Day-ahead Resource Scheduling Including Demand Response for Electric Vehicles

The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and V2G. The main focus is the comparison of different EV management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs i n the V2G approach. Three different DR programs are designed and tested (trip reduce, shifting reduce and reduce+shifting). Othe r important contribution of the paper is the comparison between deterministic and computational intelligence techniques to reduce the execution time. The proposed scheduling is solved with a modified particle swarm optimization. Mixed integer non-linear programming is also used for comparison purposes. Full ac power flow calculation is included to allow taking into account the network constraints. A case study with a 33-bus distribution network and 2000 V2G resources is used to illustrate the performance of the proposed method.

Decision Support Concerning Demand Response Programs Design and Use – A Conceptual Framework and Simulation Tool

Demand response can play a very relevant role in the context of power systems with an intensive use of distributed energy resources, from which renewable intermittent sources are a significant part. More active consumers participation can help improving the system reliability and decrease or defer the required investments. Demand response adequate use and management is even more important in competitive electricity markets. However, experience shows difficulties to make demand response be adequately used in this context, showing the need of research work in this area. The most important difficulties seem to be caused by inadequate business models and by inadequate demand response programs management. This paper contributes to developing methodologies and a computational infrastructure able to provide the involved players with adequate decision support on demand response programs and contracts design and use. The presented work uses DemSi, a demand response simulator that has been developed by the authors to simulate demand response actions and programs, which includes realistic power system simulation. It includes an optimization module for the application of demand response programs and contracts using deterministic and metaheuristic approaches. The proposed methodology is an important improvement in the simulator while providing adequate tools for demand response programs adoption by the involved players. A machine learning method based on clustering and classification techniques, resulting in a rule base concerning DR programs and contracts use, is also used. A case study concerning the use of demand response in an incident situation is presented.

Comparison of contaminant removal effectiveness and air change efficiency as indicator of air diffusion quality.

The ventilation efficiency concept is an attempt to quantify a parameter that can easily distinguish the different options for air diffusion in the building spaces. Thirteen strategies of air diffusion were measured in a test chamber through the application of the tracer gas method, with the objective to validate the calculation by Computational fluid dynamics (CFD). Were compared the Air Change Efficiency (ACE) and the Contaminant Removal Effectiveness (CRE), the two indicators most internationally accepted. The main results from this work shows that the values of the numerical simulations are in good agreement with experimental measurements and also, that the solutions to be adopted for maximizing the ventilation efficiency should be the schemes that operate with low speeds of supply air and small differences between supply air temperature and the room temperature.

The use of the boundary element method in the analysis of single lap joints

The most common techniques for stress analysis/strength prediction of adhesive joints involve analytical or numerical methods such as the Finite Element Method (FEM). However, the Boundary Element Method (BEM) is an alternative numerical technique that has been successfully applied for the solution of a wide variety of engineering problems. This work evaluates the applicability of the boundary elem ent code BEASY as a design tool to analyze adhesive joints. The linearity of peak shear and peel stresses with the applied displacement is studied and compared between BEASY and the analytical model of Frostig et al., considering a bonded single-lap joint under tensile loading. The BEM results are also compared with FEM in terms of stress distributions. To evaluate the mesh convergence of BEASY, the influence of the mesh refinement on peak shear and peel stress distributions is assessed. Joint stress predictions are carried out numerically in BEASY and ABAQUS®, and analytically by the models of Volkersen, Goland, and Reissner and Frostig et al. The failure loads for each model are compared with experimental results. The preparation, processing, and mesh creation times are compared for all models. BEASY results presented a good agreement with the conventional methods.

