Assessment of risk-averse policies for the long-term hydrothermal scheduling problem

Larroyd, Paulo Vitor; Matos, Vitor Luiz de; Finardi, Erlon Cristian

doi:10.1007/s12667-016-0191-y

Cited by 12 publications

(8 citation statements)

References 12 publications

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“…Finally, we are not paying attention to which (and how many) inflows should be gathered in aggregation and how to obtain the best participation factors in constraints ( 22) and (23). Given that such choices can be based on the reservoirs' spatial coupling and, therefore, depend on the problem, it is possible to find several possibilities in the literature.…”

Section: A Inflow Aggregationmentioning

confidence: 99%

“…Particularly for systems with a predominance of water resources, the main object of simplification is the HPF, a nonlinear and nonconvex multidimensional function [1]. In general, the works opt to represent HPF via an EER [17], an individualized model with constant productivity [23], or with minor corrections in power according to the head [24]. However, the literature lacks this trade-off, i.e., finding a balance between stochastic inflow modeling and the physical representation of the problem.…”

Section: Introductionmentioning

confidence: 99%

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Inflow Aggregation and Run-of-the-River Inflow Energy for Reducing Dimensionality in the Long-Term Generation Scheduling Problem

Fredo

Finardi

Larroyd³

et al. 2021

IEEE Access

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The long-term generation scheduling (LTGS) problem aims to build an operating policy over a multi-year planning horizon, correlating thermal generation and deficit costs with water storage. The LTGS is modeled as a linear multistage stochastic program, whose state-of-art solution is the stochastic dual dynamic programming (SDDP). One critical challenge is to represent the time-dependency of river inflows accurately. Despite recent advances, modeling simplifications are needed to allow the LTGS computational tractability via SDDP since it is necessary to increase the state space to include the time-dependent variables properly. Thus, most works simplify the hydro production function (HPF) to represent the inflows in detail to overcome this negative aspect. However, the literature lacks this trade-off, i.e., finding a balance between stochastic inflow modeling and HPF representation. Thus, this paper proposes an alternative approach to analyzing this trade-off, using the inflow aggregation and run-of-the-river energy inflow instead of individual inflow in SDDP. The proposed approach drastically reduces the number of state variables in SDDP, allowing a detailed HPF representation in the LTGS. We employ a one-sided confidence interval for expected cost estimation to show that our approach provides better performance than the individualized inflows. The analysis is performed in several large-scale computational instances using a power system with 53 geographically widespread hydro plants. The numerical results demonstrate that inflow aggregation provides, on average, a 4 % reduction on the operating cost, whereas for the run-of-the-river inflow energy, the reduction is, on average, 2 %. INDEX TERMS Inflow modeling, hydro production function, the long-term generation scheduling problem, stochastic dual dynamic programming, run-of-the-river inflow energy.

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Section: A Inflow Aggregationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inflow Aggregation and Run-of-the-River Inflow Energy for Reducing Dimensionality in the Long-Term Generation Scheduling Problem

Fredo

Finardi

Larroyd³

et al. 2021

IEEE Access

Self Cite

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“…One alternative strategy to achieve the aforementioned is to use stochastic programming because it allows one to model the random variables using their probability distribution functions, as estimated from historical data. Since adding all the random variables into a single optimization model may be impractical, given that one of the most relevant challenges in stochastic programming is related to reducing the required runtime to solve a given large-scale optimization problem [14], we only consider inflows as random variables. The stochastic programming approach, i.e., algorithms based on stochastic dynamic programming (SDP) and stochastic dual dynamic programming (SDDP), has been widely applied to estimate the optimal operation of hydrothermal power systems.…”

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

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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.

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“…Hence, this paper investigates the possible impacts of the nonstationarity on the hydropower plants reservoir's S-Y-R curves, analyzing the regularization capacity variability over the years. First, we consider a territorial-wide analysis, using the energy equivalent reservoirs (EER) approach (Larroyd et al, 2017). It is a solution commonly adopted in Brazil that aggregates all hydropower plants in four EERs (Southeast/Central-West, South, Northeast, and North subsystems).…”

Section: Introductionmentioning

confidence: 99%

Influence of nonstationarity on reservoir storage-yield-reliability relationships

Degraf

Detzel

2022

RBRH

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The reservoir storage-yield-reliability (S-Y-R) curve defines the required volume to meet a specific yield. It is typically obtained through the historical streamflow time series; however, as an effect of nonstationarity, the statistical properties of a streamflow series may vary, which might lead to a change in the reservoir’s operational risk. In this study we explore this issue by analyzing two sets of annual data: (i) natural energy flows to aggregated reservoirs, and (ii) streamflow time series of four hydropower plants currently in operation in Brazil. The study is supported by Monte Carlo simulations to account for the reliability of the S-Y-R curves. Results suggest that the time series from the Southern and Northeast regions exhibit upward and downward trends, respectively. Consequently, the regularization capacity of the Southern reservoir decreased, however only in relative terms. On the other hand, the Northeastern reservoir had an actual loss of its regularization capacity as an effect of lower average streamflow.

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Assessment of risk-averse policies for the long-term hydrothermal scheduling problem

Cited by 12 publications

References 12 publications

Inflow Aggregation and Run-of-the-River Inflow Energy for Reducing Dimensionality in the Long-Term Generation Scheduling Problem

Inflow Aggregation and Run-of-the-River Inflow Energy for Reducing Dimensionality in the Long-Term Generation Scheduling Problem

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

Influence of nonstationarity on reservoir storage-yield-reliability relationships

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