abstract: Microgrids are a subset of the modern power structure; using distributed generation (DG) to supply power to communities rather than vast regions. The reduced scale mitigates loss allowing the power produced to do more with better control, giving greater security, reliability, and design flexibility. This paper explores the performance and cost viability of a hybrid grid-tied microgrid that utilizes Photovoltaic (PV), batteries, and fuel cell (FC) technology. The concept proposes that each community home is equipped with more PV than is required for normal operation. As the homes are part of a microgrid, excess or unused energy from one home is collected for use elsewhere within the microgrid footprint. The surplus power that would have been discarded becomes a community asset, and is used to run intermittent services. In this paper, the modeled community does not have parking adjacent to each home allowing for the installment of a privately owned slower Level 2 charger, making EV ownership option untenable. A solution is to provide a Level 3 DC Quick Charger (DCQC) as the intermittent service. The addition of batteries and Fuel Cells are meant to increase load leveling, reliability, and instill limited island capability. / Dissertation/Thesis / M.S.Tech Engineering 2013

Made available in DSpace on 2016-08-17T14:53:28Z (GMT). No. of bitstreams: 1 Dissertacao Felipe Simoes Freitas e Silva.pdf: 5795737 bytes, checksum: dc589a2115d1b568db1a3c2b9de3f80c (MD5) Previous issue date: 2014-04-25 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / This work presents the study, modelling and assembling of a three-phase bidirectional DCAC converter suitable for operating as an interface between an energy storage system based on a battery bank and an isolated microgrid. The converter consists of two stages: a DC-DC stage implemented with a bidirectional half-bridge converter that operates on buck or boost mode (during charge or discharge of the ESS, respectively), and a DC-AC full-bridge three-phase bidirectional inverter. The two stages are connected through a DC link, which also works as a power-decoupling element. Isolation from the microgrid is obtained with a :-Y low frequency transformer. A control strategy is proposed where the DC-DC stage regulates the DC link voltage level while the DC-AC controls the three-phase output voltage and frequency, therefore acting as a grid-forming converter (GFC). A 15kW prototype was successfully built and tested in different situations. Simulation and experimental results are shown. / Esta dissertação apresenta o estudo, modelagem e montagem de um conversor bidirecional CC-CA trifásico de múltiplos estágios, projetado para operar como interface entre um sistema de armazenamento e uma minirrede isolada. O conversor consiste de dois estágios de conversão: um estágio de conversão CC-CC, implementado com um conversor buck-boost de meia ponte, que opera em modo buck ou boost (durante o carga ou descarga do sistema de armazenamento, respectivamente), e um estágio de conversão CC-CA composto por um conversor CC-CA trifásico. Os dois estágios são conectados por um elo CC, que funciona como um elemento de desacoplamento de potência. Isolação galvânica é obtida através de um transformador de baixa frequência. Uma estratégia de controle é utilizada na qual o estágio CC-CC controla a tensão do elo CC enquanto o estágio CC-CA controla a tensão e a frequência da minirrede, atuando, portanto, como um conversor formador de rede (CFR). Um protótipo foi montado e testado em diferentes situações. Resultados de simulação e de testes de bancada são mostrados.

