A stochastic dynamic programming model for hydropower scheduling with state-dependent maximum discharge constraints

Schäffer, Linn Emelie; Helseth, Arild; Korpås, Magnus

doi:10.1016/j.renene.2022.05.106

Cited by 21 publications

(32 citation statements)

References 27 publications

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“…The results from the strategy calculations show that the water value curves change considerably for some of the cases, but that the magnitude depends on the expected power price, the hydropower system and the type of constraint. The soft reservoir filling constraint gives higher water values for reservoir fillings close to the constraint threshold, in line with what has been shown in previous studies [32]. Furthermore, the impacts increase with increasing price variation and are found to be strongly dependent on the seasonal profile of the power price.…”

Section: Discussionsupporting

confidence: 90%

“…The method scales poorly due to the "curse of dimensionality", but is suitable for small hydropower systems. The main features of the modelling framework are presented here, while a more detailed description can be found in [32]. The scheduling problem is solved in two phases; a strategy phase and an operational simulation phase.…”

Section: Methodology 21 Hydropower Scheduling Modelmentioning

confidence: 99%

“…An underlying question is whether complex environmental constraints should be included in MTHS models, and what the operational implications of improving this aspect in the modelling could be. In [32] stochastic dynamic programming (SDP) [33] based modelling framework is used to accurately represent soft reservoir filling constraints (also referred to as state-dependent maximum discharge constraints) in the MTHS problem. The study demonstrates that the constraint has a significant impact on the water value curves and leads to a non-concave expected future profit function.…”

Section: Research Scope and Contributionmentioning

confidence: 99%

“…The expected future profit function is represented by convex combination of the expected future value points calculated in the previous stage (t + 1), as described in [32]. If the expected future value function is non-concave, special ordered sets of order 2 (SOS2) are used to ensure that adjacent points are used, turning the problem into a MILP [38].…”

Section: The Weekly Decision Problemmentioning

confidence: 99%

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Optimal operation of hydropower systems with environmental constraints on reservoir management

Schäffer

Bakken

Helseth

et al. 2023

Preprint

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Hydropower systems' unique capability to provide flexibility and long-term energy storage makes the low-carbon technology a key contributor to cost-effective and reliable decarbonisation of power systems. To ensure sustainable operation, environmental regulations are normally imposed on the plants. Some of these regulations can be difficult to model in existing scheduling tools based on optimisation because of their non-convex and logical characteristics. This study assesses the operational impacts of two types of environmental reservoir constraints, as well as the economic impacts of considering these constraints in medium-term hydropower scheduling. The results show that optimal reservoir management may change considerably due to these types of constraints. Furthermore, improved planning results in a decent increase in expected profit to the power producer under certain price assumptions in several of the simulated cases.

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Section: Discussionsupporting

confidence: 90%

Section: Methodology 21 Hydropower Scheduling Modelmentioning

confidence: 99%

Section: Research Scope and Contributionmentioning

confidence: 99%

Section: The Weekly Decision Problemmentioning

confidence: 99%

See 2 more Smart Citations

Optimal operation of hydropower systems with environmental constraints on reservoir management

Schäffer

Bakken

Helseth

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Although the numerical results were promising, the computation times of the SDDiP method are prohibitive, and it is therefore fair to claim that the algorithm is not generally suited for operational use in its current form. Recent research on the application of SDP to nonconvex environmental constraints is presented in Schäffer et al (2022).…”

Section: Introductionmentioning

confidence: 99%

Hydropower Scheduling with State-Dependent Discharge Constraints: An SDDP Approach

Helseth

Hågenvik

et al. 2022

J. Water Resour. Plann. Manage.

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Environmental constraints in hydropower systems serve to ensure sustainable use of water resources. Through accurate treatment in hydropower scheduling, one seeks to respect such constraints in the planning phase while optimizing the utilization of hydropower. However, many environmental constraints introduce state-dependencies and even nonconvexities to the scheduling problem, making them challenging to represent in stochastic hydropower scheduling models. This paper describes how the state-dependent maximum discharge constraint, which is widely enforced in the Norwegian hydropower system, can be embedded within the stochastic dual dynamic programming (SDDP) algorithm for hydropower scheduling without compromising computational time. In this work, a combination of constraint relaxation and time-dependent auxiliary lower reservoir volume bounds is applied, and the modeling is verified through computational experiments on two different systems. The results demonstrate that the addition of an auxiliary lower bound on reservoir volume has significant potential for improved system operation, and that a bound based on the minimum accumulated inflow in the constraint period is the most efficient.

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Implications of environmental constraints in hydropower scheduling for a power system with limited grid and reserve capacity

2023

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The negative impacts of power systems on biodiversity have to be mitigated, while simultaneously ensuring affordable and secure electricity supply for the future. This may lead to trade-off situations where ecological, recreational or social needs are weighted against the need for flexible power supply. This paper explores the interaction between the security of electricity supply and environmental constraints on the operation of flexible hydropower plants in the Norwegian renewable-based power system. A long-term, stochastic scheduling model of a wind- and hydropower-dominated power system is used to assess the implications of environmental constraints and reserve capacity requirements in combination. The model is used for a representative case study where three types of environmental constraints are imposed on the operation of the hydropower plants in a region of the congested Norwegian power system. In addition, requirements for spinning and non-spinning reserve capacity have to be met. The case study results demonstrate varying impacts on the operation of the hydropower plants, curtailment of demand and provision of reserve capacity depending on the type of environmental constraint being imposed.

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A stochastic dynamic programming model for hydropower scheduling with state-dependent maximum discharge constraints

Cited by 21 publications

References 27 publications

Optimal operation of hydropower systems with environmental constraints on reservoir management

Optimal operation of hydropower systems with environmental constraints on reservoir management

Hydropower Scheduling with State-Dependent Discharge Constraints: An SDDP Approach

Implications of environmental constraints in hydropower scheduling for a power system with limited grid and reserve capacity

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