nem-bidding-dashboard is a web app and python package for collating, processing and visualising data relevant to understanding participant behaviour in the Australian National Electricity Market wholesale spot market.
The web app is intended to make reviewing the bidding behaviour of market participants as easy as possible. Aggregate behaviour can be visualised at a whole of market, regional, or technology level. Alternatively, the non-aggregated bids of dispatch units, and stations can be visualised.
We have additionally published the code required to run the web app as a python package, so that it can be used to help visualise and analyse bidding behaviour in alternative or more sophisticated ways than allowed by the web app.
The development of nem-bidding-dashboard was funded by the Digital Grid Futures Institute
nem-bidding-dashboard is hosted as a web app here: https://nembiddingdashboard.org
The python api can be used to:
- run the web app interface locally
- download publicly available bidding and operational data from the Australian Energy Market Operator
- process and aggregate bidding and operational data into the format used in the web app
- build and populate a PostgresSQL database for efficiently querying and aggregating bidding and operational data
pip install nem_bidding_dashboard
Below are some quick examples that provide a taste of the api capabilities, see the full set of examples and api documentation for a complete guide.
To get the raw data used by nem-bidding-dashboard before preprocessing use functions in the fetch_data module, e.g.
get_volume_bids.
from nem_bidding_dashboard import fetch_data
volume_bids = fetch_data.volume_bids(
start_time='2022/01/01 00:00:00',
end_time='2022/01/01 00:05:00',
raw_data_cache='D:/nemosis_data_cache')
print(volume_bids.head(5))
# SETTLEMENTDATE DUID ... PASAAVAILABILITY INTERVAL_DATETIME
# 309360 2021-12-31 ADPBA1G ... 6.0 2022-01-01 00:05:00
# 309361 2021-12-31 ADPBA1G ... NaN 2022-01-01 00:05:00
# 309362 2021-12-31 ADPBA1G ... NaN 2022-01-01 00:05:00
# 309363 2021-12-31 ADPBA1G ... NaN 2022-01-01 00:05:00
# 309364 2021-12-31 ADPBA1G ... NaN 2022-01-01 00:05:00
#
# [5 rows x 18 columns]To get data in the format stored by nem-bidding-dashboard in the PostgresSQL database use functions in the module
fetch_and_preprocess, e.g. bid_data.
from nem_bidding_dashboard import fetch_and_preprocess
bids = fetch_and_preprocess.bid_data(
start_time='2022/01/01 00:00:00',
end_time='2022/01/01 00:05:00',
raw_data_cache='D:/nemosis_data_cache')
print(bids.head(5))
# INTERVAL_DATETIME DUID BIDBAND BIDVOLUME BIDVOLUMEADJUSTED BIDPRICE ONHOUR
# 0 2022-01-01 00:05:00 ADPBA1G 8 6 0.0 998.00 False
# 462 2022-01-01 00:05:00 REECE1 2 45 45.0 -55.03 False
# 463 2022-01-01 00:05:00 REECE1 4 74 74.0 -0.85 False
# 464 2022-01-01 00:05:00 REECE2 2 35 35.0 -54.77 False
# 465 2022-01-01 00:05:00 REECE2 4 84 84.0 -0.86 FalseCreate tables for storing processed data and functions, then populate the database with historical data.
from nem_bidding_dashboard import postgres_helpers, populate_postgres_db
con_string = postgres_helpers.build_connection_string(
hostname='localhost',
dbname='bidding_dashboard_db',
username='bidding_dashboard_maintainer',
password='1234abcd',
port=5433)
raw_data_cache = "D:/nemosis_cache"
start = "2022/01/01 00:00:00"
end = "2022/02/01 00:00:00"
populate_postgres_db.duid_info(con_string, raw_data_cache)
populate_postgres_db.bid_data(con_string, raw_data_cache, start, end)
populate_postgres_db.region_data(con_string, raw_data_cache, start, end)
populate_postgres_db.unit_dispatch(con_string, raw_data_cache, start, end)Filter bids by time and region, and then aggregate into price bands. Other functions in the module query_postgres_db
provide querying and aggregation and for each table in the db.
from nem_bidding_dashboard import postgres_helpers, query_postgres_db
con_string = postgres_helpers.build_connection_string(
hostname='localhost',
dbname='bidding_dashboard_db',
username='bidding_dashboard_maintainer',
password='1234abcd',
port=5433)
agg_bids = query_postgres_db.aggregate_bids(connection_string=con_string,
start_time="2022/01/01 00:00:00",
end_time="2022/01/01 01:00:00",
regions=['QLD', 'NSW', 'SA'],
dispatch_type='Generator',
tech_types=[],
resolution='hourly',
adjusted='adjusted')
print(agg_bids)
# INTERVAL_DATETIME BIN_NAME BIDVOLUME
# 0 2022-01-01 01:00:00 [-1000, -100) 9673.93400
# 1 2022-01-01 01:00:00 [-100, 0) 366.70236
# 2 2022-01-01 01:00:00 [0, 50) 1527.00000
# 3 2022-01-01 01:00:00 [50, 100) 1290.00000
# 4 2022-01-01 01:00:00 [100, 200) 908.00000
# 5 2022-01-01 01:00:00 [200, 300) 1217.00000
# 6 2022-01-01 01:00:00 [300, 500) 943.00000
# 7 2022-01-01 01:00:00 [500, 1000) 240.00000
# 8 2022-01-01 01:00:00 [1000, 5000) 210.00000
# 9 2022-01-01 01:00:00 [5000, 10000) 125.00000
# 10 2022-01-01 01:00:00 [10000, 15500) 6766.00000Interested in contributing? Check out the contributing guidelines.
Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
nem-bidding-dashboard was created by Nicholas Gorman and Patrick Chambers. It is licensed under the terms of the
BSD-3-Clause license. Please, also read the
disclaimer accompanying the licence