Die Bachelorarbeit behandelt die Themen der Selbstorganisation in Produktionssystemen im Kontext von Industrie 4.0. Dabei wird gezeigt, wie man mithilfe von einer Ameisenkolonieoptimierung die Reihenfolgeplanung organisieren kann.:Abbildungsverzeichnis Tabellenverzeichnis Formelverzeichnis 1. Einleitung 1.1. Motivation 1.2. Ziele 1.3. Vorgehensweise 2. Sich selbst organisierende Produktionen 2.1. Begriffserklärung 2.2. Stand der Technik 2.3. Reihenfolgeplanung als ein Problem der Selbstorganisation 2.3.1. Begriffserklärung 2.3.2. Stand der Technik 2.3.3. Umsetzung in einer Selbstorganisation 3. Ameisenkolonieoptimierung 3.1. Begriffserklärung 3.2. Allgemeine Umsetzung 3.3. Konkrete Umsetzungen 3.4. Vor- und Nachteile 3.5. Anwendungsbeispiele 4. Entwicklung einer Ameisenkolonieoptimierung für ein sich selbst organisierendes Produktionssystem 4.1. Analyse des gegebenen sich selbst organisierenden Produktionssystems 4.1.1. Grobanalyse des Systems 4.1.2. Feinanalyse der bisherigen Reihenfolgeplanung 4.2. Entwurf der Reihenfolgeplanung durch Prinzipien der Ameisenkolonieoptimierung 4.3. Implementierung der Prinzipien der Ameisenkolonieoptimierung 5. Empirische Untersuchung der implementierten Ameisenkolonieoptimierung 5.1. Beschreibung der gegebenen Produktionsdaten 5.2. Szenarienuntersuchung zur Funktionsfähigkeit 5.2.1. Schichtwechselszenario 5.2.2. Abnutzungs- und Wartungsszenario 5.2.3. Vergleichsszenario 5.3. Untersuchung hinsichtlich der Laufzeit und des Speicherbedarfs 5.3.1. Laufzeit 5.3.2. Speicherbedarf 6. Zusammenfassung und Ausblick 6.1. Zusammenfassung 6.2. Ausblick Quellenverzeichnis / The bachelor thesis is about self organization in production systems in the context of Industry 4.0. Its about ant colony optimization for scheduling in the production planning.:Abbildungsverzeichnis Tabellenverzeichnis Formelverzeichnis 1. Einleitung 1.1. Motivation 1.2. Ziele 1.3. Vorgehensweise 2. Sich selbst organisierende Produktionen 2.1. Begriffserklärung 2.2. Stand der Technik 2.3. Reihenfolgeplanung als ein Problem der Selbstorganisation 2.3.1. Begriffserklärung 2.3.2. Stand der Technik 2.3.3. Umsetzung in einer Selbstorganisation 3. Ameisenkolonieoptimierung 3.1. Begriffserklärung 3.2. Allgemeine Umsetzung 3.3. Konkrete Umsetzungen 3.4. Vor- und Nachteile 3.5. Anwendungsbeispiele 4. Entwicklung einer Ameisenkolonieoptimierung für ein sich selbst organisierendes Produktionssystem 4.1. Analyse des gegebenen sich selbst organisierenden Produktionssystems 4.1.1. Grobanalyse des Systems 4.1.2. Feinanalyse der bisherigen Reihenfolgeplanung 4.2. Entwurf der Reihenfolgeplanung durch Prinzipien der Ameisenkolonieoptimierung 4.3. Implementierung der Prinzipien der Ameisenkolonieoptimierung 5. Empirische Untersuchung der implementierten Ameisenkolonieoptimierung 5.1. Beschreibung der gegebenen Produktionsdaten 5.2. Szenarienuntersuchung zur Funktionsfähigkeit 5.2.1. Schichtwechselszenario 5.2.2. Abnutzungs- und Wartungsszenario 5.2.3. Vergleichsszenario 5.3. Untersuchung hinsichtlich der Laufzeit und des Speicherbedarfs 5.3.1. Laufzeit 5.3.2. Speicherbedarf 6. Zusammenfassung und Ausblick 6.1. Zusammenfassung 6.2. Ausblick Quellenverzeichnis

<p>The advent of advanced communication technologies has given rise to large-scale networks comprised of numerous interconnected agents, which need to cooperate to accomplish various tasks, such as distributed message routing, formation control, robust statistical inference, and spectrum access coordination. These tasks can be formulated as distributed optimization problems, which require agents to agree on a parameter minimizing the average of their local cost functions by communicating only with their neighbors. However, distributed optimization algorithms are typically susceptible to malicious (or "Byzantine") agents that do not follow the algorithm. This thesis offers analysis and algorithms for such scenarios. As the malicious agent's function can be modeled as an unknown function with some fundamental properties, we begin in the first two parts by analyzing the region containing