Álvaro Bueno Sánchez

Reference Network Models: A Computational Tool for Planning and Designing Large-Scale Smart Electricity Distribution Grids

Reference Network Models (RNMs) are large-scale distribution network planning tools. RNMs can be used by policy makers and regulators to estimate efficient distribution costs. This is a very challenging task, particularly being network planning a combinatorial problem, which is especially difficult to solve due to the vast size of the distribution areas, and the use of several voltage levels. This chapter presents the main features of RNMs developed by the authors, including high performance requirements related to the type and size of the problem. The model can be used to plan distribution networks either from scratch or incrementally from existing grids. Different case studies illustrate the applicability of these models to the assessment of the impact of massive deployment of renewable distributed generation, demand response actions, and plug-in electric vehicle penetration on distribution costs. The results obtained provide valuable information to guide strategic policy-making decisions regarding the implementation of renewable energy programs and smart grid initiatives.

Fast background subtraction using static and dynamic gates

Background subtraction is usually one of the first steps carried out in motion detection using static video cameras. This paper presents a new fast model for background subtraction that processes only some pixels of each image. This model achieves a significant reduction in computation time that can be used for subsequent image analysis. Some regions of interest (ROI) are located where movement can start. If no movement is present in the image, only pixels of these ROIs are processed. Once a moving object is detected, a new ROI that follows it is created. Thus, motion detection and parameter updates are executed only in the relevant areas instead of in the whole image. The proposed model has three main advantages: the computational time can be reduced drastically, motion detection performance is improved, and it can be combined with most of the existing background subtraction techniques. These features make it specially suitable for security applications.

Multi-Agent Systems Orientated to Assist with Daily Activities in the Homes of Elderly and Disabled People

This chapter will describe the objectives and elements of a multi-agent system and in particular, the state-of-the-art of the multi-agents systems orientated to helping elderly and disabled people carry out their daily activities at home. The typical agent models and roles of these multi-agents systems, their requirements, their physical devices such as robots or programmes and their communication methods will be described. As an example, a particular case of a multi-agent system will be described. It is designed in order to assist elderly and disabled people living alone in their homes. Its main objective is to prevent risky situations by monitoring key variables of their daily life. This example will describe the methodology used for the development of the agents and their different roles related to the acquisition of information, the processing of it and the alternative methods used for communicating among the different human participants involved.

ANDREA: A long-term dynamic planning tool for subtransmission electricity networks

Network expansion planning is a complex, nonconvex, combinatorial problem. Different techniques, such as mathematical optimization, heuristics, and metaheuristics, have been traditionally used to solve this problem. This paper presents a new model, ANDREA, which is intended to obtain near optimal networks for both the static and dynamic planning. This tool has been developed to determine when and where new lines and transformers have to be installed in subtransmission real size networks to supply the estimated future demand considering security of supply criteria, with a low computational effort. A fast guided tree search algorithm (GTSA) using smart selection criteria has been designed to solve this problem. Comparison of the obtained results has been made with those provided by a slower genetic algorithm (GA). Both algorithms were tested in a real subtransmission network.