A Multi-Objective Economic Load Dispatch Considering Accessibility of Wind Power with Here-And-Now Approach

The major problem of wind turbines is the great variability of wind power production. The dynamic change of the wind speed returns the quantity of the power injected to networks. Therefore, wind–thermal generation scheduling problem plays a key role to implement clean power producers in a competitive environment. In deregulated power systems, the scheduling problem has various objectives than in a traditional system which should be considered in economic scheduling. In this paper, a Multi-Objective Economic Load Dispatch (MOELD) model is developed for the system consisting of both thermal generators and wind turbines. Using two optimization methods, Sequential Quadratic Programming (SQP) and Particle Swarm Optimization (PSO), the system is optimally scheduled. The objective functions are total emission and total profit of units. The probability of stochastic wind power is included in the model as a constraint. This strategy, referred to as the Here-and-Now (HN) approach, avoids the probabilistic infeasibility appearing in conventional models. Based on the utilized model, the effect of stochastic wind speed on the objective functions can be readily assessed. Also a Total Index (TI) is presented to evaluate the simulation results. Also, the results show preference of PSO method to combine with HN approach

Building and investigating generators' bidding strategies in an electricity market

In a deregulated electricity market environment, Generation Companies (GENCOs) compete with each other in the market through spot energy trading, bilateral contracts and other financial instruments. For a GENCO, risk management is among the most important tasks. At the same time, how to maximise its profit in the electricity market is the primary objective of its operations and strategic planning. Therefore, to achieve the best risk-return trade-off, a GENCO needs to determine how to allocate its assets. This problem is also called portfolio optimization. This dissertation presents advanced techniques for generator strategic bidding, portfolio optimization, risk assessment, and a framework for system adequacy optimisation and control in an electricity market environment. Most of the generator bidding related problems can be regarded as complex optimisation problems. In this dissertation, detailed discussions of optimisation methods are given and a number of approaches are proposed based on heuristic global optimisation algorithms for optimisation purposes. The increased level of uncertainty in an electricity market can result in higher risk for market participants, especially GENCOs, and contribute significantly to the drivers for appropriate bidding and risk management tasks for GENCOs in the market. Accordingly, how to build an optimal bidding strategy considering market uncertainty is a fundamental task for GENCOs. A framework of optimal bidding strategy is developed out of this research. To further enhance the effectiveness of the optimal bidding framework; a Support Vector Machine (SVM) based method is developed to handle the incomplete information of other generators in the market, and therefore form a reliable basis for a particular GENCO to build an optimal bidding strategy. A portfolio optimisation model is proposed to maximise the return and minimise the risk of a GENCO by optimally allocating the GENCO's assets among different markets, namely spot market and financial market. A new market pnce forecasting framework is given In this dissertation as an indispensable part of the overall research topic. It further enhances the bidding and portfolio selection methods by providing more reliable market price information and therefore concludes a rather comprehensive package for GENCO risk management in a market environment. A detailed risk assessment method is presented to further the price modelling work and cover the associated risk management practices in an electricity market. In addition to the issues stemmed from the individual GENCO, issues from an electricity market should also be considered in order to draw a whole picture of a GENCO's risk management. In summary, the contributions of this thesis include: 1) a framework of GENCO strategic bidding considering market uncertainty and incomplete information from rivals; 2) a portfolio optimisation model achieving best risk-return trade-off; 3) a FIA based MCP forecasting method; and 4) a risk assessment method and portfolio evaluation framework quantifying market risk exposure; through out the research, real market data and structure from the Australian NEM are used to validate the methods. This research has led to a number of publications in book chapters, journals and refereed conference proceedings

Investigating evolutionary computation with smart mutation for three types of Economic Load Dispatch optimisation problem

The Economic Load Dispatch (ELD) problem is an optimisation task concerned with how electricity generating stations can meet their customers’ demands while minimising under/over-generation, and minimising the operational costs of running the generating units. In the conventional or Static Economic Load Dispatch (SELD), an optimal solution is sought in terms of how much power to produce from each of the individual generating units at the power station, while meeting (predicted) customers’ load demands. With the inclusion of a more realistic dynamic view of demand over time and associated constraints, the Dynamic Economic Load Dispatch (DELD) problem is an extension of the SELD, and aims at determining the optimal power generation schedule on a regular basis, revising the power system configuration (subject to constraints) at intervals during the day as demand patterns change. Both the SELD and DELD have been investigated in the recent literature with modern heuristic optimisation approaches providing excellent results in comparison with classical techniques. However, these problems are defined under the assumption of a regulated electricity market, where utilities tend to share their generating resources so as to minimise the total cost of supplying the demanded load. Currently, the electricity distribution scene is progressing towards a restructured, liberalised and competitive market. In this market the utility companies are privatised, and naturally compete with each other to increase their profits, while they also engage in bidding transactions with their customers. This formulation is referred to as: Bid-Based Dynamic Economic Load Dispatch (BBDELD). This thesis proposes a Smart Evolutionary Algorithm (SEA), which combines a standard evolutionary algorithm with a “smart mutation” approach. The so-called ‘smart’ mutation operator focuses mutation on genes contributing most to costs and penalty violations, while obeying operational constraints. We develop specialised versions of SEA for each of the SELD, DELD and BBDELD problems, and show that this approach is superior to previously published approaches in each case. The thesis also applies the approach to a new case study relevant to Nigerian electricity deregulation. Results on this case study indicate that our SEA is able to deal with larger scale energy optimisation tasks

Power System Stability Assessment and Enhancement using Computational Intelligence