Adherend thickness effect on the tensile fracture toughness of a structural adhesive using an optical data acquisition method

Adhesive bonding is nowadays a serious candidate to replace methods such as fastening or riveting, because of attractive mechanical properties. As a result, adhesives are being increasingly used in industries such as the automotive, aerospace and construction. Thus, it is highly important to predict the strength of bonded joints to assess the feasibility of joining during the fabrication process of components (e.g. due to complex geometries) or for repairing purposes. This work studies the tensile behaviour of adhesive joints between aluminium adherends considering different values of adherend thickness (h) and the double-cantilever beam (DCB) test. The experimental work consists of the definition of the tensile fracture toughness (GIC) for the different joint configurations. A conventional fracture characterization method was used, together with a J-integral approach, that take into account the plasticity effects occurring in the adhesive layer. An optical measurement method is used for the evaluation of crack tip opening and adherends rotation at the crack tip during the test, supported by a Matlab® sub-routine for the automated extraction of these quantities. As output of this work, a comparative evaluation between bonded systems with different values of adherend thickness is carried out and complete fracture data is provided in tension for the subsequent strength prediction of joints with identical conditions.

NoC contention analysis using a branch-and-prune algorithm

“Many-core” systems based on a Network-on-Chip (NoC) architecture offer various opportunities in terms of performance and computing capabilities, but at the same time they pose many challenges for the deployment of real-time systems, which must fulfill specific timing requirements at runtime. It is therefore essential to identify, at design time, the parameters that have an impact on the execution time of the tasks deployed on these systems and the upper bounds on the other key parameters. The focus of this work is to determine an upper bound on the traversal time of a packet when it is transmitted over the NoC infrastructure. Towards this aim, we first identify and explore some limitations in the existing recursive-calculus-based approaches to compute the Worst-Case Traversal Time (WCTT) of a packet. Then, we extend the existing model by integrating the characteristics of the tasks that generate the packets. For this extended model, we propose an algorithm called “Branch and Prune” (BP). Our proposed method provides tighter and safe estimates than the existing recursive-calculus-based approaches. Finally, we introduce a more general approach, namely “Branch, Prune and Collapse” (BPC) which offers a configurable parameter that provides a flexible trade-off between the computational complexity and the tightness of the computed estimate. The recursive-calculus methods and BP present two special cases of BPC when a trade-off parameter is 1 or ∞, respectively. Through simulations, we analyze this trade-off, reason about the implications of certain choices, and also provide some case studies to observe the impact of task parameters on the WCTT estimates.

Local Fractional Variational Iteration Method for Local Fractional Poisson Equations in Two Independent Variables

The local fractional Poisson equations in two independent variables that appear in mathematical physics involving the local fractional derivatives are investigated in this paper. The approximate solutions with the nondifferentiable functions are obtained by using the local fractional variational iteration method.

Road drivers performance appraisal using a MCDA method: a case study in the road transport company

Performance evaluation increasingly assumes a more important role in any organizational environment. In the transport area, the drivers are the company’s image and for this reason it is important to develop and increase their performance and commitment to the company goals. This evaluation can be used to motivate driver to improve their performance and to discover training needs. This work aims to create a performance appraisal evaluation model of the drivers based on the multi-criteria decision aid methodology. The MMASSI (Multicriteria Methodology to Support Selection of Information Systems) methodology was adapted by using a template supporting the evaluation according to the freight transportation company in study. The evaluation process involved all drivers (collaborators being evaluated), their supervisors and the company management. The final output is a ranking of the drivers, based on their performance, for each one of the scenarios used.

Análise numérica e experimental de um modelo dinâmico da ponte ferroviária de Antuã

Mestrado em Engenharia Civil – Ramo Estruturas

Estrutura a partir do Movimento em Sistemas Autónomos

Mestrado em Engenharia Electrotécnica e de Computadores - Ramo de Sistemas Autónomos

Finding a school technological partner: a multicriteria method for school information system producer selection

The choice of an information systems is a critical factor of success in an organization's performance, since, by involving multiple decision-makers, with often conflicting objectives, several alternatives with aggressive marketing, makes it particularly complex by the scope of a consensus. The main objective of this work is to make the analysis and selection of a information system to support the school management, pedagogical and administrative components, using a multicriteria decision aid system – MMASSITI – Multicriteria Method- ology to Support the Selection of Information Systems/Information Technologies – integrates a multicriteria model that seeks to provide a systematic approach in the process of choice of Information Systems, able to produce sustained recommendations concerning the decision scope. Its application to a case study has identi- fied the relevant factors in the selection process of school educational and management information system and get a solution that allows the decision maker’ to compare the quality of the various alternatives.