the potential minimizers of a sum of functions. Specifically, we explicitly characterize the boundary of this region for the sum of two unknown functions with certain properties. In the third part, we develop resilient algorithms that allow correctly functioning agents to converge to a region containing the true minimizer under the assumption of convex functions of each regular agent. Finally, we present a general algorithmic framework that includes most state-of-the-art resilient algorithms. Under the strongly convex assumption, we derive a geometric rate of convergence of all regular agents to a ball around the optimal solution (whose size we characterize) for some algorithms within the framework.</p>

[pt] Determinar a proveniência dos dados, isto é, o processo que levou a esses dados, é vital em muitas áreas, especialmente quando é essencial que os resultados ou ações sejam confiáveis. Com o crescente número de aplicações baseadas em inteligência artificial, criou-se a necessidade de torná-las capazes de explicar seu comportamento e responder às suas decisões. Isso é um desafio, especialmente se as aplicações forem distribuídas e compostas de vários agentes autônomos, formando um Sistema Multiagente (SMA). Uma maneira fundamental de tornar tais sistemas explicáveis é rastrear o comportamento do agente, isto é, registrar a origem de suas ações e raciocínios, como em uma depuração onisciente. Embora a ideia de proveniência já tenha sido explorada em alguns contextos, ela não foi extensivamente explorada no contexto de SMA, deixando muitas questões para serem compreendidas e abordadas. Nosso objetivo neste trabalho é justificar a importância da proveniência dos dados para SMA, discutindo quais perguntas podem ser respondidas em relação ao comportamento do SMA, utilizando a proveniência e ilustrando, através de cenários de aplicação, os benefícios que a proveniência proporciona para responder a essas questões. Este estudo envolve a criação de um framework de software, chamado FProvW3C, que suporta a coleta e armazenamento da proveniência dos dados produzidos pelo SMA, que foi integrado a plataforma BDI4JADE (41), formando o que denominamos de Prov-BDI4JADE. Por meio desta plataforma, utilizando exemplos de sistemas autônomos, demostramos com rigor que, o uso da proveniência de dados em SMA é uma solução sólida, para tornar transparente o processo de raciocínio e ação do agente. / [en] Determining the data provenance, that is, the process that led to those data, is vital in many areas, especially when it is essential that the results or actions be reliable. With the increasing number of applications based on artificial intelligence, the need has been created to make them capable of explaining their behavior and be responsive to their decisions. This is a challenge especially if the applications are distributed, and composed of multiple autonomous agents, forming a Multiagent System (MAS). A key way of making such systems explicable is to track the agent s behavior, that is, to record the source of their actions and reasoning, as in an omniscient debugging. Although the idea of provenance has already been explored in some contexts, it has not been extensively explored in the context of MAS, leaving many questions to be understood and addressed. Our objective in this work is to justify the importance of the data provenance to MAS, discussing which questions can be answered regarding the behavior of MAS using the provenance and illustrating, through application scenarios, to demonstrate the benefits that provenance provides to reply to these questions. This study involves the creation of a software framework, called FProvW3C, which supports the collects and stores the provenance of the data produced by the MAS, which was integrated with the platform BDI4JADE (41), forming what we call Prov-BDI4JADE. Through this platform, using examples of autonomous systems, we have rigorously demonstrated that the use of data provenance in MAS is a solid solution to make the agent’s reasoning and action process transparent.

