",
"1AMBUK_U03": "hydro",
"1ANDA_G01": "coal",
"1APEC_G01": "coal",
"1ARMSOL_G01": "solar",
"1BAKUN_G01": "hydro",
"1BINGA_U01": "hydro",
"1BINGA_U02": "hydro",
"1BINGA_U03": "hydro",
"1BINGA_U04": "hydro",
"1BOSUNG_G01": "solar",
"1BT2020_G01": "biomass",
"1BTNSOL_G01": "solar",
"1BULSOL_G01": "solar",
"1BURGOS_G01": "wind",
"1BURGOS_G02": "solar",
"1BURGOS_G03": "solar",
"1CABSOL_G01": "solar",
"1CAPRIS_G01": "wind",
"1CASECN_G01": "hydro",
"1CIP2_G01": "oil",
"1CLASOL_G01": "solar",
"1DALSOL_G01": "solar",
"1GFII_G01": "biomass",
"1GIFT_G01": "biomass",
"1IBEC_G01": "biomass",
"1IPOWER_G01": "biomass",
"1IPOWER_G02": "unknown",
"1MAEC_G01": "solar",
"1MAGAT_U01": "hydro",
"1MAGAT_U02": "hydro",
"1MAGAT_U03": "hydro",
"1MAGAT_U04": "hydro",
"1MARSOL_G01": "solar",
"1MARVEL_G01": "coal",
"1MARVEL_G02": "coal",
"1MASIWA_G01": "hydro",
"1MSINLO_G01": "coal",
"1MSINLO_G02": "coal",
"1NIABAL_G01": "hydro",
"1NMHC_G01": "hydro",
"1NMHC_G03": "hydro",
"1NWIND_G01": "wind",
"1NWIND_G02": "wind",
"1PETRON_G01": "unknown",
"1PETSOL_G01": "solar",
"1PNTBNG_U01": "hydro",
"1PNTBNG_U02": "hydro",
"1RASLAG_G01": "solar",
"1RASLAG_G02": "solar",
"1SABANG_G01": "hydro",
"1SLANGN_G01": "hydro",
"1SMBELL_G01": "hydro",
"1SMC_G01": "coal",
"1SMC_G02": "coal",
"1SMC_G03": "coal",
"1SPABUL_G01": "solar",
"1SROQUE_U01": "hydro",
"1SROQUE_U02": "hydro",
"1SROQUE_U03": "hydro",
"1SUAL_G01": "coal",
"1SUAL_G02": "coal",
"1SUBSOL_G01": "solar",
"1S_ENRO_G01": "oil",
"1T_ASIA_G01": "oil",
"1UPPC_G01": "coal",
"1YHGRN_G01": "solar",
"1ZAMSOL_G01": "solar",
"2MMPP_G01": "gas",
"2PNGEA_G01": "gas",
"2SMNRTH_G01": "solar",
"2VALSOL_G01": "solar",
"3ADISOL_G01": "solar",
"3AVION_U01": "gas",
"3AVION_U02": "gas",
"3AWOC_G01": "wind",
"3BART_G01": "hydro",
"3BBEC_G01": "biomass",
"3BOTOCA_G01": "hydro",
"3CALACA_G01": "coal",
"3CALACA_G02": "coal",
"3CALIRY_G01": "hydro",
"3CALSOL_G01": "solar",
"3ILIJAN_G01": "gas",
"3ILIJAN_G02": "gas",
"3KAL_G01": "hydro_storage",
"3KAL_G02": "hydro_storage",
"3KAL_G03": "hydro_storage",
"3KAL_G04": "hydro_storage",
"3MALAYA_G01": "gas",
"3MALAYA_G02": "gas",
"3MEC_G01": "solar",
"3MGPP_G01": "geothermal",
"3ORMAT_G01": "geothermal",
"3PAGBIL_G01": "coal",
"3PAGBIL_G02": "coal",
"3PAGBIL_G03": "coal",
"3QPPL_G01": "coal",
"3RCBMI_G01": "oil",
"3RCBMI_G02": "oil",
"3SLPGC_G01": "coal",
"3SLPGC_G02": "coal",
"3SLTEC_G01": "coal",
"3SLTEC_G02": "coal",
"3SNGAB_G01": "gas",
"3STA-RI_G01": "gas",
"3STA-RI_G02": "gas",
"3STA-RI_G03": "gas",
"3STA-RI_G04": "gas",
"3STA-RI_G05": "gas",
"3STA-RI_G06": "gas",
"1ARAYSOL_G01": "solar",
"1ARAYSOL_G02": "solar",
"1BALWIND_G01": "wind",
"1BTSOLEN_G01": "solar",
"1CONSOL_G01": "solar",
"1CURSOL_G02": "solar",
"1GIGSOL_G01": "solar",
"1PASQSOL_G01": "solar",
"1PETSOL_G02": "solar",
"2PNGYSOL_G01": "solar",
"3SOLACE_G01": "solar",
"1AMPHAW_G01": "hydro",
"1BAKSIP_G01": "hydro",
