A virtual power plant model for time-driven power flow calculations

This paper presents the implementation of a custom-made virtual power plant model in OpenDSS. The goal is to develop a model adequate for time-driven power flow calculations in distribution systems. The virtual power plant is modeled as the aggregation of renewable generation and energy storage connected to the distribution system through an inverter. The implemented operation mode allows the virtual power plant to act as a single dispatchable generation unit. The case studies presented in the paper demonstrate that the model behaves according to the specified control algorithm and show how it can be incorporated into the solution scheme of a general parallel genetic algorithm in order to obtain the optimal day-ahead dispatch. Simulation results exhibit a clear benefit from the deployment of a virtual power plant when compared to distributed generation based only on renewable intermittent generation.Peer ReviewedPostprint (published version

Reliability analysis of distribution systems with photovoltaic generation using a power flow simulator and a parallel Monte Carlo approach

This paper presents a Monte Carlo approach for reliability assessment of distribution systems with distributed generation using parallel computing. The calculations are carried out with a royalty-free power flow simulator, OpenDSS (Open Distribution System Simulator). The procedure has been implemented in an environment in which OpenDSS is driven from MATLAB. The test system is an overhead distribution system represented by means of a three-phase model that includes protective devices. The paper details the implemented procedure, which can be applied to systems with or without distributed generation, includes an illustrative case study and summarizes the results derived from the analysis of the test system during one year. The goal is to evaluate the test system performance considering different scenarios with different level of system automation and reconfiguration, and assess the impact that distributed photovoltaic generation can have on that performance. Several reliability indices, including those related to the impact of distributed generation, are obtained for every scenario.Postprint (published version

A Solid State Transformer model for power flow calculations

This paper presents the implementation of a Solid State Transformer (SST) model in OpenDSS. The goal is to develop a SST model that could be useful for assessing the impact that the replacement of the conventional iron-and-copper transformer with the SST can have on the distribution system performance. Test distribution systems of different characteristics and size have been simulated during different time periods. The simulations have been carried out assuming voltage-dependent loads and considering that power flow through either the HV/MV substation transformer or any of the MV/LV distribution transformers can be bidirectional. Simulation results prove that a positive impact should be expected on voltages at both MV and LV levels, but the efficiency of current SST designs should be improved.Postprint (author's final draft

Localización óptima de generación distribuida en sistemas de distribución trifásicos con carga variable en el tiempo utilizando el método de Monte Carlo

En el presente trabajo se desarrolla un método para determinar la localización óptima de un generador distribuido que inyecta únicamente potencia activa en un alimentador radial con carga uniformemente distribuida. El algoritmo desarrollado se basa en el método de Monte Carlo y permite obtener los valores óptimos de potencia y posición del generador. El algoritmo también sirve para determinar la localización óptima de bancos de condensadores con el objetivo de obtener una reducción óptima de las pérdidas

Towards hand biometrics in mobile devices

The increasing demand of security oriented to mobile applications has raised the attention to biometrics, as a proper and suitable solution for providing secure environment to mobile devices. With this aim, this document presents a biometric system based on hand geometry oriented to mobile devices, involving a high degree of freedom in terms of illumination, hand rotation and distance to camera. The user takes a picture of their own hand in the free space, without requiring any flat surface to locate the hand, and without removals of rings, bracelets or watches. The proposed biometric system relies on an accurate segmentation procedure, able to isolate hands from any background; a feature extraction, invariant to orientation, illumination, distance to camera and background; and a user classification, based on k-Nearest Neighbor approach, able to provide an accurate results on individual identification. The proposed method has been evaluated with two own databases collected with a HTC mobile. First database contains 120 individuals, with 20 acquisitions of both hands. Second database is a synthetic database, containing 408000 images of hand samples in different backgrounds: tiles, grass, water, sand, soil and the like. The system is able to identify individuals properly with False Reject Rate of 5.78% and False Acceptance Rate of 0.089%, using 60 features (15 features per finger

Development of an electrochemical maltose biosensor

In this work, electrochemical maltose biosensors based on mutants of the maltose binding protein (MBP) are developed. A ruthenium II complex (Ru II ), which is covalently attached to MBP, serves as an electrochemical reporter of MBP conformational changes. Biosensors were made through direct attachment of Ru II complex modified MBP to gold electrode surfaces. The responses of some individual mutants were evaluated using square wave voltammetry. A maltose-dependent change in Faradic current and capacitance was observed. It is therefore demonstrated that biosensors using generically this family of bacterial periplasmic binding proteins (bPBP) can be made lending themselves to facile biorecognition element preparation and low cost electrochemical transduction

Towards hand biometrics in mobile devices

The increasing demand of security oriented to mobile applications has raised the attention to biometrics, as a proper and suitable solution for providing secure environment to mobile devices. With this aim, this document presents a biometric system based on hand geometry oriented to mobile devices, involving a high degree of freedom in terms of illumination, hand rotation and distance to camera. The user takes a picture of their own hand in the free space, without requiring any flat surface to locate the hand, and without removals of rings, bracelets or watches. The proposed biometric system relies on an accurate segmentation procedure, able to isolate hands from any background; a feature extraction, invariant to orientation, illumination, distance to camera and background; and a user classification, based on k-Nearest Neighbor approach, able to provide an accurate results on individual identification. The proposed method has been evaluated with two own databases collected with a HTC mobile. First database contains 120 individuals, with 20 acquisitions of both hands. Second database is a synthetic database, containing 408000 images of hand samples in different backgrounds: tiles, grass, water, sand, soil and the like. The system is able to identify individuals properly with False Reject Rate of 5.78% and False Acceptance Rate of 0.089%, using 60 features (15 features per finger

Towards hand biometrics in mobile devices

The increasing demand of security oriented to mobile applications has raised the attention to biometrics, as a proper and suitable solution for providing secure environment to mobile devices. With this aim, this document presents a biometric system based on hand geometry oriented to mobile devices, involving a high degree of freedom in terms of illumination, hand rotation and distance to camera. The user takes a picture of their own hand in the free space, without requiring any flat surface to locate the hand, and without removals of rings, bracelets or watches. The proposed biometric system relies on an accurate segmentation procedure, able to isolate hands from any background; a feature extraction, invariant to orientation, illumination, distance to camera and background; and a user classification, based on k-Nearest Neighbor approach, able to provide an accurate results on individual identification. The proposed method has been evaluated with two own databases collected with a HTC mobile. First database contains 120 individuals, with 20 acquisitions of both hands. Second database is a synthetic database, containing 408000 images of hand samples in different backgrounds: tiles, grass, water, sand, soil and the like. The system is able to identify individuals properly with False Reject Rate of 5.78% and False Acceptance Rate of 0.089%, using 60 features (15 features per finger