Recent urbanization and industrialization have brought tremendous pressure and challenges to modern autonomous systems. When considering multiple complex tasks, cooperation and coordination between multiple agents can improve efficiency in a system. In real-world applications, multi-agent systems (MAS) are widely used in various fields, such as robotics, unmanned aerial systems, autonomous vehicles, distributed sensor networks, etc. Unlike traditional MAS systems based on pre-defined algorithms and rules, a special human-in-loop (HIL) based MAS involves human interactions to enhance the system’s adaptability for special scenarios, as well as apply human preferences for robot control. However, existing HIL strategies are primarily based on human involvement at a low level, such as mixed-initiative control and mixed-agent scenarios with both human-driven and intelligent robots. There are fewer investigations on applying HIL in high-level coordination. In particular, designing a coordination strategy for multi-task multi-agent scenarios, which can also deal with real-time human commands, will be one of the key topics of this Master’s thesis project. In this thesis work, different kinds of tasks described by signal temporal logic (STL) are created for agents, which can be enforced by control barrier function (CBF) constraints. Both centralized and distributed frameworks are designed for agent coordination. In detail, the centralized strategy is developed for machine-to-infrastructure (M2I) communication, by using the nonlinear model predictive control (NMPC) method to obtain collision-free trajectories. The distributed strategy utilizing graph theory is proposed for machine-to-machine (M2M), in order to reduce computation time by offloading. Most importantly, a HIL model is generated for both frameworks to apply online human commands to the coordination, with a novel task allocation protocol. Simulations and experiments are carried out on both Matlab and Python-based ROS simulators, to show that proposed frameworks can achieve obvious performance advantages in safety, smoothness, and stability for task completion. Numerical results are provided to validate the feasibility and applicability of our algorithms. / Den senaste urbaniseringen och industrialiseringen har medfört enormt tryck och utmaningar för moderna autonoma system. Vid beaktande av flera komplexa uppgifter kan samarbete och samordning mellan flera agenter förbättra effektiviteten i ett system. I verkliga tillämpningar används multiagent-system (MAS) i stor utsträckning inom olika områden, såsom robotik, obemannade luftfarkoster, autonoma fordon, distribuerade sensorsystem etc. Till skillnad från traditionella MAS-system baserade på fördefinierade algoritmer och regler, innebär ett särskilt människa-i-loop (HIL)-baserat MAS mänsklig interaktion för att förbättra systemets anpassningsförmåga till speciella scenarier samt anpassa mänskliga preferenser för robotstyrning. Emellertid är befintliga HIL-strategier främst baserade på mänsklig inblandning på en låg nivå, såsom mixad-initiativkontroll och mixade agentscenarier med både människa-drivna och intelligenta robotar. Det finns färre undersökningar om att tillämpa HIL på högnivåkoordination. Särskilt att utforma en koordineringsstrategi för fleruppgiftsfleragent-scenarier, som också kan hantera mänskliga kommandon i realtid, kommer att vara ett av huvudämnena för detta masterprojekt. I detta examensarbete skapas olika typer av uppgifter beskrivna av signaltemporallogik (STL) för agenter, som kan upprätthållas genom styrbarriärfunktions (CBF) -begränsningar. Både centraliserade och distribuerade ramverk utformas för agentkoordination. Mer specifikt utvecklas den centraliserade strategin för maskin-till-infrastruktur (M2I)-kommunikation genom att använda icke-linjär modellprediktiv reglering (NMPC) för att erhålla kollisionsfria trajektorier. Den distribuerade strategin med användning av grafteori föreslås för maskin-till-maskin (M2M) för att minska beräkningstiden genom avlastning. Viktigast av allt genereras en HIL-modell för båda ramverken för att tillämpa online-mänskliga kommandon på koordinationen med en ny protokoll för uppgiftstilldelning. Simuleringar och experiment utförs på både Matlab och Python-baserade ROS-simulatorer för att visa att de föreslagna ramverken kan uppnå tydliga prestandafördelar när det gäller säkerhet, smidighet och stabilitet för uppgiftsslutförande. Numeriska resultat presenteras för att validera genomförbarheten och tillämpligheten hos våra algoritmer.

