More and more of our physical world today is being monitored and controlled by so-called cyber-physical systems (CPSs). These are compositions of networked autonomous cyber and physical agents such as sensors, actuators, computational elements, and humans in the loop. Today, CPSs are still relatively small-scale and very limited compared to CPSs to be witnessed in the future. Future CPSs are expected to be far more complex, large-scale, wide-spread, and mission-critical, and found in a variety of domains such as transportation, medicine, manufacturing, and energy, where they will bring many advantages such as the increased efficiency, sustainability, reliability, and security. To unleash their full potential, CPSs need to be equipped with, among other features, the support for automated planning and control, where computing agents collaboratively and continuously plan and control their actions in an intelligent and well-coordinated manner to secure and optimize a physical process, e.g., electricity flow in the power grid. In today’s CPSs, the control is typically automated, but the planning is solely performed by humans. Unfortunately, it is intractable and infeasible for humans to plan every action in a future CPS due to the complexity, scale, and volatility of a physical process. Due to these properties, the control and planning has to be continuous and automated in future CPSs. Humans may only analyse and tweak the system’s operation using the set of tools supporting prescriptive analytics that allows them (1) to make predictions, (2) to get the suggestions of the most prominent set of actions (decisions) to be taken, and (3) to analyse the implications as if such actions were taken. This thesis considers the planning and control in the context of a large-scale multi-agent CPS. Based on the smart-grid use-case, it presents a so-called PrescriptiveCPS – which is (the conceptual model of) a multi-agent, multi-role, and multi-level CPS automatically and continuously taking and realizing decisions in near real-time and providing (human) users prescriptive analytics tools to analyse and manage the performance of the underlying physical system (or process). Acknowledging the complexity of CPSs, this thesis provides contributions at the following three levels of scale: (1) the level of a (full) PrescriptiveCPS, (2) the level of a single PrescriptiveCPS agent, and (3) the level of a component of a CPS agent software system. At the CPS level, the contributions include the definition of PrescriptiveCPS, according to which it is the system of interacting physical and cyber (sub-)systems. Here, the cyber system consists of hierarchically organized inter-connected agents, collectively managing instances of so-called flexibility, decision, and prescription models, which are short-lived, focus on the future, and represent a capability, an (user’s) intention, and actions to change the behaviour (state) of a physical system, respectively. At the agent level, the contributions include the three-layer architecture of an agent software system, integrating the number of components specially designed or enhanced to support the functionality of PrescriptiveCPS. At the component level, the most of the thesis contribution is provided. The contributions include the description, design, and experimental evaluation of (1) a unified multi-dimensional schema for storing flexibility and prescription models (and related data), (2) techniques to incrementally aggregate flexibility model instances and disaggregate prescription model instances, (3) a database management system (DBMS) with built-in optimization problem solving capability allowing to formulate optimization problems using SQL-like queries and to solve them “inside a database”, (4) a real-time data management architecture for processing instances of flexibility and prescription models under (soft or hard) timing constraints, and (5) a graphical user interface (GUI) to visually analyse the flexibility and prescription model instances. Additionally, the thesis discusses and exemplifies (but provides no evaluations of) (1) domain-specific and in-DBMS generic forecasting techniques allowing to forecast instances of flexibility models based on historical data, and (2) powerful ways to analyse past, current, and future based on so-called hypothetical what-if scenarios and flexibility and prescription model instances stored in a database. Most of the contributions at this level are based on the smart-grid use-case. In summary, the thesis provides (1) the model of a CPS with planning capabilities, (2) the design and experimental evaluation of prescriptive analytics techniques allowing to effectively forecast, aggregate, disaggregate, visualize, and analyse complex models of the physical world, and (3) the use-case from the energy domain, showing how the introduced concepts are applicable in the real world. We believe that all this contribution makes a significant step towards developing planning-capable CPSs in the future. / Mehr und mehr wird heute unsere physische Welt überwacht und durch sogenannte Cyber-Physical-Systems (CPS) geregelt. Dies sind Kombinationen von vernetzten autonomen cyber und physischen Agenten wie Sensoren, Aktoren, Rechenelementen und Menschen. Heute sind CPS noch relativ klein und im Vergleich zu CPS der Zukunft sehr begrenzt. Zukünftige CPS werden voraussichtlich weit komplexer, größer, weit verbreiteter und unternehmenskritischer sein sowie in einer Vielzahl von Bereichen wie Transport, Medizin, Fertigung und Energie – in denen sie viele Vorteile wie erhöhte Effizienz, Nachhaltigkeit, Zuverlässigkeit und Sicherheit bringen – anzutreffen sein. Um ihr volles Potenzial entfalten zu können, müssen CPS unter anderem mit der Unterstützung automatisierter Planungs- und Steuerungsfunktionalität ausgestattet sein, so dass Agents ihre Aktionen gemeinsam und kontinuierlich auf intelligente und gut koordinierte Weise planen und kontrollieren können, um einen physischen Prozess wie den Stromfluss im Stromnetz sicherzustellen und zu optimieren. Zwar sind in den heutigen CPS Steuerung und Kontrolle typischerweise automatisiert, aber die Planung wird weiterhin allein von Menschen durchgeführt. Leider ist diese Aufgabe nur schwer zu bewältigen, und es ist für den Menschen schlicht unmöglich, jede Aktion in einem zukünftigen CPS auf Basis der Komplexität, des Umfangs und der Volatilität eines physikalischen Prozesses zu planen. Aufgrund dieser Eigenschaften müssen Steuerung und Planung in CPS der Zukunft kontinuierlich und automatisiert ablaufen. Der Mensch soll sich dabei ganz auf die Analyse und Einflussnahme auf das System mit Hilfe einer Reihe von Werkzeugen konzentrieren können. Derartige Werkzeuge erlauben (1) Vorhersagen, (2) Vorschläge der wichtigsten auszuführenden Aktionen (Entscheidungen) und (3) die Analyse und potentiellen Auswirkungen der zu fällenden Entscheidungen. Diese Arbeit beschäftigt sich mit der Planung und Kontrolle im Rahmen großer Multi-Agent-CPS. Basierend auf dem Smart-Grid als Anwendungsfall wird ein sogenanntes PrescriptiveCPS vorgestellt, welches einem Multi-Agent-, Multi-Role- und Multi-Level-CPS bzw. dessen konzeptionellem Modell entspricht. Diese PrescriptiveCPS treffen und realisieren automatisch und kontinuierlich Entscheidungen in naher Echtzeit und stellen Benutzern (Menschen) Prescriptive-Analytics-Werkzeuge und Verwaltung der Leistung der zugrundeliegenden physischen Systeme bzw. Prozesse zur Verfügung. In Anbetracht der Komplexität von CPS leistet diese Arbeit Beiträge auf folgenden Ebenen: (1) Gesamtsystem eines PrescriptiveCPS, (2) PrescriptiveCPS-Agenten und (3) Komponenten eines CPS-Agent-Software-Systems. Auf CPS-Ebene umfassen die Beiträge die Definition von PrescriptiveCPS als ein System von wechselwirkenden physischen