Driving the Energy Revolution - An index for grid edge need and readiness
Bibliographic record
Abstract
The world’s relationship with energy is transitioning as we attempt to mitigate the impact of climate change. In the energy systems of the future, residential, commercial, and industrial consumers will no longer be passive. They may own generation sources, such as solar panels; they may be able to offer a service, such as giving flexibility for when energy may be used; or they may make lifestyle choices which impact their energy consumption. Because of this, the interface between the grid and these distributed end-users will only grow in importance. This interface is called the grid edge. The grid edge encompasses a wide range of technologies and services, from electric vehicles to heat pumps, solar panels to home batteries, and smart meters to building controls. To maximize the impact of grid edge technology roll-out, knowledge of both the need for and readiness for grid edge technologies in a specific geography can be extremely valuable for companies and governments alike. This report presents a novel index to characterize the need and readiness for grid edge technologies of a region. To achieve this, factors which affect the need or readiness are included through an extensive range of indicators - 99 in total. Indicators for need are categorized based on those that contribute to current and to future need for system flexibility. There are four components of grid edge readiness: political, economic, social, and technical. Each indicator influencing each of these components has been weighted based on its importance following expert advice. For example, the introduction of a carbon price is considered to have a substantial impact on a region’s readiness for grid edge as it incentivizes renewables and could be used as a key policy tool. Applying this hierarchical weighting scheme to data collected about various locations, grid edge need and readiness scores can be calculated for each region. Five regions form the focus of the report, these are Finland, Germany, Singapore, the UK, and California in the US. These focus regions were selected as locations which have historically been among those leading in developing and adopting modern energy-related technologies. Of these focus regions, Finland is the country with highest readiness, in part due to its plans for a flexibility market and high carbon price, while California has the highest need, in part due to significant solar panel penetration. Germany and the UK follow closely behind with the UK displaying high political ambition but slightly less need and readiness. Singapore, although exhibiting high readiness for grid edge, presents a lower need, which is due to its present dependence on dispatchable fossil-fueled generation and more moderate ambitions for introducing renewable energy generation into the future energy mix. To position these focus regions within a global context, the index is also applied to a broader range of locations, with the caveat that due to data gaps there is an aspect of uncertainty. Although the focus regions of Finland, Germany, UK, and California are the regions with greatest need and readiness, Norway, China, and Canada exhibit the next highest need and readiness, making them promising candidates for further attention. Another country to highlight is South Africa, which has relatively high need but low readiness. This is a country where grid edge technology could make a difference, and where policy could be employed to improve readiness. Three key policy levers to improve a region’s readiness for grid edge are identified. These include introducing incentives for clean energy technologies; introducing flexibility and carbon markets; and developing policy pathways to provide reliable and secure communications infrastructure to all domestic citizens. The full report can be accessed here: https://new.siemens.com/global/en/company/topic-areas/smart-infrastructure/grid-edge-whitepaper2.html
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".