Approved for public release, distribution is unlimited / This thesis analyzes the potential impact of incorporating wireless technologies, specifically an 802.11 mesh layer architecture and 802.16 Orthogonal Frequency Division Multiplexing, in order to effectively and more efficiently transmit data and create a symbiotic operational picture between Coast Guard Cutters, their boarding teams, Coast Guard Operation Centers, and various external agencies. Two distinct collaborative software programs, Groove Virtual Office and the Naval Postgraduate School's Situational Awareness Agent, are utilized over the Tactical Mesh and OFDM network configurations to improve the Common Operating Picture of involved units within a marine environment to evaluate their potential impact for the Coast Guard. This is being done to increase the effectiveness and efficiency of Coast Guard units while they carry out their Law Enforcement and Homeland Security Missions. Through multiple field experiments, including Tactical Network Topology and nuclear component sensing with Lawrence Livermore National Laboratory, we utilize commercial off the shelf (COTS) equipment and software to evaluate their impact on these missions. / Lieutenant Commander, United States Coast Guard / Lieutenant, United States Coast Guard

[ES] Recientemente, hay una fuerte y creciente influencia de aplicaciones en línea en nuestro día a día. Más concretamente las redes sociales se cuentan entre las plataformas en línea más usadas, que permiten a usuarios comunicarse e interactuar desde diferentes partes del mundo todos los días. Dado que estas interacciones conllevan diferentes riesgos, y además los adolescentes tienen características que los hacen más vulnerables a ciertos riesgos, es deseable que el sistema pueda guiar a los usuarios cuando se encuentren interactuando en línea, para intentar mitigar la probabilidad de que caigan en uno de estos riesgos. Esto conduce a una experiencia en línea más segura y satisfactoria para usuarios de este tipo de plataformas. El interés en aplicaciones de inteligencia artificial capaces de realizar análisis de sentimientos ha crecido recientemente. Los usos de la detección automática de sentimiento de usuarios en plataformas en línea son variados y útiles. Se pueden usar polaridades de sentimiento para realizar minería de opiniones en personas o productos, y así descubrir las inclinaciones y opiniones de usuarios acerca de ciertos productos (o ciertas características de ellos), para ayudar en campañas de marketing, y también opiniones acerca de personas como políticos, para descubrir la intención de voto en un periodo electoral, por ejemplo. En esta tesis, se presenta un Sistema Multi-Agente (SMA), el cual integra agentes que realizan diferentes análisis de sentimientos y de estrés usando texto y dinámicas de escritura (usando análisis unimodal y multimodal), y utiliza la respuesta de los analizadores para generar retroalimentación para los usuarios y potencialmente evitar que caigan en riesgos y difundan comentarios en plataformas sociales en línea que pudieran difundir polaridades de sentimiento negativas o niveles altos de estrés. El SMA implementa un análisis en paralelo de diferentes tipos de datos y generación de retroalimentación a través del uso de dos mecanismos diferentes. El primer mecanismo se trata de un agente que realiza generación de retroalimentación y guiado de usuarios basándose en un conjunto de reglas y la salida de los analizadores. El segundo mecanismo es un módulo de Razonamiento Basado en Casos (CBR) que usa no solo la salida de los analizadores en los mensajes del usuario interactuando para predecir si su interacción puede generar una futura repercusión negativa, sino también información de contexto de interacciones de usuarios como son los tópicos sobre los que hablan o información sobre predicciones previas en mensajes escritos por la gente que conforma la audiencia del usuario. Se han llevado a cabo experimentos con datos de una red social privada generada en laboratorio con gente real usando el sistema en tiempo real, y también con datos de Twitter.com para descubrir cuál es la eficacia de los diferentes analizadores implementados y del módulo CBR al detectar estados del usuario que se propagan más en la red social. Esto conlleva descubrir cuál de las técnicas puede prevenir mejor riesgos potenciales que los usuarios pueden sufrir cuando interactúan, y en qué casos. Se han encontrado diferencias estadísticamente significativas y la versión final del SMA incorpora los analizadores que mejores resultados obtuvieron, un agente asesor o guía basado en reglas y un módulo CBR. El trabajo de esta tesis pretende ayudar a futuros desarrolladores de sistemas inteligentes a crear sistemas que puedan detectar el estado de los usuarios interactuando en sitios en línea y prevenir riesgos que los usuarios pudiesen enfrentar. Esto propiciaría una experiencia de usuario más segura y satisfactoria. / [CA] Recentment, hi ha una forta i creixent influència d'aplicacions en línia en el nostre dia a dia, i concretament les xarxes socials es compten entre les plataformes en línia més utilitzades, que permeten a usuaris comunicar-se i interactuar des de diferents parts del món cada dia. Donat que aquestes interaccions comporten diferents riscos, i a més els adolescents tenen característiques que els fan més vulnerables a certs riscos, seria desitjable que el sistema poguera guiar als usuaris mentre es troben interactuant en línia, per així poder mitigar la