Hydrothermal scheduling in Norway using stochastic dual dynamic programming; a large-scale case study

Gjerden, Knut Skogstrand; Helseth, Arild; Mo, Birger; Warland, Geir

doi:10.1109/ptc.2015.7232278

Cited by 28 publications

(20 citation statements)

References 7 publications

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“…Import and export across the system boundary is modelled by defining a set ℳ of exogenous market areas with a sales and purchase option at the price λ mk . The future expected operating cost is represented by α o, t + 1 which is constrained by Benders cuts in (11). The hydropower system comprises hydropower modules ∈ ℋ connected through the three waterways discharge, bypass and spillage.…”

Section: Operational Decision Problemmentioning

confidence: 99%

“…An example is the prolonged dry or wet periods that sometimes occur in geographical regions. In our experience it is challenging to obtain SDDP-based strategies (cuts) for the detailed description of the Norwegian hydropower system that perform well when simulating the system using historical data [11]. A different method known as sampling stochastic dynamic programming was proposed in [21], and is compatible with the direct use of historical data.…”

Section: Introductionmentioning

confidence: 99%

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Detailed long‐term hydro‐thermal scheduling for expansion planning in the Nordic power system

Helseth

Henden

et al. 2017

IET Generation, Transmission & Distribution

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Abstract:The authors describe a method for long-term hydro-thermal scheduling allowing treatment of detailed large-scale hydro systems. Decisions for each week are determined by solving a two-stage stochastic linear programming problem considering uncertainty in weather and exogenous market prices. The overall scheduling problem is solved by embedding such two-stage problems in a rolling horizon simulator. The method is verified on data for the Nordic power system, studying the incremental changes in expected socio-economic surplus for expansions in both the transmission and generation systems. Comparisons are made with a widely used existing long-term hydro-thermal scheduling model. The results indicate that the model is well suited to valuate the flexibility of hydropower in systems with a high share of intermittent renewable generation. Nomenclature Index setsA set of price zones C o, t set of Benders cuts for scenario o and week t D a set of price-elastic demand steps in zone a G a set of thermal generators in zone a ℋ a set of hydropower modules in zone a K set of time steps within the week ℒ a set of interconnections connected to zone a ℳ set of exogenous markets N h set of efficiency-curve segments for module h P a set of pumps in zone a S set of N S scenarios S R reduced set of N R scenarios ω h

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Section: Operational Decision Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detailed long‐term hydro‐thermal scheduling for expansion planning in the Nordic power system

Helseth

Henden

et al. 2017

IET Generation, Transmission & Distribution

Self Cite

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“…An alternative method based on the stochastic dual dynamic programming approach (SDDP) was developed in [23], which overcomes the dimensionality problem so that large-scale systems can be investigated [24].…”

Section: Previous Work On Hydro System Aggregationmentioning

confidence: 99%

Aggregation Methods for Modelling Hydropower and Its Implications for a Highly Decarbonised Energy System in Europe

Härtel

Korpås

2017

Energies

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Given the pursuit of long-term decarbonisation targets, future power systems face the task of integrating the renewable power and providing flexible backup production capacity. Due to its general ability to be dispatched, hydropower offers unique features and a backup production option not to be neglected, especially when taking the flexibility potential of multireservoir systems into account. Adequate hydropower representations are a necessity when analysing future power markets and aggregation methods are crucial for overcoming computational challenges. However, a major issue is that the aggregation must not be a too flexible representation. In a first step, a novel equivalent hydro system model implementation including a possibility to integrate pumping capacity and appropriate handling of multiple water paths (hydraulic coupling) by making use of an ex-ante optimisation is proposed. In a second step, a clustered equivalent hydro system model implementation employing k-means clustering is presented. A comparison of both aggregation approaches against the detailed reference system shows that both aggregated model variants yield significant reductions in computation time while keeping an adequate level of accuracy for a highly decarbonised power system scenario in Europe. The aggregation methods can easily be applied in different model types and may also be helpful in the stochastic case.

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“…However, they do not investigate the influence of modelling uncertainty on electricity prices. The authors of [19] use a model similar to the one used in [18]. Both models are based on the SDDP approach presented by [20].…”

Section: Introductionmentioning

confidence: 99%

“…Both models are based on the SDDP approach presented by [20]. In [19], the authors compare the SDP approach of [17] with two SDDP approaches regarding electricity price modelling. The first one uses a statistical model to generate water inflow scenarios for the optimisation as well as the simulation.…”

Section: Introductionmentioning

confidence: 99%

Impact of Long-Term Water Inflow Uncertainty on Wholesale Electricity Prices in Markets with High Shares of Renewable Energies and Storages

2020

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Renewable energy shares in electricity markets are increasing and therefore also require an increase in flexibility options. Conventional electricity price modelling with optimisation models in thermally dominated markets is not appropriate in markets with high shares of renewable energies and storages because price structures are not adequately represented. Previous research has already identified the impact of uncertainty in renewable energy feed-in on investment and dispatch decisions. However, we are not aware of any work that investigates the influence of uncertainties on price structures by means of optimisation models. Appropriate modelling of electricity price structures is important for investment and policy decisions. We have investigated the influence of uncertainty concerning water inflow by applying a second stage stochastic dual dynamic programming approach in a linear optimisation model using Norway as an example. We found that the influence of uncertainty concerning water inflow combined with high shares of storages has a strong impact on the electricity price structures. The identified structures are highly influenced by seasonal water inflow, electricity demand, wind, and export profiles. Additionally, they are reinforced by seasonal primary energy source prices and import prices. Incorporating uncertainties in linear optimisation models improves the price modelling and provides, to a large extent, an explanation for the seasonal patterns of Norwegian electricity market prices. The paper explains the basic pricing mechanisms in markets with high shares of storages and renewable energies which are subject to uncertainty. To identify these fundamental mechanisms, we focused on uncertainty regarding water inflow, but the basic results hold true for uncertainties regarding other renewable energies as well.

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Hydrothermal scheduling in Norway using stochastic dual dynamic programming; a large-scale case study

Cited by 28 publications

References 7 publications

Detailed long‐term hydro‐thermal scheduling for expansion planning in the Nordic power system

Detailed long‐term hydro‐thermal scheduling for expansion planning in the Nordic power system

Aggregation Methods for Modelling Hydropower and Its Implications for a Highly Decarbonised Energy System in Europe

Impact of Long-Term Water Inflow Uncertainty on Wholesale Electricity Prices in Markets with High Shares of Renewable Energies and Storages

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