Relações inter-firmas em Barcarena/PA: um estudo das práticas de inovação e cooperação na rede de fornecedores e subcontratados da IRCC

Este trabalho tem como objetivo apresentar um estudo das relações inter-firmas estabelecidas entre empresas mínero-metalúrgicas localizadas no distrito industrial do município de Barcarena, no estado do Pará, a partir da análise das interações entre uma empresa central e sua rede de fornecedores e subcontratados, que operacionalizam o beneficiamento e a exportação do mineral conhecido como caulim. Segundo as abordagens teóricas que defendem a ocorrência e a disseminação de inovações tecnológicas como base para o desenvolvimento econômico, realizou-se um estudo sobre a forma como os arranjos produtivos são capazes de gerar e manter vantagens competitivas a partir de sistemas de inovação baseados em redes cooperativas integradas por um grupo específico de agentes públicos e privados. Os resultados da pesquisa mostram que, em que pese a existência de inovações nos processos produtivos, em função das exigências técnicas apresentadas por seus contratantes, as empresas subcontratadas, via de regra, não conseguem estabelecer relações de cooperação capazes de promover a disseminação destas inovações, assim como também geralmente não dispõem de recursos gerenciais e tecnológicos capazes de gerar inovações de produtos ou de transformar as inovações de processos em significativos ganhos de produtividade. A ausência de cooperação entre as empresas estende-se aos relacionamentos com os outros agentes, tais como instituições de ensino e pesquisa, de treinamento e capacitação técnica, órgãos de fomento e crédito, associações, cooperativas e sindicatos. Conseqüentemente, a rede de subcontratação desperdiça o potencial de ganhos sinérgicos capazes de elevar os patamares de competitividade local. Abordam-se também as implicações do estudo para a pesquisa, para o desenvolvimento local e para o planejamento e as políticas públicas.

Alocação de modem PLC utilizando SNR em uma rede elétrica de baixa tensão

Pós-graduação em Engenharia Elétrica - FEIS

Isolamento e identificação das micobactérias em águas tratadas provenientes do sistema de abastecimento de Araraquara-SP

Water quality is very important for the health and the population welfare, and the public supply system must provide water quality and suffi cient quantity for the entire population. Water treatment stations, are the main way to obtain water quality. When this doesn’t occur, several problems can affect the population, in this case, using water with poor quality is a constant risk of emergence causing various diseases. The elimination of microorganisms in treated water reduces competition, encouraging the multiplication of chlorine resistant bacteria as Mycobacterium genus frequently isolated from treated and chlorinated water. Considering the lack of indication from examinations of mycobacteria routine laboratory for quality control of drinking water and other human uses, the objective was to verify the presence isolate and identify the environmental mycobacteria in the system water source surface of Araraquara - SP. We analyzed 40 water samples, distributed as follows: ten water gross collected at Station Water Treatment Plant (WTP), harvested after ten fi ltration; ten collected in the reservoir after chlorination and ten in the network distribution. Were recovered 43 isolates of mycobacteria. All isolates were subjected to PCR-PRA. The mycobacteria were identifi ed as M. lentifl avum, M. parafortuitum, M. genavense, M. gordonae, M. fortuitum, M. confl uent, M. duvalii, M. avium subspecies paratuberculosis and M. szulgai. With these results, was concluded that water is an important source of environmental mycobacteria probably related to several human diseases, suggesting the carrying out continuous monitoring of the microorganisms in the system drinking water.

Negoziare confini: dagli stati di cose ai transiti

Negotiating boundaries: from state of affairs to matter of transit. The research deals with the everyday management of spatial uncertainty, starting with the wider historical question of terrains vagues (a French term for wastelands, dismantled areas and peripheral city voids, or interstitial spaces) and focusing later on a particular case study. The choice intended to privilege a small place (a mouth of a lagoon which crosses a beach), with ordinary features, instead of the esthetical “vague terrains”, often witnessed through artistic media or architectural reflections. This place offered the chance to explore a particular dimension of indeterminacy, mostly related with a certain kind of phenomenal instability of its limits, the hybrid character of its cultural status (neither natural, nor artificial) and its crossover position as a transitional space, between different tendencies and activities. The first theoretical part of the research develops a semiotic of vagueness, by taking under exam the structuralist idea of relation, in order to approach an interpretive notion of continuity and indeterminacy. This exploration highlights the key feature of actantial network distribution, which provides a bridge with the second methodological parts, dedicated to a “tuning” of the tools for the analysis. This section establishes a dialogue with current social sciences (like Actor-Network Theory, Situated action and Distributed Cognition), in order to define some observational methods for the documentation of social practices, which could be comprised in a semiotic ethnography framework. The last part, finally, focuses on the mediation and negotiation by which human actors are interacting with the varying conditions of the chosen environment, looking at people’s movements through space, their embodied dealings with the boundaries and the use of spatial artefacts as framing infrastructure of the site.