probabilitat de caure en un d'aquests riscos. Açò comporta una experiència en línia més segura i satisfactòria per a usuaris d'aquest tipus de plataformes. L'interés en aplicacions d'intel·ligència artificial capaces de realitzar anàlisi de sentiments ha crescut recentment. Els usos de la detecció automàtica de sentiments en usuaris en plataformes en línia són variats i útils. Es poden utilitzar polaritats de sentiment per a realitzar mineria d'opinions en persones o productes, i així descobrir les inclinacions i opinions d'usuaris sobre certs productes (o certes característiques d'ells), per a ajudar en campanyes de màrqueting, i també opinions sobre persones com polítics, per a descobrir la intenció de vot en un període electoral, per exemple. En aquesta tesi, es presenta un Sistema Multi-Agent (SMA), que integra agents que implementen diferents anàlisis de sentiments i d'estrés utilitzant text i dinàmica d'escriptura (utilitzant anàlisi unimodal i multimodal), i utilitza la resposta dels analitzadors per a generar retroalimentació per als usuaris i potencialment evitar que caiguen en riscos i difonguen comentaris en plataformes socials en línia que pogueren difondre polaritats de sentiment negatives o nivells alts d'estrés. El SMA implementa una anàlisi en paral·lel de diferents tipus de dades i generació de retroalimentació a través de l'ús de dos mecanismes diferents. El primer mecanisme es tracta d'un agent que realitza generació de retroalimentació i guia d'usuaris basant-se en un conjunt de regles i l'eixida dels analitzadors. El segon mecanisme és un mòdul de Raonament Basat en Casos (CBR) que utilitza no solament l'eixida dels analitzadors en els missatges de l'usuari per a predir si la seua interacció pot generar una futura repercussió negativa, sinó també informació de context d'interaccions d'usuaris, com són els tòpics sobre els quals es parla o informació sobre prediccions prèvies en missatges escrits per la gent que forma part de l'audiència de l'usuari. S'han realitzat experiments amb dades d'una xarxa social privada generada al laboratori amb gent real utilitzant el sistema implementat en temps real, i també amb dades de Twitter.com per a descobrir quina és l'eficàcia dels diferents analitzadors implementats i del mòdul CBR en detectar estats de l'usuari que es propaguen més a la xarxa social. Açò comporta descobrir quina de les tècniques millor pot prevenir riscos potencials que els usuaris poden sofrir quan interactuen, i en quins casos. S'han trobat diferències estadísticament significatives i la versió final del SMA incorpora els analitzadors que millors resultats obtingueren, un agent assessor o guia basat en regles i un mòdul CBR. El treball d'aquesta tesi pretén ajudar a futurs dissenyadors de sistemes intel·ligents a crear sistemes que puguen detectar l'estat dels usuaris interactuant en llocs en línia i prevenir riscos que els usuaris poguessen enfrontar. Açò propiciaria una experiència d'usuari més segura i satisfactòria. / [EN] In the present days, there is a strong and growing influence of on-line applications in our daily lives, and concretely Social Network Sites (SNSs) are one of the most used on-line social platforms that allow users to communicate and interact from different parts of the world every day. Since this interaction poses several risks, and also teenagers have characteristics that make them more vulnerable to certain risks, it is desirable that the system could be able to guide users when interacting on-line, to try and mitigate the probability of incurring one of those risks. This would in the end lead to a more satisfactory and safe experience for the users of such on-line platforms. Recently, interest in artificial intelligence applications being able to perform sentiment analysis has risen. The uses of detecting the sentiment of users in on-line platforms or sites are variated and rewarding. Sentiment polarities can be used to perform opinion mining on people or products, and discover the inclinations and opinions of users on certain products (or certain features of them) to help marketing campaigns, and also on people such as politics, to discover the voting intention for example in electoral periods. In this thesis, a Multi-Agent System (MAS) is presented, which integrates agents that perform different sentiment and stress analyses using text and keystroke dynamics data (using both unimodal and multi-modal analysis). The MAS uses the output of the analyzers for generating feedback for users and potentially avoids them from incurring risks and spreading comments in on-line social platforms that could lead to the spread of negative sentiment or high-stress levels. Moreover, the MAS incorporates parallelized analyses of different data types and feedback generation via the use of two different mechanisms. On the one hand, a rule-based advisor agent has been implemented, that generates feedback or guiding for users based on the output of the analyzers and a set of rules. On the other hand, a Case-Based Reasoning (CBR) module that uses not only the output of the different analyzers on the messages of the user interacting, but also context information from user interactions such as the topics being talked about or information about the previous states detected on messages written by people in the audience of the user. Experiments with data from a private SNS generated in a laboratory with real people using the system in real-time, and also with data from Twitter.com have been performed to ascertain the efficacy of