Impacto da integração dos veículos eléctricos nos edifícios multi-habitacionais, nas redes de distribuição de baixa tensão e no diagrama de carga nacional

Associado à escassez dos combustíveis fósseis e ao desejado controlo de emissões nocivas para a atmosfera, assistimos no mundo ao desenvolvimento do um novo paradigma — a mobilidade eléctrica. Apesar das variações de maior ou menor arbítrio político dos governos, do excelente ou débil desenvolvimento tecnológico, relacionados com os veículos eléctricos, estamos perante um caminho, no que diz respeito à mobilidade eléctrica, que já não deve ser encarado como uma moda mas como uma orientação para o futuro da mobilidade. Portugal tendo dado mostras que pretende estar na dianteira deste desafio, necessita equacionar e compreender em que condições existirá uma infra-estrutura nacional capaz de fazer o veículo eléctrico vingar. Assim, neste trabalho, analisa-se o impacto da mobilidade eléctrica em algumas dessas infra-estruturas, nomeadamente nos edifícios multi-habitacionais e redes de distribuição em baixa tensão. São criados neste âmbito, quatro perfis de carregamento dos EVs nomeadamente: nas horas de chegada a casa; nas horas de vazio com início programado pelo condutor; nas horas de vazio controlado por operador de rede (“Smart Grid”); e um cenário que contempla a utilização do V2G. Com a obrigação legal de nos novos edifícios serem instaladas tomadas para veículos eléctricos, é estudado, com os cenários anteriores a possibilidade de continuar a conceber as instalações eléctricas, sem alterar algumas das disposições legais, ao abrigo dos regulamentos existentes. É também estudado, com os cenários criados e com a previsão da venda de veículos eléctricos até 2020, o impacto deste novo consumo no diagrama de carga do Sistema Eléctrico Nacional. Mostra-se assim que a introdução de sistemas inteligentes de distribuição de energia [Smartgrid e vehicle to grid” (V2G)] deverá ser encarada como a solução que por excelência contribuirá para um aproveitamento das infra-estruturas existentes e simultaneamente um uso acessível para os veículos eléctricos.

Sistema de contagem de tráfego e classificação automática de veículos, em tempo real e sem fios

Esta tese tem por objectivo o desenho e avaliação de um sistema de contagem e classificação de veículos automóveis em tempo-real e sem fios. Pretende, também, ser uma alternativa aos actuais equipamentos, muito intrusivos nas vias rodoviárias. Esta tese inclui um estudo sobre as comunicações sem fios adequadas a uma rede de equipamentos sensores rodoviários, um estudo sobre a utilização do campo magnético como meio físico de detecção e contagem de veículos e um estudo sobre a autonomia energética dos equipamentos inseridos na via, com recurso, entre outros, à energia solar. O projecto realizado no âmbito desta tese incorpora, entre outros, a digitalização em tempo real da assinatura magnética deixada pela passagem de um veículo, no campo magnético da Terra, o respectivo envio para servidor via rádio e WAN, Wide Area Network, e o desenvolvimento de software tendo por base a pilha de protocolos ZigBee. Foram desenvolvidas aplicações para o equipamento sensor, para o coordenador, para o painel de controlo e para a biblioteca de Interface de um futuro servidor aplicacional. O software desenvolvido para o equipamento sensor incorpora ciclos de detecção e digitalização, com pausas de adormecimento de baixo consumo, e a activação das comunicações rádio durante a fase de envio, assegurando assim uma estratégia de poupança energética. Os resultados obtidos confirmam a viabilidade desta tecnologia para a detecção e contagem de veículos, assim como para a captura de assinatura usando magnetoresistências. Permitiram ainda verificar o alcance das comunicações sem fios com equipamento sensor embebido no asfalto e confirmar o modelo de cálculo da superfície do painel solar bem como o modelo de consumo energético do equipamento sensor.

Geostatistical Mapping of Outfall Plume Dispersion Data gathered with an autonomous underwater vehicle

The main purpose of this study was to examine the applicability of geostatistical modeling to obtain valuable information for assessing the environmental impact of sewage outfall discharges. The data set used was obtained in a monitoring campaign to S. Jacinto outfall, located off the Portuguese west coast near Aveiro region, using an AUV. The Matheron’s classical estimator was used the compute the experimental semivariogram which was fitted to three theoretical models: spherical, exponential and gaussian. The cross-validation procedure suggested the best semivariogram model and ordinary kriging was used to obtain the predictions of salinity at unknown locations. The generated map shows clearly the plume dispersion in the studied area, indicating that the effluent does not reach the near by beaches. Our study suggests that an optimal design for the AUV sampling trajectory from a geostatistical prediction point of view, can help to compute more precise predictions and hence to quantify more accurately dilution. Moreover, since accurate measurements of plume’s dilution are rare, these studies might be very helpful in the future for validation of dispersion models.

TINSEL – a web-based intelligent tutoring system on electrical installation design

This paper presents a project consisting on the development of an Intelligent Tutoring System, for training and support concerning the development of electrical installation projects to be used by electrical engineers, technicians and students. One of the major goals of this project is to devise a teaching model based on Intelligent Tutoring techniques, considering not only academic knowledge but also other types of more empirical knowledge, able to achieve successfully the training of electrical installation design.

