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Energy storage systems for carbon footprint reduction

2015· article· fr· W2244716988 sur OpenAlexaffabout
François Bouffard

Notice bibliographique

RevueLes Cahiers du GERAD · 2015
Typearticle
Languefr
DomaineEngineering
ThématiqueIntegrated Energy Systems Optimization
Établissements canadiensMcGill University
Organismes subventionnairesnon disponible
Mots-clésSoftware deploymentEnergy storageElectric power systemDistributed generationElectricity generationElectric energyElectrical engineeringEnvironmental scienceEnvironmental economicsEngineeringPower (physics)TelecommunicationsComputer scienceRenewable energyPhysicsOperating systemEconomics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This short note outlines how energy storage systems are becoming a core component of modern low carbon electric power systems. Challenges and opportunities are identified, while we also discuss mechanisms being put in place to favour the emergence of economically-viable grid-connected energy storage systems. Resume : Cette courte note examine comment les systemes de stockage d’energie joueront un role de plus en plus predominant dans les reseaux electriques decarbones. Nous y identifions les defis techniques ainsi que les occasions d’affaires relies au deploiement de ces systemes. De plus, nous discutons certains des mecanismes mis en place afin de favoriser l’implantation de systemes de stockage d’energie qui soient viables economiquement. Les Cahiers du GERAD G–2015–110 1 Historically, electric power systems have been developed with the assumption that energy storage systems (ESS) were either too expensive or technically inadequate to play a major role in electric power systems. The poor energy conversion efficiencies of most legacy storage technologies and centralized, generation-driven utility planning have clearly been major barriers, which held back wider deployment. Aside from classic pumped-hydro storage power stations (such as the Sir Adam Beck Complex in Niagara Falls and the wellknown Dinorwig power station in Wales), often built to help manage nighttime load in power systems with significant power nuclear generation capacity, grid-side energy storage has had limited scope and range of applications up until now. With the ongoing deployment of increasingly variable, intermittent, distributed and, most importantly, low carbon power generation from the sun and the wind, energy storage systems are finally gaining ground in the power industry. This evolution is also coupled with significant progress in battery technology as well as in less conventional technologies, like compressed air energy storage. Today’s ESS serve three main purposes in assisting power system operators and planners integrate more renewable energy sources (Figure 1). Figure 1: Energy storage application and technology map (Source: Centre for Low Carbon Futures, UK) First, ESSs can assist with reserve and response services. In such cases, the storage technologies involved are able to help grids ride through fast-acting disturbances like major wind and solar power ramps, and when large numbers of wind turbines shut down quasi-simultaneously in high wind conditions. In addition, ESSs are essential to provide bridging power in timescales of 10 to 60 minutes to assist with grid management and support. For these applications battery technologies dominate. Typical roles for storage here will involve short-term wind power generation balancing, network congestion relief and operating reserve provision. 2 G–2015–110 Les Cahiers du GERAD Over longer time scales (beyond 1 hour), ESSs are tasked with bulk energy movements potentially spanning several hours. Applications here would include profit-driven energy arbitrage (i.e., buying and storing electricity when it is cheap and reselling it when its price is high) and intra-day energy shifting with the goal of reducing peak demands. The main challenges for grid-side energy storage technologies at the moment and for the years to come are very similar to those encountered by the solar photovoltaics and wind power generation industries over the last decade. Its main goal is to reduce manufacturing costs. At the same time, in the case of batteries, it is essential that manufacturers can roll out battery packs with potential for more charging/discharging cycles and less performance degradation due to repeated cycling. In the Canadian context, two principal business cases for grid-connected storage have emerged. The first one is associated with improving the greenhouse gas emission performance and operating costs of power generation in off-grid communities and mining operations. Here, energy storage can be used, in combination with local wind power generation, as a partial substitute to local diesel-fired generation. Glencore’s Raglan nickel mine in Northern Quebec with its 3 MW wind turbine, 1.8 MW diesel units and a hydrogen-based storage system is a great example of such a hybrid diesel-wind-storage system. Another business opportunity is certainly within the Ontarian power market with its near zero (or even negative) energy prices at nighttime and very high prices during the day. Here storage operators can reap the benefits from the inherent inflexibilities of nuclear power and from the fact that the Ontario electricity market does not have a lot of spare generation capacity at peak times. Last but not least, just like with all the other now mature renewable energy generation technologies out there, energy storage technologies will most likely benefit from some regulatory stimulus. This is why several jurisdictions in North America–e.g., in Ontario, and in the state of California–have launched energy storage mandates to stimulate further technological development, get actual field deployments and attract investors. In California the energy storage mandate launched in 2014 was a great success with multiple bidders overshooting the mandate’s target. The world is now watching to see if the exercise will deliver its promises as deployments are starting.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,857
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,012
Tête enseignante GPT0,193
Écart entre enseignants0,182 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission2
Résumé présentoui

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