the different analyzers implemented and the CBR module on detecting states of the user that propagate more in the network, which leads to discovering which of the techniques is able to better prevent potential risks that users could face when interacting, and in which cases. Significant differences were found and the final version of the MAS incorporates the best-performing analyzer agents, a rule-based advisor agent, and a CBR module. In the end, this thesis aims to help intelligent systems developers to build systems that are able to detect the state of users interacting in on-line sites and prevent risks that they could face, leading to a more satisfactory and safe user experience. / This thesis was funded by the following research projects: Privacy in Social Educational Environments during Child-hood and Adolescence (PESEDIA), Ministerio de Economia y Empresa (TIN2014-55206-R) and Intelligent Agents for Privacy Advice in Social Networks (AI4PRI), Ministerio de Economia y Empresa (TIN2017-89156-R) / Aguado Sarrió, G. (2021). MAS-based affective state analysis for user guiding in on-line social environments [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/164902 / TESIS

Ce mémoire est consacré à l'étude du phénomène d'instabilité pouvant apparaître sur les bus continus des réseaux DC. En effet, l'interaction entre les différents sous-systèmes électriques (source, charge, filtre) composant le réseau DC peut conduire, sous certaines conditions, à l'instabilité du système. A partir de la modélisation des charges sous forme de "Charge à Puissance Constante" (notée CPL), des méthodes d'études permettant l'analyse de la stabilité "petit-signal" et "grand-signal" des systèmes électriques sont présentées. Celles-ci permettent de mettre en évidence le fait qu'un réseau DC ne peut pas fournir n'importe quelle puissance à ses charges sans devenir instable. Ces puissances limites dépendent à la fois de la structure du réseau et des valeurs de ses éléments passifs et de sa tension de bus. Afin de pouvoir augmenter l'amortissement/les marges de stabilité du système, des méthodes de stabilisation sont présentées dans ce mémoire. Elles proposent d'adapter les commandes des charges de manière à assurer sa stabilité. Ceci se fait grâce à l'addition d'un signal stabilisant sur la référence de chaque charge. Ce signal n'est visible que durant les régimes transitoires de la charge afin de ne pas modifier le point de fonctionnement demandé. Néanmoins, plus on voudra stabiliser une charge et plus son signal stabilisant sera important. Un bon compromis doit donc être trouvé afin d'assurer la stabilité du système sans altérer les performances dynamiques des charges. Deux approches différentes sont proposées afin de générer ces commandes stabilisantes. La première se base sur la mise en place d'un stabilisateur centralisé. Deux méthodes centralisées sont alors proposées : la première s'appuie sur la théorie des multimodèles de Takagi-Sugeno alors que la seconde s'appuie sur la théorie de Lyapunov. Cette dernière permettra d'orienter les efforts de stabilisation sur les charges souhaitées pour par exemple, les diriger principalement vers les organes de récupération d'énergie. La seconde approche se base sur la mise en place d'un système de stabilisation multi-agent. Celui-ci présente une structure décentralisée où chaque agent correspond à un bloc de stabilisation. Ceux-ci vont compenser localement les impacts déstabilisants de leur charge respective et leurs actions combinées permettront d'assurer la stabilité du système. De plus, on propose d'utiliser un algorithme d'optimisation sous contraintes qui permettra de donner un dimensionnement du système minimisant les efforts de stabilisation tout en considérant des cas de défaut tels que la perte de l'un des agents ou la reconfiguration du réseau / This thesis is devoted to the analysis of the instability phenomenon that may appear on the DC bus of DC microgrids. Indeed, interaction between the different electrical subsystems of the grid (source, load, filters) can lead, under certain conditions, to the system instability. From the "Constant Power Load" (CPL) hypothesis for the loads, this thesis presents studying methods for "small-signal" and "large-signal" stability analysis of electrical systems. This highlights that a DC microgrid cannot power the loads more than a maximum limit without becoming unstable. This power limitation depends on the structure of the grid, the value of its passive components, and its bus voltage. In order to improve the microgrid stability, stabilization methods are presented in this thesis. They propose to adapt the loads control to ensure the system stability. This is achieved by the addition of a stabilizing signal to the reference of each load. This signal is only visible during the load power transient mode to not change the requested operating point. However, a good trade-off must be found to ensure system stability without affecting the dynamic performance of its loads. Two approaches are investigated to generate the stabilizing commands. The first one is based on the establishment of a centralized stabilization block. Two centralized methods have been developed: the first one is based on the Takagi-Sugeno theory while the second is based on the Lyapunov theory. This latest permits to guide the stabilizing effort on the desired loads. For example, stabilizing effort can be oriented on the energy storage device. The second approach is based on the establishment of a multi-agent stabilizing system. It consists of a decentralized structure in which each agent corresponds to a stabilization block. These will locally compensate the destabilizing impact of their respective load on the microgrid, and their combined actions ensure the system stability. To design the system, the use of a constrained optimization algorithm is proposed. This permits to minimize stabilization efforts while considering faulty events such as the failure of one of the agents or a reconfiguration of the microgrid

