Pedro Paulo Balestrassi

A Model to Long-Term, Multiarea, Multistage, and Integrated Expansion Planning of Electricity and Natural Gas Systems

A long-term, multiarea, and multistage model for the supply/interconnections expansion planning of integrated electricity and natural gas (NG) is presented in this paper. The proposed Gas Electricity Planning (GEP) model considers the NG value chain, i.e., from the supply to end-consumers through NG pipelines and the electrical systems value chain, i.e., power generation and transmission, in an integrated way. The sources of NG can be represented by NG wells, liquefied natural gas (LNG) terminals and storages of NG and LNG. The electricity generation may be composed by hydro plants, wind farms, or thermal plants where the latter represent the link between the gas and the electricity chain. The proposed model is formulated as a mixed-integer linear optimization problem which minimizes the investment and operation costs to determine the optimal location, technologies, and installation times of any new facilities for power generation, power interconnections, and the complete natural gas chain value (supply/transmission/storage) as well as the optimal dispatch of existing and new facilities over a long range planning horizon. A didactic case study as well as the Brazilian integrated gas/electricity system are presented to illustrate the proposed framework.

Design of experiments on neural network's training for nonlinear time series forecasting

In this study, the statistical methodology of Design of Experiments (DOE) was applied to better determine the parameters of an Artificial Neural Network (ANN) in a problem of nonlinear time series forecasting. Instead of the most common trial and error technique for the ANNs training, DOE was found to be a better methodology. The main motivation for this study was to forecast seasonal nonlinear time series-that is related to many real problems such as short-term electricity loads, daily prices and returns, water consumption, etc. A case study adopting this framework is presented for six time series representing the electricity load for industrial consumers of a production company in Brazil.

Weighted Multivariate Mean Square Error for processes optimization: A case study on flux-cored arc welding for stainless steel claddings

A mathematical programming technique developed recently that optimizes multiple correlated characteristics is the Multivariate Mean Square Error (MMSE). The MMSE approach has obtained noteworthy results, by avoiding the production of inappropriate optimal points that can occur when a method fails to take into account a correlation structure. Where the MMSE approach is deficient, however, is in cases where the multiple correlated characteristics need to be optimized with varying degrees of importance. The MMSE approach, in treating all responses as having the same importance, is unable to attribute the desired weights. This paper thus introduces a strategy that weights the responses in the MMSE approach. The method, called the Weighted Multivariate Mean Square Error (WMMSE), utilizes a weighting procedure that integrates Principal Component Analysis (PCA) and Response Surface Methodology (RSM). In doing so, WMMSE obtains uncorrelated weighted objective functions from the original responses. After being mathematically programmed, these functions are optimized by employing optimization algorithms. We applied WMMSE to optimize a stainless steel cladding application executed via the flux-cored arc welding (FCAW) process. Four input parameters and eight response variables were considered. Stainless steel cladding, which carries potential benefits for a variety of industries, takes low cost materials and deposits over their surfaces materials having anti-corrosive properties. Optimal results were confirmed, which ensured the deposition of claddings with defect-free beads exhibiting the desired geometry and demonstrating good productivity indexes.

Design of experiments and focused grid search for neural network parameter optimization

The present work offers some contributions to the area of surface roughness modeling by Artificial Neural Networks (ANNs) in machining processes. It proposes a method for an optimized project of a Multi-Layer Perceptron (MLP) network architecture applied for the prediction of Average Surface Roughness (Ra). The tuning method is expressed in the format of an algorithm employing two techniques from Design of Experiments (DOE) methodology: Full factorials and Evolutionary Operations (EVOP). Datasets retrieved from literature are employed to form training and test data sets for the ANN. The proposed tuning method leads to significant reduction of roughness prediction errors in machining operations in comparison to techniques currently used. It constitutes an effective option for the systematic design models based on ANN for prediction of surface roughness, filling the gap reported in the literature on this subject. We propose a systematic approach to design and optimize MLP networks.We used DOE, Evolutionary Operation and Focused Grid Search for optimization.The proposed method is compared to previous studies in machining applications.The method presents superior results for all the comparisons.

