Paulo S. F. Barbosa

A linear programming model for cash flow management in the Brazilian construction industry

A linear programming model has been developed for optimal cash flow management addressing specific cash flow issues related to the construction industry. These include typical financial transactions, possible delays on payments, use of available credit lines, effect of changing interest rates, and budget constraints that often occur in the construction industry. A small size project from the Brazilian construction industry is provided as a case study, aiming at evaluating the potential benefits from using the model. Different changes to the basic structure of the model allow and establish the consistency of the results. Alternative formulations are suggested to deal with uncertainties, longer planning horizons, and multiple subcontractors and suppliers. In addition to the tangible financial earnings derived from the optimization process, the simple structure of the model, as a network flow and corresponding equations, provides much visual insight concerning the relationships between the external inputs and the variables of the problem. Optimal results have yet to be achieved in a real life situation, but a better view of whole cash flow management is provided when using the model.

Optimization of Large-Scale Hydrothermal System Operation

This paper presents the development of a mathematical model to optimize the management and operation of the Brazilian hydrothermal system. The system consists of a large set of individual hydropower plants and a set of aggregated thermal plants. The energy generated in the system is interconnected by a transmission network so it can be transmitted to centers of consumption throughout the country. The optimization model offered is capable of handling different types of constraints, such as interbasin water transfers, water supply for various purposes, and environmental requirements. Its overall objective is to produce energy to meet the country’s demand at a minimum cost. Called HIDROTERM, the model integrates a database with basic hydrological and technical information to run the optimization model, and provides an interface to manage the input and output data. The optimization model uses the General Algebraic Modeling System (GAMS) package and can invoke different linear as well as nonlinear programming ...

Planning and Operation of Large-Scale Water Distribution System with Hedging Rules

One of the most critical problems that all megacities (cities with more than 10 million people) face today is the shortage of water supply. Finding a solution to this problem presents a great challenge to hydrologists, urban planners and environmentalists. This paper describes the development of an optimization model for planning and operation of a large-scale water supply distribution system. An important characteristic of the model is that it considers the hedging rules during a dry period when water supply is insufficient to meet the planned demand and water shortage occurs. The water distribution system is formulated as a network flow model in terms of nodes and links and solved by the GAMS language, which has access to several linear and nonlinear algorithms. A user friendly interface is developed to facilitate the manipulation of a large amount of data and to generate graphs and tables for analysis for decision makers. The developed methodology has been implemented for the Sao Paulo Metropolitan Area Water Distribution System that supplies water to 18 million people. Some preliminary results obtained show good performance of the model.

Daily reservoir inflow forecasting using fuzzy inference systems

This paper presents the application of a methodology for daily reservoir inflow forecasting in Brazilian hydroelectric plants. The methodology is based on Fuzzy Inference Systems (FIS) and the technique used for adjusting of the model parameters is an offline version of the Expectation Maximization (EM) algorithm. In order to automate the application of the methodology and facilitate the analysis of the results, a tool that allows managing streamflow forecasting studies and visualizing their information in graphical form was developed. A case study was applied to the data from three Brazilian hydroelectric plants whose operation is under the coordination of the Electric System National Operator. They are located in the Grande basin, a part of the Parana basin with two main rivers: the Grande and the Pardo. The benefits of the model are analyzed using statistics calculations, such as: root mean square error, mean absolute percentage error, mean absolute error and mass curve coefficient. Besides that, graphics that compare the registered and predicted streamflow are presented. The results show an adequate performance of the model, leading to a promising alternative for daily streamflow forecasting.

The São Francisco River Water Transfer System: An Optimization Model for Planning Operation

Brazil’s poorest region, located in the country’s northeast, suffers mainly because of its dry climate and limited water resources. The demand for water and irrigation is very high, and shifts in Brazilian water resources policy can minimize drought social impacts. The most important source of water supply is the Sao Francisco river basin. Many years ago a water transfer system was designed in order to address the planned irrigation demand in the dry area. At that time the main conflict was between irrigation demand and the hydroelectrical impacts on the Sao Francisco hydropower system. The project changed its basic objectives and today the main water uses are irrigation and water supply in many cities located in the dry areas. Today the quantity of water planned to be transferred from the Sao Francisco river to the semi-arid area of the northeast region known as the “Drought Polygon” is small compared to the originally proposed amount, so the impacts on hydropower production are minimized. Despite this the project is still very controversial due mainly to its negative environmental impacts related to water quality in the Sao Francisco river. In order to minimize these impacts it is necessary to develop a highly efficient model to plan the operation of the system. The SISAGUA model developed by Barros et al. (2005) is a very convenient tool to accomplish this goal. This paper presents the application of SISAGUA to the optimal operation of the Sao Francisco river water transfer system and some very interesting results related to environmental impacts.

Energy Sector and Water Resources Management in the New Brazilian Private Energy Market

Abstract This work reviews the most significant stages of the past and recent evolution of the electrical energy deregulation that took place in Brazil. The past alternate cycles of the State and private ownership of hydroelectric projects across the country is discussed, as well as the recent large privatization process that is currently also including thermal plants. The issues dealt with in this paper concern the requirement that deregulation certainly be coupled with effective competition and gains in economy efficiency, but these are only some of the many issues that must be addressed. The possible different perspectives from public and private viewpoints are examined for the short and long run.

