A Unit Commitment Algorithm and a Compact MILP Model for Short-Term Hydro-Power Generation Scheduling

Guedes, L.S.M.; Maia, Pedro de Mendonca; Lisboa, Adriano C.; Vieira, D.A.G.; Saldanha, R.R.

doi:10.1109/tpwrs.2016.2641390

Cited by 51 publications

(22 citation statements)

References 38 publications

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“…The basic task of the optimal operation of the hydropower station is to rationally arrange the load distribution of the units [18,19]. These units are put into operation on the basis of the known power generation flow or output of the hydropower station to achieve the highest power generation efficiency of the whole plant and increase the economic benefits of the hydropower station.…”

Section: Establishment Of the Optimal Operation Model In The Plantmentioning

confidence: 99%

Short-Term Optimal Operation of Baluchaung II Hydropower Plant in Myanmar

Saw

Chen

2020

Water

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The short-term optimal operation model discussed in this paper uses the 2016 to 2018 daily and monthly data of Baluchaung II hydropower station to optimize power generation by minimizing water consumption effectively in order to get more revenue from optimal operation. In the first stage, run-off-river type Baluchaung II hydropower station data was applied in a mathematical model of equal micro-increment rate method for optimal hydropower generation flow distribution unit results. In the second stage, dynamic programming was used to get optimal hydropower generation unit distribution results. The resultant data indicated that optimized results can effectively guide the actual operation run of this power station. The purpose of the optimal load dispatching unit was to consider the optimal power of each unit for financial profit and numerical programming on the actual data of Baluchaung II hydropower plant to confirm that our methods are able to find good optimal solutions which satisfy the objective values of 17.75% in flow distribution units and 24.16% in load distribution units.

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Section: Establishment Of the Optimal Operation Model In The Plantmentioning

confidence: 99%

Short-Term Optimal Operation of Baluchaung II Hydropower Plant in Myanmar

Saw

Chen

2020

Water

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“…Several authors have investigated how to include variable head in the hydropower scheduling problem. Most of the publications are related to solving the STHS problem; see [17][18][19][20]. There has also been some work conducted on how to include variable head in MTHS problems [8,21].…”

Section: Generation Functionmentioning

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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“…The set C contains concave regions of the generation function, where the discharge, q c , and problem. Most of the publications are related to solving the STHS problem, see [17][18][19][20]. There has also been some work conducted on how to include variable head in MTHS problems [8,21].…”

Section: Generation Functionmentioning

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

A Unit Commitment Algorithm and a Compact MILP Model for Short-Term Hydro-Power Generation Scheduling

Cited by 51 publications

References 38 publications

Short-Term Optimal Operation of Baluchaung II Hydropower Plant in Myanmar

Short-Term Optimal Operation of Baluchaung II Hydropower Plant in Myanmar

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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