L’intégration croissante de la Génération Distribuée basée sur des sources d’énergies renouvelables présente de nouveaux défis pour le réseau électrique centralisé actuel. Le micro-réseau est une des alternatives envisagées afin d’augmenter le taux de pénétration d’énergie renouvelable et d’améliorer la qualité de l’énergie. La stabilité d’un micro-réseau est fortement sensible aux variations de puissance venant des sources d’énergie ou des charges. Dans ce contexte, un système de stockage d’énergie joue un rôle essentiel et doit satisfaire deux conditions : disposer d’une capacité de stockage importante pour adapter la production à la demande et être capable de fournir rapidement une puissance instantanée suffisante pour pallier les problèmes de qualité d’énergie. L’objectif principal de cette thèse est de concevoir et valider expérimentalement un système de conversion de puissance et l’algorithme de contrôle associé pour la gestion du stockage dans un micro-réseau afin de satisfaire les deux conditions. Suite à l’analyse de différentes technologies de stockage, on peut conclure qu’il n’y a pas de système de stockage capable de satisfaire les conditions d’énergie et de puissance en même temps. Par conséquent, l’association d’un supercondensateur et d’une batterie Redox au Vanadium dans un Système de Stockage Hybride est utilisée pour satisfaire les conditions mentionnées. Le travail de recherche est axé sur la gestion du flux d’énergie et de puissance du Système de Stockage Hybride proposé à l’aide d’un système de conversion de puissance innovant et son algorithme de commande. Un convertisseur multi-niveaux 3LNPC a été choisi pour commander en même temps les deux systèmes de stockage, en raison de faibles pertes de puissance et de distorsions harmoniques réduites en comparaison avec d’autres topologies existantes. Un algorithme de commande capable d’exploiter les limites de fonctionnement du convertisseur sur toute sa plage de fonctionnement a été conçu afin de satisfaire de manière optimale les critères spécifiés. Le fonctionnement du système de conversion et la stratégie de commande proposée ont été validés d’abord en simulation et ensuite expérimentalement en utilisant le micro-réseau installé à l’ESTIA. / The increasing penetration of Distributed Generation systems based on Renewable Energy Sources is introducing new challenges in the current centralised electric grid. The microgrid is one of the alternatives that is being analysed in order to increase the penetration level of renewable energy sources in electrical grids and improve the power quality. The microgrid stability is highly sensitive to power variations coming from the energy sources or loads. In this context, an energy storage system is essential and it must satisfy two criteria: to have a high storage capacity to adapt the generation to the demand and to be able to supply fast power variations to overcome the power quality problems that may arise. The main objective of this thesis has been to design a power conversion system and the associated control algorithm for a storage system management in order to satisfy the defined requirements, as well as to experimentally validate the proposed solution. After an analysis of different storage system technologies, it can be concluded that there is not any storage system capable of offering the energy and power requirements at the same time. Consequently, the association of a SuperCapacitor bank and a Vanadium Redox Battery is used to satisfy the mentioned requirements. This thesis has been focused on the power and energy flow management of the proposed Hybrid Energy Storage System through an innovative power conversion system and its control method. A Three-Level Neutral Point Clamped converter has been used to control at the same time the two storage systems, due to the reduced power losses and harmonic distortion compared to other existing topologies. A control algorithm that uses the operational limits of the converter in its entire operation range has been designed in order to allow selecting the best operation point according to the specified criteria. The operation of the power conversion system and the proposed control method have been first validated in simulations and then experimentally using the microgrid installed in ESTIA.

Samhället går idag bort från centraliserad energi mot decentraliserade system. Istället för att köpa från stora företag som skapar el från fossila bränslen har många förnybara alternativ kommit. Eftersom konsumenter kan generera solenergi med solpaneler kan de också bli producenter. Detta skapar en stor marknad för handel av el mellan konsumenter i stället för företag. Detta skapar ett så kallat mikronät. Syftet med denna avhandling är att hitta en lösning för att köpa och sälja på dessa mikronät. Genom att använda en Q-learning-lösning med OpenAI Gym-verktygslådan och en mikronätsimulering syftar denna avhandling till att svara på följande frågor: I vilken utsträckning kan Qlearning användas för att köpa och sälja energi i ett mikrosystem, hur lång tid tar det köp och sälj algoritm för att träna och slutligen påverkar latens genomförbarheten av Q-learning för mikronät. För att svara på dessa frågor måste jag mäta latens och utbildningstid för Q-learninglösningen. En neural nätverkslösning skapades också för att jämföra med Q-learning-lösningen. Från dessa resultat kunde jag säga att en del av det inte var så tillförlitligt, men vissa slutsatser kunde fortfarande göras. För det första är den utsträckning som Q-learning kan användas för att köpa och sälja ganska bra om man bara tittar på noggrannhetsresultaten på 97%, men detta sitter på mikronätets simulering för att vara korrekt. Hur lång tid det tar att köpa och sälja algoritm för att träna uppmättes till cirka 12 sekunder. Latensen anses vara noll med