Intelligence artificielle à la périphérie du réseau mobile avec efficacité de communication

L'intelligence artificielle (AI) et l'informatique à la périphérie du réseau (EC) ont permis de mettre en place diverses applications intelligentes incluant les maisons intelligentes, la fabrication intelligente, et les villes intelligentes. Ces progrès ont été alimentés principalement par la disponibilité d'un plus grand nombre de données, l'abondance de la puissance de calcul et les progrès de plusieurs techniques de compression. Toutefois, les principales avancées concernent le déploiement de modèles dans les dispositifs connectés. Ces modèles sont préalablement entraînés de manière centralisée. Cette prémisse exige que toutes les données générées par les dispositifs soient envoyées à un serveur centralisé, ce qui pose plusieurs problèmes de confidentialité et crée une surcharge de communication importante. Par conséquent, pour les derniers pas vers l'AI dans EC, il faut également propulser l'apprentissage des modèles ML à la périphérie du réseau. L'apprentissage fédéré (FL) est apparu comme une technique prometteuse pour l'apprentissage collaboratif de modèles ML sur des dispositifs connectés. Les dispositifs entraînent un modèle partagé sur leurs données stockées localement et ne partagent que les paramètres résultants avec une entité centralisée. Cependant, pour permettre l' utilisation de FL dans les réseaux périphériques sans fil, plusieurs défis hérités de l'AI et de EC doivent être relevés. En particulier, les défis liés à l'hétérogénéité statistique des données à travers les dispositifs ainsi que la rareté et l'hétérogénéité des ressources nécessitent une attention particulière. L'objectif de cette thèse est de proposer des moyens de relever ces défis et d'évaluer le potentiel de la FL dans de futures applications de villes intelligentes. Dans la première partie de cette thèse, l'accent est mis sur l'incorporation des propriétés des données dans la gestion de la participation des dispositifs dans FL et de l'allocation des ressources. Nous commençons par identifier les mesures de diversité des données qui peuvent être utilisées dans différentes applications. Ensuite, nous concevons un indicateur de diversité permettant de donner plus de priorité aux clients ayant des données plus informatives. Un algorithme itératif est ensuite proposé pour sélectionner conjointement les clients et allouer les ressources de communication. Cet algorithme accélère l'apprentissage et réduit le temps et l'énergie nécessaires. De plus, l'indicateur de diversité proposé est renforcé par un système de réputation pour éviter les clients malveillants, ce qui améliore sa robustesse contre les attaques par empoisonnement des données. Dans une deuxième partie de cette thèse, nous explorons les moyens de relever d'autres défis liés à la mobilité des clients et au changement de concept dans les distributions de données. De tels défis nécessitent de nouvelles mesures pour être traités. En conséquence, nous concevons un processus basé sur les clusters pour le FL dans les réseaux véhiculaires. Le processus proposé est basé sur la formation minutieuse de clusters pour contourner la congestion de la communication et est capable de traiter différents modèles en parallèle. Dans la dernière partie de cette thèse, nous démontrons le potentiel de FL dans un cas d'utilisation réel impliquant la prévision à court terme de la puissance électrique dans un réseau intelligent. Nous proposons une architecture permettant l'utilisation de FL pour encourager la collaboration entre les membres de la communauté et nous montrons son importance pour l'entraînement des modèles et la réduction du coût de communication à travers des résultats numériques.Abstract : Artificial intelligence (AI) and Edge computing (EC) have enabled various applications ranging from smart home, to intelligent manufacturing, and smart cities. This progress was fueled mainly by the availability of more data, abundance of computing power, and the progress of several compression techniques. However, the main advances are in relation to deploying cloud-trained machine learning (ML) models on edge devices. This premise requires that all data generated by end devices be sent to a centralized server, thus raising several privacy concerns and creating significant communication overhead. Accordingly, paving the last mile of AI on EC requires pushing the training of ML models to the edge of the network. Federated learning (FL) has emerged as a promising technique for the collaborative training of ML models on edge devices. The devices train a globally shared model on their locally stored data and only share the resulting parameters with a centralized entity. However, to enable FL in wireless edge networks, several challenges inherited from both AI and EC need to be addressed. In particular, challenges related to the statistical heterogeneity of the data across the devices alongside the scarcity and the heterogeneity of the resources require particular attention. The goal of this thesis is to propose ways to address these challenges and to evaluate the potential of FL in future applications. In the first part of this thesis, the focus is on incorporating the data properties of FL in handling the participation and resource allocation of devices in FL. We start by identifying data diversity measures allowing us to evaluate the richness of local datasets in different applications. Then, we design a diversity indicator allowing us to give more priority to clients with more informative data. An iterative algorithm is then proposed to jointly select clients and allocate communication resources. This algorithm accelerates the training and reduces the overall needed time and energy. Furthermore, the proposed diversity indicator is reinforced with a reputation system to avoid malicious clients, thus enhancing its robustness against poisoning attacks. In the second part of this thesis, we explore ways to tackle other challenges related to the mobility of the clients and concept-shift in data distributions. Such challenges require new measures to be handled. Accordingly, we design a cluster-based process for FL for the particular case of vehicular networks. The proposed process is based on careful clusterformation to bypass the communication bottleneck and is able to handle different models in parallel. In the last part of this thesis, we demonstrate the potential of FL in a real use-case involving short-term forecasting of electrical power in smart grid. We propose an architecture empowered with FL to encourage the collaboration among community members and show its importance for both training and judicious use of communication resources through numerical results

Discussion on drivers and proposition of approaches to support the transition of traditional electricity consumers to prosumers