The main objective of the dissertation is to develop a fast and robust tool for assessment of power system stability and design a framework for enhancing system stability. The proposed framework is - based on the investigation of the dynamic behavior of the system - a market based rescheduling strategy that increases the stability margin. The dissertation specifically puts emphasis on the following approached: Power System Stability Evaluation: System stability is investigated by simulating a set of critical contingencies to determine whether the disturbances will result in any unsafe operating conditions and extract the necessary information to classify system states. The classification is based on the computation of the critical fault clearing time (CCT) for transient stability assessment (TSA) and the minimum damping of oscillation (MDO) for power system oscillatory stability assessment (OSA). The customary method of power system transient stability analysis including time-domain simulation (TDS) is used to compute the CCT at each critical contingency and Prony analysis as an efficient identification technique to estimate the mode parameters from the actual time response. The use of Prony analysis is to account for the effects of the change in location of the small disturbances as well as the increase in system nonlinearity on oscillating modes. Fast Power System Stability Assessment Tool: An artificial neural network (ANN) is designed to serve as accurate and fast tool for dynamic stability assessment (DSA). Fast response of ANN allows system operators to take suitable control actions to enhance the system stability and to forestall any possible impending breakup of the system. Two offline trained ANN are designed to map the dynamic behavior by relating the selected input features and the calculated CCT (as indicator for transient stability) and MDO (as indicator for oscillatory stability). Input features of ANN are selected to characterize the following: Changes in system topology and power distributions due to outage of major equipment such as transmission line, generation unit or large load Change in fault location and the severity of the fault Variation in loading levels and load allocation among market participants The features are generated for a wide range of loading at each expected system topology. Initial feature sets are pre-selected by engineering judgment based on experience in power system operation. In order to improve the accuracy of ANN to map the power system dynamic behavior, final selection is performed in the following three steps. In the first step, the generators terminal voltage drops immediately after fault are selected features to characterize the severity of the contingency with respect to the generators and to detect the fault location. In the second step, new features based on the inertia constant and the generated power in each area are calculated to characterize the changes in system topology and power flow pattern during normal and abnormal operation. In the third step, a systematic clustering feature selection technique is used to select the most important features that characterize the load levels and the power flow through lines from the mathematical viewpoint. The results prove the suitability of ANN in DSA with a reasonable degree of accuracy. Dynamic Stability Enhancement: To achieve online dynamic stability enhancement an online market based rescheduling strategy is proposed in the deregulated power systems. In case of power system operation by a centralized pool in vertically integrated electric utilities, generation rescheduling based sensitivity analysis is proposed. In the proposed market for deregulated power systems, the transactions among suppliers and consumers participating in the market are reallocated based on optional power bids to enhance system stability in case the available control actions are insufficient to enhance system stability. All participants are allowed to submit voluntary power bids to increase or decrease their scheduled level with equal chance. These bids represent the offered power quantity and the corresponding price. The goal of the framework is to enhance system stability with minimum additional and opportunity costs arising from the rescheduling. In case of vertically integrated electric utility, generation rescheduling based sensitivity analysis is used to enhance the system stability. The sensitivity analysis is based on the generators response following the most probable contingency. The generators are split into critical machines with positive sensitivity and non-critical machines with negative sensitivity. The change of the generation level among critical and non-critical machines provides the trajectories for stabilization procedure. The re-allocation of power among generators in each group is calculated based on the generator capacities and inertia constant, which simplifies the optimization procedure and speeds up the iterative to find a feasible solution. The objective is to minimize the increase in the cost due to rescheduling process. Particle swarm optimization is used as an optimization tool to search for the optimal solution to enhance the system stability with a minimum cost. The handling of all system constraints including stability constraints is achieved using a self-adaptive penalty function. Comparison strategy for selecting the best individuals during the optimization process is proposed where the feasible solutions are ever preferable during selection of local and global best particles.Die Schwerpunkte der Dissertation liegen in der Entwicklung eines schnellen und robusten Echtzeit-Bewertungsinstruments für Stabilitätsuntersuchungen in elektrischen Energienetzen und in dem Entwurf von Rahmenbedingungen zur Verbesserung der Systemstabilität. Basierend auf Untersuchungen bezüglich des dynamischen Verhaltens von elektrischen Energienetzen ist das Ziel der vorgeschlagenen Rahmenbedingungen, eine Planungsstrategie zu entwickeln, die marktwirtschaftlich ausgerichtet ist, um so die Stabilitätsgrenze zu verbessern und die erforderliche Systemsicherheit zu gewährleisten. Die dynamische Stabilität von elektrischen Energienetzen wurde bezogen auf die transiente und oszillatorische Stabilität untersucht, welche zur Beurteilung des dynamischen Verhaltens des Systems während Netzstörungen genutzt wird. Das Ziel der Dissertation ist die folgenden Aspekte zu untersuchen: Evaluierung der Dynamischen Stabilität: Die dynamische Stabilität ist durch die Simulation von kritischen Netzereignissen untersucht worden. Ziel war es, Störungen zu ermitteln, die zu kritischen oder gar unsicheren Betriebszuständen führen, und wichtige Beurteilungsparameter über den Zustand des Netzes auszuwählen. Die Beurteilungsparameter über den Zustand des elektrischen Energienetzes sind unter Verwendung der kritischen Fehlerklärungszeit als Indikator für die transiente Stabilität und der minimalen Dämpfung von Oszillationen als Indikator für die ozillatorische Stabilität ermittelt worden. Die übliche Methode bei einer transienten Stabilitätsanalyse in elektrischen Energienetzen basiert auf Simulationen im Zeitbereich und wird unter der Verwendung von vordefinierten netzkritischen Ereignissen genutzt, um die kritische Fehlerklärungszeit präzise zu berechnen. Die Prony-Analyse als eine effiziente Identifizierungstechnik wird zur Schätzung der Zustandsparameter auf eine einer Störung folgenden Zeitantwort verwendet. Der Gebrauch der Prony-Analyse erfasst die Veränderungen im Fehlerort von kleinen Störungen und einen Anstieg von Systemnichtlinearitäten im oszillatorischen Modus. Die mit Hilfe der Modalanalyse berechneten Parameter für den oszillatorischen Modus werden als Referenzsignale während des Abstimmens der Parameter der Prony-Analyse verwendet. Ziel ist die Verbesserung der Identifizierung des Systemmodus. Schnelles Bewertungswerkzeug für die dynamische Stabilität: Ein präzises und schnelles Werkzeug für die Bewertung von dynamischer Stabilität wurde mit Hilfe von künstlichen, neuronalen Netzen entwickelt. Die schnelle Antwort eines künstlichen, neuronalen Netzes ermöglicht es dem Netzbetreiber, geeignete fehlerbehebende Schalthandlungen während kritischer Netzereignisse durchzuführen. So kann die Stabilität des elektrischen Netzes gewährleistet und bevorstehende Netzausfälle verhindert werden. Zwei offline trainierte künstliche neuronale Netze sind entwickelt worden, um a) das dynamische Verhalten unter Verwendung ausgewählter Eingangseigenschaften und b) die berechnete kritische Fehlerklärungszeit als Indikator für die transiente Stabilität und die minimale Dämpfung der Oszillationen als Indikator für ozillatorische Stabilität abzubilden. Künstliche, neuronale Netze bieten vielversprechende Lösungen für schnelle Berechnungen bei online Anwendungen. Als Folge kann die hohe Anzahl an Berechnungen, die zur Untersuchung aller zu erwartenden kritischen Netzereignissen in elektrischen Energienetzen benötigt werden, schnell durchgeführt werden. Dies ermöglicht eine Bewertung der Systemzustände des elektrischen Netzes und eine Initiierung der zu erwartenden Schalthandlungen, um so die Systemstabilität zu verbessern. Für eine genaue Bewertung der dynamischen Stabilität sollten die Eingangseigenschaften für das künstliche, neuronale Netz sorgfältig ausgewählt werden. In dieser Arbeit sind die Eingangseigenschaften aus den gesamten Systemdaten ausgewählt worden, um die folgenden Eigenschaften kennzuzeichnen: i. Veränderungen in der Systemtopologie und des Lastflusses durch Ausfälle oder planmäßige Wartungen