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

Intelligent decision making in electricity markets: simulated annealing Q-Learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.

Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

Particle swarm optimization based approaches to vehicle-to-grid scheduling

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

A multi-agent based approach for intelligent smart grid management

The spread and globalization of distributed generation (DG) in recent years has should highly influence the changes that occur in Electricity Markets (EMs). DG has brought a large number of new players to take action in the EMs, therefore increasing the complexity of these markets. Simulation based on multi-agent systems appears as a good way of analyzing players’ behavior and interactions, especially in a coalition perspective, and the effects these players have on the markets. MASCEM – Multi-Agent System for Competitive Electricity Markets was created to permit the study of the market operation with several different players and market mechanisms. MASGriP – Multi-Agent Smart Grid Platform is being developed to facilitate the simulation of micro grid (MG) and smart grid (SG) concepts with multiple different scenarios. This paper presents an intelligent management method for MG and SG. The simulation of different methods of control provides an advantage in comparing different possible approaches to respond to market events. Players utilize electric vehicles’ batteries and participate in Demand Response (DR) contracts, taking advantage on the best opportunities brought by the use of all resources, to improve their actions in response to MG and/or SG requests.

Intelligent management of end consumers loads including electric vehicles through a SCADA system

The large penetration of intermittent resources, such as solar and wind generation, involves the use of storage systems in order to improve power system operation. Electric Vehicles (EVs) with gridable capability (V2G) can operate as a means for storing energy. This paper proposes an algorithm to be included in a SCADA (Supervisory Control and Data Acquisition) system, which performs an intelligent management of three types of consumers: domestic, commercial and industrial, that includes the joint management of loads and the charge/discharge of EVs batteries. The proposed methodology has been implemented in a SCADA system developed by the authors of this paper – the SCADA House Intelligent Management (SHIM). Any event in the system, such as a Demand Response (DR) event, triggers the use of an optimization algorithm that performs the optimal energy resources scheduling (including loads and EVs), taking into account the priorities of each load defined by the installation users. A case study considering a specific consumer with several loads and EVs is presented in this paper.

Residential energy resources management by a SCADA system in case of excess wind generation

The end consumers in a smart grid context are seen as active players. The distributed generation resources applied in smart home system as a micro and small-scale systems can be wind generation, photovoltaic and combine heat and power facility. The paper addresses the management of domestic consumer resources, i.e. wind generation, solar photovoltaic, combined heat and power, electric vehicle with gridable capability and loads, in a SCADA system with intelligent methodology to support the user decision in real time. The main goal is to obtain the better management of excess wind generation that may arise in consumer’s distributed generation resources. The optimization methodology is performed in a SCADA House Intelligent Management context and the results are analyzed to validate the SCADA system.

Intelligent Bio-Inspired system for manufacturing scheduling under uncertainties

A novel approach to scheduling resolution by combining Autonomic Computing (AC), Multi-Agent Systems (MAS), Case-based Reasoning (CBR), and Bio-Inspired Optimization Techniques (BIT) will be described. AC has emerged as a paradigm aiming at incorporating applications with a management structure similar to the central nervous system. The main intentions are to improve resource utilization and service quality. In this paper we envisage the use of MAS paradigm for supporting dynamic and distributed scheduling in Manufacturing Systems with AC properties, in order to reduce the complexity of managing manufacturing systems and human interference. The proposed CBR based Intelligent Scheduling System was evaluated under different dynamic manufacturing scenarios.

A hybrid intelligent system for distributed dynamic scheduling

This chapter addresses the resolution of dynamic scheduling by means of meta-heuristic and multi-agent systems. Scheduling is an important aspect of automation in manufacturing systems. Several contributions have been proposed, but the problem is far from being solved satisfactorily, especially if scheduling concerns real world applications. The proposed multi-agent scheduling system assumes the existence of several resource agents (which are decision-making entities based on meta-heuristics) distributed inside the manufacturing system that interact with other agents in order to obtain optimal or near-optimal global performances.

Energy efficiency analysis considering the use of an intelligent systems management in a smart home

In this abstract is presented an energy management system included in a SCADA system existent in a intelligent home. The system control the home energy resources according to the players definitions (electricity consumption and comfort levels), the electricity prices variation in real time mode and the DR events proposed by the aggregators.

Intelligent supervisory control system for home devices using a cyber physical approach

Although we have many electric devices at home, there are just few systems to evaluate, monitor and control them. Sometimes users go out and leave their electric devices turned on what can cause energy wasting and dangerous situations. Therefore most of the users may want to know the using states of their electrical appliances through their mobile devices in a pervasive way. In this paper, we propose an Intelligent Supervisory Control System to evaluate, monitor and control the use of electric devices in home, from outside. Because of the transferring data to evaluate, monitor and control user's location and state of home (ex. nobody at home) may be opened to attacks leading to dangerous situations. In our model we include a location privacy module and encryption module to provide security to user location and data. Intelligent Supervising Control System gives to the user the ability to manage electricity loads by means of a multi-agent system involving evaluation, monitoring, control and energy resource agents.