und cyber (Sub-)Systemen. Das Cyber-System besteht hierbei aus hierarchisch organisierten verbundenen Agenten, die zusammen Instanzen sogenannter Flexibility-, Decision- und Prescription-Models verwalten, welche von kurzer Dauer sind, sich auf die Zukunft konzentrieren und Fähigkeiten, Absichten (des Benutzers) und Aktionen darstellen, die das Verhalten des physischen Systems verändern. Auf Agenten-Ebene umfassen die Beiträge die Drei-Ebenen-Architektur eines Agentensoftwaresystems sowie die Integration von Komponenten, die insbesondere zur besseren Unterstützung der Funktionalität von PrescriptiveCPS entwickelt wurden. Der Schwerpunkt dieser Arbeit bilden die Beiträge auf der Komponenten-Ebene, diese umfassen Beschreibung, Design und experimentelle Evaluation (1) eines einheitlichen multidimensionalen Schemas für die Speicherung von Flexibility- and Prescription-Models (und verwandten Daten), (2) der Techniken zur inkrementellen Aggregation von Instanzen eines Flexibilitätsmodells und Disaggregation von Prescription-Models, (3) eines Datenbankmanagementsystem (DBMS) mit integrierter Optimierungskomponente, die es erlaubt, Optimierungsprobleme mit Hilfe von SQL-ähnlichen Anfragen zu formulieren und sie „in einer Datenbank zu lösen“, (4) einer Echtzeit-Datenmanagementarchitektur zur Verarbeitung von Instanzen der Flexibility- and Prescription-Models unter (weichen oder harten) Zeitvorgaben und (5) einer grafische Benutzeroberfläche (GUI) zur Visualisierung und Analyse von Instanzen der Flexibility- and Prescription-Models. Darüber hinaus diskutiert und veranschaulicht diese Arbeit beispielhaft ohne detaillierte Evaluation (1) anwendungsspezifische und im DBMS integrierte Vorhersageverfahren, die die Vorhersage von Instanzen der Flexibility- and Prescription-Models auf Basis historischer Daten ermöglichen, und (2) leistungsfähige Möglichkeiten zur Analyse von Vergangenheit, Gegenwart und Zukunft auf Basis sogenannter hypothetischer „What-if“-Szenarien und der in der Datenbank hinterlegten Instanzen der Flexibility- and Prescription-Models. Die meisten der Beiträge auf dieser Ebene basieren auf dem Smart-Grid-Anwendungsfall. Zusammenfassend befasst sich diese Arbeit mit (1) dem Modell eines CPS mit Planungsfunktionen, (2) dem Design und der experimentellen Evaluierung von Prescriptive-Analytics-Techniken, die eine effektive Vorhersage, Aggregation, Disaggregation, Visualisierung und Analyse komplexer Modelle der physischen Welt ermöglichen und (3) dem Anwendungsfall der Energiedomäne, der zeigt, wie die vorgestellten Konzepte in der Praxis Anwendung finden. Wir glauben, dass diese Beiträge einen wesentlichen Schritt in der zukünftigen Entwicklung planender CPS darstellen. / Mere og mere af vores fysiske verden bliver overvåget og kontrolleret af såkaldte cyber-fysiske systemer (CPSer). Disse er sammensætninger af netværksbaserede autonome IT (cyber) og fysiske (physical) agenter, såsom sensorer, aktuatorer, beregningsenheder, og mennesker. I dag er CPSer stadig forholdsvis små og meget begrænsede i forhold til de CPSer vi kan forvente i fremtiden. Fremtidige CPSer forventes at være langt mere komplekse, storstilede, udbredte, og missionskritiske, og vil kunne findes i en række områder såsom transport, medicin, produktion og energi, hvor de vil give mange fordele, såsom øget effektivitet, bæredygtighed, pålidelighed og sikkerhed. For at frigøre CPSernes fulde potentiale, skal de bl.a. udstyres med støtte til automatiseret planlægning og kontrol, hvor beregningsagenter i samspil og løbende planlægger og styrer deres handlinger på en intelligent og velkoordineret måde for at sikre og optimere en fysisk proces, såsom elforsyningen i elnettet. I nuværende CPSer er styringen typisk automatiseret, mens planlægningen udelukkende er foretaget af mennesker. Det er umuligt for mennesker at planlægge hver handling i et fremtidigt CPS på grund af kompleksiteten, skalaen, og