987 research outputs found
Smart grid architecture for rural distribution networks: application to a Spanish pilot network
This paper presents a novel architecture for rural distribution grids. This architecture is designed to modernize traditional rural networks into new Smart Grid ones. The architecture tackles innovation actions on both the power plane and the management plane of the system. In the power plane, the architecture focuses on exploiting the synergies between telecommunications and innovative technologies based on power electronics managing low scale electrical storage. In the management plane, a decentralized management system is proposed based on the addition of two new agents assisting the typical Supervisory Control And Data Acquisition (SCADA) system of distribution system operators. Altogether, the proposed architecture enables operators to use more effectively—in an automated and decentralized way—weak rural distribution systems, increasing the capability to integrate new distributed energy resources. This architecture is being implemented in a real Pilot Network located in Spain, in the frame of the European Smart Rural Grid project. The paper also includes a study case showing one of the potentialities of one of the principal technologies developed in the project and underpinning the realization of the new architecture: the so-called Intelligent Distribution Power Router.Postprint (published version
A Multi-Agent System Architecture for Smart Grid Management and Forecasting of Energy Demand in Virtual Power Plants
[EN] Recent technological advances in the power generation and information technologies areas are helping to change the modern electricity supply system in order to comply with higher energy efficiency and sustainability standards. Smart grids are an emerging trend that introduce intelligence in the power grid to optimize resource usage. In order for this intelligence to be effective, it is necessary to retrieve enough information about the grid operation together with other context data such as environmental variables, and intelligently modify the behavior of the network elements accordingly. This article presents a multi-agent system model for virtual power plants, a new power plant concept in which generation no longer occurs in big installations, but is the result of the cooperation of smaller and more intelligent elements. The proposed model is not only focused on the management of the different elements, but includes a set of agents embedded with artificial neural networks for collaborative forecasting of disaggregated energy demand of domestic end users, the results of which are also shown in this article.We would like to express our thanks to the coordinators of the project OptimaGrid for the information provided on MAS-based micro-grids, and the creators of a MAS INGENIAS methodology. This article has been partially funded by the project SociAAL (Social Ambient Assisted Living), supported by Spanish Ministry for Economy and Competitiveness, with grant TIN2011-28335-C02-01, by the Programa de Creacion y Consolidacion de Grupos de Investigacion UCM-Banco Santander for the group number 921354 (GRASIA group).Hernández, L.; Baladrón Zorita, C.; Aguiar Pérez, JM.; Carro, B.; Sanchez-Esguevillas, A.; Lloret, J.; Chinarro, D.... (2013). A Multi-Agent System Architecture for Smart Grid Management and Forecasting of Energy Demand in Virtual Power Plants. IEEE Communications Magazine. 51(1):106-113. https://doi.org/10.1109/MCOM.2013.6400446S10611351
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
Traditional power grids are being transformed into Smart Grids (SGs) to
address the issues in existing power system due to uni-directional information
flow, energy wastage, growing energy demand, reliability and security. SGs
offer bi-directional energy flow between service providers and consumers,
involving power generation, transmission, distribution and utilization systems.
SGs employ various devices for the monitoring, analysis and control of the
grid, deployed at power plants, distribution centers and in consumers' premises
in a very large number. Hence, an SG requires connectivity, automation and the
tracking of such devices. This is achieved with the help of Internet of Things
(IoT). IoT helps SG systems to support various network functions throughout the
generation, transmission, distribution and consumption of energy by
incorporating IoT devices (such as sensors, actuators and smart meters), as
well as by providing the connectivity, automation and tracking for such
devices. In this paper, we provide a comprehensive survey on IoT-aided SG
systems, which includes the existing architectures, applications and prototypes
of IoT-aided SG systems. This survey also highlights the open issues,
challenges and future research directions for IoT-aided SG systems
PLC & SCADA based substation automation
lectrical power systems are a technical wonder. Electricity and its accessibility are the\ud
greatest engineering achievements of the 20th century. A modern society cannot exist without electricity.\ud
Generating stations, transmission lines and distribution systems are the main components of\ud
power system. Smaller power systems (called regional grids) are interconnected to form a larger network\ud
called national grid, in which power is exchanged between different areas depending upon surplus and\ud
deficiency. This requires a knowledge of load flows, which is impossible without meticulous planning and\ud
monitoring .Also, the system needs to operate in such a way that the losses and in turn the cost of\ud
production are minimum.\ud
The major factors that influence the operation of a power system are the changes in load and\ud
stability. As is easily understood from the different load curves and load duration curve, the connected\ud
load, load varies widely throughout the day. These changes have an impact on the stability of power\ud
system. As a severe change in a short span can even lead to loss of synchronism. Stability is also affected\ud
by the occurrence of faults, Faults need to be intercepted at an easily stage and corrective measures like\ud
isolating the faulty line must be taken.\ud
As the power consumption increases globally, unprecedented challenges are being faced,\ud
which require modern, sophisticated methods to counter them. This calls for the use of automation in the\ud
power system. The Supervisory Control and Data Acquisition (SCADA) and Programmable Logic\ud
Controllers (PLC) are an answer to this.\ud
SCADA refers to a system that enables on electricity utility to remotely monitor, co-ordinate,\ud
control and operate transmission and distribution components, equipment and real-time mode from a\ud
remote location with acquisition at date for analysis and planning from one control location.\ud
PLC on the other hand is like the brain of the system with the joint operation of the SCADA\ud
and the PLC, it is possible to control and operate the power system remotely. Task like\ud
Opening of circuit breakers, changing transformer taps and managing the load demand can be carried out\ud
efficiently.\ud
This type of an automatic network can manage load, maintain quality, detect theft of\ud
electricity and tempering of meters. It gives the operator an overall view of the entire network. Also, flow\ud
of power can be closely scrutinized and Pilferage points can be located. Human errors leading to tripping\ud
can be eliminated. This directly increases the reliability and lowers the operating cost.\ud
In short our project is an integration of network monitoring functions with geographical\ud
mapping, fault location, load management and intelligent metering
Intelligent training in control centres based on an ambient intelligence paradigm
This article describes a new approach in the Intelligent Training of Operators in Power Systems Control Centres, considering the new reality of Renewable Sources, Distributed Generation, and Electricity Markets, under the emerging paradigms of Cyber-Physical Systems and Ambient Intelligence. We propose Intelligent Tutoring Systems as the approach to deal with the intelligent training of operators in these new circumstances
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
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