Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Smart Grid Communication Technologies in the Brazilian Electrical Sector

Intelligent electrical grids can be considered as the next generation of electrical energy transportation. The enormous potential leads to worldwide focus of research on the technology of smart grids. This paper aims to present a review of the Brazilian electricity sector in context with the integration of communication technologies for smart grids. The work gives an overview of the generation, transmission and distribution of electrical energy in the Brazil and a brief summary of the current electricity market. Smart grid technologies are introduced and the requirements for the Brazilian power system are pointed out. Various technologies for communication within an intelligent network are presented and their characteristics, advantages and disadvantages are compared to the Brazilian conditions. In addition, a summary is given of current pilot projects for Smart Grid technologies within Brazil, as well as a presentation of individual selected projects.

Real-Time Simulation of energy Management in a Domestic Consumer

Recent and future changes in power systems, mainly in the smart grid operation context, are related to a high complexity of power networks operation. This leads to more complex communications and to higher network elements monitoring and control levels, both from network’s and consumers’ standpoint. The present work focuses on a real scenario of the LASIE laboratory, located at the Polytechnic of Porto. Laboratory systems are managed by the SCADA House Intelligent Management (SHIM), already developed by the authors based on a SCADA system. The SHIM capacities have been recently improved by including real-time simulation from Opal RT. This makes possible the integration of Matlab®/Simulink® real-time simulation models. The main goal of the present paper is to compare the advantages of the resulting improved system, while managing the energy consumption of a domestic consumer.

A Communication and Resources Management Scheme to Support the Smart Grid Integration of Multiplayers Access to Resources Information

The increasing and intensive integration of distributed energy resources into distribution systems requires adequate methodologies to ensure a secure operation according to the smart grid paradigm. In this context, SCADA (Supervisory Control and Data Acquisition) systems are an essential infrastructure. This paper presents a conceptual design of a communication and resources management scheme based on an intelligent SCADA with a decentralized, flexible, and intelligent approach, adaptive to the context (context awareness). The methodology is used to support the energy resource management considering all the involved costs, power flows, and electricity prices leading to the network reconfiguration. The methodology also addresses the definition of the information access permissions of each player to each resource. The paper includes a 33-bus network used in a case study that considers an intensive use of distributed energy resources in five distinct implemented operation contexts.

A Learning Algorithm and System Approach to Address Exceptional Events in Domestic Consumption Management

The integration of the Smart Grid concept into the electric grid brings to the need for an active participation of small and medium players. This active participation can be achieved using decentralized decisions, in which the end consumer can manage loads regarding the Smart Grid needs. The management of loads must handle the users’ preferences, wills and needs. However, the users’ preferences, wills and needs can suffer changes when faced with exceptional events. This paper proposes the integration of exceptional events into the SCADA House Intelligent Management (SHIM) system developed by the authors, to handle machine learning issues in the domestic consumption context. An illustrative application and learning case study is provided in this paper.

Data Mining Approach to support the Generation of Realistic Scenarios for Multi-Agent simulation of Electricity Markets

This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players’ characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.

Engineering Temporal and Spatial Aspects in OWL using Patterns

WWW is a huge, open, heterogeneous system, however its contents data is mainly human oriented. The Semantic Web needs to assure that data is readable and “understandable” to intelligent software agents, though the use of explicit and formal semantics. Ontologies constitute a privileged artifact for capturing the semantic of the WWW data. Temporal and spatial dimensions are transversal to the generality of knowledge domains and therefore are fundamental for the reasoning process of software agents. Representing temporal/spatial evolution of concepts and their relations in OWL (W3C standard for ontologies) it is not straightforward. Although proposed several strategies to tackle this problem but there is still no formal and standard approach. This work main goal consists of development of methods/tools to support the engineering of temporal and spatial aspects in intelligent systems through the use of OWL ontologies. An existing method for ontology engineering, Fonte was used as framework for the development of this work. As main contributions of this work Fonte was re-engineered in order to: i) support the spatial dimension; ii) work with OWL Ontologies; iii) and support the application of Ontology Design Patterns. Finally, the capabilities of the proposed approach were demonstrated by engineering time and space in a demo ontology about football.

Methodology for collaborative enterprise reference ontology building

Dissertation presented at Faculdade de Ciências e Tecnologia of Universidade Nova de Lisboa to obtain the Master degree in Electrical Engineering and Computer Science

Infra-estrutura de software baseada em standards de apoio à eficiência energética utilizando fontes de energia renovável

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

Algoritmo de controlo inteligente de microprodução para regulação de tensão em redes de baixa tensão

Com o constante desenvolvimento da sociedade, o consumo de energia elétrica tem aumentado gradualmente, por outro lado a preocupação com o meio ambiente e a necessidade de um desenvolvimento sustentável, faz com que a legislação atual favoreça a introdução de fontes de energia de origem renovável em detrimento de fontes de energia de origem fóssil. Cada vez mais têm surgido incentivos para a implementação de pequenos sistemas de produção em instalações de utilização, estes consumidores/produtores são denominados de prosumers, sendo este tipo de produtores ligados à rede elétrica de baixa tensão. Com a introdução deste tipo de produtores é necessário dotar a rede elétrica de meios que permitam ao operador da rede monitorizar e controlar em tempo real o estado da rede assim como destes novos produtores. No âmbito desta dissertação, foi desenvolvido um algoritmo de controlo inteligente de microprodução. Avaliando o consumo, a produção, entre outros parâmetros de gestão da rede, este algoritmo calculará um conjunto de set-points que deverão ser enviados para os microprodutores de modo a limitar a potência injetada na rede e assim controlar a tensão. Também foi realizado um estudo económico do impacto que as medidas propostas teriam do ponto de vista dos gestores da rede bem como do ponto de vista dos microprodutores.

Deteção e previsão de falhas em equipamentos de produção industrial

As empresas nacionais deparam-se com a necessidade de responder ao mercado com uma grande variedade de produtos, pequenas séries e prazos de entrega reduzidos. A competitividade das empresas num mercado global depende assim da sua eficiência, da sua flexibilidade, da qualidade dos seus produtos e de custos reduzidos. Para se atingirem estes objetivos é necessário desenvolverem-se estratégias e planos de ação que envolvem os equipamentos produtivos, incluindo: a criação de novos equipamentos complexos e mais fiáveis, alteração dos equipamentos existentes modernizando-os de forma a responderem às necessidades atuais e a aumentar a sua disponibilidade e produtividade; e implementação de políticas de manutenção mais assertiva e focada no objetivo de “zero avarias”, como é o caso da manutenção preditiva. Neste contexto, o objetivo principal deste trabalho consiste na previsão do instante temporal ótimo da manutenção de um equipamento industrial – um refinador da fábrica de Mangualde da empresa Sonae Industria, que se encontra em funcionamento contínuo 24 horas por dia, 365 dias por ano. Para o efeito são utilizadas medidas de sensores que monitorizam continuamente o estado do refinador. A principal operação de manutenção deste equipamento é a substituição de dois discos metálicos do seu principal componente – o desfibrador. Consequentemente, o sensor do refinador analisado com maior detalhe é o sensor que mede a distância entre os dois discos do desfibrador. Os modelos ARIMA consistem numa abordagem estatística avançada para previsão de séries temporais. Baseados na descrição da autocorrelação dos dados, estes modelos descrevem uma série temporal como função dos seus valores passados. Neste trabalho, a metodologia ARIMA é utilizada para determinar um modelo que efetua uma previsão dos valores futuros do sensor que mede a distância entre os dois discos do desfibrador, determinando-se assim o momento ótimo da sua substituição e evitando paragens forçadas de produção por ocorrência de uma falha por desgaste dos discos. Os resultados obtidos neste trabalho constituem uma contribuição científica importante para a área da manutenção preditiva e deteção de falhas em equipamentos industriais.

Condition-Based Diagnosis of Mechatronic Systems Using a Fractional Calculus Approach

While fractional calculus (FC) is as old as integer calculus, its application has been mainly restricted to mathematics. However, many real systems are better described using FC equations than with integer models. FC is a suitable tool for describing systems characterised by their fractal nature, long-term memory and chaotic behaviour. It is a promising methodology for failure analysis and modelling, since the behaviour of a failing system depends on factors that increase the model’s complexity. This paper explores the proficiency of FC in modelling complex behaviour by tuning only a few parameters. This work proposes a novel two-step strategy for diagnosis, first modelling common failure conditions and, second, by comparing these models with real machine signals and using the difference to feed a computational classifier. Our proposal is validated using an electrical motor coupled with a mechanical gear reducer.

A Visual Programming Framework for Wireless Sensor Networks in Smart Home Applications

International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2015). 7 to 9, Apr, 2015. Singapure, Singapore.

Forest fire detection with a small fixed wing autonomous aerial vehicle

In this work a forest fire detection solution using small autonomous aerial vehicles is proposed. The FALCOS unmanned aerial vehicle developed for remote-monitoring purposes is described. This is a small size UAV with onboard vision processing and autonomous flight capabilities. A set of custom developed navigation sensors was developed for the vehicle. Fire detection is performed through the use of low cost digital cameras and near-infrared sensors. Test results for navigation and ignition detection in real scenario are presented.

Modeling and Control of a Dragonfly-Like Robot

Dragonflies demonstrate unique and superior flight performances than most of the other insect species and birds. They are equipped with two pairs of independently controlled wings granting an unmatchable flying performance and robustness. In this paper it is studied the dynamics of a dragonfly-inspired robot. The system performance is analyzed in terms of time response and robustness. The development of computational simulation based on the dynamics of the robotic dragonfly allows the test of different control algorithms. We study different movement, the dynamics and the level of dexterity in wing motion of the dragonfly. The results are positive for the construction of flying platforms that effectively mimic the kinematics and dynamics of dragonflies and potentially exhibit superior flight performance than existing flying platforms.