omskifteligheden af en fysisk proces. På grund af disse egenskaber, skal kontrol og planlægning være kontinuerlig og automatiseret i fremtidens CPSer. Mennesker kan kun analysere og justere systemets drift ved hjælp af det sæt af værktøjer, der understøtter præskriptive analyser (prescriptive analytics), der giver dem mulighed for (1) at lave forudsigelser, (2) at få forslagene fra de mest fremtrædende sæt handlinger (beslutninger), der skal tages, og (3) at analysere konsekvenserne, hvis sådanne handlinger blev udført. Denne afhandling omhandler planlægning og kontrol i forbindelse med store multi-agent CPSer. Baseret på en smart-grid use case, præsenterer afhandlingen det såkaldte PrescriptiveCPS hvilket er (den konceptuelle model af) et multi-agent, multi-rolle, og multi-level CPS, der automatisk og kontinuerligt tager beslutninger i nær-realtid og leverer (menneskelige) brugere præskriptiveanalyseværktøjer til at analysere og håndtere det underliggende fysiske system (eller proces). I erkendelse af kompleksiteten af CPSer, giver denne afhandling bidrag til følgende tre niveauer: (1) niveauet for et (fuldt) PrescriptiveCPS, (2) niveauet for en enkelt PrescriptiveCPS agent, og (3) niveauet for en komponent af et CPS agent software system. På CPS-niveau, omfatter bidragene definitionen af PrescriptiveCPS, i henhold til hvilken det er det system med interagerende fysiske- og IT- (under-) systemer. Her består IT-systemet af hierarkisk organiserede forbundne agenter der sammen styrer instanser af såkaldte fleksibilitet (flexibility), beslutning (decision) og præskriptive (prescription) modeller, som henholdsvis er kortvarige, fokuserer på fremtiden, og repræsenterer en kapacitet, en (brugers) intention, og måder til at ændre adfærd (tilstand) af et fysisk system. På agentniveau omfatter bidragene en tre-lags arkitektur af et agent software system, der integrerer antallet af komponenter, der er specielt konstrueret eller udbygges til at understøtte funktionaliteten af PrescriptiveCPS. Komponentniveauet er hvor afhandlingen har sit hovedbidrag. Bidragene omfatter beskrivelse, design og eksperimentel evaluering af (1) et samlet multi- dimensionelt skema til at opbevare fleksibilitet og præskriptive modeller (og data), (2) teknikker til trinvis aggregering af fleksibilitet modelinstanser og disaggregering af præskriptive modelinstanser (3) et database management system (DBMS) med indbygget optimeringsproblemløsning (optimization problem solving) der gør det muligt at formulere optimeringsproblemer ved hjælp af SQL-lignende forespørgsler og at løse dem "inde i en database", (4) en realtids data management arkitektur til at behandle instanser af fleksibilitet og præskriptive modeller under (bløde eller hårde) tidsbegrænsninger, og (5) en grafisk brugergrænseflade (GUI) til visuelt at analysere fleksibilitet og præskriptive modelinstanser. Derudover diskuterer og eksemplificerer afhandlingen (men giver ingen evalueringer af) (1) domæne-specifikke og in-DBMS generiske prognosemetoder der gør det muligt at forudsige instanser af fleksibilitet modeller baseret på historiske data, og (2) kraftfulde måder at analysere tidligere-, nutids- og fremtidsbaserede såkaldte hypotetiske hvad-hvis scenarier og fleksibilitet og præskriptive modelinstanser gemt i en database. De fleste af bidragene på dette niveau er baseret på et smart-grid brugsscenarie. Sammenfattende giver afhandlingen (1) modellen for et CPS med planlægningsmulighed, (2) design og eksperimentel evaluering af præskriptive analyse teknikker der gør det muligt effektivt at forudsige, aggregere, disaggregere, visualisere og analysere komplekse modeller af den fysiske verden, og (3) brugsscenariet fra energiområdet, der viser, hvordan de indførte begreber kan anvendes i den virkelige verden. Vi mener, at dette bidrag udgør et betydeligt skridt i retning af at udvikle CPSer til planlægningsbrug i fremtiden.