Mathematical Modeling of Weld Bead Geometry, Quality, and Productivity for Stainless Steel Claddings Deposited by FCAW

In recent years, industrial settings are seeing a rise in the use of stainless steel claddings. The anti-corrosive surfaces are made from low cost materials such as carbon steel or low alloy steels. To ensure the final quality of claddings, however, it is important to know how the welding parameters affect the process’s outcome. Beads should be defect free and deposited with the desired geometry, with efficiency, and with a minimal waste of material. The objective of this study then is to analyze how the flux-cored arc welding (FCAW) parameters influence geometry, productivity, and the surface quality of the stainless steel claddings. It examines AISI 1020 carbon steel cladded with 316L stainless steel. Geometry was analyzed in terms of bead width, penetration, reinforcement, and dilution. Productivity was analyzed according to deposition rate and process yield, and surface quality according to surface appearance and slag formation. The FCAW parameters chosen included the wire feed rate, voltage, welding speed, and contact-tip-workpiece distance. To analyze the parameters’ influences, mathematical models were developed based on response surface methodology. The results show that all parameters were significant. The degrees of importance among them varied according to the responses of interest. What also proved to be significant was the interaction between parameters. It was found that the combined effect of two parameters significantly affected a response; even when taken individually, the two might produce little effect. Finally, the development of Pareto frontiers confirmed the existence of conflicts of interest in this process, suggesting the application of multi-objective optimization techniques to the sequence of this study.

Simulating Electricity Spot Prices in Brazil Using Neural Network and Design of Experiments

The electricity price has been one of the most important variables since the introduction of deregulation on the electricity sector. On this way, efficient forecasting methods of spot prices have become crucial to maximize the agent benefits. In Brazil the electricity price is based on the marginal cost provided by an optimization software (NEWAVE). Forecasting the operational marginal cost (OMC) and its volatility has been one big problem in the Brazilian market because of the computational time taken by this software. This work presents a fast and efficient model to simulate the OMC using DOE (design of experiments) and ANN (artificial neural networks) techniques. The paper proved that the combined techniques provided a promising result and may be applied to risk management and investment analysis.

Entropy-Based Weighting for Multiobjective Optimization: An Application on Vertical Turning

In practical situations, solving a given problem usually calls for the systematic and simultaneous analysis of more than one objective function. Hence, a worthwhile research question may be posed thus: In multiobjective optimization, what can facilitate for the decision maker choosing the best weighting? Thus, this study attempts to propose a method that can identify the optimal weights involved in a multiobjective formulation. The proposed method uses functions of Entropy and Global Percentage Error as selection criteria of optimal weights. To demonstrate its applicability, this method was employed to optimize the machining process for vertical turning, maximizing the productivity and the life of cutting tool, and minimizing the cost, using as the decision variables feed rate and rotation of the cutting tool. The proposed optimization goals were achieved with feed rate = 0.37 mm/rev and rotation = 250 rpm. Thus, the main contributions of this study are the proposal of a structured method, differentiated in relation to the techniques found in the literature, of identifying optimal weights for multiobjective problems and the possibility of viewing the optimal result on the Pareto frontier of the problem. This viewing possibility is very relevant information for the more efficient management of processes.

A multivariate robust parameter optimization approach based on Principal Component Analysis with combined arrays

Todays modern industries have found a wide array of applications for optimization methods based on modeling with Robust Parameter Designs (RPD). Methods of carrying out RPD have thus multiplied. However, little attention has been given to the multiobjective optimization of correlated multiple responses using response surface with combined arrays. Considering this gap, this paper presents a multiobjective hybrid approach combining response surface methodology (RSM) with Principal Component Analysis (PCA) to study a multi-response dataset with an embedded noise factor, using a DOE combined array. How this approach differs from the most common approaches to RPD is that it derives the mean and variance equations using the propagation of error principle (POE). This comes from a control-noise response surface equation written with the most significant principal component scores that can be used to replace the original correlated dataset. Besides the dimensional reduction, this multiobjective programming approach has the benefit of considering the correlation among the multiple responses while generating convex Pareto frontiers to mean square error (MSE) functions. To demonstrate the procedure of the proposed approach, we used a bivariate case of AISI 52100 hardened steel turning employing wiper mixed ceramic tools. Theoretical and experimental results are convergent and confirm the effectiveness of the proposed approach.

A multivariate descriptor method for change-point detection in nonlinear time series

The purpose of this paper is to present a novel method that is applied to detect dynamic changes in nonlinear time series. The method combines a multivariate control chart that monitors the variation of three normalized descriptors – Hjorths descriptors of activity, mobility and complexity – and is applied to the change-point detection problem of nonlinear time series. The approach is estimated using six simulated nonlinear time series. In addition, a case study of six time series of short-term electricity load consumption was used to illustrate the power of the method.

A study of electricity price volatility for the Brazilian energy market

In the recent months, the price of the electricity in Brazil has presented a high level of volatility. As an example, the verified highest electricity price return in March 2007 was almost 260%. The volatility of a commodity plays an important role in the study of the risk management. It also improves the efficiency in parameter estimation and the accuracy in interval forecast. In this work, the Generalized Autoregressive Conditional Heteroscedastic (GARCH) model is used to study the price volatility in the Brazilian market in four geographical regions. The results have shown that the model is able to estimate the behavior of the volatility.