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A microgrid is a group of interconnected loads and distributed energy resources that can fill the gap between the dependence on a bulk power grid and the transition to renewable energies. The islanded mode presents itself as the most interesting scenario, when local controllers should maintain the power quality standards based on several parameters. A tool specifically focused on the process of parameter tuning of the secondary consensus-based control for inverter-based islanded microgrids was proposed in this paper. One often-quoted drawback in this process is the great number of parameters that must be tuned, even for a very simple microgrid structure. To manage such a large number of parameters, the design of experiments was used in this study. The main motivation for this work was to present an optimized way to define the correct parameters for the secondary consensus control for inverter-based islanded microgrids. The study shows how experimental design methodology can be an efficient tool to tune microgrid parameters, which are typically multi-objective-based experiments. From the results, it is correct to state that the design of experiments is able to reach the optimal setting with a minimal number of experiments, which would be almost impossible to obtain with the trial-and-error method. [ABSTRACT FROM AUTHOR]

Electric power systems have experienced the rapid insertion of distributed renewable generating sources and, as a result, are facing planning and operational challenges as new grid connections are made. The complexity of this management and the degree of uncertainty increase significantly and need to be better estimated. Considering the high volatility of photovoltaic generation and its impacts on agents in the electricity sector, this work proposes a multivariate strategy based on design of experiments (DOE), principal component analysis (PCA), artificial neural networks (ANN) that combines the resulting outputs using Mixture DOE (MDOE) for photovoltaic generation prediction a day ahead. The approach separates the data into seasons of the year and considers multiple climatic variables for each period. Here, the dimensionality reduction of climate variables is performed through PCA. Through DOE, the possibilities of combining prediction parameters, such as those of ANN, were reduced, without compromising the statistical reliability of the results. Thus, 17 generation plants distributed in the Brazilian territory were tested. The one-day-ahead PV generation forecast has been considered for each generation plant in each season of the year, reaching mean percentage errors of 10.45% for summer, 9.29% for autumn, 9.11% for winter and 6.75% for spring. The versatility of the proposed approach allows the choice of parameters in a systematic way and reduces the computational cost, since there is a reduction in dimensionality and in the number of experimental simulations. [ABSTRACT FROM AUTHOR]

The design of the control system in an inverter‐based microgrid (μGs) is a challenging problem due to the large number of parameters involved. Different optimisation methods based on obtaining an approximated mathematical model of the μG can be found in the literature. In these approaches, the non‐linearities and uncertainties of the real system are typically not considered, which may result in a non‐optimal tuning of the control parameters. In addition, in most applications, the problem has been simplified, assuming that all controllers have the same value for their control parameters. However, in this case, the behaviour of the system is sub‐optimal since the particularities of each node of the μG are not taken into account. In this study, an experimental approach for tuning the control parameters of an inverter‐based μG is introduced. The approach is based on the methodology of design of experiments and it considers different values for the control parameters of all controllers. In this study, this methodology is applied to the design of a droop‐free control scheme; however, it can be easily extended to other control schemes. The validity of the proposal is verified through selected experimental results. [ABSTRACT FROM AUTHOR]

This study proposes an approach to help the bidding processes of hiring wind‐photovoltaic farms in long‐term energy auctions. The proposed approach aims to define an optimal solution to configure wind‐photovoltaic farms based on mixture design of experiments and the Lp method, as well as an efficiency metric designed to achieve diversification and to identify the Pareto dominant optimal portfolio. The proposed method is simple and flexible for practical applications. Moreover, its associated goals of choosing the Pareto dominant optimal solutions are aligned with the goals of the electricity regulators responsible to manage the hiring process for a new generation. To validate the method, wind‐solar photovoltaic generation configurations in three Brazilian cities are analysed and the results are compared with other methods previously proposed in the literature. The results show that the proposed method has more intuitive criteria for the investor and regulator, without reducing the quality of the information provided to decision making. [ABSTRACT FROM AUTHOR]

We propose to control the mean vector by taking larger samples but inspecting fewer quality characteristics if we work with samples of size n to control bivariate processes, then 2n observations are usually collected, half are observations of X1 and the other half are observations of X2; alternatively, we might work with samples of size 2n if only observations of X1 (or X2) are collected; in both cases the cardinality c of the sample data set is c = 2n. If only one of the two quality characteristics, X1 or X2, is measured at each sampling time, then only one of the two statistics ( X ¯ 1 or X ¯ 2 ) is computed. The variable charting statistic (VCS) chart works as follows: if a X ¯ 1 point falls in the central region, then the statistic for the next sample changes to X ¯ 2 and vice-versa; yet, if a X ¯ 1 point reaches the warning region, then the statistic for the next sample remains X ¯ 1 . [ABSTRACT FROM AUTHOR]

This research evaluates the influence of the Brazilian accreditation methodology on the sustainability of the organizations. Critical factors for implementing accreditation were also examined, including measuring the relationships established between these factors in the organization sustainability. The present study was developed based on the survey methodology applied in the organizations accredited by ONA (National Accreditation Organization); 288 responses were received from the top level managers. The analysis of quantitative data of the measurement models was made with factorial analysis from principal components. The final model was evaluated from the confirmatory factorial analysis and structural equation modeling techniques. The results from the research are vital for the definition of factors that interfere in the accreditation processes, providing a better understanding for accredited organizations and for Brazilian accreditation. [ABSTRACT FROM AUTHOR]

Recently, different methods have been proposed for portfolio optimization and decision making on investment issues. This article aims to present a new method for portfolio formation based on Data Envelopment Analysis (DEA) and Entropy function. This new portfolio optimization method applies DEA in association with a model resulting from the insertion of the Entropy function directly into the optimization procedure. First, the DEA model was applied to perform a pre-selection of the assets. Then, assets given as efficient were submitted to the proposed model, resulting from the insertion of the Entropy function into the simplified Sharpe's portfolio optimization model. As a result, an improved asset participation was provided in the portfolio. In the DEA model, several variables were evaluated and a low value of beta was achieved, guaranteeing greater robustness to the portfolio. Entropy function has provided not only greater diversity but also more feasible asset allocation. Additionally, the proposed method has obtained a better portfolio performance, measured by the Sharpe Ratio, in relation to the comparative methods. [ABSTRACT FROM AUTHOR]

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This paper discusses a methodology to determine the ampacity of transmission lines based on their dynamic thermal limits. In this sense, temperature, wind speed, season conditions, and system loading are taken into account, rendering a methodology that determines, for each operating point and weather condition, the actual thermal limit of a transmission line. The proposed technique is tested in a real scenario and compared with the technique currently employed for this purpose. [ABSTRACT FROM PUBLISHER]

Copyright of Production / Produção is the property of Associacao Brasileira de Engenharia de Producao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)