La raréfaction des sources de production conventionnelles et leurs émissions nocives ont favorisé l’essor notable de la production renouvelable, plus durable et mieux répartie géographiquement. Toutefois, son intégration au système électrique est problématique. En effet, la production renouvelable est peu prédictible et issue de sources majoritairement incontrôlables, ce qui compromet la stabilité du réseau, la viabilité économique des producteurs et rend nécessaire la définition de solutions adaptées pour leur participation au marché de l’électricité. Dans ce contexte, le projet scientifique Winpower propose de relier par un réseau à courant continu les ressources de plusieurs acteurs possédant respectivement des fermes éoliennes offshore (acteurs EnR) et des centrales de stockage de masse (acteurs CSM). Cette configuration impose aux acteurs d’assurer conjointement la gestion du réseau électrique.Nous supposons que les acteurs participent au marché comme une entité unique : cette hypothèse permet aux acteurs EnR de tirer profit de la flexibilité des ressources contrôlables pour minimiser le risque de pénalités sur le marché de l’électricité, aux acteurs CSM de valoriser leurs ressources auprès des acteurs EnR et/ou auprès du marché et à la coalition de faciliter la gestion des déséquilibres sur le réseau électrique, en agrégeant les ressources disponibles. Dans ce cadre, notre travail s’attaque à la problématique de la participation au marché EPEX SPOT Day-Ahead de la coalition comme une centrale électrique virtuelle ou CVPP (Cooperative Virtual Power Plant). Nous proposons une architecture de pilotage multi-acteurs basée sur les systèmes multi-agents (SMA) : elle permet d’allier les objectifs et contraintes locaux des acteurs et les objectifs globaux de la coalition.Nous formalisons alors l’agrégation et la planification de l’utilisation des ressources comme un processus décisionnel de Markov (MDP), un modèle formel adapté à la décision séquentielle en environnement incertain, pour déterminer la séquence d’actions sur les ressources contrôlables qui maximise l’espérance des revenus effectifs de la coalition. Toutefois, au moment de la planification des ressources de la coalition, l’état de la production renouvelable n’est pas connue et le MDP n’est pas résoluble en l’état : on parle de MDP partiellement observable (POMDP). Nous décomposons le POMDP en un MDP classique et un état d’information (la distribution de probabilités des erreurs de prévision de la production renouvelable) ; en extrayant cet état d’information de l’expression du POMDP, nous obtenons un MDP à état d’information (IS-MDP), pour la résolution duquel nous proposons une adaptation d’un algorithme de résolution classique des MDP, le Backwards Induction.Nous décrivons alors un cadre de simulation commun pour comparer dans les mêmes conditions nos propositions et quelques autres stratégies de participation au marché dont l’état de l’art dans la gestion des ressources renouvelables et contrôlables. Les résultats obtenus confortent l’hypothèse de la minimisation du risque associé à la production renouvelable, grâce à l’agrégation des ressources et confirment l’intérêt de la coopération des acteurs EnR et CSM dans leur participation au marché de l’électricité. Enfin, l’architecture proposée offre la possibilité de distribuer le processus de décision optimale entre les différents acteurs de la coalition : nous proposons quelques pistes de solution dans cette direction. / Renewable Energy Sources (RES) has grown remarkably in last few decades. Compared to conventional energy sources, renewable generation is more available, sustainable and environment-friendly - for example, there is no greenhouse gases emission during the energy generation. However, while electrical network stability requires production and consumption equality and the electricity market constrains producers to contract future production a priori and respect their furniture commitments or pay substantial penalties, RES are mainly uncontrollable and their behavior is difficult to forecast accurately. De facto, they jeopardize the stability of the physical network and renewable producers competitiveness in the market. The Winpower project aims to design realistic, robust and stable control strategies for offshore networks connecting to the main electricity system renewable sources and controllable storage devices owned by different autonomous actors. Each actor must embed its own local physical device control strategy but a global network management mechanism, jointly decided between connected actors, should be designed as well.We assume a market participation of the actors as an unique entity (the coalition of actors connected by the Winpower network) allowing the coalition to facilitate the network management through resources aggregation, renewable producers to take advantage of controllable sources flexibility to handle market penalties risks, as well as storage devices owners to leverage their resources on the market and/or with