Synthetic scenario generation of monthly streamflows conditioned to the El Niño–Southern Oscillation: application to operation planning of hydrothermal systems

Treistman, Felipe; Maceira, M.E.P.; Penna, D.D.J.; Damázio, Jorge Machado; Filho, Otto Corrêa Rotunno

doi:10.1007/s00477-019-01763-2

Cited by 4 publications

(5 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the sake of simplicity, we consider only three inflow scenarios, i.e., N = 3 as Figure 3 shows. The set of constraints is composed of those related to the first stage ( 7)-( 8) and those associated with each possible inflow scenario ( 9)- (12). Then, Table 6 shows the optimal solution of the different variables involved in the problem.…”

Section: Stochastic Htsmentioning

confidence: 99%

“…Officially, the deterministic approach has been employed in Mexico to solve the MTHS problem. Although this approach is the basis for the stochastic programming approach, its main drawback is that it does not model the stochastic nature of the problem due to its assumption that only one scenario will occur [12]; therefore, through this approach, it is not possible to obtain a decision that considers the other possible scenarios that can also happen, and one can only obtain a decision that is optimal for a particular realization [13]. Given the stochastic nature of the MTHS problem, it is essential to consider this ingredient in the optimization model in order to obtain more accurate solutions and reduce the expected total cost.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Medium-Term Hydrothermal Scheduling of the Infiernillo Reservoir Using Stochastic Dual Dynamic Programming (SDDP): A Case Study in Mexico

Marín Cruz,

Badaoui,

Mota Palomino

2023

Energies

View full text Add to dashboard Cite

This article aims to obtain and evaluate medium-term operating policies for the hydrothermal scheduling problem by using the stochastic dual dynamic programming (SDDP) approach. To this end, to feed the mathematical model and build the probability distribution functions that best fit each month of the actual inflow volume, monthly inflow data recorded from 1938 to 2018 for the Infiernillo reservoir located in Mexico were employed. Moreover, we simulated inflow volume scenarios using the Monte Carlo method for each month of a one-year planning period. The SDDP approach to solving the optimization problem consisted of the simulation of one forward scenario per iteration and the stabilization of the total operating cost as a convergence criterion, which results in an operating policy. We then assessed its quality by estimating the one-sided optimality gap. It is worth mentioning that the best operation policy required scenario trees of up to 17,000 inflow realizations per stage. Additionally, to study the evolution of the expected value along the planning horizon of the main variables involved in the medium-term hydrothermal scheduling problem, we simulated the best operation policy over 10,000 inflow scenarios. Finally, to show the practical value of the proposed approach, we report its computational complexity.

show abstract

Section: Stochastic Htsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Medium-Term Hydrothermal Scheduling of the Infiernillo Reservoir Using Stochastic Dual Dynamic Programming (SDDP): A Case Study in Mexico

Marín Cruz,

Badaoui,

Mota Palomino

2023

Energies

View full text Add to dashboard Cite

show abstract

“…The fourth methodology consists of a Markov-switching periodic autoregressive model with -MS-PAR(p) (Treistman et al, 2020b;Pessanha et al, 2023), which is the combination of the classic PAR model with the addition of climate variables as a state model by a Markov chain. This evaluation uses the El Niño Southern Oscillation as the represented climate variable, which is one of the most impacting climate variability in the Brazilian streamflow.…”

Section: /17mentioning

confidence: 99%

A framework to evaluate and compare synthetic streamflow scenario generation models

Treistman,

Penna,

Khenayfis

et al. 2023

RBRH

Self Cite

View full text Add to dashboard Cite

Synthetic streamflow scenario generation is particularly important in countries like Brazil, where hydroelectric power generation plays a key role and properly handling the uncertainty of future streamflow is crucial. This paper showcases a collaborative effort within the Brazilian electrical sector to enhance streamflow scenario models, focusing on horizons up to one year. Five institutions proposed diverse methodologies, and their effectiveness was evaluated using a comparative framework. The results reveal the strengths and areas for improvement in each model. GHCen emerged as the top performer, excelling in both short-term and moving average analyses, while the PARX model demonstrated superior performance in specific regions. The PAR(p)-A, which is the official methodology in Brazil, was the second-best model in the moving average analysis. This research offers valuable insights for countries facing similar hydrothermal scheduling and scenario generation challenges.

show abstract

“…In Treistman et al. (2020), ENSO variables are rather treated as additional state variables modeled by a Markov chain along with the PAR(p) model for the streamflows. More recently, Mbeutcha et al.…”

Section: Introductionmentioning

confidence: 99%

“…Pina et al (2017) propose an approach to incorporate exogenous hydrological variables (e.g., snow water equivalent, ENSO variables) into the SDDP algorithm through a MPARX model. In Treistman et al (2020) However, none of the reported extensions of the SDDP algorithm can handle low-frequency signals. The main focus of this study is to extend the SDDP algorithm so as to determine reservoir operating policies that explicitly capture shifting flow regimes due to climate variability.…”

mentioning

confidence: 99%

Optimizing the Management of Multireservoir Systems Under Shifting Flow Regimes

Espanmanesh

Tilmant

2022

Water Resources Research

View full text Add to dashboard Cite

Anthropogenic disturbances of the terrestrial water cycle, climate change, and low frequency climate signals all challenge the stationarity assumption, which has been the backbone of water resources planning techniques during much of the 20th century (Loucks & Van Beek, 2017b). Time series of hydrological observations can exhibit nonstationarity in different forms such as increasing or decreasing trends (

show abstract

Synthetic scenario generation of monthly streamflows conditioned to the El Niño–Southern Oscillation: application to operation planning of hydrothermal systems

Cited by 4 publications

References 49 publications

Medium-Term Hydrothermal Scheduling of the Infiernillo Reservoir Using Stochastic Dual Dynamic Programming (SDDP): A Case Study in Mexico

Medium-Term Hydrothermal Scheduling of the Infiernillo Reservoir Using Stochastic Dual Dynamic Programming (SDDP): A Case Study in Mexico

A framework to evaluate and compare synthetic streamflow scenario generation models

Optimizing the Management of Multireservoir Systems Under Shifting Flow Regimes

Contact Info

Product

Resources

About