Oxidative Leaching of metals from electronic waste with solutions based on quaternary ammonium salts

The treatment of electric and electronic waste (WEEE) is a problem which receives ever more attention. An inadequate treatment results in harmful products ending up in the environment. This project intends to investigate the possibilities of an alternative route for recycling of metals from printed circuit boards (PCBs) obtained from rejected computers. The process is based on aqueous solutions composed of an etchant, either 0.2 M CuCl2.2H2O or 0.2 M FeCl3.6H2O, and a quaternary ammonium salt (quat) such as choline chloride or chlormequat. These solutions are reminiscent of deep eutectic solvents (DES) based on quats. DES are quite similar to ionic liquids (ILs) and are used as well as alternative solvents with a great diversity of physical properties, making them attractive for replacement of hazardous, volatile solvents (e.g. VOCs). A remarkable difference between genuine DES and ILs with the solutions used in this project is the addition of rather large quantities of water. It is shown the presence of water has a lot of advantages on the leaching of metals, while the properties typical for DES still remain. The oxidizing capacities of Cu(II) stem from the existence of a stable Cu(I) component in quat based DES and thus the leaching stems from the activity of the Cu(II)/Cu(I) redox couple. The advantage of Fe(III) in combination with DES is the fact that the Fe(III)/Fe(II) redox couple becomes reversible, which is not true in pure water. This opens perspectives for regeneration of the etching solution. In this project the leaching of copper was studied as a function of gradual increasing water content from 0 - 100w% with the same concentration of copper chloride or iron(III) chloride at room temperature and 80ºC. The solutions were also tested on real PCBs. At room temperature a maximum leaching effect for copper was obtained with 30w% choline chloride with 0.2 M CuCl2.2H2O. The leaching effect is still stronger at 80°C, b ut of course these solutions are more energy consuming. For aluminium, tin, zinc and lead, the leaching was faster at 80ºC. Iron and nickel dissolved easily at room temperature. The solutions were not able to dissolve gold, silver, rhodium and platinum.

Produção Sustentável em BT- Análise de Características de Contadores Inteligentes

A crescente necessidade de reduzir a dependência energética e a emissão de gases de efeito de estufa levou à adoção de uma série de políticas a nível europeu com vista a aumentar a eficiência energética e nível de controlo de equipamentos, reduzir o consumo e aumentar a percentagem de energia produzida a partir de fontes renováveis. Estas medidas levaram ao desenvolvimento de duas situações críticas para o setor elétrico: a substituição das cargas lineares tradicionais, pouco eficientes, por cargas não-lineares mais eficientes e o aparecimento da produção distribuída de energia a partir de fontes renováveis. Embora apresentem vantagens bem documentadas, ambas as situações podem afetar negativamente a qualidade de energia elétrica na rede de distribuição, principalmente na rede de baixa tensão onde é feita a ligação com a maior parte dos clientes e onde se encontram as cargas não-lineares e a ligação às fontes de energia descentralizadas. Isto significa que a monitorização da qualidade de energia tem, atualmente, uma importância acrescida devido aos custos relacionados com perdas inerentes à falta de qualidade de energia elétrica na rede e à necessidade de verificar que determinados parâmetros relacionados com a qualidade de energia elétrica se encontram dentro dos limites previstos nas normas e nos contratos com clientes de forma a evitar disputas ou reclamações. Neste sentido, a rede de distribuição tem vindo a sofrer alterações a nível das subestações e dos postos de transformação que visam aumentar a visibilidade da qualidade de energia na rede em tempo real. No entanto, estas medidas só permitem monitorizar a qualidade de energia até aos postos de transformação de média para baixa tensão, não revelando o estado real da qualidade de energia nos pontos de entrega ao cliente. A monitorização nestes pontos é feita periodicamente e não em tempo real, ficando aquém do necessário para assegurar a deteção correta de problemas de qualidade de energia no lado do consumidor. De facto, a metodologia de monitorização utilizada atualmente envolve o envio de técnicos ao local onde surgiu uma reclamação ou a um ponto de medição previsto para instalar um analisador de energia que permanece na instalação durante um determinado período de tempo. Este tipo de monitorização à posteriori impossibilita desde logo a deteção do problema de qualidade de energia que levou à reclamação, caso não se trate de um problema contínuo. Na melhor situação, o aparelho poderá detetar uma réplica do evento, mas a larga percentagem anomalias ficam fora deste processo por serem extemporâneas. De facto, para detetar o evento que deu origem ao problema é necessário monitorizar permanentemente a qualidade de energia. No entanto este método de monitorização implica a instalação permanente de equipamentos e não é viável do ponto de vista das empresas de distribuição de energia já que os equipamentos têm custos demasiado elevados e implicam a necessidade de espaços maiores nos pontos de entrega para conter os equipamentos e o contador elétrico. Uma alternativa possível que pode tornar viável a monitorização permanente da qualidade de energia consiste na introdução de uma funcionalidade de monitorização nos contadores de energia de determinados pontos da rede de distribuição. Os contadores são obrigatórios em todas as instalações ligadas à rede, para efeitos de faturação. Tradicionalmente estes contadores são eletromecânicos e recentemente começaram a ser substituídos por contadores inteligentes (smart meters), de natureza eletrónica, que para além de fazer a contagem de energia permitem a recolha de informação sobre outros parâmetros e aplicação de uma serie de funcionalidades pelo operador de rede de distribuição devido às suas capacidades de comunicação. A reutilização deste equipamento com finalidade de analisar a qualidade da energia junto dos pontos de entrega surge assim como uma forma privilegiada dado que se trata essencialmente de explorar algumas das suas características adicionais. Este trabalho tem como objetivo analisar a possibilidade descrita de monitorizar a qualidade de energia elétrica de forma permanente no ponto de entrega ao cliente através da utilização do contador elétrico do mesmo e elaborar um conjunto de requisitos para o contador tendo em conta a normalização aplicável, as características dos equipamentos utilizados atualmente pelo operador de rede e as necessidades do sistema elétrico relativamente à monitorização de qualidade de energia.

