Stochastic Model Predictive Control and Machine Learning for the Participation of Virtual Power Plants in Simultaneous Energy Markets

The emergence of distributed energy resources in the electricity system involves new scenarios in which domestic consumers (end-users) can be aggregated to participate in energy markets, acting as prosumers. Every prosumer is considered to work as an individual energy node, which has its own renewable generation source, its controllable and non-controllable energy loads, or even its own individual tariffs to trade. The nodes can build aggregations which are managed by a system operator. The participation in energy markets is not trivial for individual prosumers due to different aspects such as the technical requirements which must be satisfied, or the need to trade with a minimum volume of energy. These requirements can be solved by the definition of aggregated participations. In this context, the aggregators handle the difficult task of coordinating and stabilizing the prosumers' operations, not only at an individual level, but also at a system level, so that the set of energy nodes behaves as a single entity with respect to the market. The system operators can act as a trading-distributing company, or only as a trading one. For this reason, the optimization model must consider not only aggregated tariffs, but also individual tariffs to allow individual billing for each energy node. The energy node must have the required technical and legal competences, as well as the necessary equipment to manage their participation in energy markets or to delegate it to the system operator. This aggregation, according to business rules and not only to physical locations, is known as virtual power plant. The optimization of the aggregated participation in the different energy markets requires the introduction of the concept of dynamic storage virtualization. Therefore, every energy node in the system under study will have a battery installed to store excess energy. This dynamic virtualization defines logical partitions in the storage system to allow its use for different purposes. As an example, two different partitions can be defined: one for the aggregated participation in the day-ahead market, and the other one for the demand-response program. There are several criteria which must be considered when defining the participation strategy. A risky strategy will report more benefits in terms of trading; however, this strategy will also be more likely to get penalties for not meeting the contract due to uncertainties or operation errors. On the other hand, a conservative strategy would result worse economically in terms of trading, but it will reduce these potential penalties. The inclusion of dynamic intent profiles allows to set risky bids when there exist a potential low error of forecast in terms of generation, load or failures; and conservative bids otherwise. The system operator is the agent who decides how much energy will be reserved to trade, how much to energy node self consumption, how much to demand-response program participation etc. The large number of variables and states makes this problem too complex to be solved by classical methods, especially considering the fact that slight differences in wrong decisions would imply important economic issues in the short term. The concept of dynamic storage virtualization has been studied and implemented to allow the simultaneous participation in multiple energy markets. The simultaneous participations can be optimized considering the objective of potential profits, potential risks or even a combination of both considering more advanced criteria related to the system operator's know-how. Day-ahead bidding algorithms, demand-response program participation optimization and a penalty-reduction operation control algorithm have been developed. A stochastic layer has been defined and implemented to improve the robustness inherent to forecast-dependent systems. This layer has been developed with chance-constraints, which includes the possibility of combining an intelligent agent based on a encoder-decoder arquitecture built with neural networks composed of gated recurrent units. The formulation and the implementation allow a total decouplement among all the algorithms without any dependency among them. Nevertheless, they are completely engaged because the individual execution of each one considers both the current scenario and the selected strategy. This makes possible a wider and better context definition and a more real and accurate situation awareness. In addition to the relevant simulation runs, the platform has also been tested on a real system composed of 40 energy nodes during one year in the German island of Borkum. This experience allowed the extraction of very satisfactory conclusions about the deployment of the platform in real environments.La irrupción de los sistemas de generación distribuidos en los sistemas eléctricos dan lugar a nuevos escenarios donde los consumidores domésticos (usuarios finales) pueden participar en los mercados de energía actuando como prosumidores. Cada prosumidor es considerado como un nodo de energía con su propia fuente de generación de energía renovable, sus cargas controlables y no controlables e incluso sus propias tarifas. Los nodos pueden formar agregaciones que serán gestionadas por un agente denominado operador del sistema. La participación en los mercados energéticos no es trivial, bien sea