Diseño de un estabilizador de sistemas de potencia neuro-borroso adaptativo ajustado mediante algoritmos genéticos

Los sistemas eléctricos de potencia constituyen una de las realizaciones más complejas y sofisticadas que ha conseguido la ingeniería eléctrica. Los sistemas eléctricos se encargan de asegurar el suministro de energía conectando los centros productores con los centros de consumo, a través de un gran número de redes eléctricas. Para que el sistema funcione adecuadamente, todos los generadores deben estar interconectados entre sí de forma que se asegure el suministro eléctrico. La conexión sólo es posible cuando todos los generadores se mantienen girando a la misma velocidad, garantizando así el valor constante de la frecuencia de la tensión de salida. Para controlar estos sistemas se disponen de reguladores automáticos que mantienen constante los valores de tensión y frecuencia generadas por el alternador. Al realizar estas interconexiones se han observado inestabilidades en el comportamiento dinámico del sistema, apareciendo oscilaciones espontáneas de muy baja frecuencia en el margen entre 0.2 a 3 Hz. Estas oscilaciones limitan la capacidad de transmisión de potencia entre los generadores y las cargas y por ello, se han diseñado los Estabilizadores de Sistemas de Potencia (Power System Stabilizer-PSS) como elementos adicionales que permiten amortiguar y estabilizar estos sistemas. La mayor parte de los PSS utilizados en aplicaciones reales son células de adelanto-retraso de tipo analógico con parámetros fijos. El diseño y la selección de la mejor estructura del PSS, así como el valor de sus parámetros es un proceso iterativo y muy complejo, optimizándose en el punto de trabajo habitual de la máquina. El comportamiento del PSS empeora cuando, debido a cambios en la topología de la red, cambios en la demanda de carga o cualquier otro tipo de perturbaciones, se modifica el punto de funcionamiento del sistema y los parámetros del estabilizador dejan de ser óptimos. El objetivo principal de esta Tesis Doctoral consiste en diseñar un sistema de estabilización adaptativo que utilice estas técnicas de sistemas expertos. El diseño del estabilizador está dividido en dos partes: En primer lugar se diseña un PSS robusto, utilizando algoritmos genéticos, capaz de estabilizar al sistema de potencia en un amplio margen de puntos de operación y posteriormente se calculan los mejores parámetros de los estabilizadores para distintos puntos de trabajo. La relación entre estos puntos y el valor de los parámetros del estabilizador es aprendida por un sistema neuro-borroso que se encarga de ajustar los parámetros del un PSS clásico a medida que el sistema de potencia evoluciona y cambia su punto de trabajo. Con objeto de validar el correcto funcionamiento del sistema de control propuesto, se han realizado una serie de ensayos que permiten comparar este esquema de control con otros sistemas de control clásicos

Charge Scheduling Strategies for Managing an Electric Vehicle Fleet Parking

In this work, different charging scheduling algorithms for managing the recharge process of an electric vehicle fleet in a centralized parking are developed. This tool is tested on a real-world electric fleet which are charged using five charging stations. These algorithms are also use to size the charging infrastructure, determining the minimum number of chargers that are required to charge all electric vehicles

Reducing the impact of EV charging on the electric grid

One of the main objectives of European Commission related to climate and energy is the well-known 20-20-20 targets to be achieved in 2020: Europe has to reduce greenhouse gas emissions of at least 20% below 1990 levels, 20% of EU energy consumption has to come from renewable resources and, finally, a 20% reduction in primary energy use compared with projected levels, has to be achieved by improving energy efficiency. In order to reach these objectives, it is necessary to reduce the overall emissions, mainly in transport (reducing CO2, NOx and other pollutants), and to increase the penetration of the intermittent renewable energy. A high deployment of battery electric (BEVs) and plug-in hybrid electric vehicles (PHEVs), with a low-cost source of energy storage, could help to achieve both targets. Hybrid electric vehicles (HEVs) use a combination of a conventional internal combustion engine (ICE) with one (or more) electric motor. There are different grades of hybridation from micro-hybrids with start-stop capability, mild hybrids (with kinetic energy recovery), medium hybrids (mild hybrids plus energy assist) and full hybrids (medium hybrids plus electric launch capability). These last types of vehicles use a typical battery capacity around 1-2 kWh. Plug in hybrid electric vehicles (PHEVs) use larger battery capacities to achieve limited electric-only driving range. These vehicles are charged by on-board electricity generation or either plugging into electric outlets. Typical battery capacity is around 10 kWh. Battery Electric Vehicles (BEVs) are only driven by electric power and their typical battery capacity is around 15-20 kWh. One type of PHEV, the Extended Range Electric Vehicle (EREV), operates as a BEV until its plug-in battery capacity is depleted; at which point its gasoline engine powers an electric generator to extend the vehicle's range. The charging of PHEVs (including EREVs) and BEVs will have different impacts to the electric grid, depending on the number of vehicles and the start time for charging. Initially, the lecture will start analyzing the electrical power requirements for charging PHEVs-BEVs in Flanders region (Belgium) under different charging scenarios. Secondly and based on an activity-based microsimulation mobility model, an efficient method to reduce this impact will be presented

Algorithm development for night charging electric vehicles optimization in big data applications

In this paper a night charging method that optimizes the recharging process of electric vehicles (EVs) depending on hourly energy price in a peer to peer (P2P) energy trading system is presented. This algorithm determines how much energy should be recharged in the battery of each EV and the corresponding time slot to do it, avoiding the discontinuities in the charging process and considering the users’ personal mobility constraints

Neural Networks for optimal operation of a run-of-river adjustable speed hydro power plant with axial-flow propeller turbine

This paper analyzes the regulating capabilities of both turbine speed and guide vanes position in an axial-flow propeller turbine. Two neural networks are implemented in order to simulate the turbine behavior and the turbine efficiency. A maximum-efficiency-tracking algorithm is developed to set the guide vanes position. An experimental power plant built in the Hydraulics Laboratory is described. In order to validate the proposed operation control, the dynamics of this run-of- river pilot plant has been simulated. Substantial increases in the turbine efficiency have been found

La Evaluación de la docencia: Ventajas e Inconvenientes del procedimiento "Docentia" propuesto por la ANECA

La evaluación de la actividad docente e investigadora del profesorado se implantó normativamente en 1989, si bien con fines exclusivamente retributivos. Desde entonces, los profesores se han sometido cada 5 años a la evaluación de su actividad docente y cada 6 años a la de su actividad investigadora. Sin embargo, la evaluación de la actividad investigadora ha tenido una importancia y trascendencia muchísimo mayor que la docente. De hecho, los sexenios investigadores se han convertido en la clave de los concursos a los cuerpos de docente universitarios, tanto para la formación de tribunales como para la selección de los candidatos. La evaluación de la actividad docente ha estado, por el contrario, sometida a la rutina e indiferencia. La mayoría de los profesores universitarios están en posesión de todos los quinquenios docentes que su antigüedad le permite, ya tengan o no actividad docente real. A la larga, esta generalización ha minusvalorado la propia actividad docente, ha marginado a todo un conjunto de profesores vocacionales y ha deteriorado la calidad de la enseñanza que reciben los alumnos. Incluso, mientras esto acontecía, y coincidiendo con el desarrollo del EEES, se proclamaban admirables principios acerca de la enseñanza de calidad, que en nada se materializaban o que invariablemente se reconducían a la tenencia de sexenios investigadores. Finalmente, en 2007, casi 13 años más tarde de que se estableciera un procedimiento para la evaluación de la actividad investigadora (1994), la ANECA propuso DOCENTIA. La UPM, que se ha sumado a este programa aun año más tarde, ya tenía, en teoría, su propio procedimiento de evaluación docente, pero sólo diseñado para otorgar quinquenios, sin que en modo alguno se evalúe la actividad docente real del profesor. A la vista de esta apatía institucional, la ETSI de Caminos, Canales y Puertos de la UPM estableció en 2005, un procedimiento de evaluación docente que ha cambiado en la forma de valorar esta actividad dentro de nuestra Escuela. Se encuesta a los alumnos de forma eficiente y a través de Internet (lo que ya es novedoso de por sí), sobre la actividad docente real del profesor en el aula y fuera de ella. Pero la gran novedad del sistema está en la divulgación general de los resultados, que son presentados de forma rápida, clara, precisa y comparativa. Actualmente estos resultados están siendo tenidos en cuenta para la renovación de contratos de los profesores. La experiencia acumulada tras estos años, permite a los autores comentar en este artículo la viabilidad práctica del procedimiento de evaluación propuesto por la ANECA, y por la UPM, que parece una copia directa de aquel. Se proponen modificaciones a este procedimiento si se pretende que sirva para conseguir una docencia de calidad, y no sólo para seguir estableciendo una retribución económica de forma general, o para cumplir sobre el papel, los requisitos de calidad exigidos a los nuevos planes de estudi

Using mobility information to perform a feasibility study and the evaluation of spatio-temporal energy demanded by an electric taxi fleet

Half of the global population already lives in urban areas, facing to the problem of air pollution mainly caused by the transportation system. The recently worsening of urban air quality has a direct impact on the human health. Replacing today’s internal combustion engine vehicles with electric ones in public fleets could provide a deep impact on the air quality in the cities. In this paper, real mobility information is used as decision support for the taxi fleet manager to promote the adoption of electric taxi cabs in the city of San Francisco, USA. Firstly, mobility characteristics and energy requirements of a single taxi are analyzed. Then, the results are generalized to all vehicles from the taxi fleet. An electrificability rate of the taxi fleet is generated, providing information about the number of current trips that could be performed by electric taxis without modifying the current driver mobility patterns. The analysis results reveal that 75.2% of the current taxis could be replaced by electric vehicles, considering a current standard battery capacity (24–30 kWh). This value can increase significantly (to 100%), taking into account the evolution of the price and capacity of the batteries installed in the last models of electric vehicles that are coming to the market. The economic analysis shows that the purchasing costs of an electric taxi are bigger than conventional one. However, fuel, maintenance and repair costs are much lower. Using the expected energy consumption information evaluated in this study, the total spatio-temporal demand of electric energy required to recharge the electric fleet is also calculated, allowing identifying optimal location of charging infrastructure based on realistic routing patterns. This information could also be used by the distribution system operator to identify possible reinforcement actions in the electric grid in order to promote introducing electric vehicles

Management Strategies for Electric Vehicle Fleets

The research leading to these results has received funding from the EU 7th FP under the project DATA science for SIMulating the era of electric vehicles (DATASIM, FP7-ICT-270833). DATA SIM aims at providing an entirely new and highly detailed spatial-temporal microsimulation methodology for human mobility with the goal to forecast the nation-wide consequences of a massive switch to electric vehicles. The objective of this work is focused in the development of charging management strategies for electric vehicle (EV) fleets. Its purpose is to maximize the integration of EVs in the current electric grid considering their consumption and their charging limits, both temporal and spatially. The main contribution of this work is the development of a novel Peer to Peer Energy Trading System (P2PETS) between EVs in order to reduce the impact of charging EVs over the electric grid

Electrical machines-based multi-disturbance device for testing distribution grid technologies

The growth of distributed energy resources in power systems, mainly converter-coupled generators, has forced the evolution of grid codes, increasing the requirements to be fulfilled by the generators before their connection to the mains. Solving this problem is crucial to guarantee the reliability and stability of the power system. Current standards force the generators to remain connected when facing voltage and/or frequency disturbances and, in some advanced grid codes, also when the generators are facing transient phase jumps produced as a consequence of those disturbances. If considering that the increment of the distributed generation is mainly done at a distribution level, it is unrealistic to consider voltage and frequency events as decoupled phenomena in the testing and certification procedures, as it is currently done. The testing device proposed in this paper is able to reproduce voltage, frequency and phase jumps disturbances of a controlled value simultaneously, being able to reproduce the real grid behavior reliably. Its design is based on standard components with a simple control system, which makes it easily replicable and scalable up to the power of the device to be tested. Therefore, the proposed device is an interesting commercially competitive testing equipment suitable for certification.This work has been funded by the Spanish Ministry of Economy and Competiveness under research grants PN-ENE2009-13276 and BES- 2010-034386

Dam seepage analysis based on artificial neural networks: the hysteresis phenomenon

Seepage flow measurement is an important behavior indicator when providing information about dam performance. The main objective of this study is to analyze seepage by means of an artificial neural network model. The model is trained and validated with data measured at a case study. The dam behavior towards different water level changes is reproduced by the model and a hysteresis phenomenon detected and studied. Artificial neural network models are shown to be a powerful tool for predicting and understanding seepage phenomenon