Future Roles for Natural Gas in Decarbonising the Australian Electricity Supply within the NEM: Total System Costs are Key
Bibliographic record
Abstract
Abstract Electricity systems around the world are changing, with the Paris Agreement of 2015 a catalyst for much of this current change.1 The Australian Government ratified this agreement, by commiting to 26-28% emissions reductions below 2005 levels by 2030.2 Additionally, State governments are adopting more ambitious targets to increase variable renewable energy and focusing on net emissions reductions, while the Australian Energy Market Operator (AEMO) warns of power instabilities and load shedding3. Given the difficulties in reducing emissions in other sectors, reductions in emissions from electricity generation has become the focus of these targets. Natural gas is often referred to as a ‘transition fuel’ towards a low emissions future, but this requires that it is in abundance and cost competitive. While the majority of the electricity generated in the National Energy Market (NEM) is coal-based, a vast majority of these plants are due to retire gradually between now and 2050, and this generation loss will need to be replaced in the context of low emissions aspirations. How this is done has significant implications for how much the system will cost, with natural gas playing a pivotal role. In order to decarbonise the grid to meet targets, while building firm, dispatchable generation capacity to support the system, a new metric is required to measure success. The changing generation mix, along with the need to maintain a competent grid, is resulting in previously acceptable cost comparison metrics being used outside of their limited range of applicability. Electricity generation facilities do not only provide energy, they also provide an array of additional services which are fundamental to maintaining a permanent and reliable electricity supply across the system. These services, corresponding costs and operational implications need to be included in the evaluation of technologies in order to ensure the grids emerge transformed, resilient and genuinely sustainable. Total System Cost is the most appropriate economic metric for analysis and decision making in a future, low emissions grid. This paper explores the outputs of the MEGS model (Model of Energy and Grid Services), showing the outcomes if a single technology group is favoured. High renewables, gas and carbon capture and storage scenarios are discussed. The optimal route to power grid decarbonisation needs to be be viewed as a team sport, not a race. It's an "and" not an "or" solution. There's a range of technologies that have very different, yet important, roles to play in providing the pathway to a low emissions, competent and reliable supply at the lowest total system cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".