We’ve published the latest hydrological modelling dataset (HMD) including data to the end of 2016.
The HMD includes:
- infrastructure and hydro constraint attributes (including access to contingent storage)
- storage and spill information.
Due to the high proportion of hydro generation in the New Zealand electricity system, a good understanding of hydrology is required for many functions.
1. Operationally, this data is used heavily within the industry to inform planning and decision making related to the inherent uncertainty of hydrology, i.e. the timing and volume of energy supply from inflows to the broader electricity system. The information may be used as inputs into studies employing tools such as DOASA, a hydro-thermal scheduling model which seeks to optimally dispatch hydro resources.
2. The dataset holds an accurate record of hydro data that assists monitoring market outcomes as well as providing important historical context to observed market responses. In addition, this information is used to assist in the calculation of the security standards for energy and capacity.
3. Broader studies around possible future scenarios for the New Zealand electricity system are also enabled through the use of this data.
We continue to improve the organisation of the dataset to avoid confusion and errors in the use of this data. These changes mean that file names or formats may have changed slightly from the previous dataset. This year we have included an index file with each set of files to assist users in interpreting the data.
This update is an interim update meaning the 2016 year has been appended to the existing series. Next year we intend to undertake a full update. This update will entail revisiting historical correlations.
Users of the dataset are encouraged to post feedback - especially if they believe there is an error or something has been missed in the infrastructure and hydro constraints file.
The Authority appreciates the cooperation of the hydro generators and other data providers in compiling and publishing this dataset.