Resistência ao fogo de alvenarias sem função estrutural

Um incêndio é algo difícil de prever, assim como a sua consequência nos elementos de construção. Dessa forma, ao longo das últimas décadas, os elementos de construção têm sido alvo de diversos estudos a fim de avaliar os seus comportamentos quando solicitados em situação de incêndio. O International Building Code (IBC) descreve um método de cálculo analítico para a determinação da resistência ao fogo dos elementos da construção de acordo com os procedimentos de teste estabelecidos na ASTM E119 (Standard Test Methods for Fire Tests of Building Construction and Materials). Nesta dissertação foi feita uma análise desse método, que se mostrou inadequado para estimar a resistência ao fogo das alvenarias, sem função estrutural, de blocos cerâmicos e blocos de betão, uma vez que despreza qualquer efeito do ar no interior das mesmas. No seguimento desta análise, é apresentado um desenvolvimento do método descrito tendo em conta o efeito do ar. Depois de uma análise aos vários tipos de blocos cerâmicos e de betão com diferentes dimensões e geometrias foi possível obter uma relação entre a espessura equivalente de ar existente num bloco e a sua respectiva resistência ao fogo, de modo a serem obtidos os valores descritos na normalização existente. O efeito do ar mostrou ter uma maior influência na resistência ao fogo nas alvenarias constituídas por blocos cerâmicos de furação vertical, já que a sua geometria caracterizada por um elevado número de pequenos alvéolos contribui para o aumento do isolamento térmico, e consequentemente da sua resistência ao fogo. Nas alvenarias rebocadas os valores da resistência ao fogo aumentam cerca de 50%, quando revestidos com argamassa de cimento, e 70% quando revestidos com gesso, logo, o emprego de revestimentos representam uma boa alternativa para aumentar a resistência ao fogo.

Using Baseline Methods to Identify Non-technical Losses in the Context of Smart Grids

Demand response has gained increasing importance in the context of competitive electricity markets and smart grid environments. In addition to the importance that has been given to the development of business models for integrating demand response, several methods have been developed to evaluate the consumers’ performance after the participation in a demand response event. The present paper uses those performance evaluation methods, namely customer baseline load calculation methods, to determine the expected consumption in each period of the consumer historic data. In the cases in which there is a certain difference between the actual consumption and the estimated consumption, the consumer is identified as a potential cause of non-technical losses. A case study demonstrates the application of the proposed method to real consumption data.

Multi-objective Parallel Particle Swarm Optimization for Day-ahead Vehicle-To-Grid Scheduling

This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle- To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Consumers Performance Evaluation of the Participation in Demand Response Programs Using Baseline Methods

Demand response has gain increasing importance in the context of competitive electricity markets environment. The use of demand resources is also advantageous in the context of smart grid operation. In addition to the need of new business models for integrating demand response, adequate methods are necessary for an accurate determination of the consumers’ performance evaluation after the participation in a demand response event. The present paper makes a comparison between some of the existing baseline methods related to the consumers’ performance evaluation, comparing the results obtained with these methods and also with a method proposed by the authors of the paper. A case study demonstrates the application of the referred methods to real consumption data belonging to a consumer connected to a distribution network.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Distributed energy resources scheduling considering real-time resources forecast

Energy resource scheduling is becoming increasingly important, such as the use of more distributed generators and electric vehicles connected to the distribution network. This paper proposes a methodology to be used by Virtual Power Players (VPPs), regarding the energy resource scheduling in smart grids and considering day-ahead, hour-ahead and realtime time horizons. This method considers that energy resources are managed by a VPP which establishes contracts with their owners. The full AC power flow calculation included in the model takes into account network constraints. In this paper, distribution function errors are used to simulate variations between time horizons, and to measure the performance of the proposed methodology. A 33-bus distribution network with large number of distributed resources is used.