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

Abstract

RESUMO NEWAVE Versus ODIN: Comparação entre Modelo Estocástico e Determinístico no Planejamento da Operação Energética do Sistema Interligado NacionalEste artigo compara o modelo NEWAVE, uma abordagem baseada em programação dinâmica dual estocástica usada no Brasil para o planejamento da operação de médio prazo, com o modelo ODIN (Otimização de Despacho Interligado Nacional), uma abordagem determinística baseada em modelo de controle preditivo. A primeira adota uma representação agregada do sistema e aproximações lineares para possibilitar a aplicação da técnica de programação dinâmica ao sistema brasileiro. A última usa um algoritmo de otimização não linear considerando vazões futuras previstas com uma representação detalhada das usinas individualmente. Dados de fontes oficiais foram usados para formular um caso de estudo sobre o planejamento mensal de Janeiro de 2011 que 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.

Revealing Invisible Water: Moisture Recycling as an Ecosystem Service

“…The region downwind of this VMR source is the VMR sink region, also known as the evaporationshed for Mato Grosso (c). The temporal dynamics of VMR precipitation (d), depicting average, monthly variation between the current (blue) and desert (red) vegetation scenarios are shown alongside the seasonal overlap of important sectors affected by precipitation including the growing season for rainfed maize [ 36 ], reservoir filling [ 37 ], and seasonal rangeland grazing, with the hashed area indicating decreased quality, wintertime forage [ 38 ]. This case study forms the basis for our conceptual framework of VMR ecosystem services (e) running parallel to the Mato Grosso figures.…”

Direct Model Predictive Control: A Theoretical and Numerical Analysis

“…MPC is quite convenient, robust, simple and remains a main tool in spite of theoretical shortcomings. In particular, it often outperforms [20] the far more sophisticated SDDP which relies on a modelization by a usually stage-wise independent random process [16] or at least a Markov process with moderate size.…”

Optimization of Large‐Scale Daily Hydrothermal System Operations With Multiple Objectives

“…This generally is undertaken for a selected planning horizon to meet the forecasted load demand across the entire power grid. The goal of optimal hydrothermal operation is to meet load demand at minimum costs and minimum emissions as well as store sufficient water in the reservoirs for future hydropower generation (de Queiroz, ; Zambelli et al, ; Zambon et al, ).…”

“…However, aggregation, disaggregation, and approximation introduce errors. Zambelli et al () compared the results obtained from a SDDP model and a deterministic model for the Brazilian hydrothermal system. The results show the deterministic model performs better.…”