por requerimientos técnicos de instalación o debido a la necesidad de cubrir un volumen mínimo de energía por transacción, que cada nodo debe cumplir individualmente. Estas limitaciones hacen casi imposible la participación individual, pero pueden ser salvadas mediante participaciones agregadas. El agregador llevará a cabo la ardua tarea de coordinar y estabilizar las operaciones de los nodos de energía, tanto individualmente como a nivel de sistema, para que todo el conjunto se comporte como una unidad con respecto al mercado. Las entidades que gestionan el sistema pueden ser meras comercializadoras, o distribuidoras y comercializadoras simultáneamente. Por este motivo, el modelo de optimización sobre el que basarán sus decisiones deberá considerar, además de las tarifas agregadas, otras individuales para permitir facturaciones independientes. Los nodos deberán tener autonomía legal y técnica, así como el equipamiento necesario y suficiente para poder gestionar, o delegar en el operador del sistema, su participación en los mercados de energía. Esta agregación atendiendo a reglas de negocio y no solamente a restricciones de localización física es lo que se conoce como Virtual Power Plant. La optimización de la participación agregada en los mercados, desde el punto de vista técnico y económico, requiere de la introducción del concepto de virtualización dinámica del almacenamiento, para lo que será indispensable que los nodos pertenecientes al sistema bajo estudio consten de una batería para almacenar la energía sobrante. Esta virtualización dinámica definirá particiones lógicas en el sistema de almacenamiento para dedicar diferentes porcentajes de la energía almacenada para propósitos distintos. Como ejemplo, se podría hacer una virtualización en dos particiones lógicas diferentes: una de demand-response. Así, el sistema podría operar y satisfacer ambos mercados de manera simultánea con el mismo grid y el mismo almacenamiento. El potencial de estas particiones lógicas es que se pueden definir de manera dinámica, dependiendo del contexto de ejecución y del estado, tanto de la red, como de cada uno de los nodos a nivel individual. Para establecer una estrategia de participación se pueden considerar apuestas arriesgadas que reportarán más beneficios en términos de compra-venta, pero también posibles penalizaciones por no poder cumplir con el contrato. Por el contrario, una estrategia conservadora podría resultar menos beneficiosa económicamente en dichos términos de compra-venta, pero reducirá las penalizaciones. La inclusión del concepto de perfiles de intención dinámicos permitirá hacer pujas que sean arriesgadas, cuando existan errores de predicción potencialmente pequeños en términos de generación, consumo o fallos; y pujas más conservadoras en caso contrario. El operador del sistema es el agente que definirá cuánta energía utiliza para comercializar, cuánta para asegurar autoconsumo, cuánta desea tener disponible para participar en el programa de demand-response etc. El gran número de variables y de situaciones posibles hacen que este problema sea muy costoso y complejo de resolver mediante métodos clásicos, sobre todo teniendo en cuenta que pequeñas variaciones en la toma de decisiones pueden tener grandes implicaciones económicas incluso a corto plazo. En esta tesis se ha investigado en el concepto de virtualización dinámica del almacenamiento para permitir una participación simultánea en múltiples mercados. La estrategia de optimización definida permite participaciones simultáneas en diferentes mercados que pueden ser controladas con el objetivo de optimizar el beneficio potencial, el riesgo potencial, o incluso una combinación mixta de ambas en base a otros criterios más avanzados marcados por el know-how del operador del sistema. Se han desarrollado algoritmos de optimización para el mercado del day-ahead, para la participación en el programa de demand-response y un algoritmo de control para reducir las penalizaciones durante la operación mediante modelos de control predictivo. Se ha realizado la definición e implementación de un componente estocástico para hacer el sistema más robusto frente a la incertidumbre inherente a estos sistemas en los que hay tanto peso de una componente de tipo forecasing. La formulación de esta capa se ha realizado mediante chance-constraints, que incluye la posibilidad de combinar diferentes componentes para mejorar la precisión de la optimización. Para el caso de uso presentado se ha elegido la combinación de métodos estadísticos por probabilidad junto a un agente inteligente basado en una arquitectura de codificador-decodificador construida con redes neuronales compuestas de Gated Recurrent Units. La formulación y la implementación utilizada permiten que, aunque todos los algoritmos estén completamente desacoplados y no presenten dependencias entre ellos, todos se actual como la estrategia seleccionada. Esto permite la definición de un contexto mucho más amplio en la ejecución de las optimizaciones y una toma de decisiones más consciente, real y ajustada a la situación que condiciona al proceso. Además de las pertinentes pruebas de simulación, parte de la herramienta ha sido probada en un sistema real compuesto por 40 nodos domésticos, convenientemente equipados, durante un año en una infraestructura implantada en la isla alemana de Borkum. Esta experiencia ha permitido extraer conclusiones muy interesantes sobre la implantación de la plataforma en entornos reales