the management of renewable imbalances. This work tackles the market participation of the coalition as a Cooperative Virtual Power Plant. For this purpose, we describe a multi-agent architecture trough the definition of intelligent agents managing and operating actors resources and the description of these agents interactions; it allows the alliance of local constraints and objectives and the global network management objective.We formalize the aggregation and planning of resources utilization as a Markov Decision Process (MDP), a formal model suited for sequential decision making in uncertain environments. Its aim is to define the sequence of actions which maximize expected actual incomes of the market participation, while decisions over controllable resources have uncertain outcomes. However, market participation decision is prior to the actual operation when renewable generation still is uncertain. Thus, the Markov Decision Process is intractable as its state in each decision time-slot is not fully observable. To solve such a Partially Observable MDP (POMDP), we decompose it into a classical MDP and an information state (a probability distribution over renewable generation errors). The Information State MDP (IS-MDP) obtained is solved with an adaptation of the Backwards Induction, a classical MDP resolution algorithm.Then, we describe a common simulation framework to compare our proposed methodology to some other strategies, including the state of the art in renewable generation market participation. Simulations results validate the resources aggregation strategy and confirm that cooperation is beneficial to renewable producers and storage devices owners when they participate in electricity market. The proposed architecture is designed to allow the distribution of the decision making between the coalition’s actors, through the implementation of a suitable coordination mechanism. We propose some distribution methodologies, to this end.

In this work are summarized the theoretical basis of retail and simulation models usable as decision making support in the management of retail units. There are described the specifics of retail and specifics of retail units in terms of their classification and basic theoretical foundations for the creation of simulation models. The work also describes the default multi-agent simulation model created by the leader of this work,. Ing. Viktor Vojtko, Ph.D., on which this work builds. Then work describes creation of case studies using multi-agent simulation model, including the calibration process of models for these case studies. General methodology of creating case studies is described in next part of the work. Then the created methodology is varified by scenarios and the last part describes proposals for further editing of simulation model.

<div><div><div><p>The process of acquiring the large-scale complex systems is usually characterized with cost and schedule overruns. To investigate the causes of this problem, we may view the acquisition of a complex system in several different time scales. At finer time scales, one may study different stages of the acquisition process from the intricate details of the entire systems engineering process to communication between design teams to how individual designers solve problems. At the largest time scale one may consider the acquisition process as series of actions which are, request for bids, bidding and auctioning, contracting, and finally building and deploying the system, without resolving the fine details that occur within each step. In this work, we study the acquisition processes in multiple scales. First, we develop a game-theoretic model for engineering of the systems in the building and deploying stage. We model the interactions among the systems and subsystem engineers as a principal-agent problem. We develop a one-shot shallow systems engineering process and obtain the optimum transfer functions that best incentivize the subsystem engineers to maximize the expected system-level utility. The core of the principal-agent model is the quality function which maps the effort of the agent to the performance (quality) of the system. Therefore, we build the stochastic quality function by modeling the design process as a sequential decision-making problem. Second, we develop and evaluate a model of the acquisition process that accounts for the strategic behavior of different parties. We cast our model in terms of government-funded projects and assume the following steps. First, the government publishes a request for bids. Then, private firms offer their proposals in a bidding process and the winner bidder enters in a con- tract with the government. The contract describes the system requirements and the corresponding monetary transfers for meeting them. The winner firm devotes effort to deliver a system that fulfills the requirements. This can be assumed as a game that the government plays with the bidder firms. We study how different parameters in the acquisition procedure affect the bidders’ behaviors and therefore, the utility of the government. Using reinforcement learning, we seek to learn the optimal policies of involved actors in this game. In particular, we study how the requirements, contract types such as cost-plus and incentive-based contracts, number of bidders, problem complexity, etc., affect the acquisition procedure. Furthermore, we study the bidding strategy of the private firms and how the contract types affect their strategic behavior.</p></div></div></div>