Paulo de Barros Correia

Multiobjective linear model for pre-feasibility design of cogeneration systems

This article deals with some methodologies for economic and technical evaluations of cogeneration projects proposed by several authors. A discussion on design philosophy applied to thermal power plants leads to the decision problem of a conflicting, multiobjective formulation that includes the most important parameters. This model is formulated to help decision makers and designers in choosing compromise values for included parameters.

Hybrid Model for Short-Term Scheduling of Hydropower Systems

AbstractIn this paper the authors propose a global–local methodology for optimizing the short-term operation of hydroelectric plants. The authors determine the tradeoffs between minimizing the daily release from the plant and minimizing the number of startups and shutdowns of the generating units. The model is formulated as a mixed integer, nonlinear programming optimization problem with multiple objectives. The authors consider the nonlinearities of the generating units without simplifications or approximations. The authors develop a solution method that combines an evolutionary algorithm for the global search of the integer variables and a gradient-based local optimizer for the continuous variables. The local optimizer is embedded in the global search algorithm. Convergence is achieved by iterating between the global search and the local optimizer. The proposed methodology is applied to a moderately sized Brazilian hydroelectric plant that belongs to the national interconnected system. Additionally, a c...

Metaheuristic Approaches for Hydropower System Scheduling

This paper deals with the short-term scheduling problem of hydropower systems. The objective is to meet the daily energy demand in an economic and safe way. The individuality of the generating units and the nonlinearity of their efficiency curves are taken into account. The mathematical model is formulated as a dynamic, mixed integer, nonlinear, nonconvex, combinatorial, and multiobjective optimization problem. We propose two solution methods using metaheuristic approaches. They combine Genetic Algorithm with Strength Pareto Evolutionary Algorithm and Ant Colony Optimization. Both approaches are divided into two phases. In the first one, to maximize the plant’s net generation, the problem is solved for each hour of the day (static dispatch). In the second phase, to minimize the units’ switching on-off, the day is considered as a whole (dynamic dispatch). The proposed methodology is applied to two Brazilian hydroelectric plants, in cascade, that belong to the national interconnected system. The nondominated solutions from both approaches are presented. All of them meet demand respecting the physical, electrical, and hydraulic constraints.

Multiobjective dispatch of hydrogenerating units using a two-step genetic algorithm method

This paper proposes a multiobjective dispatch model to operate hydroelectric power plants. The model is composed of two algorithms that are based on Genetic Algorithms. The first algorithm is used for the static dispatch of generating units and is aimed at maximizing plant efficiency on an hourly basis. The second step is a multiobjective technique for the daily operation of generating units. The two objectives are to maximize the plant efficiency and to minimize the number of startups and shutdowns of generating units. Data from a Brazilian power plant were used in the simulation of a daily operation. A daily load curve contains 24 static problems, each one solved on average in approximately 2 minutes. The second step was executed in approximately 99 seconds. The proposed model proved suitable for the daily operation of the hydroelectric power plant studied, given the low computational time, satisfactory efficiency and low number of generating units startups and shutdowns (only 12).

A genetic algorithm solution for optimization of the power generation potential in hydroelectric plants

This paper presents an optimization model of the power generation potential for either new or repowered hydroelectric plants. It is based on curves that represent the unit efficiency as a function of the nominal output. The objective is to choose the combination of efficiency curve types that maximizes the power generation for certain load levels. The mathematical formulation results in a mixed integer, nonlinear programming problem. Genetic Algorithm is employed to solve this. The operators and parameters of the model are chosen by simulation using the objective function values as a selection method. A case study is carried out for two Brazilian hydroelectric plants: Sobradinho and Ilha Solteira. The results show the importance of the turbines model choice in order to get the maximum benefit of a plant.

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.

Binomial Lattice Model: Application on Carbon Credits Market

It is known from literature that many models of financial mathematics are based on the assumption of normality returns. Thus, the normal distribution is not a single model to fit the log-return distributions. It is very important to consider an alternative class of probability distributions which is able to model the effects caused by asymmetric data. This paper is a survey about the log-returns of Certified Emission Reductions (CERs), carbon credits generated by projects of the Clean Development Mechanism. The contracts are priced through the binomial lattice model proposed by Cox. Therefore, the model is discussed in order to represent the random parameter behaviors of CERs contracts and evaluate the benefits and exposure to them.

Feasibility on using carbon credits: A multiobjective model

This paper aims to examine the economic feasibility on trading Certified Emission Reductions (CERs) from Clean Development Mechanisms (CDM) projects that are related to electricity generation from renewable energy sources in Brazil. Its purpose is to identify favorable conditions for combining CERs trade obtained by generating electricity from wind power, biomass cogeneration and small hydro-power plants, in replace of fossil fuel plants. As those are all seasonal sources, which means that the energy offers swing along the months of the years, some risks arise associated with the CER’s net benefit. Instead of being examined alone, given that some sources can hedge others, the projects are analyzed in a portfolio framework.

A Multiobjective Model for Urban Transport Energy Cost and Environment

Abstract This paper proposes a multiobjective approach for energy planning on transport sector, including environmental considerations. The optimization problem is formulated with two objective functions: energy cost and carbon dioxide emission. Efficient configurations for transport fleet on the planning horizon are generated by the weighted method. A trade-off curve quantifies the conflict of objectives, defining the concept of implicit carbon tax . The model variables are the road vehicle fuels: Diesel, gasoline, natural gas, electricity and alcohol. Constraints on available technologies for road transport of passenger and cargo are considered. This approach is applied to Brazilian energy scenario for road transport sector on the year 2000.