von Hauptkomponenten des Systems, wie zum Beispiel Übertragungsleitungen, Erzeugereinheiten oder großen Lasten ii. Veränderungen des Fehlerortes und des Einflusses des Fehlers auf die elektrischen Komponenten iii. Laständerungen und Lastaufteilung zwischen Netzversorgern Die Eingangseigenschaften wurden für viele, unterschiedliche Lastszenarien in Verbindung mit den zu erwartenden Netztopologien erzeugt. Die Anfangsbedingungen sind auf Grund von Erfahrungen mit dem Betrieb von elektrischen Energienetzen und bedingt durch das zu schätzende Ziel vorausgewählt. Die endgültige Auswahl der Eingangseigenschaften ist in drei Schritte unterteilt, um so die Genauigkeit des künstlichen, neuronalen Netzes zu erhöhen, welches die dynamische Stabilität des Energienetzes abbildet. Im ersten Schritt sind die Generatorklemmenspannungseinbrüche direkt nach der Netzstörung die wichtigen ausgewählten Eigenschaften. Hierdurch wird die Schwere des kritischen Netzereignisses aus der Sicht der Erzeugungseinheit gekennzeichnet und die Fehlerstelle lokalisiert. In dem zweiten Schritt werden neue Eingangseigenschaften basierend auf der Massenträgheitskonstante des Systems und der erzeugten Leistung in jedem Gebiet berechnet. So können Veränderungen in der Netztopologie und des Lastflusses unter normalen und gestörten Betriebsbedingungen gekennzeichnet werden. Im dritten Schritt wird eine systematische Cluster-Bildung der Eigenschaften genutzt, um so die wichtigsten Eigenschaften auszuwählen, die Aussagen über die Lastzustände und den Lastfluss über die Leitungen zulassen. Alle ausgewählten Eigenschaften repräsentieren das Eingangsmuster, wobei das Ausgangsmuster der Index der dynamischen Stabilitätsanalyse ist. Die Ergebnisse stellen die Eignung des künstlichen, neuronalen Netzes bei der Bewertung der dynamischen Stabilität dar. Verbesserung der dynamischen Stabilität: Eine online Verbesserung der dynamischen Stabilität kann durch eine vorgeschlagene marktwirtschaftliche Neuplanung des deregulierten Energiesystems und durch eine Neuplanung der Erzeugungseinheiten basierend auf der Empfindlichkeitsanalyse im Falle des Betriebs des Energienetzes durch eine zentrale Einheit erreicht werden. In dem vorgeschlagenen Markt für deregulierte Energiesysteme wird im Falle, dass vorgesehenen Schalthandlungen das Netz nicht in einen stabilen Zustand zurückbringen kann, die Energie zwischen Versorgern und Verbrauchern basierend auf optionalen Leistungsgeboten umgeschichtet. Alle Erzeuger und Verbraucher sind berechtigt an diesem Markt durch freiwillige Leistungsgebote teilzunehmen, um so ihre geplante Menge chancengleich zu erhöhen oder zu verkleinern. Diese Gebote der Marktteilnehmer repräsentieren die angebotene Leistungsmenge und den darauf bezogenen Preis. Teilnehmer, von denen es verlangt ist, Erzeugung oder Verbrauch zu reduzieren, werden für diese Möglichkeit zur Reduzierung bezahlt. So kann der Verlust der Serviceleistung kompensiert werden, während Teilnehmer, deren Leistung erhöht wird, durch den Marktpreis plus zusätzlicher Kosten für zusätzliche Veränderungen entlohnt werden. Das Ziel dieser Rahmenbedingungen ist eine Verbesserung der Systemstabilität kombiniert mit einem Minimum an zusätzlichen Kosten auftretend durch die Neuplanung. Im Falle eines zentralen Energiemarktes wird die Neuplanung der Erzeuger basierend auf der Empfindlichkeitsanalyse durchgeführt, um so eine Verbesserung der Systemstabilität zu erreichen. Die Empfindlichkeitsanalyse bezieht sich auf die Systemantwort des Generators während des belastbarsten kritischen Netzereignisses. Dieses kritische Netzereignis trennt die Erzeugungseinheiten a) in kritische Maschinen, die eine positive Empfindlichkeit besitzen, und b) in nicht-kritische Maschinen mit einer negativen Empfindlichkeit. Die Einteilung in kritische und nicht-kritische Maschinen ermöglicht eine Lösung für die Stabilisierung des Systems. Die Verteilung der verschobenen Leistung zwischen den Generatoren in jeder Gruppe wird unter Verwendung der Generatorleistungen und der Massenträgheitskonstanten berechnet. Dies erleichtert den Optimierungsalgorithmus und beschleunigt das Erhalten einer möglichen Lösung. Das Ziel ist die Minimierung der Erhöhung der Kosten für die absolut erzeugte Leistung auf Grund der Abweichung vom wirtschaftlichen Arbeitspunkt. In dieser Arbeit wird die Particle Swarm Optimierung als Werkzeug verwendet, um damit eine optimale Lösung mit den minimalen Kosten zu erlangen. Dadurch kann eine Verbesserung der dynamischen Stabilität des elektrischen Energienetzes unter Berücksichtigung aller systembedingten Nebenbedingungen erlangt werden. Die Handhabung aller systembedingten Nebenbedingungen inklusive der Nebenbedingungen der dynamischen Stabilität kann durch eine selbstanpassende Straffunktion erreicht werden