Cette thèse est consacrée à l'étude d’agents appelés prosumers parce qu’ils peuvent, à partir d’énergies renouvelables, à la fois produire et consommer de l’électricité. Si leurs productions excèdent leurs propres besoins, ceux-ci cherchent à vendre leur surplus sur des marchés de l’électricité. Nous proposons de modéliser ces prosumers à partir de données météorologiques, ce qui nous a permit de mettre en évidence des corrélations spatio-temporelles non triviales, d'une grande importance pour les agrégateurs qui forment des portefeuilles d’équipements afin de vendre des services à l'opérateur du réseau. Comme un agrégateur est lié par un contrat avec l'opérateur, il peut faire l'objet de sanctions s’il ne remplit pas son rôle. Nous montrons que ces corrélations impactent la stabilité des agrégats, et donc le risque encouru par les agrégateurs. Nous proposons un algorithme minimisant le risque d'un ensemble d’agrégations, tout en maximisant le gain attendu. La mise en place de dispositifs de stockage dans un réseau où les générateurs et les charges sont dynamiques et stochastiques est complexe. Nous proposons de répondre à cette question grâce à la théorie du contrôle. Nous modélisons le système électrique par un réseau d'oscillateurs couplés, dont la dynamique des angles de phase est une approximation de la dynamique réelle du système. Le but est de trouver le sous-ensemble des nœuds du graphe qui, lors d'une perturbation du système, permet le retour à l'équilibre si les bons signaux sont injectés, et ceci avec une énergie minimum. Nous proposons un algorithme pour trouver un placement proche de l'optimum permettant de minimiser l'énergie moyenne de contrôle / This thesis is devoted to the study of agents called prosumers because they can, from renewable, both produce and consume electricity. If their production exceeds their own needs, they are looking to sell their surplus on electricity markets. We propose to model these prosumers from meteorological data, which has allowed us to highlight non trivial spatial and temporal correlations. This is of great importance for aggregators that form portfolios of equipments to sell services to the network operator. As an aggregator is bound by a contract with the operator, it can be subject to penalties if it does not fulfill its role. We show that these correlations impact the stability of aggregates, and therefore the risk taken by the aggregators. We propose an algorithm minimizing the risk of the aggregations, while maximizing the expected gain. The placement of storage devices in a network where generators and loads are stochastic and not fixed is complex. We propose to answer this question with control theory. We model the electrical system as a network of coupled oscillators, whose phase angles dynamics is an approximation of the actual dynamics of the system. The goal is to find the subset of nodes in the graph that, during a disturbance of the system, allows returning to equilibrium if the right signals are injected and this with a minimum energy. We propose an algorithm to find a near optimal placement to minimize the average energy control

The main focus of this thesis is to test a model that compares the present grid and a Smart Grid system. The thesis discusses the major issues faced by our electricity infrastructure and the possible solutions offered by the Smart Grid. Present grid limitations based on operational, technological, planning, and policy issues are covered. The thesis initially focuses on the limitations of our present grid, and describes severe limitations of our current grid during blackouts. The thesis outlines possible solutions for these problems offered by the concept of the Smart Grid, whose technology and features are described in detail. The thesis details Smart Grid technologies for power generation and the latest electronic devices available to aid the current aging power grid. Further, this thesis offers an analysis that compares the ‘present grid’ to a particular ‘Smart Grid’ configuration consisting of a Combined-Heat & Power (CHP) plant, a Photovoltaic system, and a Demand Response with real-time pricing. The analysis reveals the economic and operational benefit of the Smart Grid system under consideration.

Master of Science / Department of Electrical and Computer Engineering / Caterina M. Scoglio / The need for reliable and quick communication in the power grid is growing and becoming more critical. With the Smart Grid initiative, an increasing number of intelligent devices, such as smart meters and new sensors, are being added to the grid. The traffic demand on the communications network increases as these new devices are being added. This can cause issues such as longer delay, dropped packets, and equipment failure. The power grid relies on this data to function properly. The power grid will lose reliability and will not be able to provide customers with power unless it has correct and timely data. The current communications network architecture needs to be evaluated and improved. In this thesis, a simulator program is developed to study the communications network. The simulation model is written in C++ and models the components of the communications network. The simulation results provide insight on how to design the communications network in order for the system to be robust from failures. We are using the simulator to study different topologies of the communications network. The communications network often has a simular topology to the power grid. This is because of right-a-ways and ownership of equipment. Modifying the topology of the communications network slightly can improve the performance of the network. Security of the communications network is a very important aspect. There is a risk of successful attacks on the communications network without the implementation of security protocols. Attacks can come from malicious users of the communications network or from entities outside the network. These attacks may lead to damaged equipment, loss of power to consumers, network overload, loss of data, and loss of privacy. This thesis presents a short overview of the major issues related to the security of the communications network. The department of Electrical and Computer Engineering (ECE) at Kansas State University (K-State) is working on developing a Smart Grid lab. Burns and McDonnell has collaborated with the ECE department at K-State to develop the Smart Grid Lab. This lab will be located inside of the ECE department. The lab will consist of both power grid equipment and network communication equipment. This thesis describes similar labs. It then describes the initial plan for the lab, which is currently in the planning stage.