Comparison of Disposable Pipette Extraction and Dispersive Solid-Phase Extraction in the QuEChERS Method for Analysis of Pesticides in Strawberries

In this study, we sought to assess the applicability of GC–MS/MS for the identification and quantification of 36 pesticides in strawberry from integrated pest management (IPM) and organic farming (OF). Citrate versions of QuEChERS (quick, easy, cheap, effective, rugged and safe) using dispersive solid-phase extraction (d-SPE) and disposable pipette extraction (DPX) for cleanup were compared for pesticide extraction. For cleanup, a combination of MgSO4, primary secondary amine and C18 was used for both the versions. Significant differences were observed in recovery results between the two sample preparation versions (DPX and d-SPE). Overall, 86% of the pesticides achieved recoveries (three spiking levels 10, 50 and 200 µg/kg) in the range of 70–120%, with <13% RSD. The matrix effects were also evaluated in both the versions and in strawberries from different crop types. Although not evidencing significant differences between the two methodologies were observed, however, the DPX cleanup proved to be a faster technique and easy to execute. The results indicate that QuEChERS with d-SPE and DPX and GC–MS/MS analysis achieved reliable quantification and identification of 36 pesticide residues in strawberries from OF and IPM.

Contribuição da produção em regime especial na garantia do abastecimento de energia elétrica e o impacto no planeamento da rede

Esta dissertação apresenta um estudo sobre a garantia de fornecimento de energia elétrica por parte dos produtores em regime especial com tecnologia cogeração e o impacto que estes traduzem na fase de planeamento da rede. Este trabalho foi realizado na Energias de Portugal - Distribuição (EDP-D) na direção de planeamento da rede (DPL). Para este estudo foi utilizado o caso de uma subestação com dezoito produtores em regime especial agregados à sua rede, em que dezasseis desses produtores são cogeração. A proposta de estudo para o caso concreto, passa pela análise das condições de funcionamento da subestação e apurar se a mesma necessita de alguma reformulação, tendo em vista as cargas a satisfazer atuais e possível incremento de carga futura. Considerando que a subestação está inserida num ambiente industrial e atendendo que existem diversos produtores de energia elétrica nas imediações da subestação. Para a resolução da garantia do fornecimento de energia por parte da cogeração, estudou-se a possibilidade de prever a energia produzida por estes produtores, através dos seguintes modelos de previsão: árvore de regressão, árvore de regressão com aplicação bagging e uma rede neuronal (unidirecional). Com a implementação destes modelos pretende-se estimar qual a potência que se pode esperar na garantia de abastecimento da carga, prevenindo maior solicitação de potência por parte da subestação. A metodologia utilizada baseia-se em simulações computacionais.

Dispatch of distributed Energy Resources to Provide Energy and Reserve in Smart Grids using a Particle Swarm Optimization Approach

The smart grid concept is a key issue in the future power systems, namely at the distribution level, with deep concerns in the operation and planning of these systems. Several advantages and benefits for both technical and economic operation of the power system and of the electricity markets are recognized. The increasing integration of demand response and distributed generation resources, all of them mostly with small scale distributed characteristics, leads to the need of aggregating entities such as Virtual Power Players. The operation business models become more complex in the context of smart grid operation. Computational intelligence methods can be used to give a suitable solution for the resources scheduling problem considering the time constraints. 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 optimal schedule minimizes the operation costs and it is obtained using a particle swarm optimization approach, which is compared with a deterministic approach used as reference methodology. The proposed method is applied to a 33-bus distribution network with 32 medium voltage consumers and 66 distributed generation units.