Monica S. Zambelli

Long-term hydropower scheduling based on deterministic nonlinear optimization and annual inflow forecasting models

This paper proposes an operational policy for long-term hydropower scheduling based on deterministic nonlinear optimization and annual inflow forecasting models using an open-loop feedback control framework. The optimization model precisely represents hydropower generation by taking into consideration water head as a nonlinear function of storage, discharge and spillage. The inflow is made available by a forecasting model based on a fuzzy inference system that captures the nonlinear correlation of consecutive inflows on an annual basis, then disaggregating it on a monthly basis. In order to focus on the ability of the approach to handle the stochastic nature of the problem, a case study with a single-reservoir system is considered. The performance of the proposed approach is evaluated by simulation over the historical inflow records and compared to that of the stochastic dynamic programming approach. The results show that the proposed approach leads to a better operational performance of the plant, providing lower spillages and higher average hydropower efficiency and generation.

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.

A predictive control approach for long term hydrothermal scheduling

This paper presents a predictive control approach for long-term generation scheduling of hydro-thermal power systems. The approach is based on an open-loop feedback control scheme that uses a neural fuzzy network forecasting model, for handling the stochastic nature of inflows, and a deterministic nonlinear optimization model, to determine the discharge decisions to be implemented. As a consequence, inflow correlations on time can be represented by nonlinear relationships, and hydropower generation and thermal fuel cost can be handled by nonlinear functions, allowing a more precise modeling of the problem. A simulation model was also developed for performance assessment of the proposed approach. A comparison with the classical stochastic dynamic programming approach, in the case of single reservoir systems, revealed that the latter and the proposed approach perform similarly. The approach was also applied to a multi-reservoir system composed of 19 hydro plants and 10 reservoirs corresponding to a major cascade of the Brazilian power system. The results show that the proposed approach performs as well as in the single reservoir case study.

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.

Hydropower Scheduling in Large Scale Power Systems

Brazil is the third largest producer of hydroelectricity in the world, preceded only by China and Canada. (Source: www.eia.gov). In 2009, hydropower accounted for 87 percent of Brazilian electric power generation, with smaller amounts coming from conventional thermal, nuclear, and other renewable sources. But managing a power system with over 110 GW of installed capacity, most of it coming from around 150 hydro plants, is a daunting task.

Model predictive control applied to the long-term hydrothermal scheduling of the Brazilian power system

This paper presents a case study concerning the application of model predictive control (MPC) to the long term hydrothermal scheduling of the Brazilian power system. According to MPC, the hydro and thermal generation decisions at each stage are provided by a deterministic nonlinear optimization model considering predicted inflows. The model, which is solved by interior point method, also takes into account tie line constraints between interconnected areas. In order to evaluate the performance of the approach several simulations over historical inflow scenarios were performed, and statistics about operation costs, hydro and thermal generation, power flow interchange, reservoir storage, load shortage, among others, are obtained. General results are compared to those from the stochastic model in use in Brazil and the results have shown substantial decrease in expected operation costs and load shortages, as well as an increase on water storage, both cause and effect of the better management of water resources.

Deterministic versus stochastic dynamic programming for long term hydropower scheduling

This paper is concerned with the performance assessment of deterministic and stochastic dynamic programming approaches in long term hydropower scheduling. In order to focus the analysis on the stochastic nature of inflows, only single reservoir systems are considered, where the so-called “curse of dimensionality” is not a concern. The paper reviews the framework of dynamic programming for hydropower scheduling, highlighting the differences between deterministic and stochastic approaches. The performances of both methods are evaluated through simulation with the historical inflow records considering hydro plants from different river basins in Brazil. The conclusions indicate that the deterministic approach leads to operation rules which maintain the reservoirs fuller than does the stochastic one, providing greater hydropower efficiency but also greater water spillage. Despite these differences, both approaches yield a similar performance, which suggests that no significant benefit is obtained from considering stochastic inflows in long term hydropower scheduling. Furthermore, in critical hydrologic periods where no spillage occurs, the deterministic approach leads to a better performance than does the stochastic one.

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.

Predictive Control Approach for Long-Term Hydropower Scheduling Using Annual Inflow Forecasting Model

Abstract This paper proposes an annual inflow forecasting model in an open-loop feedback control operational policy for long-term hydropower scheduling. A deterministic optimization model precisely represents hydropower generation by taking into consideration water head as a nonlinear function of storage, discharge and spillage. The inflow is made available by a forecasting model based on a fuzzy inference system that captures the nonlinear correlation of consecutive inflows on an annual basis, with disaggregation of the results on a monthly basis. The performance of the proposed approach is evaluated by simulation for a multi-reservoir system, based on historical inflow records and compared to the same approach on monthly basis. The results show that the proposed approach leads to an operational performance closer to that of the perfect foresight solution, providing lower spillages and higher average hydropower efficiency and generation.