Dans de proches années, les véhicules électriques (VEs) vont faire leur apparition massive sur les marchés. Cela peut avoir un impact important sur le fonctionnement des réseaux d’électricité actuels qui devront ajuster leur fonctionnement à la nouvelle demande massive d'électricité provenant des VEs. Par contre, les VEs peuvent aussi être vus comme une nouvelle opportunité dans le futur marché d’électricité. En effet, une décharge/recharge intelligente peut permettre aux VEs d’être un support de stockage d’électricité important, valable, et permanent dont la capacité croit en fonction du nombre des VEs. Ce projet a comme objectifs de : (1) proposer un schéma d’interaction V2G (Vehicle-to-Grid) intégrant des techniques permettant de : (a) adapter le fonctionnement de la grille aux contraintes temporelles et spatiales relatives au processus de recharge des VEs dans un milieu résidentiel. On cherchera à satisfaire de différentes demandes en puissance des VEs branchés au secteur sans trop stresser la grille intelligente, (b) optimiser les opérations de chargement/déchargement entre les VEs et la grille dans les deux sens. (2) Proposer de nouveaux schémas de communication sans fil, entre les VEs et la grille intelligente loin des bornes de recharge, qui soient basés sur les standards de communications véhiculaires (VANETs) ainsi que sur d’autres standards de communication à grande échelle. On introduira des techniques d’accès à la grille intelligente pour négocier le coût de recharge/décharge des batteries, le temps d’attente du service, les emplacements et aussi pour planifier la motivation du consommateur afin de favoriser la stabilité de la grille.

Renewable and green energy development is vigorously supported by most countries to suppress the continuously increasing greenhouse gas (GHG) emissions. However, as the total renewable capacity expands, the growth rate of emissions is not effectively restrained. An unforeseen factor contributing to this growth is the regulation service, which aims to mitigate power frequency deviations caused by the intermittent renewable power generation and unbalanced power supply and demand. Regulation services, normally issued by supply-side balancing authorities, leads to inefficient operations of regulating generators, thus directly contributing to the emissions growth. Therefore, it is urged to find solutions that can stabilize the power frequency with an increased energy using efficiency. Demand response (DR) is an ideal candidate to solve this problem. The current smart grid infrastructure enables a high penetration of smart residential electric loads, including heating, ventilation, and air conditioning systems (HVACs), air conditioners (A/Cs), electric water heaters (EWHs), and plug-in hybrid electric vehicles (PHEVs). Beyond simply drawing power from the grid for local electric demand, those loads can also adjust their power consumption patterns by responding to the control signals sent to them. It has been proved that, if appropriately aggregated and controlled, power consumption of demand-side residential loads possesses a huge potential for providing regulation services. The research of DR is pivotal from the the application perspective due to the efficient usage of renewable energy generation and the high power quality. However, many problems remain open in this area due to the load heterogeneity, device physical constraints, and computational and communication restrictions. In order to move one step further toward industry applications, this PhD thesis is concerned with two cruxes in DR program design: Aggregation Modeling and Control; it deals with two main types of terminal loads: Thermostatically Controlled Appliances (TCAs) (Chapters 2-4) and PHEVs (Chapter 5). This thesis proceeds with Chapter 1 by reviewing the state-of-the-art of DR. Then in Chapter 2, the focus is put on modeling and control of TCAs for secondary frequency control. In order to explicitly describe local TCA dynamics and to provide the aggregator a clear global view, TCAs are aggregated by directly stacking their individual dynamics. Terminal TCAs are assumed in a general case that an arbitrary number of TCAs are equipped with varying frequency drives (VFDs). A centralized model predictive control (MPC) scheme is firstly constructed. In the design, to tackle the TCA lockout effect and to facilitate the MPC scheme, a novel approach for converting time-integrated interdependent logic constraints into inequality constraints are proposed. Since a centralized MPC scheme may introduce non-trivial computational load by using this aggregation model, especially when the number of TCAs increases, a distributed MPC (DMPC) scheme is proposed. This DMPC scheme is validated through a more practical case study that all TCAs are subject to pure ON/OFF control. Chapter 3 targets on aggregation modeling and control of TCAs for the provision of primary frequency control. To efficiently reduce the computational load to