260 resultados para architetture, TDC


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In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.

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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.

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The management of energy resources for islanded operation is of crucial importance for the successful use of renewable energy sources. A Virtual Power Producer (VPP) can optimally operate the resources taking into account the maintenance, operation and load control considering all the involved cost. This paper presents the methodology approach to formulate and solve the problem of determining the optimal resource allocation applied to a real case study in Budapest Tech’s. The problem is formulated as a mixed-integer linear programming model (MILP) and solved by a deterministic optimization technique CPLEX-based implemented in General Algebraic Modeling Systems (GAMS). The problem has also been solved by Evolutionary Particle Swarm Optimization (EPSO). The obtained results are presented and compared.

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This paper presents a Unit Commitment model with reactive power compensation that has been solved by Genetic Algorithm (GA) optimization techniques. The GA has been developed a computational tools programmed/coded in MATLAB. The main objective is to find the best generations scheduling whose active power losses are minimal and the reactive power to be compensated, subjected to the power system technical constraints. Those are: full AC power flow equations, active and reactive power generation constraints. All constraints that have been represented in the objective function are weighted with a penalty factors. The IEEE 14-bus system has been used as test case to demonstrate the effectiveness of the proposed algorithm. Results and conclusions are dully drawn.

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Relatório Final de Estágio apresentado à Escola Superior de Dança com vista à obtenção do Grau de Mestre em Ensino de Dança.

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Relatório Final de Estágio apresentado à Escola Superior de Dança, com vista à obtenção do grau de Mestre em Ensino de Dança.

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Relatório Final de Estágio apresentado à Escola Superior de Dança, com vista à obtenção do grau de Mestre em Ensino de Dança.

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Relatório Final de Estágio apresentado à Escola Superior de Dança com vista à obtenção do Grau de Mestre em Ensino de Dança.

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Relatório Final de Estágio apresentado à Escola Superior de Dança com vista à obtenção do Grau de Mestre em Ensino de Dança.

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Relatório Final de Estágio apresentado à Escola Superior de Dança com vista à obtenção do Grau de Mestre em Ensino de Dança.

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Relatório Final de Estágio apresentado à Escola Superior de Dança, com vista à obtenção do grau de Mestre em Ensino de Dança.

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Relatório Final de Estágio apresentado à Escola Superior de Dança, com vista à obtenção do grau de Mestre em Ensino de Dança.

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The long term evolution (LTE) is one of the latest standards in the mobile communications market. To achieve its performance, LTE networks use several techniques, such as multi-carrier technique, multiple-input-multiple-output and cooperative communications. Inside cooperative communications, this paper focuses on the fixed relaying technique, presenting a way for determining the best position to deploy the relay station (RS), from a set of empirical good solutions, and also to quantify the associated performance gain using different cluster size configurations. The best RS position was obtained through realistic simulations, which set it as the middle of the cell's circumference arc. Additionally, it also confirmed that network's performance is improved when the number of RSs is increased. It was possible to conclude that, for each deployed RS, the percentage of area served by an RS increases about 10 %. Furthermore, the mean data rate in the cell has been increased by approximately 60 % through the use of RSs. Finally, a given scenario with a larger number of RSs, can experience the same performance as an equivalent scenario without RSs, but with higher reuse distance. This conduces to a compromise solution between RS installation and cluster size, in order to maximize capacity, as well as performance.

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The use of distribution networks in the current scenario of high penetration of Distributed Generation (DG) is a problem of great importance. In the competitive environment of electricity markets and smart grids, Demand Response (DR) is also gaining notable impact with several benefits for the whole system. The work presented in this paper comprises a methodology able to define the cost allocation in distribution networks considering large integration of DG and DR resources. The proposed methodology is divided into three phases and it is based on an AC Optimal Power Flow (OPF) including the determination of topological distribution factors, and consequent application of the MW-mile method. The application of the proposed tariffs definition methodology is illustrated in a distribution network with 33 buses, 66 DG units, and 32 consumers with DR capacity.

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This paper presents the characterization of high voltage (HV) electric power consumers based on a data clustering approach. The typical load profiles (TLP) are obtained selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The choice of the best partition is supported using several cluster validity indices. The proposed data-mining (DM) based methodology, that includes all steps presented in the process of knowledge discovery in databases (KDD), presents an automatic data treatment application in order to preprocess the initial database in an automatic way, allowing time saving and better accuracy during this phase. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ consumption behavior. To validate our approach, a case study with a real database of 185 HV consumers was used.