Medium-term optimization of pumped hydro storage with stochastic intrastage subproblems

Abgottspon, Hubert; Andersson, Göran

doi:10.1109/pscc.2014.7038352

Cited by 13 publications

(12 citation statements)

References 13 publications

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“…how much water to use in a given week, while intra-stage decisions concern the detailed operation using a much finer time-resolution. Based on this scheme [18] proposed a method for stochastic mediumterm hydropower scheduling considering participation in both the spot and secondary reserve markets. Inter-stage decisions regarding operation of seasonal reservoirs are found by use of SDP, and the shorter term intra-stage decisions, e.g.…”

Section: Introductionmentioning

confidence: 99%

Optimal Medium-Term Hydropower Scheduling Considering Energy and Reserve Capacity Markets

Helseth

Fodstad

2016

IEEE Trans. Sustain. Energy

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This paper describes a method for optimal scheduling of hydropower systems for a profit maximizing, price-taking and risk neutral producer selling energy and capacity to separate and sequentially cleared markets. The method is based on a combination of stochastic dynamic programming (SDP) and stochastic dual dynamic programming (SDDP), and treats inflow to reservoirs and prices for energy and capacity as stochastic variables.The proposed method is applied in a case study for a Norwegian watercourse, quantifying the expected changes in schedules and water values when going from an energy-only market to a joint treatment of energy and reserve capacity markets.Arild Helseth (M'10) was born on Stord, Norway in 1977. He received the M.Sc. and Ph.D. degrees in electrical power engineering from the Norwegian University of Science and Technology. Currently he works at SINTEF Energy Research with hydro-thermal and hydropower scheduling models and methods.

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Section: Introductionmentioning

confidence: 99%

Optimal Medium-Term Hydropower Scheduling Considering Energy and Reserve Capacity Markets

Helseth

Fodstad

2016

IEEE Trans. Sustain. Energy

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“…A start-up cost β g is incurred whenever a generator goes from a non-spinning to a spinning state. Incurred start-up costs are given by the variable y gk in (16). The time-coupling in (16) indicates that each unit's initial spinning state u g0 at the beginning of the week should enter the state space.…”

Section: Decision Problemmentioning

confidence: 99%

“…Incurred start-up costs are given by the variable y gk in (16). The time-coupling in (16) indicates that each unit's initial spinning state u g0 at the beginning of the week should enter the state space. In our experience, this detail has little impact on the computed water values.…”

Section: Decision Problemmentioning

confidence: 99%

Assessing hydropower operational profitability considering energy and reserve markets

Helseth

Fodstad

Askeland

et al. 2017

IET Renewable Power Generation

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The authors analyse the operational profitability of a hydropower system selling both energy and reserve capacity in a competitive market setting. A mathematical model based on stochastic dynamic programming is used to compute the water values for the system considering different power plant configurations. The uncertainties in inflow and both energy and reserve capacity prices are considered through a discrete Markov chain. Subsequently, the system operation is simulated based on the obtained water values to assess system performance and expected revenues from the two markets. The model is applied in a case study for a Norwegian hydropower producer, showing how the power plant operation changes and profitability increases when considering sale of reserve capacity. The authors emphasise on how the water values are influenced by the opportunity to sell reserve capacity, and assess how the representation of non-convex relationships in the water value computations as well as simulation influence the profitability. P g max , P g min Max./Min. capacity, MW Q min minimum river flow, m 3 /s Q gm discharge in point m, m 3 /s T number of weeks in planning horizon V max , V min Max./Min. reservoir volume, Mm 3 V n reservoir volume at point n, Mm 3

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“…A considerable amount of research has been conducted for solving the nonconvex MTHS problem, such as [8][9][10][11]. Except [10], which proposed a novel approach that uses step functions to model a nonconvex EFP function, they all rely on solving some relaxation of the original problem.…”

Section: Introductionmentioning

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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Medium-term optimization of pumped hydro storage with stochastic intrastage subproblems

Cited by 13 publications

References 13 publications

Optimal Medium-Term Hydropower Scheduling Considering Energy and Reserve Capacity Markets

Optimal Medium-Term Hydropower Scheduling Considering Energy and Reserve Capacity Markets

Assessing hydropower operational profitability considering energy and reserve markets

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

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