facilitate the primary frequency control, the explicit monitoring of terminal TCAs must be compromised. To this end, a 2-D population-based model is proposed, in which TCAs are clustered into state bins according to their temperature information and running status. Within the proposed aggregation framework, individual TCA dynamics' evolutions develop into TCA population migration probabilities, thus the computational load of the centralized controller is dramatically reduced. Based on this model, a centralized MPC scheme is proposed for the primary frequency control. The previously proposed population-based model provides a promising direction for the centralized control. However, in traditional population-based model, TCA lockout effect can only be considered when implementing the control signals. This will cause a mismatch between the nominal control signals and the actually implemented ones. To conquer this, in Chapter 4, an improved population-based model is studied to explicitly formulate the TCA lockout effect in the aggregation model. A DMPC scheme is firstly constructed based on this model. Furthermore, since the predictions of regulation signals may not be available or they may include severe disturbances, a control scheme that does not require future regulation signals is urged. To this end, an optimal control scheme, in which a novel penalty is included to maximize the regulation capability, is proposed to facilitate the most practical scenario. Another type of terminal loads that has a huge potential in providing grid services is PHEV. At this point, Chapter 5 presents the aggregation and charging control of heterogeneous PHEVs for the provision of DR. In contrast to using battery state-of-charge (SOC) solely as the system state, a new aggregation model is proposed by introducing a novel concept, i.e., charging requirement index. This index combines the SOC with drivers' specified charging requirements, thus inherently providing the aggregation model with richer information. A centralized MPC scheme is proposed based on this novel model. Both of the model and controller are validated through an overnight valley-filling case study. Finally, the conclusions of the thesis are summarized and future research topics are presented. / Graduate / 0537 / 0544 / 0548 / mingxiliu419@gmail.com

Les réseaux de distribution sont aujourd'hui confrontés à un accroissement significatif de la Génération d'Énergie Dispersée. Cet accroissement pose, notamment, des problèmes des tension que les moyens de réglage de la tension utilisés aujourd'hui ne parviennent plus à éviter. En conséquence, la capacité d'accueil des réseaux de distribution est aujourd'hui très limitée. Cette thèse propose une méthode de réglage de tension novatrice appelée OMD pour Optimisation Mixte Découplée. Cette méthode se base sur une gestion optimale en temps réel de la puissance réactive des GED et des variables discrètes du système. Les simulations entreprises montrent que l'algorithme développé permet d'augmenter significativement le nombre de GED connectées au réseau et d'optimiser les pertes Joule. De plus, l'OMD apporte une plus value intéressante de par sa modularité et sa flexibilité en comparaison avec d'autres méthodes proposées dans la littérature. Une étude complémentaire montre que le concept de l'OMD peut être repris et appliqué à d'autres systèmes. Ainsi, un réglage de tension optimal, basé sur l'OMD a été développé pour les parcs éoliens. Il permet un contrôle optimal de la tension à l'intérieur des parcs tout en assurant le maintien d'une consigne de puissance réactive ou de tension au point de connexion avec le réseau. Le réglage de tension proposé dans cette thèse est donc une des solutions qui pourra permettre un meilleur fonctionnement des réseaux de distribution du futur : les Smart Grids

Enabled by advanced communication and information technologies, the smart grid represents a major transformation for the electricity sector. Vast quantities of data and two-way communications abilities create the potential for a flexible, data-driven, multi-directional supply and consumption network well equipped to meet the challenges of the next century. For electricity service providers (“utilities”), the smart grid provides opportunities for improved business practices and new business models; however, a transformation of such magnitude is not without risks. Three related studies are conducted to explore the implications of the smart grid on utilities’ demand-side activities. An initial conceptual framework, based on organizational information processing theory, suggests that utilities’ performance depends on the fit between the information processing requirements and capacities associated with a given demand-side activity. Using secondary data and multiple regression analyses, the first study finds, consistent with OIPT, a positive relationship between utilities’ advanced meter deployments and demand-side management performance. However, it also