"1BINENG_G01": "hydro",
"1BT2020_G02": "biomass",
"1BUTAO_G01": "hydro",
"1CAGBIO_G01": "biomass",
"1CLEANG_G01": "biomass",
"1GIFT_G02": "biomass",
"1GNPD_U01": "coal",
"1GNPD_U02": "coal",
"1GRGOLD_G01": "biomass",
"1HYPGRN_G01": "biomass",
"1LASUER_U01": "biomass",
"1LIMAY_U01": "gas",
"1LIMAY_U02": "gas",
"1LIMAY_U03": "gas",
"1LIMAY_U04": "gas",
"1LIMAY_U05": "gas",
"1LIMAY_U06": "gas",
"1LIMAY_U07": "gas",
"1LIMAY_U08": "gas",
"1MARIS_U01": "hydro",
"1MARIS_U02": "hydro",
"1MATUNO_G01": "hydro",
"1MPGC_U01": "coal",
"1MSINLO_G03": "coal",
"1RASLAG_G03": "solar",
"1SMC_G04": "coal",
"1TERASU_G01": "solar",
"1VSGRIP_G01": "biomass",
"2ECOPRK_G01": "solar",
"2ECOTAGA_G01": "solar",
"2TMOBIL_G01": "oil",
"2TMOBIL_G02": "oil",
"2TMOBIL_G03": "oil",
"2TMOBIL_G04": "oil",
"3BACMAN_U01": "geothermal",
"3BACMAN_U02": "geothermal",
"3BACMAN_U03": "geothermal",
"3BALUG_G01": "hydro",
"3CADPI_G01": "biomass",
"3CLBATO_G01": "hydro",
"3INARI_G01": "hydro",
"3LWERLAB_G01": "hydro",
"3MAJAY_G01": "hydro",
"3MGI_G02": "geothermal",
"3PALAK_G01": "hydro",
"3SBPL_G01": "coal",
"3TIBAG_G01": "hydro",
"3UPLAB_G01": "hydro",
"4LGPP_G01": "geothermal",
"4PHSOL_G01": "solar",
"4SEPSOL_G01": "solar",
"5CEDC_U01": "coal",
"5CEDC_U02": "coal",
"5CEDC_U03": "coal",
"5KSPC_G01": "coal",
"5KSPC_G02": "coal",
"5TOLSOL_G01": "solar",
"5TPC_G01": "oil",
"5TPC_G02": "coal",
"6AMLA_G01": "hydro",
"6CARSOL_G01": "solar",
"6CENPRI_U01": "oil",
"6CENPRI_U02": "oil",
"6CENPRI_U03": "oil",
"6CENPRI_U04": "oil",
"6FFHC_G01": "biomass",
"6HELIOS_G01": "solar",
"6HPCO_G01": "biomass",
"6MANSOL_G01": "solar",
"6MNTSOL_G01": "solar",
"6NASULO_G01": "geothermal",
"6PAL1A_G01": "geothermal",
"6PAL2A_U01": "geothermal",
"6PAL2A_U02": "geothermal",
"6PAL2A_U03": "geothermal",
"6PAL2A_U04": "geothermal",
"6SACASL_G01": "solar",
"6SACASL_G02": "solar",
"6SACSUN_G01": "solar",
"6SCBE_G01": "oil",
"6SLYSOL_G01": "solar",
"6URC_G01": "biomass",
"6VMC_G01": "biomass",
"7JANOPO_G01": "hydro",
"7LOBOC_G01": "hydro",
"8CASA_G01": "biomass",
"8COSMO_G01": "solar",
"8GLOBAL_G01": "unknown",
"8PALM_G01": "coal",
"8PEDC_U01": "coal",
"8PEDC_U02": "coal",
"8PEDC_U03": "coal",
"8PWIND_G01": "wind",
"8SLWIND_G01": "wind",
"8SUWECO_G01": "hydro",
"4CLBYBNK_G01": "oil",
"4IASMOD_G01": "oil",
"4IASMOD_G02": "oil",
"4IASMOD_G03": "oil",
"4IASMOD_G04": "oil",
"4IASMOD_G05": "oil",
"4IASMOD_G06": "oil",
"4TAFT_G01": "hydro",
"5CPPC_U01": "oil",
"5CPPC_U02": "oil",
"5CPPC_U03": "oil",
"5CPPC_U04": "oil",
"5CPPC_U05": "oil",
"5CPPC_U06": "oil",
"5CPPC_U07": "oil",
"5CPPC_U08": "oil",
"5CPPC_U09": "oil",
"5EAUC_U01": "coal",
"5EAUC_U02": "coal",
"5EAUC_U03": "coal",
"5EAUC_U04": "coal",
"5THVI_U01": "coal",
"5THVI_U02": "coal",
"5TPVI_U01": "oil",
"5TPVI_U02": "oil",
"5TPVI_U03": "oil",
"5TPVI_U04": "oil",
"5TPVI_U05": "oil",
"5TPVI_U06": "oil",
"6BISCOM_G01": "biomass",
