Luís Neves
Polytechnic Institute of Leiria
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Publication
Featured researches published by Luís Neves.
European Journal of Operational Research | 2009
Luís Neves; Luis C. Dias; Carlos Henggeler Antunes; António Martins
This work presents the use of a problem structuring method, Soft Systems Methodology (SSM), to structure a Multi-Criteria Decision Analysis (MCDA) model, aimed at appraising energy efficiency initiatives. SSM was useful to help defining clearly the decision problem context and the main actors involved, as well as to unveil the relevant objectives for each stakeholder. Keeneys Value Focused Thinking approach was then used to refine and structure the list of objectives according to the perspective of the main evaluators identified. In addition to describing this particular case study, this paper aims at providing some general guidelines on how SSM may facilitate the emergence of objectives for MCDA models.
Journal of the Operational Research Society | 2004
Luís Neves; António Martins; Carlos Henggeler Antunes; Luis C. Dias
This paper reflects an attempt to rethink the process of analysis of energy efficiency initiatives using soft systems methodology (SSM) as a problem structuring tool. The aim of the work is to provide public and private initiative promoters or evaluators with a structured support for a more informed decision regarding the implementation of energy efficiency measures. The SSM approach contributed with the identification of all market players and their relations, as well as the insight into the deficiencies of current methodologies. Some future work directions are also proposed.
ieee powertech conference | 2009
Romeu M. Vitorino; Luís Neves; Humberto M. Jorge
This paper presents a new method to improve reliability and also minimize active power losses in radial distribution systems (RDS) through a process of network reconfiguration. The methodology adopted to enhance reliability, uses the Monte Carlo (MC) simulation and an historical data of the network such as the severity of the potential contingencies in each branch. Due to the greater number of possible configurations and the need of an efficient search, is also used an improved genetic algorithm (IGA), with adaptive crossover and mutation probabilities and with other new features. The method analyses the RDS in a perspective of optimization considering no investment, and a perspective of optimization where is given the possibility to place a limited number of tie-switches, defined by a decision agent, in certain branches. The effectiveness of the proposed method is demonstrated through the analysis of a 69 bus RDS.
ieee international symposium on sustainable systems and technology | 2012
Marta A. R. Lopes; Carlos Henggeler Antunes; A. R. Soares; Andreia M. Carreiro; F. Rodrigues; D. Livengood; Luís Neves; Humberto M. Jorge; Álvaro Gomes; António Martins; Luis C. Dias; Paulo G. Pereirinha; Joao P. Trovao; R. Larson; W. L. Leow; A. Mónica; M. Oliveira; S. J. Breda; R. Viegas; P. Peixoto
The ongoing transformation of electric grids into smart grids provides the technological basis to implement demand-sensitive pricing strategies aimed at using the electric power infrastructure more efficiently. These strategies, also designated by demand response [1], already proved to be effective in altering patterns of electricity usage [2-6], and create benefits not only for end users (by lowering their electricity bill without degrading comfort levels), but also for the utilities (by managing the peak, flattening the aggregate demand curve, and meeting supply with demand) and the environment (by avoiding, or delaying, building new generation units and other network infrastructures). In fact, demand-sensitive pricing of electricity is expected to become the standard pricing mechanism in smart grids [3, 7, 8] and is considered essential to accelerate the deployment of variable renewable generation while maintaining electric system security and reliability at least cost [9].
international conference on european electricity market | 2008
Bruno André Gomes; João Tomé Saraiva; Luís Neves
Modeling uncertainties in power systems has long interested researchers. Nowadays, as in 70psilas, the volatility associated with generation or fuel prices, for one side, and the uncertainties related with load forecasting and generation capacity, for another, places a new emphasis on this kind of problems. As a result of this renewed interest, in this paper we are enlarging the original fuzzy optimal power flow, FOPF, model in order to consider not only load uncertainties, but also uncertainties in generation or fuel prices, specified using trapezoidal fuzzy numbers. This new approach is based on multiparametric linear programming techniques that lead to the identification of a number of critical regions covering all the uncertainty space. This contributes to build more accurate membership functions of all variables, namely generations, branch flows and power not supplied.