finds that meters with only data collection capacities are associated with lower performance, suggesting the presence of information waste causing operational inefficiencies. In the second study, interviews with industry participants provide partial support for the initial conceptual model, new insights are gained with respect to information processing fit and information waste, and “big data” is identified as a central theme of the smart grid. To derive richer theoretical insights, the third study employs a grounded theory approach examining the experience of one successful utility in detail. Based on interviews and documentary data, the paradox of dynamic stability emerges as an essential enabler of utilities’ performance in the smart grid environment. Within this context, the frames of opportunity, control, and data limitation interact to support dynamic stability and contribute to innovation within tradition. The main contributions of this thesis include theoretical extensions to OIPT and the development of an emergent model of dynamic stability in relation to big data. The thesis also adds to the green IS literature and identifies important practical implications for utilities as they endeavour to bring the smart grid to reality. / Thesis (Ph.D, Management) -- Queen's University, 2013-01-21 12:04:43.652

Rapid advances in the smart grid technology are making it possible to tackle a lot of problems in the aged power systems. High-speed data acquisition system, high-voltage power electronic equipment, advanced utility and customer interaction technologies, as well as distributed renewable generation are enabling the revolution in the electric power generation, delivery and distribution. Through the implementation of ubiquitous metering and communication networks, the customers would no longer be a passive receiver of the electrical energy, but instead, an active participant in the power system and electricity market. They can not only sell their own energy to the utility, but also take part in the emergency restoration in the power grid. Nonetheless, some technical barriers are encountered during this revolution, such as difficulties in integrating home automation, smart metering, customer interaction and power system operation into the whole system. This thesis proposes a customer involved load shedding algorithm for both the power system frequency control and the micro-grid islanding. This new algorithm possesses the features of centralized load control and distributed load control, which fully utilizes the advantages of hierarchical communication networks along with the home automation. The proposed algorithm considers the reliability of the power grid as well as the comfort of the electricity users. In the power distribution system, the high-level control centre is responsible for coordinating the local load controllers, whilst the local controller takes charge of frequency monitoring and decision making. In the micro-grid, a centralized control strategy is adopted to better serve the system with the wide set of information available at the micro-grid control centre. The simulation results have demonstrated the correctness and feasibility of the proposed algorithm. Finally, the hardware implementation further tests the validity of the wireless sensor networks serving as the system’s monitoring and communication technology. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2013-09-24 20:01:37.098

n the electrical power grid, the power load is not constant but continuouslychanging. This depends on many different factors, among which the habits of theconsumers, the yearly seasons and the hour of the day. The continuous change inenergy consumption requires the power grid to be flexible. If the energy provided bygenerators is lower than the demand, this is usually compensated by using renewablepower sources or stored energy until the power generators have adapted to the newdemand. However, if buffers are depleted the output may not meet the demandedpower and could cause power outages. The currently adopted practice in the indus-try is based on configuring the grid depending on some expected power draw. Thisanalysis is usually performed at a high level and provide only some basic load aggre-gate as an output. In this thesis, we aim at investigating techniques that are able topredict the behaviour of loads with fine-grained precision. These techniques couldbe used as predictors to dynamically adapt the grid at run time. We have investigatedthe field of time series forecasting and evaluated and compared different techniquesusing a real data set of the load of the Swedish power grid recorded hourly throughyears. In particular, we have compared the traditional ARIMA models to a neuralnetwork and a long short-term memory (LSTM) model to see which of these tech-niques had the lowest forecasting error in our scenario. Our results show that theLSTM model outperformed the other tested models with an average error of 6,1%.