A Stochastic Hydrothermal Decision Support System for Planning Operation: New Developments for the HIDROTERM Model

The Brazilian hydrothermal system consists of a completely linked network of 143 medium and large hydropower plants, 888 small hydropower plants, and 1602 thermal and 82 wind power plants, with a total installed capacity of 128,570 MW. The hydropower plants produce, on average, 91% of the total electricity consumed in the country, while the complementary thermal system is dispatched mainly during periods of drought. In our previous studies, we developed a nonlinear programming model, HIDROTERM, to optimize the management and operation of the hydrothermal system, considering individual hydropower plants, thermal generation and exchange, multiple uses of the water, and system expansion. The model is deterministic. In this paper, we develop a new version of HIDROTERM to incorporate the stochasticity of the inflows in the formulation. Additionally, hourly load variation is represented by three levels: peak, base, and low. The proposed approach is based on a two-stage stochastic programming with recourse. In the first stage (first time period) the system passes from its initial state to the final state based on deterministic inflow forecasts. From the second time period on (the second stage), the state of the system branches out according to inflow scenarios. The branching can be scaled up gradually until the end of the planning horizon. New developments are made in the formulation, interface, database and the underlying simulation model. The newly developed integrated decision support system is being tested on the entire Brazilian hydrothermal system. 1 Assistant Professor, University of Sao Paulo; e-mail: rczambon@usp.br; ASCE/EWRI Member 2 Professor, University of Sao Paulo, Brazil; e-mail: mtbarros@usp.br; ASCE Member 3 Professor, University of Campinas; e-mail: franco@fec.unicamp.br 4 Assistant Professor, University of Campinas; e-mail: francato@fec.unicamp.br 5 Consulting Engineer; e-mail: jelopes1@gmail.com 6 Engineer, Companhia Energetica de Sao Paulo; e-mail: luis.nogueira@cesp.com.br 7 Distinguished Professor, UCLA; e-mail: williamy@seas.ucla.edu; ASCE Honorary Member 1563 World Environmental and Water Resources Congress 2013: Showcasing the Future

Towards Brazilian Corporations Better Stock Price Valuation and Operational Performance with Corporate Social Responsibility and Environmental Socio Responsibility

In this chapter is presented a Brazilian capital market good initiatives and improvements: we address some stock market improvements due to corporate governance practices structure development; we discuss correlation between CSR/ESG with operating performance and stock market returns; we describe initiatives undertaken since the middle of 1990s aiming at developing a more robust capital market; we also discuss CSR in a specific sector—the electricity industry, a big business in Brazil.

FUZZY INFERENCE SYSTEMS FOR MULTI-STEP AHEAD DAILY INFLOW FORECASTING

This paper presents the evaluation of a daily inflow forecasting model using a tool that facilitates the analysis of mathematical models for hydroelectric plants. The model is based on a Fuzzy Inference System. An offline version of the Expectation Maximization algorithm is employed to adjust the model parameters. The tool integrates different inflow forecasting models into a single physical structure. It makes uniform and streamlines the management of data, prediction studies, and presentation of results. A case study is carried out using data from three Brazilian hydroelectric plants of the Parana basin, Tiete River, in southern Brazil. Their activities are coordinated by Operator of the National Electric System (ONS) and inspected by the National Agency for Electricity (ANEEL). The model is evaluated considering a multi-step ahead forecasting task. The graphs allow a comparison between observed and forecasted inflows. For statistical analysis, it is used the mean absolute percentage error, the root mean square error, the mean absolute error, and the mass curve coefficient. The results show an adequate performance of the model, leading to a promising alternative for daily inflow forecasting.

Metrics and Risk Criteria Acceptance for Flood Control in Valleys Downstream Hydropower Plants

Given the importance of controlling floods in valleys and their impacts on the operation planning of hydropower plants, this research work aims at developing a contribution for finding a systematic metrics and risk criteria acceptance. It is assumed that each hydropower plant has a maximum flow constraint at downstream valley, thus requiring protection through operational procedures of the spillway and turbines. The decision about which risk level is acceptable for protection against flooding in the corresponding valleys should consider the nature, the complexity for control and the magnitude of the consequences of flooding. In general terms, high impacts will require low acceptable risk levels and, high complexity for flood control operation will require low acceptable risk levels. In order to describe the magnitude of consequences under flooding beyond the threshold level (maximum flow constraint), six severity descriptors were defined based on social impacts, possible fatalities, economic damages, operational damages, environmental damages and reputation losses to the image of the generation company. The severity descriptors are measured by using qualitative scales (low, moderate, high, catastrophic) with detailed instructions about the situations that leads to each position in the several scale levels. Similarly, five complexity operational flood control descriptors are also defined, with the corresponding qualitative scales. The proposed methodology is applied on a system of hydropower plants of the power generation company Companhia Energetica de Sao Paulo (CESP). This case study was developed with the collaboration of the technical staff of CESP, including professionals of different branches of the company. The results exhibits consistency, thus revealing the proposed methodology as an useful tool for supporting decision about risk criteria acceptance.