Advantages of deterministic optimization in long-term hydrothermal scheduling of large-scale power systems

Zambelli, Monica S.; Martins, L. S. A.; Filho, Secundino Soares

doi:10.1109/pesmg.2013.6673063

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…A wide range of applications on GEP are found in the literature [1]- [4], [15]- [22], each of which considering different aspects and system characteristics. In this work, however, we focus on the specific challenges of hydrothermal power systems [16]- [18], [20]. In this setting, the main challenge is to consider, within the expansion problem, an integrated and computationally efficient nonanticipative water-value assessment.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Progressive Hedging and Benders Decomposition with Multiple Master Method to Solve the Brazilian Generation Expansion Problem

Soares¹,

Street²,

Andrade³

et al. 2021

Preprint

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Section: Introductionmentioning

confidence: 99%

An Integrated Progressive Hedging and Benders Decomposition with Multiple Master Method to Solve the Brazilian Generation Expansion Problem

Soares¹,

Street²,

Andrade³

et al. 2021

Preprint

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“…Now we present a new regularization scheme based on the PH method to accelerate our BDMM. To do that, we add the PH penalty terms in the master problem formulation (3-13)- (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18). So, we rewrite the master problem (3-13)- (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18) as problem (3-21)- (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)…”

Section: Accelerating Convergence Of the Benders Decomposition With Multiple Mastersmentioning

confidence: 99%

“…Therefore, for the sake of conciseness, we omit the proof here. Finally, the upper bound remains unchanged and follows expression (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20).…”

Section: St Constraints (3-14)-(3-18) (3-25)mentioning

confidence: 99%

“…While investment decisions are binary and already appear in the original objective function weighted by investment costs, LDR coefficients are real numbers and do not participate in the original objective function. Therefore, in our implementation, we modified the penalization term ρ 2 x −x k 2 from expression (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21) to consider different penalty weights for components in x IN V and (x LDR t,0 , x LDR t ). Therefore, we used the following quadratic penalty term:…”

Section: St Constraints (3-14)-(3-18) (3-25)mentioning

confidence: 99%

“…A wide range of applications on GEP are found in the literature [8][9][10][11][12][13][14][15][16][17][18][19][20], each of which considering different aspects and system characteristics. In this work, we focus on hydrothermal power systems [13-16, 19, 20].…”

Section: Gary Kasparov Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Regularized Benders Decomposition With Multiple Master Problems to Solve the Hydrothermal Generation Expansion Problem

SILVA¹

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

Wang

Cheng

Shen

et al. 2018

Water Resources Research

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This paper proposes a practical procedure for optimizing the daily operation of a large‐scale hydrothermal system. The overall procedure optimizes a monthly model over a period of 1 year and a daily model over a period of up to 1 month. The outputs from the monthly model are used as inputs and boundary conditions for the daily model. The models iterate and update when new information becomes available. The monthly hydrothermal model uses nonlinear programing (NLP) to minimize fuel costs, while maximizing hydropower production. The daily model consists of a hydro model, a thermal model, and a combined hydrothermal model. The hydro model and thermal model generate the initial feasible solutions for the hydrothermal model. The two competing objectives considered in the daily hydrothermal model are minimizing fuel costs and minimizing thermal emissions. We use the constraint method to develop the trade‐off curve (Pareto front) between these two objectives. We apply the proposed methodology on the Yunnan hydrothermal system in China. The system consists of 163 individual hydropower plants with an installed capacity of 48,477 MW and 11 individual thermal plants with an installed capacity of 12,400 MW. We use historical operational records to verify the correctness of the model and to test the robustness of the methodology. The results demonstrate the practicability and validity of the proposed procedure.

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Advantages of deterministic optimization in long-term hydrothermal scheduling of large-scale power systems

Cited by 5 publications

References 12 publications

An Integrated Progressive Hedging and Benders Decomposition with Multiple Master Method to Solve the Brazilian Generation Expansion Problem

An Integrated Progressive Hedging and Benders Decomposition with Multiple Master Method to Solve the Brazilian Generation Expansion Problem

A Regularized Benders Decomposition With Multiple Master Problems to Solve the Hydrothermal Generation Expansion Problem

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

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