Projects:2020s1-2510 Energy Storage Requirements for the SA Grid
Contents
Team Member
- Kyle Piper
Supervisors
- Dr. Andrew Allison
- Mr. David Vowles
Overview
SA obtains approximately 45% of its electrical energy from renewable sources – large scale wind and small scale solar PV. To reliably integrate these intermittent sources into the grid will increasingly require energy storage in a variety of forms such as pumped-hydro and batteries together with virtual energy storage in the form of demand side management. The storage requirement will progressively increase as controllable fossil fuel generation sources are withdrawn from the system. The objective of this project is to develop tools to assess the energy storage requirements to ensure reliable supply with high levels of intermittent generation. This project commenced in 2017 and the two honours students involved did a very fine job in achieving several early objectives.
Introduction
Motivation
As the world, Australia, and South Australia inherently moves away from controllable generation such as coal and gas towards renewable energy sources in the forms of Solar PV, Wind, Geothermal, Hydro, etc. it is of paramount importance that we are able to make this transition as smooth as possible. This involves ensuring that excess generated renewable energy can be stored, in the forms of primarily batteries and other methods such as pumped hydro. Storage of this renewable energy is a large primary step towards reducing coal and gas generation. As storage increases and coal/gas generation decreases, more renewable sources can be brought into the power system. This allows us to slowly change the proportion for controllable generation (coal/gas) and intermittent sources (renewable energy) until the eventual scenario in which controllable is no longer present and we run 100% renewable. There however are inherent problems with large amounts of intermittent sources on the power system and these are to be explored and hopefully resolved throughout this project.
Objectives
(1) Serve the time-series data via the web so that it can be analysed and graphically displayed in various ways online.
(2) Extend the data analysis and graphical display toolbox to allow the user to rapidly identify and display interesting features and periods within the data.
(3) Extend the storage optimization approaches to consider alternative optimization objectives and future scenarios.
(4) To estimate storage requirements for Australia as a whole assuming a hypothetical 100% renewable scenario and hypothetical interconnected Australia.
Background
As aforementioned, South Australia obtains 45% of its electrical energy from renewable sources, this makes us one of the leading states in renewable energy proportion. To adopt the proposed increase in amounts of intermittent sources many technical problems arise and provoke issues towards the power grid. These problems occur because unlike the current coal and gas generation which are run synchronously due to governors, renewable sources aren't available 24/7 on demand due to the nature of them having factors we physically can't change. One of the primary problems and key factors to running a secure, stable and reliable power system is frequency regulation. There are national standards in place that cause this to be such a paramount factor and hence maintaining reliable frequency is a must do. Devices are in place, called frequency controllers that help as much as possible to keep frequency within acceptable limits (similarly to governors but for renewable sources) however these are not 100% effective and as the proportion of intermittent sources increases these conditions may change. One of the aims of this project is to find suitable specifications to ensure that once a high proportion of intermittent sources is implemented into the power grid there are measures in place to ensure stability reliability and security.
Data Processing Method
Data Collection
Python Code going through AEMO historical data for generation, supply, demand, pricing
Data Collating
Use of a Structures Query Language (SQL) to take AEMO data and store it in a database for ease of access
Data Analysis and Display
Use the SQL to obtain specified data sets (as per user input) and display them appropriately (tabulated / graphical)