"6CABI_G01": "biomass",
"6CENPRI_U05": "oil",
"6HPCO_G02": "biomass",
"6NTNEGB_G01": "biomass",
"6SCBIOP_G01": "biomass",
"6STNEGB_G01": "biomass",
"6VMC_G02": "biomass",
"7BDPP_U01": "oil",
"7BDPP_U02": "oil",
"7BDPP_U03": "oil",
"7BDPP_U04": "oil",
"7LOBOC_G02": "hydro",
"7SEVILL_G01": "hydro",
"7TPLPB4_U01": "oil",
"7TPLPB4_U02": "oil",
"7TPLPB4_U03": "oil",
"7TPLPB4_U04": "oil",
"8PDPP1_U02": "oil",
"8PDPP1_U03": "oil",
"8PDPP1_U05": "oil",
"8STBPB1_U01": "oil",
"8STBPB1_U02": "oil",
"8STBPB1_U03": "oil",
"8STBPB1_U04": "oil",
"8TIMBA_G01": "hydro",
"11KBSOL_G01": "solar",
"13DIGSOL_G01": "solar",
"14ASTROSOL_G01": "solar",
"14NVSOL_G01": "solar",
"9KEGJIM_G01": "oil",
"9PANABNK_U01": "unknown",
"9PANABNK_U02": "unknown",
"9WMPC_U01": "oil",
"9WMPC_U02": "oil",
"9WMPC_U03": "oil",
"9WMPC_U04": "oil",
"9WMPC_U05": "oil",
"9WMPC_U06": "oil",
"9WMPC_U07": "oil",
"9WMPC_U08": "oil",
"9WMPC_U09": "oil",
"10AGUS1_U01": "hydro",
"10AGUS1_U02": "hydro",
"10AGUS2_U01": "hydro",
"10AGUS2_U02": "hydro",
"10AGUS2_U03": "hydro",
"10AGUS4_U01": "hydro",
"10AGUS4_U02": "hydro",
"10AGUS4_U03": "hydro",
"10AGUS5_U01": "hydro",
"10AGUS5_U02": "hydro",
"10AGUS6_U01": "hydro",
"10AGUS6_U02": "hydro",
"10AGUS6_U03": "hydro",
"10AGUS6_U04": "hydro",
"10AGUS6_U05": "hydro",
"10AGUS7_U01": "hydro",
"10AGUS7_U02": "hydro",
"10GNPK_U01": "coal",
"10GNPK_U02": "coal",
"10GNPK_U03": "coal",
"10GNPK_U04": "coal",
"10IDPP_G01": "oil",
"10MEGC_G01": "oil",
"10PPEI_U01": "coal",
"11FDC_U01": "coal",
"11FDC_U02": "coal",
"11FDC_U03": "coal",
"11FGBPC_G01": "hydro",
"11KEGMAR_G01": "biomass",
"11KIRAS_G01": "solar",
"11MANFOR_G01": "hydro",
"11MANFOR_G02": "hydro",
"11MINBAL_G01": "oil",
"11MINBU_G01": "hydro",
"11MNCBLG_G01": "hydro",
"11MNRGY_G01": "oil",
"11MNRGY_G02": "oil",
"11NBPC_G01": "oil",
"11PACERM_G01": "oil",
"11PKBUK_G01": "oil",
"11PULA4_U01": "hydro",
"11PULA4_U02": "hydro",
"11PULA4_U03": "hydro",
"11STEAG_U01": "coal",
"11STEAG_U02": "coal",
"12ASIGA_G01": "hydro",
"12KEGGIN_G01": "oil",
"12KEGTAN_G01": "oil",
"12LKMAINIT_G01": "hydro",
"12NACSUR_G01": "oil",
"12PKSFRA_U01": "oil",
"12PKSFRA_U02": "oil",
"12TM2_U01": "oil",
"12TM2_U02": "oil",
"13DCPP_U01": "coal",
"13DCPP_U02": "coal",
"13EUROH_G01": "hydro",
"13MAGDBNK_G01": "oil",
"13MATIBNK_G01": "oil",
"13MPCDIG_G01": "oil",
"13SIBULAN_G01": "hydro",
"13SMC_U01": "coal",
"13SMC_U02": "coal",
"13TALOM1_G01": "hydro",
"13TALOM23_G01": "hydro",
"13TM1_U01": "oil",
"13TM1_U02": "oil",
"13TUDAY2_G01": "hydro",
"14ALMADA_G01": "hydro",
"14BIOGS_G01": "biomass",
"14BIOMS_U01": "biomass",
"14GERBIO_G01": "biomass",
"14LAMSAN_G01": "biomass",
"14MARBEL_U01": "hydro",
"14MTAPO_U01": "geothermal",
"14MTAPO_U02": "geothermal",
"14MTAPO_U03": "geothermal",
"14PKPSOC_G01": "oil",
"14SARANG_U01": "coal",
"14SARANG_U02": "coal",
"14SPGI_U01": "biomass",
"14SUPKOR_G01": "oil",
"1ANGAT_A": "hydro",
