Efficient Parallelization of the Stochastic Dual Dynamic Programming Algorithm Applied to Hydropower Scheduling

Helseth, Arild; Braaten, Hallvard

doi:10.3390/en81212431

Cited by 15 publications

(19 citation statements)

References 12 publications

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“…With the above Equations (20)- (22), the net head function can be rewritten as a fourth-order polynomial of storage volume, turbine discharge, and spillage, shown in (23):…”

Section: Reformulating Nonlinearities Of the Hydropower Systemmentioning

confidence: 99%

“…Mathematically, the present problem is a high-dimensional, nonlinear, multistage, and multi-objective optimization [18]. In the past decades, three kinds of techniques have been developed for this problem: Mathematical programming [19,20], dynamic programming (DP) and a DP-based method [21][22][23], and population-based algorithms [24][25][26]. In mathematical programming, linear programming and nonlinear programming methods are widely used.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Operation of Interprovincial Hydropower System Including Xiluodu and Local Plants in Multiple Recipient Regions

et al. 2019

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This paper focuses on the monthly operations of an interprovincial hydropower system (IHS) connected by ultrahigh voltage direct current lines. The IHS consists of the Xiluodu Hydropower Project, which ranks second in China, and local plants in multiple recipient regions. It simultaneously provides electricity for Zhejiang and Guangdong provinces and thus meets their complex operation requirements. This paper develops a multi-objective optimization model of maximizing the minimum of total hydropower generation for each provincial power grid while considering network security constraints, electricity contracts, and plant constraints. The purpose is to enhance the minimum power in dry season by using the differences in hydrology and regulating storage of multiple rivers. The TOPSIS method is utilized to handle this multi-objective optimization, where the complex minimax objective function is transformed into a group of easily solved linear formulations. Nonlinearities of the hydropower system are approximatively described as polynomial formulations. The model was used to solve the problem using mixed integer nonlinear programming that is based on the branch-and-bound technique. The proposed method was applied to the monthly generation scheduling of the IHS. Compared to the conventional method, both the total electricity for Guangdong Power Grid and Zhejiang Power Grid during dry season increased by 6% and 4%, respectively. The minimum monthly power also showed a significant increase of 40% and 31%. It was demonstrated that the hydrological differences between Xiluodu Plant and local hydropower plants in receiving power grids can be fully used to improve monthly hydropower generation.

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“…With the above Equations (20)- (22), the net head function can be rewritten as a fourth-order polynomial of storage volume, turbine discharge, and spillage, shown in (23):…”

Section: Reformulating Nonlinearities Of the Hydropower Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Operation of Interprovincial Hydropower System Including Xiluodu and Local Plants in Multiple Recipient Regions

et al. 2019

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“…No parallelization was carried out except the one from the optimization solver. Parallelization in the SDDP framework is well studied, as in [26], and thus neglected in this work. It would, however, contribute to significantly reducing the CPU time.…”

Section: Solution Approachmentioning

confidence: 99%

Medium-Term Hydropower Scheduling with Variable Head under Inflow, Energy and Reserve Capacity Price Uncertainty

2019

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We propose a model for medium-term hydropower scheduling (MTHS) with variable head and uncertainty in inflow, reserve capacity, and energy price. With an increase of intermittent energy sources in the generation mix, it is expected that a flexible hydropower producer can obtain added profits by participating in markets other than just the energy market. To capture this added potential, the hydropower system should be modeled with a higher level of detail. In this context, we apply an algorithm based on stochastic dual dynamic programming (SDDP) to solve the nonconvex MTHS problem and show that the use of strengthened Benders (SB) cuts to represent the expected future profit (EFP) function provides accurate scheduling results for slightly nonconvex problems. A method to visualize the EFP function in a dynamic programming setting is provided, serving as a useful tool for a priori inspection of the EFP shape and its nonconvexity.

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“…No parallelization was carried out except the one from the optimization solver. Parallelization in the SDDP framework is well studied, as in [25], and thus neglected for this work. It would, however, contribute to significantly reducing the CPU time.…”

Section: Case Studymentioning

confidence: 99%

Medium-Term Hydropower Scheduling with Variable Head under Inflow, Energy and Reserve Capacity Price Uncertainty

Hjelmeland¹,

Helseth²,

Korpås³

2018

Preprint

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We propose a model for medium-term hydropower scheduling (MTHS) with variable head and uncertainty in inflow, reserve capacity, and energy price. With an increase of intermittent energy sources in the generation mix, it is expected that a flexible hydropower producer can obtain added profits by participating in other markets than just the energy market. To capture this added potential the hydropower system should be modeled with a higher level of details. In this context, we apply an algorithm based on stochastic dual dynamic programming (SDDP) to solve the nonconvex MTHS problem, and show that the use of Strengthened Benders (SB) cuts to represent the expected future profit (EFP) function provide accurate scheduling results for slightly nonconvex problems. A method to visualize the EFP function in a dynamic programming setting is provided, serving as a useful tool for a priori inspection of the EFP shape and its nonconvexity.

show abstract

Efficient Parallelization of the Stochastic Dual Dynamic Programming Algorithm Applied to Hydropower Scheduling

Cited by 15 publications

References 12 publications

Optimal Operation of Interprovincial Hydropower System Including Xiluodu and Local Plants in Multiple Recipient Regions

Optimal Operation of Interprovincial Hydropower System Including Xiluodu and Local Plants in Multiple Recipient Regions

Medium-Term Hydropower Scheduling with Variable Head under Inflow, Energy and Reserve Capacity Price Uncertainty

Medium-Term Hydropower Scheduling with Variable Head under Inflow, Energy and Reserve Capacity Price Uncertainty

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