Building adaptable agent-based models – application to the electricity distribution network

Agent-based modelling (ABM), like other modelling techniques, is used to answer specific questions from real world systems that could otherwise be expensive or impractical. Its recent gain in popularity can be attributed to some degree to its capacity to use information at a fine level of detail of the system, both geographically and temporally, and generate information at a higher level, where emerging patterns can be observed. This technique is data-intensive, as explicit data at a fine level of detail is used and it is computer-intensive as many interactions between agents, which can learn and have a goal, are required. With the growing availability of data and the increase in computer power, these concerns are however fading. Nonetheless, being able to update or extend the model as more information becomes available can become problematic, because of the tight coupling of the agents and their dependence on the data, especially when modelling very large systems. One large system to which ABM is currently applied is the electricity distribution where thousands of agents representing the network and the consumers’ behaviours are interacting with one another. A framework that aims at answering a range of questions regarding the potential evolution of the grid has been developed and is presented here. It uses agent-based modelling to represent the engineering infrastructure of the distribution network and has been built with flexibility and extensibility in mind. What distinguishes the method presented here from the usual ABMs is that this ABM has been developed in a compositional manner. This encompasses not only the software tool, which core is named MODAM (MODular Agent-based Model) but the model itself. Using such approach enables the model to be extended as more information becomes available or modified as the electricity system evolves, leading to an adaptable model. Two well-known modularity principles in the software engineering domain are information hiding and separation of concerns. These principles were used to develop the agent-based model on top of OSGi and Eclipse plugins which have good support for modularity. Information regarding the model entities was separated into a) assets which describe the entities’ physical characteristics, and b) agents which describe their behaviour according to their goal and previous learning experiences. This approach diverges from the traditional approach where both aspects are often conflated. It has many advantages in terms of reusability of one or the other aspect for different purposes as well as composability when building simulations. For example, the way an asset is used on a network can greatly vary while its physical characteristics are the same – this is the case for two identical battery systems which usage will vary depending on the purpose of their installation. While any battery can be described by its physical properties (e.g. capacity, lifetime, and depth of discharge), its behaviour will vary depending on who is using it and what their aim is. The model is populated using data describing both aspects (physical characteristics and behaviour) and can be updated as required depending on what simulation is to be run. For example, data can be used to describe the environment to which the agents respond to – e.g. weather for solar panels, or to describe the assets and their relation to one another – e.g. the network assets. Finally, when running a simulation, MODAM calls on its module manager that coordinates the different plugins, automates the creation of the assets and agents using factories, and schedules their execution which can be done sequentially or in parallel for faster execution. Building agent-based models in this way has proven fast when adding new complex behaviours, as well as new types of assets. Simulations have been run to understand the potential impact of changes on the network in terms of assets (e.g. installation of decentralised generators) or behaviours (e.g. response to different management aims). While this platform has been developed within the context of a project focussing on the electricity domain, the core of the software, MODAM, can be extended to other domains such as transport which is part of future work with the addition of electric vehicles.

Modelling for the electricity distribution network

Global awareness for cleaner and renewable energy is transforming the electricity sector at many levels. New technologies are being increasingly integrated into the electricity grid at high, medium and low voltage levels, new taxes on carbon emissions are being introduced and individuals can now produce electricity, mainly through rooftop photovoltaic (PV) systems. While leading to improvements, these changes also introduce challenges, and a question that often rises is ‘how can we manage this constantly evolving grid?’ The Queensland Government and Ergon Energy, one of the two Queensland distribution companies, have partnered with some Australian and German universities on a project to answer this question in a holistic manner. The project investigates the impact the integration of renewables and other new technologies has on the physical structure of the grid, and how this evolving system can be managed in a sustainable and economical manner. To aid understanding of what the future might bring, a software platform has been developed that integrates two modelling techniques: agent-based modelling (ABM) to capture the characteristics of the different system units accurately and dynamically, and particle swarm optimization (PSO) to find the most economical mix of network extension and integration of distributed generation over long periods of time. Using data from Ergon Energy, two types of networks (3 phase, and Single Wired Earth Return or SWER) have been modelled; three-phase networks are usually used in dense networks such as urban areas, while SWER networks are widely used in rural Queensland. Simulations can be performed on these networks to identify the required upgrades, following a three-step process: a) what is already in place and how it performs under current and future loads, b) what can be done to manage it and plan the future grid and c) how these upgrades/new installations will perform over time. The number of small-scale distributed generators, e.g. PV and battery, is now sufficient (and expected to increase) to impact the operation of the grid, which in turn needs to be considered by the distribution network manager when planning for upgrades and/or installations to stay within regulatory limits. Different scenarios can be simulated, with different levels of distributed generation, in-place as well as expected, so that a large number of options can be assessed (Step a). Once the location, sizing and timing of assets upgrade and/or installation are found using optimisation techniques (Step b), it is possible to assess the adequacy of their daily performance using agent-based modelling (Step c). One distinguishing feature of this software is that it is possible to analyse a whole area at once, while still having a tailored solution for each of the sub-areas. To illustrate this, using the impact of battery and PV can have on the two types of networks mentioned above, three design conditions can be identified (amongst others): · Urban conditions o Feeders that have a low take-up of solar generators, may benefit from adding solar panels o Feeders that need voltage support at specific times, may be assisted by installing batteries · Rural conditions - SWER network o Feeders that need voltage support as well as peak lopping may benefit from both battery and solar panel installations. This small example demonstrates that no single solution can be applied across all three areas, and there is a need to be selective in which one is applied to each branch of the network. This is currently the function of the engineer who can define various scenarios against a configuration, test them and iterate towards an appropriate solution. Future work will focus on increasing the level of automation in identifying areas where particular solutions are applicable.