Q-learning-lösningen, så den har stor genomförbarhet. Genom dessa frågor kan jag dra slutsatsen att en Q-learning OpenAI Gym-lösning är genomförbart. / Society is today moving away from centralized power towards decentralized systems. Instead of buying from large companies that create electricity from fossil fuels, many renewable alternatives have arrived. Since consumers can generate solar power with solar panels, they can also become the producers. This creates a large market for trading electricity between consumer instead of companies. This creates a so called microgrid. The purpose of this thesis is to find a solution to buying and selling on these microgrids. By using a Q-learning solution with the OpenAI Gym toolkit and a microgrid simulation this thesis aims to answer the following questions: To what extent can Q-learning be used to buy and sell energy in a microgrid system, how long does it take the buy and sell algorithm to train and finally does latency affect the feasibility of Q-learning for microgrids. To answer these questions, I must measure the latency and training time of the Q-learning solution. A neural network solution was also created to compare to the Q-learning solution. From these results I could tell some of it was not that reliable, but some conclusions could still be made. First, the extent that Q-learning can be used to buy and sell is quite great if just looking at the accuracy results of 97%, but this is on the microgrid simulation to be correct. How long it takes to buy and sell algorithm to train was measured to about 12 seconds. The latency is considered zero with the Q-learning solution, so it has great feasibility. Through these questions I can conclude that a Qlearning OpenAI Gym solution is a viable one.

The electric power industry and society are facing the challenges and opportunities of transforming the present power grid into a smart grid. To meet these challenges, new types of control systems are connected over IT infrastructures. While this is done to meet highly set economical and environmental goals, it also introduces new sources of uncertainty in the control loops. In this thesis, we consider control design taking some of these uncertainties into account. In Part I of the thesis, some economical and environmental concerns in smart grids are taken into account, and a scheduling framework for static loads (e.g., smart appliances in residential areas) and dynamic loads (e.g., energy storage systems) in the distribution level is investigated. A robust formulation is proposed taking the user behavior uncertainty into account, so that the optimal scheduling cost is less sensitive to unpredictable changes in user preferences. In addition, a novel distributed algorithm for the studied scheduling framework is proposed, which aims at minimizing the aggregated electricity cost of a network of apartments sharing an energy storage system. We point out that the proposed scheduling framework is applicable to various uncertainty sources, storage technologies, and programmable electrical loads. In Part II of the thesis, we study smart grid uncertainty resulting from possible security threats. Smart grids are one of the most complex cyber-physical systems considered, and are vulnerable to various cyber and physical attacks. The attack scenarios consider cyber adversaries that may corrupt a few measurements and reference signals, which may degrade the system’s reliability and even destabilize the voltage magnitudes. In addition, a practical attack-resilient framework for networked control systems is proposed. This framework includes security information analytics to detect attacks and a resiliency policy to improve the performance of the system running under the attack. Stability and optimal performance of the networked control system under attack and by applying the proposed framework, is proved here. The framework has been applied to an energy management system and its efficiency is demonstrated on a critical attack scenario. / <p>QC 20160830</p>

Although Indonesia’s electrification ratio reached 99.2% in 2020, it has shown stagnating electrification since 2018. This is because most of the remaining areas that need to be electrified are remote and have unique characteristics that hamper implementation of microgrids for providing energy access. Furthermore, not only the deployment but also the long-term sustainability of microgrids is crucial for ensuring continuity of energy access. This paper aims to investigate the scaling and sustainability challenges of remote microgrid development in Indonesia by analyzing microgrids in the Maluku and North Maluku provinces. This study is a two-part publication; the first part focuses on identifying challenges in Indonesia’s remote microgrid development, while the second part focuses on potential technology solutions. In the first part, an assessment of energy access within a multi-tier framework was conducted, which was then analyzed using a multi-dimensional (institutional, social, technical, economic, environmental, and policy) approach adapted from the literature. The framework was expanded by mapping the challenges onto specific phases of the microgrid development, which is intended to be helpful for the parties involved in specific phases. It is shown that the challenges related to unclear land status, lack of social engagement, preliminary survey, technical and practical knowledge, and O&M procedures—especially for remote microgrids with renewable energy sources—are the most prominent issues. Additionally, issues caused by electrical events and environmental conditions such as relatively humid and high-temperatures, and uncontrolled vegetation, rodents, insects, and lizards are often found. Furthermore, a high-level technological outlook to address some of these issues is presented.

