Ieda Geriberto Hidalgo

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...

Data Consolidation from Hydroelectric Plants

This paper presents a sequence of procedures for data analysis and correction from hydroelectric plants. The procedures are based on variable data recorded by the plant. Therefore, they do not require additional financial investment in instrumentation for measurements. The proposed methodology makes use of a data manager, a queries builder, a generator of the overall efficiency function and an electronic spreadsheet. It has been applied to the data from a large Brazilian hydroelectric plant whose operation is under the coordination of the Electric System National Operator (ONS). The benefits of the data correction are analyzed using a simulator of the hydroelectric plants operation. This simulator is used to reproduce the past operation of the plant, once with official data and the next with adjusted data. The results show significant improvements in terms of quality of the data, contributing to the efficiency and reliability of the computational models in use by the energy sector in the operation planning.

Evaluation of Optimization Algorithms to Adjust Efficiency Curves for Hydroelectric Generating Units

AbstractThe power generated by a hydroelectric plant depends on the penstock head loss, the turbine efficiency, and the generator efficiency among other factors. Initially, the functions related to these variables are provided by the equipment manufacturer; however, over time they change as the plant ages. This paper presents a methodology to adjust an efficiency function for each generating unit based on measured data. It is applied using two optimization methods: the generalized reduced gradient and the evolutionary algorithm. A case study with data from a large Brazilian hydroelectric plant demonstrates how the methodology can be employed and compares the performance of the optimization tools. A comparison metric is used to show that the optimal unit efficiency function significantly improves the performance of simulation models to reproduce observed data and better describe the actual operation of the hydroelectric plant.

Efficiency Curves for Hydroelectric Generating Units

AbstractThis paper presents a methodology for obtaining and adjusting of efficiency curves for hydroelectric generating units. It is based on measured data of power, gross head, and water discharge recorded by the company that manages the plant operation. The objective is to determine the actual performance characteristics of the set: turbine, generator, and penstock. In order to obtain the efficiency functions, an iterative calculation is used. Its input data are the functions currently in use of turbine efficiency, generator efficiency, and penstock head losses. For the adjustment of the efficiency functions, the Generalized Reduced Gradient optimization method is employed. A case study was applied to the data from a large Brazilian hydroelectric plant whose operation is under the coordination of the Electric System National Operator. The benefits of the proposed methodology are analyzed using a simulation tool for the hydroelectric operation. The simulator is used to reproduce the past operation of the...

Management and analysis of data from hydroelectric plants

This paper presents a computer aided system that aims to improve the quality of data for hydroelectric plants. This system consists of two modules. The first is a relational database that organizes, standardizes and manages data from hydroelectric plants. The second module allows a user to build, store and manage advanced queries on this or certain other databases. The data manager and the queries builder were tested with data from Brazilian hydroelectric plants and the results show the potential for a significant improvement in terms of efficiency for the operation planning of electrical power systems. Planning has strong dependence on the quality of the data used in mathematical models and other built-in computational tools. The software project and the computational implementation of these modules use the Object-Oriented Paradigm, the C++ Programming Language and the Structured Query Language (SQL). This assures a modern and efficient structure for the system.

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.

A Simulator of the Hydroelectric Plants Operation as Tool for Analyzing Data

This paper presents the use of a simulator of the hydroelectric plants operation as tool for analyzing data. Two approaches are shown. In the first the simulator must reproduce the water discharge trajectory recorded by the plant. In the second the simulator’s aim is reproduce the generation trajectory of a certain operation period. The two methods for analyzing data are described and the advantages and disadvantages of each are discussed. The data analysis via simulation has been applied to Brazilian hydroelectric plants which are under the coordination of the Brazilian Electric System National Operator. The results show the potential of this tool for the consistency evaluation of the data used in the hydroelectric operation planning.

Multireservoir Simulation Using Multipurpose Constraints and Object-Oriented Software design

The hydroelectric short term planning is quite challenging in order to respect all the operational restrictions concerned with hydropower generation. A detailed simulation model combining multipurpose constraints and object-oriented software design is presented for short term decision support. The object-oriented design permits a mathematical structure that separates the decision tasks from the regular hydroelectric computation. The main operational policy allows users to evaluate a hydro generation schedule using inflow forecasting. In other words, the simulator can accept hydro generation as data input and convert it to water flow variables (turbines discharge and spillage), using the inflow forecast and the initial reservoir’s storage. The water balance equation, hydro generation and efficiency calculation modules are synchronized with multireservoir operation. As an important result, this simulator can automatically correct the input data of the operational policy, saving substantially time in the decision-making process. A test problem on 94-reservoir subsystem within the Brazilian Integrated Hydropower Generation System was simulated using an hourly time-step over a one-week horizon with good performance, demonstrating the simulation model’s capabilities for solving a large scale hydropower operation problem.

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.

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.