Demand Response for Residential Appliances in a Smart Electricity Distribution Network: Utility and Customer Perspectives

This thesis introduces advanced Demand Response algorithms for residential appliances to provide benefits for both utility and customers. The algorithms are engaged in scheduling appliances appropriately in a critical peak day to alleviate network peak, adverse voltage conditions and wholesale price spikes also reducing the cost of residential energy consumption. Initially, a demand response technique via customer reward is proposed, where the utility controls appliances to achieve network improvement. Then, an improved real-time pricing scheme is introduced and customers are supported by energy management schedulers to actively participate in it. Finally, the demand response algorithm is improved to provide frequency regulation services.

Enhanced Operation of Electricity Distribution Grids Through Smart Metering PLC Network Monitoring, Analysis and Grid Conditioning

Low Voltage (LV) electricity distribution grid operations can be improved through a combination of new smart metering systems' capabilities based on real time Power Line Communications (PLC) and LV grid topology mapping. This paper presents two novel contributions. The first one is a new methodology developed for smart metering PLC network monitoring and analysis. It can be used to obtain relevant information from the grid, thus adding value to existing smart metering deployments and facilitating utility operational activities. A second contribution describes grid conditioning used to obtain LV feeder and phase identification of all connected smart electric meters. Real time availability of such information may help utilities with grid planning, fault location and a more accurate point of supply management.

A hybrid simulation framework to assess the impact of renewable generators on a distribution network

With an increasing number of small-scale renewable generator installations, distribution network planners are faced with new technical challenges (intermittent load flows, network imbalances…). Then again, these decentralized generators (DGs) present opportunities regarding savings on network infrastructure if installed at strategic locations. How can we consider both of these aspects when building decision tools for planning future distribution networks? This paper presents a simulation framework which combines two modeling techniques: agent-based modeling (ABM) and particle swarm optimization (PSO). ABM is used to represent the different system units of the network accurately and dynamically, simulating over short time-periods. PSO is then used to find the most economical configuration of DGs over longer periods of time. The infrastructure of the framework is introduced, presenting the two modeling techniques and their integration. A case study of Townsville, Australia, is then used to illustrate the platform implementation and the outputs of a simulation.

Parallel ABM for electricity distribution grids : a case study

This paper introduces a parallel implementation of an agent-based model applied to electricity distribution grids. A fine-grained shared memory parallel implementation is presented, detailing the way the agents are grouped and executed on a multi-threaded machine, as well as the way the model is built (in a composable manner) which is an aid to the parallelisation. Current results show a medium level speedup of 2.6, but improvements are expected by incor-porating newer distributed or parallel ABM schedulers into this implementa-tion. While domain-specific, this parallel algorithm can be applied to similarly structured ABMs (directed acyclic graphs).

Estimating benefits of energy storage for aggregate storage applications in electricity distribution networks in Queensland

Electric Energy Storage (EES) is considered as one of the promising options for reducing the need for costly upgrades in distribution networks in Queensland (QLD). However, It is expected, the full potential for storage for distribution upgrade deferral cannot be fully realized due to high cost of EES. On the other hand, EES used for distribution deferral application can support a variety of complementary storage applications such as energy price arbitrage, time of use (TOU) energy cost reduction, wholesale electricity market ancillary services, and transmission upgrade deferral. Aggregation of benefits of these complementary storage applications would have the potential for increasing the amount of EES that may be financially attractive to defer distribution network augmentation in QLD. In this context, this paper analyzes distribution upgrade deferral, energy price arbitrage, TOU energy cost reduction, and integrated solar PV-storage benefits of EES devices in QLD.

Smart resource control in distribution network to improve the integration level of PV

This project was an innovative approach in developing smart coordination of available energy resources to improve the integration level of PV in distribution network. Voltage and loading issues are considered as the main concerns for future electricity grid which need to be avoided using such resources. A distributed control structure was proposed for the resources in distribution network to avoid noted power quality issues.

Consumer phase identification in a three phase unbalanced LV distribution network

A new technique is presented for automatically identifying the phase connection of domestic customers. Voltage information from a reference three phase house is correlated with voltage information from other customer electricity meters on the same network to determine the highest probability phase connection. The techniques are purely based upon a time series of electrical voltage measurements taken by the household smart meters and no additional equipment is required. The method is demonstrated using real smart meter datasets to correctly identify the phase connections of 75 consumers on a low voltage distribution feeder.

Systems modelling of the socio-technical aspects of residential electricity use and network peak demand

Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers’ peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers’ location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price,managed supply, etc., in a conceptual ‘map’ of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tick box interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments.