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Record W6987458641

Stockage d'énergie solaire par l'intermédiaire de véhicules électriques

2024· other· fr· W6987458641 on OpenAlexaboutno aff

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2024
Typeother
Languagefr
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Electric energyElectronic equipment
DOInot available

Abstract

fetched live from OpenAlex

RÉSUMÉ: L’urgence environnementale que nous vivons actuellement a pour conséquence que d’ici 2030, le Québec prévoit 2 millions de véhicules électriques sur ses routes. Mais par manque d’infrastructures suffisantes, la majorité de ces véhicules continueront à se connecter directement au réseau électrique à partir de leurs résidences pour se recharger. Ceci pourrait venir surcharger un réseau souvent congestionné pendant les heures de pointe du soir. Plus généralement, même si les véhicules étaient coordonnés pour se charger plus tard la nuit, passé la pointe, globalement on ne serait pas en train d’exploiter le rôle potentiellement éminent de stockage d’énergie renouvelable que pourraient remplir les batteries de véhicules électriques. Cette thèse pose alors de façon rigoureuse les bases d’une gestion adéquate de stockage d’énergie à partir de ces batteries pour intégrer les sources renouvelables fluctuantes dans le réseau. Un modèle économique est envisagé où à la fois les consommateurs et une classe d’agrégateurs de charges pourraient trouver leur compte. Ces derniers considéreraient alors l’établissement des infrastructures nécessaires pour la mise en oeuvre d’un processus de stockage temporaire et de restitution partielle d’énergie renouvelable, en appui au réseau aux heures de pointe. Le schéma présenté dans la thèse vise à créer un cercle vertueux entre la mobilité électrique, l’énergie non polluante et le stockage d’énergie. Il est proposé de développer une formule de parkings alimentés en énergie solaire. Les propriétaires des véhicules ayant signé un contrat avec l’agrégateur (compagnie propriétaire des parkings) verraient leurs batteries de véhicules électriques rechargées quotidiennement, aux niveaux permis par le rayonnement solaire disponible d’une journée à l’autre. Plus tard, ces mêmes propriétaires, sous la coordination de l’agrégateur, pourraient envisager un soutien au réseau électrique à partir d’une utilisation partielle des stocks d’énergie dans leurs batteries pendant les heures de pointe en soirée. Au niveau de l’analyse, des algorithmes nouveaux basés sur la théorie des jeux à champ moyen sont développés pour réaliser un partage efficace des ressources solaires parmi les véhicules. Ceux-ci mènent à des commandes quasi-décentralisées. La théorie est ensuite étendue pour tenir compte des caractères stochastiques du rayonnement solaire quotidien ainsi que des arrivées et départs aléatoires des véhicules. Ces phénomènes stochastiques sont respectivement capturés par un modèle ARMA, et par un modèle de Poisson d’arrivées et de départs dans une population finie. Mathématiquement, le contexte est celui d’une commande de type prédictif dans un jeu à champ moyen. ABSTRACT: The climate change crisis we are currently experiencing obliges Quebec to plan to have 2 million electric vehicles by 2030. But due to lack of sufficient infrastructure, the majority of these vehicles will continue to connect directly to the grid in their homes to recharge their batteries. That may create eventually system wide overloads to the grid in the evening peak. More generally, even if vehicles were coordinated to charge later at night, after peak times, overall we would not be exploiting the potentially prominent renewable energy storage role that electric vehicle batteries could fill. Therefore, this thesis lays the foundations of energy storage adequate management from battery electric vehicles to integrate fluctuating renewable sources into the grid. Our envisioned business model is where both the owners of electric vehicles and a class of aggregators could benefit. The latter would then consider building the necessary infrastructure for the implementation of a process of temporary storage and partial restitution of renewable energy, to support the grid during evening peak. The proposed scheme in this thesis is to accelerate the energy transition with electric vehicles, clean energy and battery energy storage. We propose to develop a formula of parking lots powered by solar energy. Vehicle owners who have signed a contract with the aggregator (company that owns the parking lots) would see their electric vehicle batteries recharged daily, to the levels permitted by the available solar radiation. Later, these same owners, under the coordination of the aggregator, could consider to restore the energy to the grid during the evening peak in their homes. We use classical theory of Mean Field Games which, successful when a fixed number of agents is very large, allows us to develop individual strategies where each agent minimizes its cost in a non-cooperative game. We develop decentralized algorithms to share the daily energy available between battery electric vehicles in solar powered parking lots. We add to the classical theory of Mean Field Games a Model Predictive Control with an autoregressive model, which takes into account the stochastic solar acquisition in the parking lot, and a Poisson model with finite population, which takes into account the random arrivals and random departures of vehicles in the parking lot. In this context, we extend the classic theory of Mean Field Games to consider a fluctuating number of battery electric vehicles in the parking lot, by developing heuristic algorithms. Our results show a considerable reduction of standard deviation of states of charge of batteries when vehicles leave the parking lot and, therefore according to our envisioned business model, will allow a majority of vehicles’ owners to participate in a possible grid decongestion during eveving peak.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.002

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.

Opus teacher head0.009
GPT teacher head0.241
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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Same venuePolyPublie (École Polytechnique de Montréal)French-language works237,207