Control Architecture Modeling for Future Power Systems

Uncontrollable power generation, distributed energy resources, controllable demand, etc. are fundamental aspects of energy systems largely based on renewable energy supply. These technologies have in common that they contradict the conventional categories of electric power system operation. As their introduction has proceeded incrementally in the past, operation strategies of the power system could be adapted. For example much more wind power could be integrated than originally anticipated, largely due to the flexibility reserves already present in the power system, and the possibility of interregional electricity exchange. However, at the same time, it seems that the overall system design cannot keep up by simply adapting in response to changes, but that also new strategies have to be designed in anticipation. Changes to the electricity markets have been suggested to adapt to the limited predictability of wind power, and several new control strategies have been proposed, in particular to enable the control of distributed energy resources, including for example, distributed generation or electric vehicles. Market designs addressing the procurement of balancing resources are highly dependent on the operation strategies specifying the resource requirements. How should one decide which control strategy and market configuration is best for a future power system? Most research up to this point has addressed single isolated aspects of this design problem. Those of the ideas that fit with current markets and operation concepts are lucky; they can be evaluated on the present design. But how could they be evaluated on a potential future power system? Approaches are required that support the design and evaluation of power system operation and control in context of future energy scenarios. This work addresses this challenge, not by providing a universal solution, but by providing basic modeling methodology that enables better problem formulation and by suggesting an approach to addressing the general chicken/egg problem of planning and re-design of system operation and control. The dissertation first focuses on the development of models, diagrams, that support the conceptual design of control and operation strategies, where a central theme is the focus on modeling system goals and functions rather than system structure. The perspective is then shifted toward long-term energy scenarios and adaptation of power system operation, considering the integration of energy scenario models with the re-design of operation strategies. The main contributions in the first part are, firstly, by adaptation of an existing functional modeling approach called Multilevel Flow Modeling (MFM) to the power systems domain, identifying the means-ends composition of control levels and development of principles for the consistent modeling of control structures, a formalization of control-as-a-service; secondly, the formal mapping of fluctuating and controllable resources to a multi-scale and multi-stage representation of control and operation structures; and finally the application to some concrete study cases, including a present system balancing, and proposed control structures such as Microgrids and Cells. In the second part, the main contributions are the outline of a formation strategy, integrating the design and model-based evaluation of future power system operation concepts with iterative energy scenario development. Finally, a new modeling framework for development and evaluation of power system operation in context of energy-storage based power system balancing is introduced.<br/

What is Robotics: Why Do We Need It and How Can We Get It?

Robotics is an emerging synthetic science concerned with programming work. Robot technologies are quickly advancing beyond the insights of the existing science. More secure intellectual foundations will be required to achieve better, more reliable and safer capabilities as their penetration into society deepens. Presently missing foundations include the identification of fundamental physical limits, the development of new dynamical systems theory and the invention of physically grounded programming languages. The new discipline needs a departmental home in the universities which it can justify both intellectually and by its capacity to attract new diverse populations inspired by the age old human fascination with robots. For more information: Kod*la

