A Case Study on Medium-Term Hydropower Scheduling with Sales of Capacity

Hjelmeland, Martin N.; Helseth, Arild; Korpås, Magnus

doi:10.1016/j.egypro.2015.12.341

Cited by 8 publications

(12 citation statements)

References 4 publications

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“…[20], [21], [22], [23], [24], the SDDP method does not easily facilitate nonconvexities. As mentioned previously, nonconvexities arise, for example, when representing the detailed relationship between power output and water discharge [25], and the exact unit commitment of generators [26]. The core issue is how the nonconvex EFP function can be modeled.…”

Section: B Stochastic Dual Dynamic Programmingmentioning

confidence: 99%

See 1 more Smart Citation

Nonconvex Medium-Term Hydropower Scheduling by Stochastic Dual Dynamic Integer Programming

Hjelmeland

Zou

Helseth

et al. 2019

IEEE Trans. Sustain. Energy

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Hydropower producers rely on stochastic optimization when scheduling their resources over long periods of time. Due to its computational complexity, the optimization problem is normally cast as a stochastic linear program. In a future power market with more volatile power prices, it becomes increasingly important to capture parts of the hydropower operational characteristics that are not easily linearized, e.g. unit commitment and nonconvex generation curves.Stochastic dual dynamic programming (SDDP) is a stateof-the-art algorithm for long-and medium-term hydropower scheduling with a linear problem formulation. A recently proposed extension of the SDDP method known as stochastic dual dynamic integer programming (SDDiP) has proven convergence also in the nonconvex case. We apply the SDDiP algorithm to the medium-term hydropower scheduling (MTHS) problem and elaborate on how to incorporate stagewise dependent stochastic variables on the right-hand sides and the objective of the optimization problem. Finally, we demonstrate the capability of the SDDiP algorithm on a case study for a Norwegian hydropower producer.The case study demonstrates that it is possible but timeconsuming to solve the MTHS problem to optimality. However, the case study shows that a new type of cut, known as strengthened Benders cut, significantly contributes to closing the optimality gap compared to classical Benders cuts.

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Section: B Stochastic Dual Dynamic Programmingmentioning

confidence: 99%

“…For the case study, we considered a Norwegian hydropower reservoir system that consists of reservoirs with both shortand long-term storage capacity. The case study is documented in previous publications [15], [26]. The system contains three hydropower reservoirs, with two power stations and a total installed capacity of 414 MW.…”

Section: Case Studymentioning

confidence: 99%

Nonconvex Medium-Term Hydropower Scheduling by Stochastic Dual Dynamic Integer Programming

Hjelmeland

Zou

Helseth

et al. 2019

IEEE Trans. Sustain. Energy

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“…Note that this flexibility is highly dependent on characteristics of the hydropower system being studied, see e.g. [20] for a similar test on a different system. Comparing run 3 with run 1, reduction in expected total profit of 1.03 and 0.64 percentage points is observed for cases A and B, respectively, in the E + C mode.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…It extends the hybrid SDP/SDDP algorithm in [18, 19] by allowing sequential sales of reserve capacity and energy, treating both prices as stochastic. However, as documented in [20], the approximation error introduced when linearising non‐convex system characteristics in the system simulation can be substantial, particularly when considering sales of reserve capacity.…”

Section: Introductionmentioning

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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“…The use of tank water is heavily penalized in the objective function and will influence the cuts which will be described in Section 3.1.8. For the following iteration, the model will have a strong incentive to avoid emptying the reservoir [38].…”

Section: Hydropower Modulementioning

confidence: 99%

Medium-term hydropower scheduling with provision of capacity reserves and inertia

Brekke

Hjelmeland

Korpås

2016

2016 51st International Universities Power Engineering Conference (UPEC)

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The thesis was carried out during the autumn semester of 2015, in cooperation with SINTEF Energy Research AS.I would like to thank my supervisor Magnus Korpås for his guidance and encouragement throughout the semester. I also wish to thank my co-supervisor Martin Nødland Hjelmeland for his help with understanding the model, which has been of paramount importance for the work achieved. Further, I am grateful for the help of Erik Alexander Jansson at Statnett, for providing me with data of the rotational energy in the Nordic power system. I am also grateful for all the help and hydropower system data provided by Magnus Landstad and his colleagues at Lyse Produksjon AS.Finally, I am thankful for the many fruitful discussions with my fellow students, providing valuable help with problems concerning my own thesis, as well as giving me insight into the interesting topics of theirs.

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A Case Study on Medium-Term Hydropower Scheduling with Sales of Capacity

Cited by 8 publications

References 4 publications

Nonconvex Medium-Term Hydropower Scheduling by Stochastic Dual Dynamic Integer Programming

Nonconvex Medium-Term Hydropower Scheduling by Stochastic Dual Dynamic Integer Programming

Assessing hydropower operational profitability considering energy and reserve markets

Medium-term hydropower scheduling with provision of capacity reserves and inertia

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