Driving the Energy Revolution - An index for grid edge need and readiness
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,006 | 0,006 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».