Tesis por compendio / [ES] Las fuentes de energía renovable (ER) permiten una alta disgregación, por lo que hacen posible generar la energía que se utilizará en el mismo sitio de su aprovechamiento. Esto favorece un cambio en la estructura de las redes eléctricas, permitiendo pasar de un esquema de generación centralizado a un esquema distribuido. Sin embargo, las fuentes de ER son altamente dependientes de las condiciones medioambientales como la radiación solar, la nubosidad, el viento, entre otros, por lo que lograr un sistema de generación basado en energías renovables es un reto en la actualidad. Los sistemas de generación que integran fuentes renovables tienen que ser capaces de establecer estrategias de control y gestión de la energía que para hacer un uso eficiente de ella e intentar cubrir la demanda de energía de forma óptima al combinar más de un tipo de fuente y sistema de almacenamiento, siendo posible operar de manera aislada o conectada a la red eléctrica. En la actualidad es de interés el estudio, desarrollo e implementación de sistemas gestores de la energía (SGE) para microrredes eléctricas híbridas, que permitan aumentar su eficiencia, fiabilidad, y disminuir los costes de instalación, operación y mantenimiento. Diversos estudios de investigación han probado múltiples estrategias, desde SGE basados en reglas, algoritmos comparativos, controladores clásicos, y en años recientes, la integración de algoritmos de optimización bio-inspirados e inteligencia artificial. Estos algoritmos han mostrado ser una alternativa interesante a las técnicas clásicas para la solución de problemas de optimización y control en diversos problemas de ingeniería, su aplicación en el ámbito de las microrredes sigue en estudio y en ello se basa este trabajo de investigación. Los algoritmos bio-inspirados se fundamentan en imitar matemáticamente los mecanismos y estrategias que la naturaleza ha implementado a lo largo de millones de años para lograr un equilibrio en su funcionamiento, por ejemplo, imitando el cómo las aves vuelan en parvada buscando alimento, o como las hormigas y los lobos siguen patrones para la búsqueda de su alimento, o como las especies llevan a cabo mecanismos de cruce con el objetivo de mejorar su raza haciéndolas una especie optimizada y mejorando su supervivencia. Por tanto, se puede hacer una analogía con los sistemas artificiales para la mejora de controladores y diseño de sistemas en microrredes eléctricas. En este trabajo de investigación se muestra el modelo y desarrollo de un sistema de gestión óptima para una microrred empleando algoritmos bio-inspirados con el objetivo de mejorar su desempeño, partiendo desde el control primario, con la mejora de los convertidores de potencia, hasta el control terciario con las transacciones energéticas de la microrred. Se exploran diversos algoritmos, evaluando su desempeño. Los resultados para las diferentes etapas de esta investigación se encuentran plasmados en cuatro diferentes publicaciones científicas que se detallan en el Capítulo 2 del presente documento, donde se explica la metodología y los principales resultados y hallazgos para cada una de ellas. / [CA] Les fonts d'energia renovables (ER) permeten una alta desagregació, pel que fan possible generar l'energia que s'utilitzarà en el mateix lloc del seu aprofitament. Això afavoreix un canvi en l'estructura de les xarxes elèctriques, permetent passar d'un esquema de generació centralitzat a un esquema distribuït. No obstant, les fonts d'ER són altament dependents de les condicions mediambientals com la radiació solar, la nuvolositat, el vent, entre altres; pel que aconseguir un sistema de generació basat en energies renovables és un repte. Els sistemes de generació que integren energies renovables han de ser capaços de: establir estratègies de control i gestió de l'energia que es genera per fer un ús eficient d'ella i intentar cobrir la demanda d'energia de la millor manera possible al combinar més d'un tipus de font d'energia, i sistemes d'emmagatzemament. Aquest esquema es coneix com a microxarxa elèctrica, la qual és capaç d'operar de manera aïllada de la xarxa elèctrica principal, o de manera interconnectada. Actualment s'està interessant en l'estudi, desenvolupament i implementació de sistemes gestors de l'energia (SGE) per a microxarxes elèctriques híbrides, que permeten augmentar la seua eficiència, fiabilitat i reduir els costos de la seua instal·lació i d'operació i manteniment. S'han provat múltiples estratègies, des de SGE basats en regles, algorismes comparatius, controladors clàssics i, en anys