Distributed Market-Grid Coupling Using Model Predictive Control

In this dissertation, a feedback control concept is proposed for modeling a market-grid coupling. The contributions are fourfold: 1) Identification and characterization of an interoperable control between the power market and the power grid; 2) Design of a closed-loop MPC for the market-grid coupling; 3) Extension of the single control loop with a collaborative distributed MPC strategy for coupling distributed markets and grids; 4) Development of an adaptive load forecasting framework

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area

Competitive power control of distributed power plants

Joint Doctoral Programme in Electric Energy Systems : Universidad de Málaga, Universidad de Sevilla, Universidad del País Vasco y Universitat Politècnica de CatalunyaNowadays, the electrical energy sector is currently found in a dramatic changing paradigm, which moves towards an increasing trend in generating power at distribution levels, where electricity is typically consumed, by means of non-conventional/renewable based generation units. These new generation technologies, termed as distributed generation, not only offers a non-pollutant, cheap and efficient source of energy to cover increasing demand, but also enhance the reliability of supply to critical loads and reduce the need for additional grid reinforcements. Aside of the technical benefits provided, distributed generation will massively integrate renewable energy resources, with new type of loads and end-user actors, such as prosumers, demand responsive loads, or electric vehicles. Where these actors will actively participate in energy and auxiliary service markets, depending on their available or constrained energy needs. For this reason, the work presented in this Thesis deals with designing and implementing advanced hierarchical control solutions to renewable-based power plants with the purpose of achieving advanced grid conection performance while reaching maximum economic benefits from its optimum real-time operation. Initially, an extensive analysis on the main renewable-based power plant hierarchical control solutions currently on the shelf, is performed. This study not only covered the specific case of renewable-based power plants, but also advanced microgrid and smart grid control solutions. Once the main renewable-based power plant hierarchical solutions were analized, a novel Hierarchical Distributed Control Structure (HDCS) is proposed for increased management of renewable-based active distributed plants. This hierarchical control structure comprises all possible functional levels from the higher long-term economic scheduling layer, to the instantaneous supervisory control of the resource, emphasizing the entire operation and control functionalities needed for increasing the integration of active distributed power plants. In order to achieve real-time control capabilities in active distribution systems, the present thesis introduces a novel power sharing control strategy, based on the competitive operation of multiple active participating agents (distributed generators, demand response and energy storage systems) through the implementation of market rules. Such control capabilities are satisfied by applying a price control signal over the entire grid control architecture, being the final-end participating agent, the responsible entity in charge of deciding its own generation/demand involvement based on its marginal or affordable electricity costs. In addition, it reduces the information volume to be transmitted and processing requirements, as the higher control levels do not need to have knowledge on the detailed distribution system topology and contributing actors. In order to have a meaningful evaluation of the proposed competitive control capabilities, a wave power plant application has been selected, which constitutes a challenging scenario for the controller itself to achieve advanced real-time control capabilities in such an oscillating renewable energy resource. In order to suitably characterize the wave energy resource profile resulting from maximum energy absorption, this Thesis introduce a novel adaptive vector controller, which maximizes the energy extraction from the resource regardless of the dominant irregular wave frequency characteristics. For the specific wave power plant application considered, the competitive control does not only ensures real-time optimum resource allocation for satisfying a given production objective, but also provides optimum long term operation of the system. As a result, overall plant costs reductions can be achieved under the competitive operation, since the plant scheduled energy is satisfied by making use of the generation units with cheaper cumulative operation costsActualmente, el sector eléctrico se encuentra inmerso en un profundo proceso de restructuración, donde de cada vez más se tiende a generar energía a nivel de distribución, mediante el uso de generación no convencional/renovable. Estas nuevas tecnologías de generación, referidas como generación distribuida, no proporcionan unicamente una fuente de energía no-contaminante, barata y eficiente para cubrir el incremento de demanda, sinó que también pueden proporcionar seguridad de suministro a cargas críticas, así como reducir la necesidad de expansiones futuras de red. Además de las capacidades técnicas proporcionadas, la generación distribuida hará posible la integración masiva de sistemas de generación renovable, con nuevos tipos de cargas y usuarios finales, como prosumidores, cargas regulables, o vehiculos eléctricos, donde todos estos usuarios participaran activamente en mercados de energía y servicios auxiliares, dependiendo de sus requisitos de uso de energía. Por lo tanto, el trabajo realizado en esta tesis se centra en el diseño e implementación de soluciones jerárquicas de control avanzado en plantas de generación renovable, con el objetivo de obtener un comportamiento harmonioso de intercacción con la red, mientras la operación de la planta maximiza los beneficios derivados de su operación en tiempo real. Inicialmente, se ha llevado a cabo una revisión extensa sobre los sistemas de control jerárquico comunmente implementados en plantas de generación renovable, en microredes y en redes inteligentes. Una vez revisados los principales sistemas de control jerárquico en este tipo de aplicaciones, se propone un una novedosa estructura de control, que cubre todos los niveles de control posibles, desde el más alto nivel de gestión económica, hasta el control detallado del recurso de generación. Para lograr capacidades de control en tiempo real en sistemas activos de distribución, la presente tesis propone una nueva estrategia de control de reparto de potencia, basada en la operación competitiva de múltiples agentes participantes activos (generadores distribuidos, respuesta de demanda y sistemas de almacenamiento de energía) mediante la implementación de reglas del mercado. Dichas capacidades de control se satisfacen aplicando una señal de precio a lo largo de toda la arquitectura de control, siendo el agente de final, el ente responsable de decidir su propia participación en la generación/demanda en función de sus propios costes de electricidad marginales o asumibles. Además, reduce el volumen de información a transmitir y los requisitos de procesamiento de datos, ya que los niveles de control más altos no necesitan tener conocimiento sobre la topología del sistema de distribución detallado ni de la contribución de los actores adyacentes. Para llevar a cabo una evaluación significativa de las capacidades del controlador competitivo propuesto, se ha seleccionado una planta de generación undimotriz, como escenario más desfavorable, ya que el controlador debe asegurar un control estable de la potencia inyectada en un escenario altamente oscilante. Con el fin de caracterizar adecuadamente el perfil de recursos de energía de las olas resultante de la máxima absorción de energía, esta Tesis introduce un nuevo controlador de vector adaptativo, que maximiza la extracción de energía del recurso independientemente de las características dominantes de frecuencia de onda irregular. Para la aplicación de la planta de energía de onda específica considerada, el control competitivo no solo garantiza la asignación óptima de recursos en tiempo real para satisfacer un objetivo de producción dado, sino que también proporciona una operación óptima del sistema a largo plazo. Como resultado, se pueden lograr reducciones generales de los costos de la planta en el marco de la operación competitiva, ya que la energía programada de la planta se satisface haciendo uso de las unidadPostprint (published version

