Stochastic Dynamic Programming for Long Term Hydrothermal Scheduling Considering Different Streamflow Models

Siqueira, Thais Gama de; Zambelli, Monica S.; Cicogna, Marcelo Augusto; Andrade, Matheus Diógenes; Soares, S.

doi:10.1109/pmaps.2006.360203

Cited by 19 publications

(10 citation statements)

References 12 publications

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“…Stochasticity comes from the limited precision forecasts: e.g. demand [5] and inflows [6] are stochastic. Moreover, the recent years have seen an increase in production volatility, due to the raising use of renewable energies [7].…”

Section: A Unit Commitment and Long Term Planningmentioning

confidence: 99%

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Multivariate bias reduction in capacity expansion planning

Cauwet

Teytaud

2016

2016 Power Systems Computation Conference (PSCC)

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The optimization of capacities in large scale power systems is a stochastic problem, because the need for storage and connections (i.e. exchange capacities) varies a lot from one week to another (e.g. power generation is subject to the vagaries of wind) and from one winter to another (e.g. water inflows due to snow melting). It is usually tackled through sample average approximation, i.e. assuming that the system which is optimal on average over the last 40 years (corrected for climate change) is also approximately optimal in general. However, in many cases, data are high-dimensional; the sample complexity, i.e. the amount of data necessary for a relevant optimization of capacities, increases linearly with the number of parameters and can be scarcely available at the relevant scale. This leads to an underestimation of capacities. We suggest the use of bias correction in capacity estimation. The present paper investigates the importance of the bias phenomenon, and the efficiency of bias correction tools (jackknife, bootstrap; combined with possibly penalized cross-validation) including new ones (dimension reduction tools, margin method).

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Section: A Unit Commitment and Long Term Planningmentioning

confidence: 99%

“…Figure 2. Each dot corresponds to one test case, averaged over the different sample sizes (6,12,18,24). The markers ×, * , •, +, ,ˆ, ♦, stand for penalties p = 0.1, 1, 3, 10, 30, 100, 3000 respectively.…”

Section: Further Workmentioning

confidence: 99%

Multivariate bias reduction in capacity expansion planning

Cauwet

Teytaud

2016

2016 Power Systems Computation Conference (PSCC)

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“…In scheduling of hydro-thermal problem, we have to consider time length of scheduling of hydro-thermal problem. It is separated into three types: long range scheduling [9,11,12], medium range scheduling [12], short range scheduling [1][2][3], [8,10]. With different cared period of time, input data as well as constraints are different.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Hydrothermal Scheduling Based on Lagrange Function and Determining Initial Hydrothermal Generations

Vo¹,

Nguyen²,

Dao³

2015

IJUNESST

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The paper presents an effective method based on the Lagrange multiplier theory to solve optimal scheduling of hydrothermal power system. Optimal scheduling of hydrothermal power systems is a great important problem to electric utility systems, the main objective of the problem is to determine the generation for each plant during scheduling period of time such that the total system generation cost is minimum while satisfying the system constrains of the generating limits and available water. The problem of optimal economic operation of hydrothermal power systems with fixed head hydro plants is considered and has had many researches about this. Determining of water discharge at the first interval is performed in this paper and lead to a low number of iteration and short computation time for convergence. The proposed method is tested on one system consisting of one hydro and one thermal plant through two examples.

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“…Instead of water, potential energy (PE) concept which represented the system's generating capability was used in composite model with a view to referring to actual electric power generated. Meanwhile, three distinct stream-flow models that were progressively more complex and sophisticated were offered for SDP performance in [6]. An application of Stochastic Dual Dynamic Programming (SDDP) was found by exerting a relaxation in the solution of the sub-problems and new technique, so-called PRE SEGMENT, some improvements was exhibited noticeable effect on computing times [7].…”

Section: Introductionmentioning

confidence: 99%

Water valuation in Vietnamese electric power generation market

Duc

Singh

Ongsakul

2011

2011 International Conference &Amp; Utility Exhibition on Power and Energy Systems: Issues and Prospects for Asia (ICUE)

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This work has been devoted to water valuation regarding Vietnamese reservoirs in a competitive electricity market environment. Water value concept, which has been assumed as fuel cost of water, helped to reflect the exploiting cost of water resource in the context of hydrothermal power system. In this work, the Stochastic Dual Dynamic Programming (SDDP) approach has been suggested for modeling the water valuation problem. Further, a trade-off has been obtained between im mediate and future operating cost. Effectiveness of the developed approach has been investigated in Vietnamese electric power generation market for the period of 2011-2012. The results have been obtained in terms of the water value for major reservoirs, short-run marginal cost (SRMC) and classification of generating units as well.Moreover, optimal generation scheduling of cascading reservoirs and their respective trajectories have also been analyzed in this work.

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Stochastic Dynamic Programming for Long Term Hydrothermal Scheduling Considering Different Streamflow Models

Cited by 19 publications

References 12 publications

Multivariate bias reduction in capacity expansion planning

Multivariate bias reduction in capacity expansion planning

Short-Term Hydrothermal Scheduling Based on Lagrange Function and Determining Initial Hydrothermal Generations

Water valuation in Vietnamese electric power generation market

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