"1ANGAT_M": "hydro",
"1BAUANG_GS2": "oil",
"1BAUANG_GS3": "oil",
"1BAUANG_GS1": "oil",
"1CNCEP_BAT": "battery",
"1LAMAO_BAT": "battery",
"1LIMAY_BAT": "battery",
"1BTSOLEN_BAT": "battery",
"1MAGAT_BAT": "battery",
"1MSINLO_BAT": "battery",
"1SNMAN_BAT": "battery",
"1MAGAPIT_BAT": "battery",
"3INGRID_GS1": "oil",
"3INGRID_GS4": "oil",
"3INGRID_GS6": "oil",
"3INGRID_GS2": "oil",
"3INGRID_GS3": "oil",
"3INGRID_GS5": "oil",
"3MKBN_A": "geothermal",
"3MKBN_E": "geothermal",
"3ALMNOS_BAT1": "battery",
"3ALMNOS_BAT2": "battery",
"3MKBN_B": "geothermal",
"3MKBN_C": "geothermal",
"3MKBN_D": "geothermal",
"3TIWI_A": "geothermal",
"3TIWI_C": "geothermal",
"4ORMOC_BAT": "battery",
"5CPPC_U10": "oil",
"5TOLEDO_BAT": "battery",
"6KABAN_BAT": "battery",
"7UBAY_BAT": "battery",
"8PDPP3_C": "oil",
"8PDPP3_E": "oil",
"8PDPP3_G": "oil",
"8PDPP3_H": "oil",
"9WMPC_U10": "oil",
"11JASA_BAT": "battery",
"11VILLA_BAT": "battery",
"13MALITA_BAT": "battery",
"13MACO_BAT": "battery",
}
EXCHANGE_KEY_MAPPING = {
"MINVIS1": {"zone_key": "PH-MI->PH-VI", "flow": 1},
"VISLUZ1": {"zone_key": "PH-LU->PH-VI", "flow": -1},
}
KIND_TO_URL = {"production": DIPC_URL, "exchange": RTDHS_URL}
KIND_TO_POST_ID = {"production": "5754", "exchange": "5770"}
STORAGE_METHODS_TO_MODE = {"hydro_storage": "hydro", "battery": "battery"}
MODES_TO_RESOURCE_KIND = {
**{m: "production" for m in PRODUCTION_MODES},
**{m: "storage" for m in STORAGE_METHODS_TO_MODE},
}
def _validate_resource_name_to_mode_mapping():
for resource_name, mode in RESOURCE_NAME_TO_MODE.items():
if mode not in set(PRODUCTION_MODES).union(STORAGE_METHODS_TO_MODE.keys()):
raise ValueError(
f"Resource {resource_name} has an unknown mode {mode} in RESOURCE_NAME_TO_MODE"
)
class MarketReportsItem(NamedTuple):
datetime: datetime
filename: str
link: str
def get_all_market_reports_items(
session: Session, zone_key: ZoneKey, kind: str, logger: Logger = getLogger(__name__)
) -> dict[datetime, MarketReportsItem]:
"""
Gets a dictionary that converts a date into its code and filename
"""
logger.debug("Fetching list of available market reports")
form_data = {
"action": "display_filtered_market_data_files",
"datefilter%5Bstart%5D": "2021-09-13+00%3A00",
"datefilter%5Bend%5D": "2021-09-13+12%3A20",
"sort": "",
"page": "1",
"post_id": KIND_TO_POST_ID[kind],
}
res = session.post(REPORTS_ADMIN_URL, data=form_data, verify=False)
id_to_items = res.json().get("data", {})
if not id_to_items:
raise ParserException(
parser="IEMOP.py",
zone_key=zone_key,
message=f"No reports available to fetch {kind} data",
)
datetime_to_items = {}
for report_id, items in id_to_items.items():
market_reports_item = MarketReportsItem(
datetime.strptime(items["date"], "%d %B %Y %H:%M"),
items["filename"],
KIND_TO_URL[kind] + f"?md_file={report_id}",
)
datetime_to_items[market_reports_item.datetime] = market_reports_item
logger.info(
