EM - Escola de Minas
URI permanente desta comunidadehttp://www.hml.repositorio.ufop.br/handle/123456789/6
Notícias
A Escola de Minas de Ouro Preto foi fundada pelo cientista Claude Henri Gorceix e inaugurada em 12 de outubro de 1876.
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3 resultados
Resultados da Pesquisa
Item A multi-objective green UAV routing problem.(2017) Coelho, Bruno Nazário; Coelho, Vitor Nazário; Coelho, Igor Machado; Ochi, Luiz Satoru; Koochaksaraei, Roozbeh Haghnazar; Zuidema, Demetrius; Lima, Milton Sérgio Fernandes de; Costa, Adilson Rodrigues daThis paper introduces an Unmanned Aerial Vehicle (UAV) heterogeneous fleet routing problem, dealing with vehicles limited autonomy by considering multiple charging stations and respecting operational re- quirements. A green routing problem is designed for overcoming difficulties that exist as a result of lim- ited vehicle driving range. Due to the large amount of drones emerging in the society, UAVs use and efficiency should be optimized. In particular, these kinds of vehicles have been recently used for deliver- ing and collecting products. Here, we design a new real-time routing problem, in which different types of drones can collect and deliver packages. These aerial vehicles are able to collect more than one deliver- able at the same time if it fits their maximum capacity. Inspired by a multi-criteria view of real systems, seven different objective functions are considered and sought to be minimized using a Mixed-Integer Lin- ear Programming (MILP) model solved by a matheuristic algorithm. The latter filters the non-dominated solutions from the pool of solutions found in the branch-and-bound optimization tree, using a black-box dynamic search algorithm. A case of study, considering a bi-layer scenario, is presented in order to val- idate the proposal, which showed to be able to provide good quality solutions for supporting decision making.Item A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment.(2016) Coelho, Vitor Nazário; Coelho, Igor Machado; Coelho, Bruno Nazário; Reis, Agnaldo José da Rocha; Enayatifar, Rasul; Souza, Marcone Jamilson Freitas; Guimarães, Frederico GadelhaThe importance of load forecasting has been increasing lately and improving the use of energy resources remains a great challenge. The amount of data collected from Microgrid (MG) systems is growing while systems are becoming more sensitive, depending on small changes in the daily routine. The need for flexible and adaptive models has been increased for dealing with these problems. In this paper, a novel hybrid evolutionary fuzzy model with parameter optimization is proposed. Since finding optimal values for the fuzzy rules and weights is a highly combinatorial task, the parameter optimization of the model is tackled by a bio-inspired optimizer, so-called GES, which stems from a combination between two heuristic approaches, namely the Evolution Strategies and the GRASP procedure. Real data from electric utilities extracted from the literature are used to validate the proposed methodology. Computational results show that the proposed framework is suitable for short-term forecasting over microgrids and large-grids, being able to accurately predict data in short computational time. Compared to other hybrid model from the literature, our hybrid metaheuristic model obtained better forecasts for load forecasting in aMG scenario, reporting solutions with low variability of its forecasting errors.Item Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid.(2016) Coelho, Vitor Nazário; Coelho, Igor Machado; Coelho, Bruno Nazário; Cohen, Miri Weiss; Reis, Agnaldo José da Rocha; Silva, Sidelmo Magalhães; Souza, Marcone Jamilson Freitas; Fleming, Peter J.; Guimarães, Frederico GadelhaThis paper describes a multi-objective power dispatching problem that uses Plug-in Electric Vehicle (PEV) as storage units.We formulate the energy storage planning as a Mixed-Integer Linear Programming (MILP) problem, respecting PEV requirements, minimizing three different objectives and analyzing three different criteria. Two novel cost-to-variability indicators, based on Sharpe Ratio, are introduced for analyzing the volatility of the energy storage schedules. By adding these additional criteria, energy storage planning is optimized seeking to minimize the following: total Microgrid (MG) costs; PEVs batteries usage; maximum peak load; difference between extreme scenarios and two Sharpe Ratio indices. Different scenarios are considered, which are generated with the use of probabilistic forecasting, since prediction involves inherent uncertainty. Energy storage planning scenarios are scheduled according to information provided by lower and upper bounds extracted from probabilistic forecasts. A MicroGrid (MG) scenario composed of two renewable energy resources, a wind energy turbine and photovoltaic cells, a residential MG user and different PEVs is analyzed. Candidate non-dominated solutions are searched from the pool of feasible solutions obtained during different Branch and Bound optimizations. Pareto fronts are discussed and analyzed for different energy storage scenarios. Perhaps the most important conclusion from this study is that schedules that minimize the total system cost may increase maximum peak load and its volatility over different possible scenarios, therefore may be less robust.