Secundino Soares Filho

Métodos de pontos interiores para problema de fluxo de potência ótimo DC

The primal-dual and predictor-corrector versions of interior point methods are developed for an optimal DC power flow model where Kirchhoff laws are represented by a network flow model with surrogate constraints. The resulting matrix structure is explored reducing the linear system to be solved either to the number of buses or to the number of independent loops, leading to very fast iterations. Either matrix is invariant and can be factored off-line. As a consequence of such matrix manipulations, a linear system which changes at each iteration must be solved; its size, however, reduces to the number of generating units. Numerical results with C implementation are presented for IEEE test systems and large scale Brazilian systems. The interior point method shows to be robust, achieving fast convergence in all instances tested.

NEWAVE versus ODIN: comparison of stochastic and deterministic models for the long term hydropower scheduling of the interconnected brazilian system

⎯ This paper presents a comparison between the NEWAVE model, which employs a stochastic approach by using Stochastic Dual Dynamic Programming (SDDP), and the ODIN model (Optimization of Brazilian Interconnected Hydrothermal Power System), which uses a deterministic approach with a network flow optimization algorithm. The ODIN model uses deterministic and nonlinear optimization with forecasted inflows, with detailed representation of the hydroelectric system by individual plants. The NEWAVE model uses an equivalent composite representation of interconnected subsystems. In order to perform a comparison on the same terms, the SUISHI-O model was used to decompose the strategy of the NEWAVE model into individualized plants. The ODIN model was compared to the NEWAVE SUISHI-O methodology for a case study based on data from the deck of CCEE for the PMO of September 2008, regarding the generation capacity expansion plan for December 2012. The results was obtained by simulation of 70 historical scenarios, indicating a significantly superior performance by the ODIN model, which leads to a more security and economic operation of SIN by more efficient use of water resources. Keywords⎯ Energy Systems Planning, nonlinear optimization, stochastic optimization, reservoir operation, NEWAVE model, ODIN model. Resumo⎯ Este artigo apresenta uma comparacao entre o modelo NEWAVE, que utiliza uma abordagem estocastica atraves da utilizacao da Programacao Dinâmica Dual Estocastica (PDDE), e o modelo ODIN (Otimizacao do Despacho Interligado Nacional), que utiliza uma abordagem deterministica atraves de um algoritmo de otimizacao por fluxo em redes. O modelo ODIN usa otimizacao deterministica e nao linear sob vazoes previstas, com representacao detalhada do sistema hidreletrico atraves de usinas individualizadas. O modelo NEWAVE usa representacao agregada a subsistemas equivalentes interligados, de modo que para viabilizar a comparacao em igualdade de condicoes foi utilizado o modelo SUISHI-O para desagregar a estrategia do NEWAVE a usinas individualizadas. O modelo ODIN demonstra seu desempenho relativo a metodologia NEWAVESUISHI-O em um estudo de caso baseado no deck de dados da CCEE para o PMO de setembro de 2008, considerando o planejamento da expansao do parque gerador ate dezembro de 2012. Os resultados, obtidos por simulacao em 70 cenarios historicos, indicam um desempenho expressivamente melhor do modelo ODIN, proporcionando mais seguranca e economia ao SIN atraves do aproveitamento mais eficiente dos recursos hidraulicos. Palavras-chave⎯ Planejamento da operacao energetica, otimizacao nao linear, otimizacao estocastica, operacao de reservatorios, modelo NEWAVE, modelo ODIN.This paper compares the NEWAVE model, a stochastic dual dynamic programming based approach used in Brazil for the long term hydropower scheduling of the interconnected Brazilian power system, to the ODIN model (Optimal Dispatch for the Interconnected Brazilian National system), a deterministic approach based on model predictive control. The former adopts a composite representation of the hydro system and piecewise linear approximations to make the application of dynamic programming solution technique possible to the Brazilian system. The latter uses a nonlinear optimization algorithm considering predicted future inflows with a detailed representation of the individual power plants. Data from official sources were used to formulate a case study on the monthly operation planning of January 2011 that considers the projected expansion plans up to December 2015. Tests were performed by simulation using 75 historical inflow scenarios. In comparison to the scheduling provided by the stochastic approach, the proposed deterministic one was found to provide a superior performance due to the more efficient use of water resources, leading to a more secure and economic operation.

Aggregated inflows on stochastic dynamic programming for long term hydropower scheduling

This paper aims to present and analyze a different approach for long term hydropower scheduling. In opposition to the Markovian stochastic dynamic programming, where monthly inflows are modeled according to probability distribution functions conditioned to some occurrence of inflow in the previous month, in the proposed approach inflows are aggregated in groups of k months to establish the Markovian modelling. Initial tests were conducted on hypothetical singlereservoirs hydrothermal systems based on four real Brazilian hydro plants with distinct hydrological regimes. The performance of both regular and proposed methods was evaluated through simulation using the historical data available in Brazil, between January 1931 and December 2012. The results show that performance of both methods are very similar comparing mean spillage and mean power generation but with lower costs for the proposed method, with differences surpassing 1% in some cases.

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.

Advantages of deterministic optimization in long-term hydrothermal scheduling of large-scale power systems

This paper investigates the advantages of deterministic optimization in long-term hydrothermal scheduling of large-scale power systems. A specialized nonlinear optimization model was developed and coupled with a simulation model, taking into account expected values of inflows, in a model predictive control (MPC) approach. It is compared to stochastic dual dynamic programming (SDDP), as currently in use by the Brazilian ISO. Two distinct case studies were conducted for the monthly scheduling of the Brazilian power system on critical hydrology conditions: first, MPC is enforced to equally dispatch the thermal generation as calculated by SDDP; secondly, MPC is free to compute the optimal thermal dispatch, as well as hydro. The results show that MPC provides lower operation costs and less energy shortages due to better hydrothermal coordination and improved cascade reservoir management resulting from the deterministic optimization model, even under unfavorable scenarios.