In recent years, traditional power systems have undergone a significant transition, mainly related to the massive penetration of Renewable Energy Sources (RES). More specifically, the transformation of residential consumers into prosumers has been challenging to the traditional operation of electricity markets. This transition brings new challenges and opportunities to the power system, leading to new Business Model (BM). One widely discussed change is related to a consumer-centric or prosumer-driven approach, promoting increased participation of small consumers in power systems. The present thesis aims at discussing the recent BMs as enablers of the increasing prosumers’ role in the energy market and power system worldwide, deepening the discussion with a holistic view of the Brazilian context. To do so, it defines the main features of prosumers and their general related regulation as well as possible market designs within power systems. Moreover, the work intends to contribute to the knowledge, identification and understanding of the main regulatory barriers and enablers for the development of those BMs in the Brazilian energy market. In addition, it discusses enabling technologies to properly create the conditions that sustain new prosumer-driven markets. Then, it presents a comprehensive review of existing and innovative BMs and a discussion on their future roles in modern power systems and, in the Brazilian regulatory framework seeking to guide the decisions for the country to develop its political and regulatory environment in the future. Moreover, a set of recommendations for promoting these BMs in the power system worldwide is provided along with policy recommendations to promote prosumers aggregation in the Brazilian energy sector. An important conclusion is that, even though economically possible, not all innovative BMs can spread around the world due to regulatory issues. Seeking to further explore one of the prosumer-driven approaches presented and the challenges imposed by this innovative BM, a study of energy and reserve markets based on the Peer-to-Peer (P2P) structure is carried out. This structure is very promising for the prosumers’ promotion but presents some challenges for the network operation. A critical challenge is to ensure that network constraints are not violated due to energy trades between peers and neither due to the use of reserve capacity. Therefore, two methodologies are proposed. First, is proposed a three-step approach (P2PTDF), using Topological Distribution Factors (TDF) to penalize peers responsible for violations that may occur in the network constraints, ensuring a feasible solution. Second, it is proposed a new integrated prosumers-DSO approach applied in P2P energy and reserve tradings that also ensures the feasibility of both energy and reserve transactions under network constraints. The proposed approach includes the estimation of reserve requirements based on the RES uncertain behavior from historical generation data, which allows identifying RES patterns. The proposed models are assessed through a case study that uses a 14-bus system, under the technical and economic criteria. The results show that the approaches can ensure a feasible network operation.Nos últimos anos, os sistemas tradicionais de energia passaram por uma transição significativa, principalmente relacionada à penetração massiva de fontes de energia renováveis (do inglês, Renewable energy sources-RES). Mais especificamente, a transformação de consumidores residenciais em prosumidores tem desafiado a atual operação do mercado de energia elétrica. Essa transição traz novos desafios e oportunidades para o sistema elétrico, levando a novos modelos de negócios (do inglês, Business Models-BM). Uma mudança amplamente discutida está relacionada a uma abordagem centrada no consumidor ou direcionada ao prossumidor, promovendo maior participação de pequenos consumidores nos sistemas de energia. A presente tese tem como objetivo discutir os recentes BMs como facilitadores do crescente papel dos prosumidores no mercado de energia e no sistema elétrico mundial, aprofundando a discussão com uma visão holística do contexto brasileiro. Para tanto, define as principais características dos prosumidores e sua regulamentação geral relacionada, bem como possíveis designs de mercado dentro dos sistemas de energia. Além disso, o trabalho pretende contribuir para o conhecimento, identificação e compreensão das principais barreiras regulatórias e facilitadoras para o desenvolvimento desses BMs no mercado brasileiro de energia. Assim como, discutir as tecnologias importantes para criar adequadamente as condições que sustentam novos mercados orientados ao consumidor final. Em seguida, apresenta uma revisão abrangente dos BMs existentes e inovadores e uma discussão sobre seus papéis futuros nos sistemas de energia modernos e, no quadro regulatório brasileiro, buscando orientar as decisões para que o país desenvolva seu ambiente político e regulatório no futuro. Além disso, um conjunto de recomendações para promover esses BMs no sistema de energia em todo o mundo é fornecido juntamente com recomendações de políticas para promover a agregação de prosumidores no setor de energia brasileiro. Uma conclusão importante é que, mesmo sendo economicamente possível, nem todos os BMs inovadores podem se espalhar pelo mundo devido a obstáculos regulatórias. Buscando explorar ainda mais uma das abordagens orientadas ao prosumidor apresentadas e os desafios impostos por este BM inovador, é realizado um estudo dos mercados de energia e de reserva com base na estrutura ponto a ponto (do inglês, peer-to-peer-P2P). Esta estrutura é muito promissora para a promoção dos prosumidores mas apresenta alguns desafios para o funcionamento da rede. Um desafio crítico é garantir que as restrições da rede não sejam violadas devido a negociações de energia entre pares e nem devido ao uso da capacidade de reserva. Portanto, duas metodologias são propostas. Primeiramente, é proposta uma abordagem em três passos (P2PTDF), utilizando Fatores de Distribuição Topológica (do inglês, Topological Distribution Factors-TDF ) para penalizar os peers responsáveis por violações que possam ocorrer nas restrições da rede, garantindo uma solução viável. Em segundo lugar, é proposta uma nova abordagem integrada de prosumidores-DSO aplicada em transações P2P de energia e reserva que também garante a viabilidade de transações de energia e reserva sob restrições de rede. A abordagem proposta inclui a estimativa dos requisitos de reserva com base no comportamento incerto da RES a partir de dados históricos de geração, o que permite identificar padrões de RES. Os modelos propostos são avaliados através de um estudo de caso que utiliza um sistema de 14 barras, sob os critérios técnico e econômico. Os resultados mostram que as abordagens podem garantir uma operação de rede viável abrangendo energia e mercados de reserva