recents, la integració d'algorismes d'optimització bio-inspirats i intel·ligència artificial. Aquests algorismes han demostrat ser una alternativa interessant a les tècniques clàssiques per a la solució de problemes d'optimització i control en diversos problemes d'enginyeria, la seua aplicació en l'àmbit de les microxarxes continua en estudi. Els algorismes bio-inspirats es basen en imitar matemàticament els mecanismes i estratègies que la Natura ha implementat al llarg de milions d'anys per aconseguir equilibri en el seu funcionament, per exemple, imitant com les aus volen en ramat buscant menjar, o com les formigues i els llops segueixen patrons per a la recerca del seu menjar, o com les espècies porten a terme mecanismes de creuament amb mira a millorar la seua raça fent-les una espècie més apta per a la supervivència;, el qual es pot fer una analogia a sistemes artificials per a la millora de controladors i disseny de sistemes en microxarxes elèctriques. En aquest treball de recerca es mostra el model i desenvolupament d'un sistema de gestió òptima per a una microxarxa emprant algorismes bio-inspirats amb l'objectiu de millorar el seu rendiment, partint des del control primari, amb la millora dels convertidors de potència, fins al control terciari amb les transaccions energètiques de la microxarxa. S'exploren diversos algorismes, avaluant el seu rendiment. Els resultats per a les diferents etapes d'aquesta recerca es troben plasmats en quatre diferents publicacions científiques que es detallen al Capítol 2 del present document, on s'explica la metodologia i els principals resultats i troballes per a cada una d'elles. / [EN] Renewable energy sources (RES) allow for high disaggregation, making it possible to generate energy at the site of its use. This favors a change in the structure of electrical grids, allowing for a transition from a centralized generation scheme to a distributed scheme. However, RES are highly dependent on environmental conditions such as solar radiation, cloudiness, wind, among others, making the creation of a renewable energy generation system a challenge. Generation systems that integrate renewable energies must be able to establish control and energy management strategies to make efficient use of the energy generated and try to meet the energy demand in the best possible way by combining more than one type of energy source and storage systems. This scheme is known as a microgrid, which is capable of operating independently from the main electrical grid or interconnecting with it. Currently, the study, development, and implementation of energy management systems (EMS) for hybrid microgrids are of interest in order to increase their efficiency, reliability, and reduce installation, operation, and maintenance costs. Multiple strategies have been tested, including rule-based EMS, comparative algorithms, classical controllers, and in recent years, the integration of bio-inspired optimization algorithms and artificial intelligence. These algorithms have shown to be an interesting alternative to classical techniques for solving optimization and control problems in various engineering problems, although their application in the field of microgrids is still under study. Bio-inspired algorithms are based on mathematically imitating the mechanisms and strategies that Nature has implemented over millions of years to achieve balance in its operation, for example, by imitating how birds fly in flocks in search of food, or how ants and wolves follow patterns to search for food, or how species carry out crossing mechanisms in order to improve their breed and make them more suitable for survival; in other words, they are based on how Nature optimizes its resources to prosper. Therefore, an analogy can be made with artificial systems for improving controllers and designing systems in microgrids. In this research work, the model and development of an optimal management system for a microgrid using bio-inspired algorithms is presented with the aim of improving its performance, starting from primary control, with the improvement of power converters, to tertiary control with the energy transactions of the microgrid. Various algorithms are explored, evaluating their performance. The results for the different stages of this research are reflected in four different scientific publications that are detailed in Chapter 2 of this document, where the methodology and main results and findings for each of them are explained. / The authors wish to acknowledge the National Council of Science and Technology of Mexico (CONACYT) for funding this work through the Ph.D. scholarship number 486670. The authors would also thank the Institute of Energy Engineering of the Polytechnic University of Valencia, Spain, and the Department of Water and Energy Studies of the University of Guadalajara, Mexico, for all their support and collaboration. This study has also been supported by Food and Agriculture Organization of the United Nations through the project “Design of a Hybrid Renewable Microgrid System”. / Águila León, J. (2023). Modelo y desarrollo de un sistema de gestión óptima para una microrred empleando algoritmos bio-inspirados [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/196747 / Compendio