Learning business strategies for competitive electronic marketplaces

This paper presents a Multi-Agent Market simulator designed for developing new agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. This tool studies negotiations based on different market mechanisms and, time and behavior dependent strategies. The results of the negotiations between agents are analyzed by data mining algorithms in order to extract rules that give agents feedback to improve their strategies. The system also includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agent reactions.

Short-term wind forecasting for energy resources scheduling

This paper proposes a wind power forecasting methodology based on two methods: direct wind power forecasting and wind speed forecasting in the first phase followed by wind power forecasting using turbines characteristics and the aforementioned wind speed forecast. The proposed forecasting methodology aims to support the operation in the scope of the intraday resources scheduling model, namely with a time horizon of 5 minutes. This intraday model supports distribution network operators in the short-term scheduling problem, in the smart grid context. A case study using a real database of 12 months recorded from a Portuguese wind power farm was used. The results show that the straightforward methodology can be applied in the intraday model with high wind speed and wind power accuracy. The wind power forecast direct method shows better performance than wind power forecast using turbine characteristics and wind speed forecast obtained in first phase.

Real-time energy resources scheduling considering intensive wind penetration

The use of distributed energy resources, based on natural intermittent power sources, like wind generation, in power systems imposes the development of new adequate operation management and control methodologies. A short-term Energy Resource Management (ERM) methodology performed in two phases is proposed in this paper. The first one addresses the day-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. The ERM scheduling is a complex optimization problem due to the high quantity of variables and constraints. In this paper the main goal is 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 mixedinteger non-linear programming approach. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units and 1000 electric vehicles has been implemented in a 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.

Energy resource scheduling in a real distribution network managed by several virtual power players

Smart Grids (SGs) appeared as the new paradigm for power system management and operation, being designed to integrate large amounts of distributed energy resources. This new paradigm requires a more efficient Energy Resource Management (ERM) and, simultaneously, makes this a more complex problem, due to the intensive use of distributed energy resources (DER), such as distributed generation, active consumers with demand response contracts, and storage units. This paper presents a methodology to address the energy resource scheduling, considering an intensive use of distributed generation and demand response contracts. A case study of a 30 kV real distribution network, including a substation with 6 feeders and 937 buses, is used to demonstrate the effectiveness of the proposed methodology. This network is managed by six virtual power players (VPP) with capability to manage the DER and the distribution network.

Energy resource scheduling considering reactive power management

The reactive power management is an important task in future power systems. The control of reactive power allows the increase of distributed energy resources penetration as well as the optimal operation of distribution networks. Currently, the control of reactive power is only controlled in large power units and in high and very high voltage substations. In this paper a reactive power control in smart grids paradigm is proposed, considering the management of distributed energy resources and of the distribution network by an aggregator namely Virtual Power Player (VPP).

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.

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.

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.

Energy and reserve provision dispatch considering distributed generation and demand response

In competitive electricity markets with deep concerns at the efficiency level, demand response programs gain considerable significance. In the same way, distributed generation has gained increasing importance in the operation and planning of power systems. Grid operators and utilities are taking new initiatives, recognizing the value of demand response and of distributed generation for grid reliability and for the enhancement of organized spot market´s efficiency. Grid operators and utilities become able to act in both energy and reserve components of electricity markets. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The proposed method has been computationally implemented and its application is illustrated in this paper using a 32 bus distribution network with 32 medium voltage consumers.

Increase of the delivered energy probability in DES using a fuzzy probabilistic modeling

The paper proposes a methodology to increase the probability of delivering power to any load point by identifying new investments in distribution energy systems. The proposed methodology is based on statistical failure and repair data of distribution components and it uses a fuzzy-probabilistic modeling for the components outage parameters. The fuzzy membership functions of the outage parameters of each component are based on statistical records. A mixed integer nonlinear programming optimization model is developed in order to identify the adequate investments in distribution energy system components which allow increasing the probability of delivering power to any customer in the distribution system at the minimum possible cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 180 bus distribution network.

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.

Real-time energy resources scheduling considering short-term and very short-term wind forecast

This paper proposes an energy resources management methodology based on three distinct time horizons: day-ahead scheduling, hour-ahead scheduling, and real-time scheduling. In each scheduling process it is necessary the update of generation and consumption operation and of the storage and electric vehicles storage status. Besides the new operation condition, it is important more accurate forecast values of wind generation and of consumption using results of in short-term and very short-term methods. A case study considering a distribution network with intensive use of distributed generation and electric vehicles is presented.

Agent-based simulation of electronic marketplaces with decision support

This paper presents a Multi-Agent Market simulator designed for analyzing agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. The system includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agents reactions.