Flexible management of bandwidth and redundancy in fieldbuses

Os sistemas distribuídos embarcados (Distributed Embedded Systems – DES) têm sido usados ao longo dos últimos anos em muitos domínios de aplicação, da robótica, ao controlo de processos industriais passando pela aviónica e pelas aplicações veiculares, esperando-se que esta tendência continue nos próximos anos. A confiança no funcionamento é uma propriedade importante nestes domínios de aplicação, visto que os serviços têm de ser executados em tempo útil e de forma previsível, caso contrário, podem ocorrer danos económicos ou a vida de seres humanos poderá ser posta em causa. Na fase de projecto destes sistemas é impossível prever todos os cenários de falhas devido ao não determinismo do ambiente envolvente, sendo necessária a inclusão de mecanismos de tolerância a falhas. Adicionalmente, algumas destas aplicações requerem muita largura de banda, que também poderá ser usada para a evolução dos sistemas, adicionandolhes novas funcionalidades. A flexibilidade de um sistema é uma propriedade importante, pois permite a sua adaptação às condições e requisitos envolventes, contribuindo também para a simplicidade de manutenção e reparação. Adicionalmente, nos sistemas embarcados, a flexibilidade também é importante por potenciar uma melhor utilização dos, muitas vezes escassos, recursos existentes. Uma forma evidente de aumentar a largura de banda e a tolerância a falhas dos sistemas embarcados distribuídos é a replicação dos barramentos do sistema. Algumas soluções existentes, quer comerciais quer académicas, propõem a replicação dos barramentos para aumento da largura de banda ou para aumento da tolerância a falhas. No entanto e quase invariavelmente, o propósito é apenas um, sendo raras as soluções que disponibilizam uma maior largura de banda e um aumento da tolerância a falhas. Um destes raros exemplos é o FlexRay, com a limitação de apenas ser permitido o uso de dois barramentos. Esta tese apresentada e discute uma proposta para usar a replicação de barramentos de uma forma flexível com o objectivo duplo de aumentar a largura de banda e a tolerância a falhas. A flexibilidade dos protocolos propostos também permite a gestão dinâmica da topologia da rede, sendo o número de barramentos apenas limitado pelo hardware/software. As propostas desta tese foram validadas recorrendo ao barramento de campo CAN – Controller Area Network, escolhido devido à sua grande implantação no mercado. Mais especificamente, as soluções propostas foram implementadas e validadas usando um paradigma que combina flexibilidade com comunicações event-triggered e time-triggered: o FTT – Flexible Time- Triggered. No entanto, uma generalização para CAN nativo é também apresentada e discutida. A inclusão de mecanismos de replicação do barramento impõe a alteração dos antigos protocolos de replicação e substituição do nó mestre, bem como a definição de novos protocolos para esta finalidade. Este trabalho tira partido da arquitectura centralizada e da replicação do nó mestre para suportar de forma eficiente e flexível a replicação de barramentos. Em caso de ocorrência de uma falta num barramento (ou barramentos) que poderia provocar uma falha no sistema, os protocolos e componentes propostos nesta tese fazem com que o sistema reaja, mudando para um modo de funcionamento degradado. As mensagens que estavam a ser transmitidas nos barramentos onde ocorreu a falta são reencaminhadas para os outros barramentos. A replicação do nó mestre baseia-se numa estratégia líder-seguidores (leaderfollowers), onde o líder (leader) controla todo o sistema enquanto os seguidores (followers) servem como nós de reserva. Se um erro ocorrer no nó líder, um dos nós seguidores passará a controlar o sistema de uma forma transparente e mantendo as mesmas funcionalidades. As propostas desta tese foram também generalizadas para CAN nativo, tendo sido para tal propostos dois componentes adicionais. É, desta forma possível ter as mesmas capacidades de tolerância a falhas ao nível dos barramentos juntamente com a gestão dinâmica da topologia de rede. Todas as propostas desta tese foram implementadas e avaliadas. Uma implementação inicial, apenas com um barramento foi avaliada recorrendo a uma aplicação real, uma equipa de futebol robótico onde o protocolo FTT-CAN foi usado no controlo de movimento e da odometria. A avaliação do sistema com múltiplos barramentos foi feita numa plataforma de teste em laboratório. Para tal foi desenvolvido um sistema de injecção de faltas que permite impor faltas nos barramentos e nos nós mestre, e um sistema de medida de atrasos destinado a medir o tempo de resposta após a ocorrência de uma falta.

Context-based wireless mesh networks

In the modern society, new devices, applications and technologies, with sophisticated capabilities, are converging in the same network infrastructure. Users are also increasingly demanding in personal preferences and expectations, desiring Internet connectivity anytime and everywhere. These aspects have triggered many research efforts, since the current Internet is reaching a breaking point trying to provide enough flexibility for users and profits for operators, while dealing with the complex requirements raised by the recent evolution. Fully aligned with the future Internet research, many solutions have been proposed to enhance the current Internet-based architectures and protocols, in order to become context-aware, that is, to be dynamically adapted to the change of the information characterizing any network entity. In this sense, the presented Thesis proposes a new architecture that allows to create several networks with different characteristics according to their context, on the top of a single Wireless Mesh Network (WMN), which infrastructure and protocols are very flexible and self-adaptable. More specifically, this Thesis models the context of users, which can span from their security, cost and mobility preferences, devices’ capabilities or services’ quality requirements, in order to turn a WMN into a set of logical networks. Each logical network is configured to meet a set of user context needs (for instance, support of high mobility and low security). To implement this user-centric architecture, this Thesis uses the network virtualization, which has often been advocated as a mean to deploy independent network architectures and services towards the future Internet, while allowing a dynamic resource management. This way, network virtualization can allow a flexible and programmable configuration of a WMN, in order to be shared by multiple logical networks (or virtual networks - VNs). Moreover, the high level of isolation introduced by network virtualization can be used to differentiate the protocols and mechanisms of each context-aware VN. This architecture raises several challenges to control and manage the VNs on-demand, in response to user and WMN dynamics. In this context, we target the mechanisms to: (i) discover and select the VN to assign to an user; (ii) create, adapt and remove the VN topologies and routes. We also explore how the rate of variation of the user context requirements can be considered to improve the performance and reduce the complexity of the VN control and management. Finally, due to the scalability limitations of centralized control solutions, we propose a mechanism to distribute the control functionalities along the architectural entities, which can cooperate to control and manage the VNs in a distributed way.

A framework for evaluating the European airline costs of disabled persons and persons with reduced mobility

In recent years, airlines have been servicing a greater variety, and increasing numbers, of disabled persons and persons with reduced mobility (PRMs), particularly associated with ageing, obesity and medical needs. With the quantity of PRMs likely to increase in the future, there will be a growing impact on the airlines' associated actual and opportunity costs, about which there is minimal literature and data. Therefore the aim of this paper is to identify standard functional key factors (FKFs) with which airlines could audit their PRMs costs, and which could be used by other interested bodies, such as governments, when considering relevant aviation policy. These FKFs are related to nine areas, namely PRMs’ transfers; mobility aids; aircraft delays/diversions costs; staff training costs; staff health, safety and welfare; aircraft fixtures and equipment costs; airport costs; transaction costs; and opportunity costs. Further research is needed to obtain the data for these FKFs.

Virtual power players demand response programs management based on locational marginal prices

Power systems have been suffering huge changes mainly due to the substantial increase of distributed generation and to the operation in competitive environments. Virtual power players can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. Resource management gains an increasing relevance in this competitive context, while demand side active role provides managers with increased demand elasticity. This makes demand response use more interesting and flexible, giving rise to a wide range of new opportunities.This paper proposes a methodology for managing demand response programs in the scope of virtual power players. The proposed method is based on the calculation of locational marginal prices (LMP). The evaluation of the impact of using demand response specific programs on the LMP value supports the manager decision concerning demand response use. The proposed method has been computationally implemented and its application is illustrated in this paper using a 32 bus network with intensive use of distributed generation.

Short-term scheduling considering five-minute and hour-ahead energy resource management

The introduction of new distributed energy resources, based on natural intermittent power sources, in power systems imposes the development of new adequate operation management and control methods. This paper proposes a short-term Energy Resource Management (ERM) methodology performed in two phases. The first one addresses the hour-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. Both phases consider the day-ahead resource scheduling solution. The ERM scheduling is formulated as an optimization problem that aims to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixed-integer non-linear programming approach and by a heuristic approach based on genetic algorithms. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units has been implemented in a PSCADbased simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

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 electric vehicle heuristic for energy resource management using simulated annealing

The smart grid concept appears as a suitable solution to guarantee the power system operation in the new electricity paradigm with electricity markets and integration of large amounts of Distributed Energy Resources (DERs). Virtual Power Player (VPP) will have a significant importance in the management of a smart grid. In the context of this new paradigm, Electric Vehicles (EVs) rise as a good available resource to be used as a DER by a VPP. This paper presents the application of the Simulated Annealing (SA) technique to solve the Energy Resource Management (ERM) of a VPP. It is also presented a new heuristic approach to intelligently handle the charge and discharge of the EVs. This heuristic process is incorporated in the SA technique, in order to improve the results of the ERM. The case study shows the results of the ERM for a 33-bus distribution network with three different EVs penetration levels, i. e., with 1000, 2000 and 3000 EVs. The results of the proposed adaptation of the SA technique are compared with a previous SA version and a deterministic technique.

Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach

This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G user´s profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

An integrated approach for distributed energy resource short term scheduling in smart grids considering realistic power system simulation

The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD , is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD .

ANN based support for distributed energy resources scheduling in smart grids

The future scenarios for operation of smart grids are likely to include a large diversity of players, of different types and sizes. With control and decision making being decentralized over the network, intelligence should also be decentralized so that every player is able to play in the market environment. In the new context, aggregator players, enabling medium, small, and even micro size players to act in a competitive environment, will be very relevant. Virtual Power Players (VPP) and single players must optimize their energy resource management in order to accomplish their goals. This is relatively easy to larger players, with financial means to have access to adequate decision support tools, to support decision making concerning their optimal resource schedule. However, the smaller players have difficulties in accessing this kind of tools. So, it is required that these smaller players can be offered alternative methods to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), intended to support smaller players’ resource scheduling. The used methodology uses a training set that is built using the energy resource scheduling solutions obtained with a reference optimization methodology, a mixed-integer non-linear programming (MINLP) in this case. The trained network is able to achieve good schedule results requiring modest computational means.

Intelligent energy resources management in the context of smart grids

Smart grids are envisaged as infrastructures able to accommodate all centralized and distributed energy resources (DER), including intensive use of renewable and distributed generation (DG), storage, demand response (DR), and also electric vehicles (EV), from which plug-in vehicles, i.e. gridable vehicles, are especially relevant. Moreover, smart grids must accommodate a large number of diverse types or players in the context of a competitive business environment. Smart grids should also provide the required means to efficiently manage all these resources what is especially important in order to make the better possible use of renewable based power generation, namely to minimize wind curtailment. An integrated approach, considering all the available energy resources, including demand response and storage, is crucial to attain these goals. This paper proposes a methodology for energy resource management that considers several Virtual Power Players (VPPs) managing a network with high penetration of distributed generation, demand response, storage units and network reconfiguration. The resources are controlled through a flexible SCADA (Supervisory Control And Data Acquisition) system that can be accessed by the evolved entities (VPPs) under contracted use conditions. A case study evidences the advantages of the proposed methodology to support a Virtual Power Player (VPP) managing the energy resources that it can access in an incident situation.

A new approach for multi-agent coalition formation and management in the scope of electricity markets

This paper presents a new methodology for the creation and management of coalitions in Electricity Markets. This approach is tested using the multi-agent market simulator MASCEM, taking advantage of its ability to provide the means to model and simulate VPP (Virtual Power Producers). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market, and internally, with their members, in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. The new features include the development of particular individual facilitators to manage the communications amongst the members of each coalition independently from the rest of the simulation, and also the mechanisms for the classification of the agents that are candidates to join the coalition. In addition, a global study on the results of the Iberian Electricity Market is performed, to compare and analyze different approaches for defining consistent and adequate strategies to integrate into the agents of MASCEM. This, combined with the application of learning and prediction techniques provide the agents with the ability to learn and adapt themselves, by adjusting their actions to the continued evolving states of the world they are playing in.

Virtual power producers simulation - negotiating renewable distributed generation in competitive electricity markets

Sustainable development concerns made renewable energy sources to be increasingly used for electricity distributed generation. However, this is mainly due to incentives or mandatory targets determined by energy policies as in European Union. Assuring a sustainable future requires distributed generation to be able to participate in competitive electricity markets. To get more negotiation power in the market and to get advantages of scale economy, distributed generators can be aggregated giving place to a new concept: the Virtual Power Producer (VPP). VPPs are multi-technology and multisite heterogeneous entities that should adopt organization and management methodologies so that they can make distributed generation a really profitable activity, able to participate in the market. This paper presents ViProd, a simulation tool that allows simulating VPPs operation, in the context of MASCEM, a multi-agent based eletricity market simulator.