Competitive power control of distributed power plants

Nowadays, the electrical energy sector is currently found in a dramatic changing paradigm, which moves towards an increasing trend in generating power at distribution levels, where electricity is typically consumed, by means of non-conventional/renewable based generation units. These new generation technologies, termed as distributed generation, not only offers a non-pollutant, cheap and efficient source of energy to cover increasing demand, but also enhance the reliability of supply to critical loads and reduce the need for additional grid reinforcements. Aside of the technical benefits provided, distributed generation will massively integrate renewable energy resources, with new type of loads and end-user actors, such as prosumers, demand responsive loads, or electric vehicles. Where these actors will actively participate in energy and auxiliary service markets, depending on their available or constrained energy needs. For this reason, the work presented in this Thesis deals with designing and implementing advanced hierarchical control solutions to renewable-based power plants with the purpose of achieving advanced grid conection performance while reaching maximum economic benefits from its optimum real-time operation. Initially, an extensive analysis on the main renewable-based power plant hierarchical control solutions currently on the shelf, is performed. This study not only covered the specific case of renewable-based power plants, but also advanced microgrid and smart grid control solutions. Once the main renewable-based power plant hierarchical solutions were analized, a novel Hierarchical Distributed Control Structure (HDCS) is proposed for increased management of renewable-based active distributed plants. This hierarchical control structure comprises all possible functional levels from the higher long-term economic scheduling layer, to the instantaneous supervisory control of the resource, emphasizing the entire operation and control functionalities needed for increasing the integration of active distributed power plants. In order to achieve real-time control capabilities in active distribution systems, the present thesis introduces a novel power sharing control strategy, based on the competitive operation of multiple active participating agents (distributed generators, demand response and energy storage systems) through the implementation of market rules. Such control capabilities are satisfied by applying a price control signal over the entire grid control architecture, being the final-end participating agent, the responsible entity in charge of deciding its own generation/demand involvement based on its marginal or affordable electricity costs. In addition, it reduces the information volume to be transmitted and processing requirements, as the higher control levels do not need to have knowledge on the detailed distribution system topology and contributing actors. In order to have a meaningful evaluation of the proposed competitive control capabilities, a wave power plant application has been selected, which constitutes a challenging scenario for the controller itself to achieve advanced real-time control capabilities in such an oscillating renewable energy resource. In order to suitably characterize the wave energy resource profile resulting from maximum energy absorption, this Thesis introduce a novel adaptive vector controller, which maximizes the energy extraction from the resource regardless of the dominant irregular wave frequency characteristics. For the specific wave power plant application considered, the competitive control does not only ensures real-time optimum resource allocation for satisfying a given production objective, but also provides optimum long term operation of the system. As a result, overall plant costs reductions can be achieved under the competitive operation, since the plant scheduled energy is satisfied by making use of the generation units with cheaper cumulative operation costsActualmente, el sector eléctrico se encuentra inmerso en un profundo proceso de restructuración, donde de cada vez más se tiende a generar energía a nivel de distribución, mediante el uso de generación no convencional/renovable. Estas nuevas tecnologías de generación, referidas como generación distribuida, no proporcionan unicamente una fuente de energía no-contaminante, barata y eficiente para cubrir el incremento de demanda, sinó que también pueden proporcionar seguridad de suministro a cargas críticas, así como reducir la necesidad de expansiones futuras de red. Además de las capacidades técnicas proporcionadas, la generación distribuida hará posible la integración masiva de sistemas de generación renovable, con nuevos tipos de cargas y usuarios finales, como prosumidores, cargas regulables, o vehiculos eléctricos, donde todos estos usuarios participaran activamente en mercados de energía y servicios auxiliares, dependiendo de sus requisitos de uso de energía. Por lo tanto, el trabajo realizado en esta tesis se centra en el diseño e implementación de soluciones jerárquicas de control avanzado en plantas de generación renovable, con el objetivo de obtener un comportamiento harmonioso de intercacción con la red, mientras la operación de la planta maximiza los beneficios derivados de su operación en tiempo real. Inicialmente, se ha llevado a cabo una revisión extensa sobre los sistemas de control jerárquico comunmente implementados en plantas de generación renovable, en microredes y en redes inteligentes. Una vez revisados los principales sistemas de control jerárquico en este tipo de aplicaciones, se propone un una novedosa estructura de control, que cubre todos los niveles de control posibles, desde el más alto nivel de gestión económica, hasta el control detallado del recurso de generación. Para lograr capacidades de control en tiempo real en sistemas activos de distribución, la presente tesis propone una nueva estrategia de control de reparto de potencia, basada en la operación competitiva de múltiples agentes participantes activos (generadores distribuidos, respuesta de demanda y sistemas de almacenamiento de energía) mediante la implementación de reglas del mercado. Dichas capacidades de control se satisfacen aplicando una señal de precio a lo largo de toda la arquitectura de control, siendo el agente de final, el ente responsable de decidir su propia participación en la generación/demanda en función de sus propios costes de electricidad marginales o asumibles. Además, reduce el volumen de información a transmitir y los requisitos de procesamiento de datos, ya que los niveles de control más altos no necesitan tener conocimiento sobre la topología del sistema de distribución detallado ni de la contribución de los actores adyacentes. Para llevar a cabo una evaluación significativa de las capacidades del controlador competitivo propuesto, se ha seleccionado una planta de generación undimotriz, como escenario más desfavorable, ya que el controlador debe asegurar un control estable de la potencia inyectada en un escenario altamente oscilante. Con el fin de caracterizar adecuadamente el perfil de recursos de energía de las olas resultante de la máxima absorción de energía, esta Tesis introduce un nuevo controlador de vector adaptativo, que maximiza la extracción de energía del recurso independientemente de las características dominantes de frecuencia de onda irregular. Para la aplicación de la planta de energía de onda específica considerada, el control competitivo no solo garantiza la asignación óptima de recursos en tiempo real para satisfacer un objetivo de producción dado, sino que también proporciona una operación óptima del sistema a largo plazo. Como resultado, se pueden lograr reducciones generales de los costos de la planta en el marco de la operación competitiva, ya que la energía programada de la planta se satisface haciendo uso de las unida