international conference on power engineering, energy and electrical drives | 2009
Romeu M. Vitorino; Humberto M. Jorge; Luís Neves
This paper presents a new method to improve reliability and also minimize losses in radial distribution systems (RDS), trough a process of network reconfiguration, using a genetic algorithm approach. The methodology adopted to enhance reliability, uses the Monte Carlo simulation and an historical data of the network such as the level of reliability and the severity of potential contingencies in each branch. The method analyses the RDS in two perspectives. A first perspective of optimization considering no investment, therefore using only the switches presented in the network, and a second perspective of optimization where is given the possibility to place a limited number of tie-switches and thus get better results. Here, the number of tie-switches and the branches that can receive them are defined by a decision agent. The effectiveness of the proposed method is demonstrated through the analysis of a 69 bus RDS.
ieee powertech conference | 2009
J. C. Sousa; Luís Neves; Humberto M. Jorge
This work presents a novel perspective of load forecasting based on neural networks and load profiling. In addition to the variables that are typically used to predict future load demand, such as past load values, meteorological variables, seasonal effects or macroeconomic indexes, it is expected that the use of load profiles and detailed information of individual consumers could favor the forecasting process. The methodology can be extended to different temporal horizons being predicted and the eventual threat of overparametrization is attenuated by the use of neural networks since the complexity of the model does not necessarily depends on the number of its weights and biases, as some of these parameters might be found irrelevant in the process. Another way to reduce the risk of overparametrization and overfitting is through the use of a considerable number of data points (whenever historical data is available) to train the network.
international conference on the european energy market | 2009
Bruno André Gomes; João Tomé Saraiva; Luís Neves
Marginal prices have been recognized as the core approach to the economic evaluation of generation and transmission services in an electricity market environment. In this context, this paper presents the New Fuzzy Optimal Power Flow algorithm as a model to addresses the impact of load and generation cost uncertainties in nodal marginal prices. Since loads and generation costs are represented by fuzzy numbers, nodal marginal prices will no longer be represented by deterministic values, but rather by fuzzy membership functions reflecting the specified uncertainties. The paper also presents the algorithm used for the integration of the transmission losses effect on the results. Since the proposed algorithm uses multiparametric programming techniques, it contributes to characterize in a better way the system behavior. Finally, it includes results based on the IEEE 24 bus/38 branch test system to illustrate the proposed approach.
International Journal of Sustainable Energy | 2017
José Gonçalves; Luís Neves; António Martins
This paper presents a methodology to provide information to a decision maker on the associated impacts, both of economic and technical nature, of possible management schemes of storage units for choosing the best location of distributed storage devices, with a multiobjective optimisation approach based on genetic algorithms. The methodology was applied to a case study, a known distribution network model in which the installation of distributed storage units was tested, using lithium-ion batteries. The obtained results show a significant influence of the charging/discharging profile of batteries on the choice of their best location, as well as the relevance that these choices may have for the different network management objectives, for example, for reducing network energy losses or minimising voltage deviations. Results also show a difficult cost-effectiveness of an energy-only service, with the tested systems, both due to capital cost and due to the efficiency of conversion.
european conference on applications of evolutionary computation | 2015
José Gonçalves; Luís Neves; António Martins
The perception of the associated impacts among possible management schemes introduces a new way to assess energy storage systems. The ability to define a specific management scheme considering the different stakeholder objectives, both technical and economic, will increase the perception of available installation options. This paper presents a multiobjective feasibility assessment methodology using an improved version of the Non-dominated Sorting Genetic Algorithm II, to optimize the placement of electric energy storage units in order to improve the operation of distribution networks. The model is applied to a case study, using lithium-ion battery technology as an example. The results show the influence of different charging/discharging profiles on the choice of the best battery location, as well as the influence that these choices may have on the different network management objectives, e.g. increasing the integration of renewable generation. As an additional outcome, the authors propose a pricing scheme for filling the present regulatory gap regarding the pricing scheme to be applied to energy storage in order to allow the exploitation of viable business models.