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This dissertation aims to develop decision-making tools that aid power grid operators in mitigating extreme events. Two distinct areas are focused on: a) improving grid performance after a severe disturbance, and b) enhancing grid monitoring to facilitate timely preventive actions. The first part of the dissertation presents a proactive islanding strategy to split the bulk power transmission system into smaller self-adequate islands in order to arrest the propagation of cascading failures after an event. Heuristic methods are proposed to determine in what sequence should the island boundary lines be disconnected such that there are no operation constraint violations. The idea of optimal partitioning is further extended to the distribution network. A planning problem for determining which parts of the existing distribution grid can be converted to microgrids is formulated. This partitioning formulation addresses safety limits, uncertainties in load and generation, availability of grid-forming units, and topology constraints such as maintaining network radiality. Microgrids help maintain energy supply to critical loads during grid outages, thereby improving resilience. The second part of the dissertation focuses on wide-area monitoring using Phasor Measurement Unit (PMU) data. Strategies for data imputation and prediction exploiting the spatio-temporal correlation in PMU measurements are outlined. A deep-learning-based methodology for identifying the location of temporary power systems faults is also illustrated. As severe weather events become more frequent, and the threats from coordinated cyber intrusions increase, formulating strategies to reduce the impact of such events on the power grid becomes important; and the approaches outlined in this work can find application in this context. / Doctor of Philosophy / The modern power grid faces multiple threats, including extreme-weather events, solar storms, and potential cyber-physical attacks. Towards the larger goal of enhancing power systems resilience, this dissertation develops strategies to mitigate the impact of such extreme events. The proposed schemes broadly aim to- a) improve grid performance in the immediate aftermath of a disruptive event, and b) enhance grid monitoring to identify precursors of impending failures. To improve grid performance after a disruption, we propose a proactive islanding strategy for the bulk power grid, aimed at arresting the propagation of cascading failures. For the distribution network, a mixed-integer linear program is formulated for identifying optimal sub-networks with load and distributed generators that may be retrofitted to operate as self-adequate microgrids, if supply from the bulk power systems is lost. To address the question of enhanced monitoring, we develop model-agnostic, computationally efficient recovery algorithms for archived and streamed data from Phasor Measurement Units (PMU) with data drops and additive noise. PMUs are highly precise sensors that provide high-resolution insight into grid dynamics. We also illustrate an application where PMU data is used to identify the location of temporary line faults.

Microgrids containing photovoltaic (PV) cells and wind power gain more and more interest. These microgrids may work in stand-alone mode ("islanding") or be conncted to the main grid. In both modes of operation, power quality must be monitored and controlled. This report focuses on microgrids and aims to implement a monitoring system based on FPGA. In the monitoring system, two applications can be achieved, firstly a PAS-MPPT algorithm in a DC-DC boost converter to improve the maximun power point tracking of a PV unit, and secondly a detection and switching system of the grid mode - stand-alone or connected to the main grid. Simulation results prove the Verilog programs in FPGA are suitable to be used in microgrids.