f"{zone_key} - {kind}: Succesfully recovered {len(datetime_to_items)} market reports items"
)
return datetime_to_items
def filter_reports_items(
kind: str,
zone_key: ZoneKey,
reports_items: dict[datetime, MarketReportsItem],
target_datetime: datetime | None,
) -> list[MarketReportsItem]:
# Date filtering
if target_datetime is None:
last_available_datetime = max(reports_items.keys())
start_datetime = last_available_datetime - timedelta(days=1)
_exception_date = start_datetime.date()
_reports_items = [
item for item in reports_items.values() if item.datetime >= start_datetime
]
else:
# Refetch the whole day
_exception_date = target_datetime.date()
_reports_items = [
item
for item in reports_items.values()
if item.datetime.date() == target_datetime.date()
]
if len(_reports_items) == 0:
raise ParserException(
parser="IEMOP.py",
zone_key=zone_key,
message=f"{zone_key}: No {kind} data available for {_exception_date} ",
)
return _reports_items
def download_production_market_reports_items(
session: Session, reports_items: list[MarketReportsItem], logger: Logger
) -> pd.DataFrame:
from io import BytesIO
from zipfile import ZipFile
COLUMNS_MAPPING = {
"REGION_NAME": "zone_key",
"RESOURCE_NAME": "resource_name",
"SCHED_MW": "value",
}
_all_items_df = []
for reports_item in reports_items:
logger.debug(f"Downloading market reports for {reports_item.filename}")
res: Response = session.get(reports_item.link)
_item_dfs = []
# zip containing a list of csv files which we want to concatenate in a single dataframe
with ZipFile(BytesIO(res.content)) as zip_file:
for csv_filename in zip_file.namelist():
if csv_filename.endswith(".csv"):
with zip_file.open(csv_filename) as csv_file:
_df = pd.read_csv(csv_file, header=0)
# The last line is EOF
_df = _df[:-1]
# All numeric types
_df = _df.apply(pd.to_numeric, errors="ignore")
# Add datetime column
_df = convert_column_to_datetime(_df, "TIME_INTERVAL")
# Localize to TIMEZONE
_df["datetime"] = _df["datetime"].dt.tz_localize(TIMEZONE)
# Remove 5 minute interval as we want start of interval convention
_df["datetime"] = _df["datetime"] - timedelta(minutes=5)
# Misc
_df["REGION_NAME"] = _df["REGION_NAME"].apply(
lambda x: REGION_TO_ZONE_KEY[x]
)
_df["filename"] = csv_filename
_df = _df[["datetime", "filename", *COLUMNS_MAPPING.keys()]]
_df = _df.rename(columns=COLUMNS_MAPPING)
_item_dfs.append(_df)
_all_items_df.append(pd.concat(_item_dfs, ignore_index=True))
df = pd.concat(_all_items_df, ignore_index=True)
# Add kind column for production/storage
df.loc[:, "resource_kind"] = ""
logger.info(
f"Succesfully extracted unit level production between the {df.datetime.min()} and the {df.datetime.max()}"
)
return df
def filter_for_zone(df: pd.DataFrame, zone_key: ZoneKey) -> pd.DataFrame:
df = df.copy()
return df.query(f"zone_key == '{zone_key}'")
def filter_valid_values(df: pd.DataFrame, logger: Logger) -> pd.DataFrame:
df = df.copy()
# Filter out non generation resources - with the exception of storage resources
df_production = df[df["resource_kind"] == "production"]
df_production = df_production[df_production["value"] >= 0]
df_storage = df[df["resource_kind"] == "storage"]
df = pd.concat([df_production, df_storage])
# Remove duplicates if there is discharge
df = df.drop_duplicates(
subset=["datetime", "zone_key", "resource_name"], keep="first"
)
# Fill a list of all unknown resources
unknown_resources = df[df["mode"] == "unknown"]["resource_name"].unique()
logger.warning(
f"PH - production: Unknown resources found: {unknown_resources}. Please report this issue to the Electricity Maps team"
)
return df
def match_resources_to_modes(df: pd.DataFrame) -> pd.DataFrame:
df = df.copy()
# Remove 0 padding in resource name
df["resource_name"] = df["resource_name"].apply(lambda x: x.strip("0"))
# Match resource name to mode
df["mode"] = df["resource_name"].apply(
lambda x: RESOURCE_NAME_TO_MODE.get(x, "unknown")
)
# Match resource name to kind
df["resource_kind"] = df["mode"].map(MODES_TO_RESOURCE_KIND)
return df
def aggregate_per_datetime_mode(df: pd.DataFrame) -> pd.DataFrame:
return (
df.groupby(["datetime", "mode", "resource_kind"])
.sum(numeric_only=True)
.reset_index()
)
def pivot_per_mode(df: pd.DataFrame) -> pd.DataFrame:
df = df.copy()
# Flatten columns and make them "{resource_kind}.{mode}"
df["resource_kind_mode"] = df["resource_kind"] + "." + df["mode"]
df = df[["resource_kind_mode", "datetime", "value"]].pivot(
index="datetime", columns="resource_kind_mode"
)
df.columns = df.columns.droplevel() # reset columns index
# Handle storage
df = df.rename(
columns={
f"storage.{storage_method}": f"storage.{storage_mode}"
for storage_method, storage_mode in STORAGE_METHODS_TO_MODE.items()
}
)
for storage_mode in STORAGE_METHODS_TO_MODE.values():
col_name = f"storage.{storage_mode}"
if col_name in df.columns:
df[col_name] *= -1
# With the pivot if some modes only have data for some datetimes, we will have NaNs
# Fill them with 0
df = df.fillna(0)
return df
def download_exchange_market_reports_items(
session: Session, reports_items: list[MarketReportsItem], logger: Logger
) -> pd.DataFrame:
_all_items_df = []
for reports_item in reports_items:
logger.debug(f"Downloading market reports for {reports_item.filename}")
r: Response = session.get(reports_item.link)
_df = pd.read_csv(r.url)
_df = _df[:-1]
_df = convert_column_to_datetime(_df, "RUN_TIME")
_df["zone_key"] = _df["HVDC_NAME"].apply(
lambda x: EXCHANGE_KEY_MAPPING[x]["zone_key"]
)
_df["net_flow"] = _df.apply(
lambda x: EXCHANGE_KEY_MAPPING[x["HVDC_NAME"]]["flow"] * x["FLOW_FROM"],
axis=1,
)
_df = _df[["datetime", "zone_key", "net_flow"]]
_all_items_df.append(_df)
df = (
pd.concat(_all_items_df, ignore_index=True)
.set_index("datetime")
.tz_localize(TIMEZONE)
)
return df
def convert_column_to_datetime(df: pd.DataFrame, datetime_column: str) -> pd.DataFrame:
"""convert datetime column to datetime format"""
dt1 = pd.to_datetime(
df[datetime_column],
format="%m/%d/%Y %I:%M:%S %p",
errors="coerce",
)
dt2 = pd.to_datetime(df[datetime_column], format="%m/%d/%Y", errors="coerce")
df["datetime"] = dt1.combine_first(dt2)
return df
@refetch_frequency(timedelta(days=1))
def fetch_production(
zone_key: ZoneKey = ZoneKey("PH-LU"),
session: Session | None = None,
target_datetime: datetime | None = None,
logger: Logger = getLogger(__name__),
) -> list:
session = session or Session()
_validate_resource_name_to_mode_mapping()
reports_items = get_all_market_reports_items(
session, zone_key, "production", logger
)
reports_items = filter_reports_items(
"production", zone_key, reports_items, target_datetime
)
df = download_production_market_reports_items(session, reports_items, logger)
df = (
df.pipe(filter_for_zone, zone_key)
.pipe(match_resources_to_modes)
.pipe(filter_valid_values, logger)
.pipe(aggregate_per_datetime_mode)
.pipe(pivot_per_mode)
)
production_breakdown = ProductionBreakdownList(logger)
for tstamp, row in df.iterrows():
production_mix = ProductionMix()
for mode in [m for m in row.index if "production." in m]:
production_mix.add_value(mode.replace("production.", ""), row[mode])
storage_mix = StorageMix()
for mode in [m for m in row.index if "storage." in m]:
storage_mix.add_value(mode.replace("storage.", ""), row[mode])
production_breakdown.append(
zone_key,
tstamp.to_pydatetime(),
SOURCE,
production_mix,
storage_mix,
sourceType=EventSourceType.measured,
)
return production_breakdown.to_list()
def fetch_exchange(
zone_key1: ZoneKey,
zone_key2: ZoneKey,
session: Session | None = None,
target_datetime: datetime | None = None,
logger: Logger = getLogger(__name__),
) -> list[dict] | dict:
session = session or Session()
sorted_zone_keys = ZoneKey("->".join(sorted([zone_key1, zone_key2])))
all_exchange_items = get_all_market_reports_items(
session, sorted_zone_keys, "exchange", logger
)
reports_items = filter_reports_items(
"exchange", sorted_zone_keys, all_exchange_items, target_datetime
)
df = download_exchange_market_reports_items(session, reports_items, logger).pipe(
filter_for_zone, sorted_zone_keys
)
# Convert to EventList
exchange_list = ExchangeList(logger)
for tstamp, row in df.iterrows():
exchange_list.append(
zoneKey=row["zone_key"],
datetime=tstamp.to_pydatetime(),
netFlow=row["net_flow"],
source=SOURCE,
)
return exchange_list.to_list()
if __name__ == "__main__":
logger = getLogger(__name__)
logging.basicConfig(level=logging.DEBUG)
# print(fetch_production())
# print(fetch_production(ZoneKey("PH-VI"), target_datetime=datetime(2023, 8, 15)))
print(fetch_exchange(ZoneKey("PH-LU"), ZoneKey("PH-VI")))
print(
fetch_exchange(
ZoneKey("PH-MI"), ZoneKey("PH-VI"), target_datetime=datetime(2023, 8, 15)
)
)