Geostatistical Prediction of Ocean Outfall Plume Characteristics Based on an Autonomous Underwater Vehicle

Geostatistics has been successfully used to analyze and characterize the spatial variability of environmental properties. Besides giving estimated values at unsampled locations, it provides a measure of the accuracy of the estimate, which is a significant advantage over traditional methods used to assess pollution. In this work universal block kriging is novelty used to model and map the spatial distribution of salinity measurements gathered by an Autonomous Underwater Vehicle in a sea outfall monitoring campaign, with the aim of distinguishing the effluent plume from the receiving waters, characterizing its spatial variability in the vicinity of the discharge and estimating dilution. The results demonstrate that geostatistical methodology can provide good estimates of the dispersion of effluents that are very valuable in assessing the environmental impact and managing sea outfalls. Moreover, since accurate measurements of the plume’s dilution are rare, these studies might be very helpful in the future to validate dispersion models.

Energy Resource Management under the Influence of the Weekend Transition Considering an Intensive use of Electric Vehicles

Energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and of massive electric vehicle is envisaged. The present paper proposes a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and Vehicle-to-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with their owners. It takes into account these contracts, the users' requirements subjected to the VPP, and several discharge price steps. The full AC power flow calculation included in the model takes into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33-bus distribution network and V2G is used to illustrate the good performance of the proposed method.

ROAZ and ROAZ II Autonomous Surface Vehicle Design and Implementation

International Lifesaving Congress 2007, La Coruna, Spain, December, 2007

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.

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.

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.

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.

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.

Electric vehicle scenario simulator tool for smart grid operators

This paper presents a simulator for electric vehicles in the context of smart grids and distribution networks. It aims to support network operator´s planning and operations but can be used by other entities for related studies. The paper describes the parameters supported by the current version of the Electric Vehicle Scenario Simulator (EVeSSi) tool and its current algorithm. EVeSSi enables the definition of electric vehicles scenarios on distribution networks using a built-in movement engine. The scenarios created with EVeSSi can be used by external tools (e.g., power flow) for specific analysis, for instance grid impacts. Two scenarios are briefly presented for illustration of the simulator capabilities.

Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints

Energy resource scheduling becomes increasingly important, as the use of distributed resources is intensified and massive gridable vehicle use is envisaged. The present paper proposes a methodology for dayahead energy resource scheduling for smart grids considering the intensive use of distributed generation and of gridable vehicles, usually referred as Vehicle- o-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with V2G owners. It takes into account these contracts, the user´s requirements subjected to the VPP, and several discharge price steps. Full AC power flow calculation included in the model allows taking into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33 bus distribution network and V2G is used to illustrate the good performance of the proposed method.

Dgenco market balancing - cost repartition under light regulation markets

In this paper we present a new methodology, based in game theory, to obtain the market balancing between Distribution Generation Companies (DGENCO), in liberalized electricity markets. The new contribution of this methodology is the verification of the participation rate of each agent based in Nucléolo Balancing and in Shapley Value. To validate the results we use the Zaragoza Distribution Network with 42 Bus and 5 DGENCO.

Sistema de visão para percepção e navegação de um UAV Quadrotor

Mestrado em Engenharia Electrotécnica e de Computadores

Sistema de localização de um veículo autónomo terrestre

Mestrado em Engenharia Electrotécnica e de Computadores.

Emissões de carbono: caracterização do perfil de emissões de gases de efeito de estufa associadas à Monteiro, Ribas - Embalagens Flexíveis, S.A.

A presente dissertação tem como principal objectivo estimar as emissões de carbono resultantes das actividades da Monteiro, Ribas- Embalagens Flexíveis, S.A. A realização do inventário de gases de efeito estufa permite que a Monteiro, Ribas- Embalagens Flexíveis, S.A, identifique quais as suas fontes emissoras e quantifique as emissões de gases de efeito estufa, permitindo criar estratégias de redução das mesmas. A elaboração do inventário foi fundamentada nas directrizes do Greenhouse Gas Protocol, obedecendo aos princípios de relevância, integrabilidade, consistência, transparência e exactidão. A metodologia adoptada utiliza factores de emissão documentados para efectuar o cálculo das emissões de gases de efeito de estufa (GEE). Estes factores são rácios que relacionam as emissões de GEE com dados de actividade específicos para cada fonte de emissão. Como emissões directas (âmbito 1), foram quantificadas as emissões provenientes do uso de gás natural nas caldeiras, consumo de vapor e de água quente, e as emissões do veículo comercial da empresa. Como emissões indirectas de âmbito 2, incluem-se as resultantes da electricidade consumida. As emissões indirectas estimadas de âmbito 3 referem-se, no caso em estudo, ao transporte de resíduos, ao deslocamento de funcionários para a empresa e às viagens de negócio. Face ao tipo de emissões identificadas, criou-se uma ferramenta de cálculo que contém todos os valores de factores de emissão que podem ser utilizados em função das características específicas dos dados de actividade relativos às várias fontes emissoras da Empresa. Esta ferramenta permitirá, no futuro, aperfeiçoar o cálculo das emissões, a partir de uma melhor sistematização da informação disponível. Com este trabalho também foi possível identificar a necessidade de recolher e organizar alguma informação complementar à já existente. O ano base considerado foi 2011. Os resultados obtidos mostram que, neste ano, as actividades da Monteiro, Ribas- Embalagens Flexíveis, S.A serão responsáveis pela emissão de 2968,6 toneladas de CO2e (dióxido de carbono equivalente). De acordo com a Decisão 2007/589/CE da Comissão de 18 de Julho de 2007 conclui-se que a Monteiro, Ribas Embalagens e Flexíveis S.A. se enquadra na categoria de instalações com baixo níveis de emissões pois as suas emissões médias anuais são inferiores a 25000 toneladas de CO2e. Conclui-se que a percentagem maior das emissões estimadas (50,7 %) é proveniente do consumo de electricidade (emissões indirectas, âmbito 2), seguida pelo consumo de gás natural (emissões directas) que representa 39,4% das emissões. Relacionando os resultados obtidos com a produção total da Monteiro, Ribas- Embalagens Flexíveis, S.A, em 2011, obtém-se o valor de 0,65 kg de CO2e por cada quilograma de produto final. Algumas das fontes emissoras identificadas não foram incorporadas no inventário da empresa, nomeadamente o transporte das matérias-primas e dos produtos. Isto deve-se ao facto de não ter sido possível compilar a informação necessária, em tempo útil. Apesar de se tratar de emissões indirectas de âmbito 3, consideradas opcionais, recomenda-se que num próximo trabalho deste tipo, essas emissões possam vir a ser quantificadas. As principais incertezas associadas às estimativas de emissão dizem respeito aos dados de actividade uma vez que foi a primeira vez que a empresa realizou um inventário de gases de efeito de estufa. Há informações mais específicas sobre os dados de actividade que a empresa dispõe e que poderá, de futuro, sistematizar de uma forma mais adequada para a sua utilização com este fim.