Design and Optimization of Power Delivery and Distribution Systems Using Evolutionary Computation Techniques

Nowadays computing platforms consist of a very large number of components that require to be supplied with diferent voltage levels and power requirements. Even a very small platform, like a handheld computer, may contain more than twenty diferent loads and voltage regulators. The power delivery designers of these systems are required to provide, in a very short time, the right power architecture that optimizes the performance, meets electrical specifications plus cost and size targets. The appropriate selection of the architecture and converters directly defines the performance of a given solution. Therefore, the designer needs to be able to evaluate a significant number of options in order to know with good certainty whether the selected solutions meet the size, energy eficiency and cost targets. The design dificulties of selecting the right solution arise due to the wide range of power conversion products provided by diferent manufacturers. These products range from discrete components (to build converters) to complete power conversion modules that employ diferent manufacturing technologies. Consequently, in most cases it is not possible to analyze all the alternatives (combinations of power architectures and converters) that can be built. The designer has to select a limited number of converters in order to simplify the analysis. In this thesis, in order to overcome the mentioned dificulties, a new design methodology for power supply systems is proposed. This methodology integrates evolutionary computation techniques in order to make possible analyzing a large number of possibilities. This exhaustive analysis helps the designer to quickly define a set of feasible solutions and select the best trade-off in performance according to each application. The proposed approach consists of two key steps, one for the automatic generation of architectures and other for the optimized selection of components. In this thesis are detailed the implementation of these two steps. The usefulness of the methodology is corroborated by contrasting the results using real problems and experiments designed to test the limits of the algorithms.

Distributed power generation in the United States

With electricity consumption increasing within the UnitedStates, new paradigms of delivering electricity are required in order to meet demand. One promising option is the increased use of distributedpowergeneration. Already a growing percentage of electricity generation, distributedgeneration locates the power plant physically close to the consumer, avoiding transmission and distribution losses as well as providing the possibility of combined heat and power. Despite the efficiency gains possible, regulators and utilities have been reluctant to implement distributedgeneration, creating numerous technical, regulatory, and business barriers. Certain governments, most notable California, are making concerted efforts to overcome these barriers in order to ensure distributedgeneration plays a part as the country meets demand while shifting to cleaner sources of energy.

A simplified energy consumption model for fiber-based Next Generation Access Networks

ICTs account nowadays for 2% of total carbon emissions. However, in a time when strict measures to reduce energyconsumption in all the industrial and services sectors are required, the ICT sector faces an increase in services and bandwidth demand. The deployment of NextGenerationNetworks (NGN) will be the answer to this new demand and specifically, the NextGenerationAccessNetworks (NGANs) will provide higher bandwidth access to users. Several policy and cost analysis are being carried out to understand the risks and opportunities of new deployments, though the question of which is the role of energyconsumption in NGANs seems off the table. Thus, this paper proposes amodel to analyze the energyconsumption of the main fiber-based NGAN architectures, i.e. Fiber To The House (FTTH) in both Passive Optical Network (PON) and Point-to-Point (PtP) variations, and FTTx/VDSL. The aim of this analysis is to provide deeper insight on the impact of new deployments on the energyconsumption of the ICT sector and the effects of energyconsumption on the life-cycle cost of NGANs. The paper presents also an energyconsumption comparison of the presented architectures, particularized in the specific geographic and demographic distribution of users of Spain, but easily extendable to other countries.

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

A Simplified Energy Consumption Model for Fibre-Based Next Generation Access Networks

Presentación realizada en el PhD Seminar del ITS 2011 en Budapest. ICTs (Information and Communication Technologies) currently account for 2% of total carbon emissions. However, although modern standards require strict measures to reduce energy consumption across all industrial and services sectors, the ICT sector also faces an increase in services and bandwidth demand. The deployment of Next Generation Networks (NGN) will be the answer to this new demand; more specifically, Next Generation Access Networks (NGANs) will provide higher bandwidth access to users. Several policy and cost analyses are being carried out to understand the risks and opportunities of new deployments, but the question of what role energy consumption plays in NGANs seems off the table. Thus, this paper proposes a model to analyse the energy consumption of the main fibre-based NGAN architectures: Fibre To The House (FTTH), in both Passive Optical Network (PON) and Point-to-Point (PtP) variations, and FTTx/VDSL. The aim of this analysis is to provide deeper insight on the impact of new deployments on the energy consumption of the ICT sector and the effects of energy consumption on the life-cycle cost of NGANs. The paper also presents an energy consumption comparison of the presented architectures, particularised to the specific geographic and demographic distribution of users of Spain but easily extendable to other countries.

Decentralized multi-tasks distribution in heterogeneous robot teams by means of ant colony optimization and learning automata

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-election of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested on decentralized solution where the robots themselves autonomously and in an individual manner, are responsible of selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-tasks distribution problem and we propose a solution using two different approaches by applying Ant Colony Optimization-based deterministic algorithms as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithm, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Application of self-organizing techniques for the distribution of heterogeneous multi-tasks in multi-robot systems

This paper focuses on the general problem of coordinating of multi-robot systems, more specifically, it addresses the self-election of heterogeneous and specialized tasks by autonomous robots. In this regard, it has proposed experimenting with two different techniques based chiefly on selforganization and emergence biologically inspired, by applying response threshold models as well as ant colony optimization. Under this approach it can speak of multi-tasks selection instead of multi-tasks allocation, that means, as the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. It has evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems.

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-selection of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested in a decentralized solution where the robots themselves autonomously and in an individual manner, are responsible for selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-task distribution problem and we propose a solution using two different approaches by applying Response Threshold Models as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

SolarPaces 2013: Innovations on direct steam generation in linear fresnel collectors

Direct Steam Generation (DSG) in Linear Fresnel (LF) solar collectors is being consolidated as a feasible technology for Concentrating Solar Power (CSP) plants. The competitiveness of this technology relies on the following main features: water as heat transfer fluid (HTF) in Solar Field (SF), obtaining high superheated steam temperatures and pressures at turbine inlet (500ºC and 90 bar), no heat tracing required to avoid HTF freezing, no HTF degradation, no environmental impacts, any heat exchanger between SF and Balance Of Plant (BOP), and low cost installation and maintenance. Regarding to LF solar collectors, were recently developed as an alternative to Parabolic Trough Collector (PTC) technology. The main advantages of LF are: the reduced collector manufacturing cost and maintenance, linear mirrors shapes versus parabolic mirror, fixed receiver pipes (no ball joints reducing leaking for high pressures), lower susceptibility to wind damages, and light supporting structures allowing reduced driving devices. Companies as Novatec, Areva, Solar Euromed, etc., are investing in LF DSG technology and constructing different pilot plants to demonstrate the benefits and feasibility of this solution for defined locations and conditions (Puerto Errado 1 and 2 in Murcia Spain, Lidellin Newcastle Australia, Kogran Creek in South West Queensland Australia, Kimberlina in Bakersfield California USA, Llo Solar in Pyrénées France,Dhursar in India,etc). There are several critical decisions that must be taken in order to obtain a compromise and optimization between plant performance, cost, and durability. Some of these decisions go through the SF design: proper thermodynamic operational parameters, receiver material selection for high pressures, phase separators and recirculation pumps number and location, pipes distribution to reduce the amount of tubes (reducing possible leaks points and transient time, etc.), etc. Attending to these aspects, the correct design parameters selection and its correct assessment are the main target for designing DSG LF power plants. For this purpose in the recent few years some commercial software tools were developed to simulatesolar thermal power plants, the most focused on LF DSG design are Thermoflex and System Advisor Model (SAM). Once the simulation tool is selected,it is made the study of the proposed SFconfiguration that constitutes the main innovation of this work, and also a comparison with one of the most typical state-of-the-art configuration. The transient analysis must be simulated with high detail level, mainly in the BOP during start up, shut down, stand by, and partial loads are crucial, to obtain the annual plant performance. An innovative SF configurationwas proposed and analyzed to improve plant performance. Finally it was demonstrated thermal inertia and BOP regulation mode are critical points in low sun irradiation day plant behavior, impacting in annual performance depending on power plant location.

Information reconciliation for Quantum Key Distribution

Secret-key agreement, a well-known problem in cryptography, allows two parties holding correlated sequences to agree on a secret key communicating over a public channel. It is usually divided into three different procedures: advantage distillation, information reconciliation and privacy amplification. The efficiency of each one of these procedures is needed if a positive key rate is to be attained from the legitimate parties? correlated sequences. Quantum key distribution (QKD) allows the two parties to obtain correlated sequences, provided that they have access to an authenticated channel. The new generation of QKD devices is able to work at higher speeds and in noisier or more absorbing environments. This exposes the weaknesses of current information reconciliation protocols, a key component to their performance. Here we present a new protocol based in low-density parity-check (LDPC) codes that presents the advantages of low interactivity, rate adaptability and high efficiency,characteristics that make it highly suitable for next generation QKD devices.

Entanglement generation and routing in optical networks

New telecom wavelength sources of polarization entangled photon pairs allow the distribution of entanglement through metro-access networks using standard equipment. This is essential to ease the deployment of future applications that can profit from quantum entanglement, such as quantum cryptography.

Integration of quantum key distribution in metropolitan area networks

The deployment of Quantum Key Distribution forces the development of QKD-links to be operated in current and next-generation photonic metro-access networks. These highly heterogeneous architectures determine the conditions QKD-links need to be optimized for.

Effects of cross sections tables generation and optimization on rod ejection transient analyses

Best estimate analysis of rod ejection transients requires 3D kinetics core simulators. If they use cross sections libraries compiled in multidimensional tables,interpolation errors – originated when the core simulator computes the cross sections from the table values – are a source of uncertainty in k-effective calculations that should be accounted for. Those errors depend on the grid covering the domain of state variables and can be easily reduced, in contrast with other sources of uncertainties such as the ones due to nuclear data, by choosing an optimized grid distribution. The present paper assesses the impact of the grid structure on a PWR rod ejection transient analysis using the coupled neutron-kinetics/thermal-hydraulicsCOBAYA3/COBRA-TF system. Forthispurpose, the OECD/NEA PWR MOX/UO2 core transient benchmark has been chosen, as material compositions and geometries are available, allowing the use of lattice codes to generate libraries with different grid structures. Since a complete nodal cross-section library is also provided as part of the benchmark specifications, the effects of the library generation on transient behavior are also analyzed.Results showed large discrepancies when using the benchmark library and own-generated libraries when compared with benchmark participants’ solutions. The origin of the discrepancies was found to lie in the nodal cross sections provided in the benchmark.

Generation of human computational models with machine learning

Services in smart environments pursue to increase the quality of people?s lives. The most important issues when developing this kind of environments is testing and validating such services. These tasks usually imply high costs and annoying or unfeasible real-world testing. In such cases, artificial societies may be used to simulate the smart environment (i.e. physical environment, equipment and humans). With this aim, the CHROMUBE methodology guides test engineers when modeling human beings. Such models reproduce behaviors which are highly similar to the real ones. Originally, these models are based on automata whose transitions are governed by random variables. Automaton?s structure and the probability distribution functions of each random variable are determined by a manual test and error process. In this paper, it is presented an alternative extension of this methodology which avoids the said manual process. It is based on learning human behavior patterns automatically from sensor data by using machine learning techniques. The presented approach has been tested on a real scenario, where this extension has given highly accurate human behavior models,