Políticas de Copyright de Publicações Científicas em Repositórios Institucionais: O Caso do INESC TEC

A progressiva transformação das práticas científicas, impulsionada pelo desenvolvimento das novas Tecnologias de Informação e Comunicação (TIC), têm possibilitado aumentar o acesso à informação, caminhando gradualmente para uma abertura do ciclo de pesquisa. Isto permitirá resolver a longo prazo uma adversidade que se tem colocado aos investigadores, que passa pela existência de barreiras que limitam as condições de acesso, sejam estas geográficas ou financeiras. Apesar da produção científica ser dominada, maioritariamente, por grandes editoras comerciais, estando sujeita às regras por estas impostas, o Movimento do Acesso Aberto cuja primeira declaração pública, a Declaração de Budapeste (BOAI), é de 2002, vem propor alterações significativas que beneficiam os autores e os leitores. Este Movimento vem a ganhar importância em Portugal desde 2003, com a constituição do primeiro repositório institucional a nível nacional. Os repositórios institucionais surgiram como uma ferramenta de divulgação da produção científica de uma instituição, com o intuito de permitir abrir aos resultados da investigação, quer antes da publicação e do próprio processo de arbitragem (preprint), quer depois (postprint), e, consequentemente, aumentar a visibilidade do trabalho desenvolvido por um investigador e a respetiva instituição. O estudo apresentado, que passou por uma análise das políticas de copyright das publicações científicas mais relevantes do INESC TEC, permitiu não só perceber que as editoras adotam cada vez mais políticas que possibilitam o auto-arquivo das publicações em repositórios institucionais, como também que existe todo um trabalho de sensibilização a percorrer, não só para os investigadores, como para a instituição e toda a sociedade. A produção de um conjunto de recomendações, que passam pela implementação de uma política institucional que incentive o auto-arquivo das publicações desenvolvidas no âmbito institucional no repositório, serve como mote para uma maior valorização da produção científica do INESC TEC.The progressive transformation of scientific practices, driven by the development of new Information and Communication Technologies (ICT), which made it possible to increase access to information, gradually moving towards an opening of the research cycle. This opening makes it possible to resolve, in the long term, the adversity that has been placed on researchers, which involves the existence of barriers that limit access conditions, whether geographical or financial. Although large commercial publishers predominantly dominate scientific production and subject it to the rules imposed by them, the Open Access movement whose first public declaration, the Budapest Declaration (BOAI), was in 2002, proposes significant changes that benefit the authors and the readers. This Movement has gained importance in Portugal since 2003, with the constitution of the first institutional repository at the national level. Institutional repositories have emerged as a tool for disseminating the scientific production of an institution to open the results of the research, both before publication and the preprint process and postprint, increase the visibility of work done by an investigator and his or her institution. The present study, which underwent an analysis of the copyright policies of INESC TEC most relevant scientific publications, allowed not only to realize that publishers are increasingly adopting policies that make it possible to self-archive publications in institutional repositories, all the work of raising awareness, not only for researchers but also for the institution and the whole society. The production of a set of recommendations, which go through the implementation of an institutional policy that encourages the self-archiving of the publications developed in the institutional scope in the repository, serves as a motto for a greater appreciation of the scientific production of INESC TEC