Power network and smart grids analysis from a graph theoretic perspective

The growing size and complexity of power systems has given raise to the use of complex network theory in their modelling, analysis, and synthesis. Though most of the previous studies in this area have focused on distributed control through well established protocols like synchronization and consensus, recently, a few fundamental concepts from graph theory have also been applied, for example in symmetry-based cluster synchronization. Among the existing notions of graph theory, graph symmetry is the focus of this proposal. However, there are other development around some concepts from complex network theory such as graph clustering in the study. In spite of the widespread applications of symmetry concepts in many real world complex networks, one can rarely find an article exploiting the symmetry in power systems. In addition, no study has been conducted in analysing controllability and robustness for a power network employing graph symmetry. It has been verified that graph symmetry promotes robustness but impedes controllability. A largely absent work, even in other fields outside power systems, is the simultaneous investigation of the symmetry effect on controllability and robustness. The thesis can be divided into two section. The first section, including Chapters 2-3, establishes the major theoretical development around the applications of graph symmetry in power networks. A few important topics in power systems and smart grids such as controllability and robustness are addressed using the symmetry concept. These topics are directed toward solving specific problems in complex power networks. The controllability analysis will lead to new algorithms elaborating current controllability benchmarks such as the maximum matching and the minimum dominant set. The resulting algorithms will optimize the number of required driver nodes indicated as FACTS devices in power networks. The second topic, robustness, will be tackled by the symmetry analysis of the network to investigate three aspects of network robustness: robustness of controllability, disturbance decoupling, and fault tolerance against failure in a network element. In the second section, including Chapters 4-8, in addition to theoretical development, a few novel applications are proposed for the theoretical development proposed in both sections one and two. In Chapter 4, an application for the proposed approaches is introduced and developed. The placement of flexible AC transmission systems (FACTS) is investigated where the cybersecurity of the associated data exchange under the wide area power networks is also considered. A new notion of security, i.e. moderated-k-symmetry, is introduced to leverage on the symmetry characteristics of the network to obscure the network data from the adversary perspective. In chapters 5-8, the use of graph theory, and in particular, graph symmetry and centrality, are adapted for the complex network of charging stations. In Chapter 5, the placement and sizing of charging stations (CSs) of the network of electric vehicles are addressed by proposing a novel complex network model of the charging stations. The problems of placement and sizing are then reformulated in a control framework and the impact of symmetry on the number and locations of charging stations is also investigated. These results are developed in Chapters 6-7 to robust placement and sizing of charging stations for the Tesla network of Sydney where the problem of extending the capacity having a set of pre-existing CSs are addressed. The role of centrality in placement of CSs is investigated in Chapter 8. Finally, concluding remarks and future works are presented in Chapter 9

Adaptive tension, self-organization and emergence : A complex system perspective of supply chain disruptions

The purpose of this thesis was to explore how microstate human interactions produce macro level self-organization and emergence in a supply disruption scenario, as well as discover factors and typical human behaviour that bring about disruptions. This study argues that the complex adaptive system’s view of complexity is most suited scholarly foundation for this research enquiry. Drawing on the dissipative structure based explanation of emergence and self-organization in a complex adaptive system, this thesis further argues that an energy gradient between the ongoing and designed system conditions, known as adaptive tension, causes supply chains to self-organize and emerge. This study adopts a critical realist ontology operationalized by a qualitative case research and grounded theory based analysis. The data was collected using repertory grid interviews of 22 supply chain executives from 21 firms. In all 167 cases of supply disruptions were investigated. Findings illustrate that agent behaviours like loss of trust, over ambitious pursuit, use of power and privilege, conspiring against best practices and heedless performance were contributing to disruption. Impacted by these behaviours, supply chains demonstrated impaired disruption management capabilities and increased disruption probability. It was also discovered that some of these system patterns and microstate agent behaviours pushed the supply chains to a zone of emergent complexity where these networks self-organized and emerged into new structures or embraced changes in prevailing processes or goals. A conceptual model was developed to explain the transition from micro agent behaviour to system level self-organization and emergence. The model described alternate pathways of a supply chain under adaptive tension. The research makes three primary research contributions. Firstly, based upon the theoretical model, this research presents a conceptualization of supply chain emergence and self-organization from dissipative structures and adaptive tension based view of complexity. Secondly, it formally introduces and validates the role of behavioural and cognitive element of human actions in a supply chain scenario. Lastly, it affirms the complex adaptive system based conceptualization of supply chain networks. These contributions succeed in providing organizations with an explanation for observed deviations in their operations performance using a behavioural aspect of human agents