Hydropower Aggregation by Spatial Decomposition—An SDDP Approach

Helseth, Arild; Mo, Birger

doi:10.1109/tste.2022.3214497

Cited by 8 publications

(2 citation statements)

References 41 publications

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“…Therefore aggregated hydropower representations are used to calculate the strategic management of hydro resources. The disaggregation of aggregated complex water courses requires several manual calibrations and is implemented in a rule-based reservoir drawdown model today [5]. Increasing renewable energy sources integration in the Nordic power system and more interconnection to the central European power system makes it challenging to establish precise approximations of the aggregate hydro system [6].…”

Section: Normencluaturementioning

confidence: 99%

Scenario Clustering for Long-term Hydropower-Thermal Scheduling using Shape Feature Extraction

Wang¹,

Yousefi²,

Rajasekharan³

et al. 2023

Preprint

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<p>This paper proposes a framework consisting of a forepart precursor and the Scenario Fan Problem (SFP) for long-term hydropower scheduling, using shape-based feature extraction. The domain feature of the forepart precursor is scenario decomposition and scenario selection. Then, the selected scenarios will be the input of SFP, based on the feature of the new inflow scenario. Besides, we compared the performance of 5 clustering methods to generate the boundary variation on a hydro-thermal test case, using in-sample and out-of-sample analysis. Our results show that the proposed framework can significantly reduce the computational time of SFP by around 90%. The results also reveal that the shape-based method performs better regarding the boundary variation generation of the reservoir levels. In addition, this study demonstrates the possibility of performing long-term hydropower scheduling at a disaggregated level with detailed topology information and a higher time resolution. This provides more opportunities to deal with the penetration of Renewable Energy Sources (RES) in long-term hydropower scheduling and take into account more uncertainties in other variable renewable energy sources.</p>

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Section: Normencluaturementioning

confidence: 99%

Scenario Clustering for Long-term Hydropower-Thermal Scheduling using Shape Feature Extraction

Wang¹,

Yousefi²,

Rajasekharan³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The classical long‐term hydro‐thermal scheduling models in the Nordic system use an aggregated hydropower representation to calculate the strategic management of hydro resources. Disaggregation of aggregated hydropower plants along complex water courses requires multiple manual calibrations and is implemented as a rule‐based reservoir drawdown model today [6]. High penetration of VRES in the Nordic power system and interconnection to the European power system introduces several challenges in determining precise approximations of the aggregated hydro system [7].…”

Section: Introductionmentioning

confidence: 99%

Exploring the application of machine‐learning techniques in the next generation of long‐term hydropower‐thermal scheduling

Wang,

Yousefi,

Rajasekharan

et al. 2024

IET Renewable Power Gen

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This paper introduces a shape‐based inflow scenarios reduction framework applied in long‐term hydro‐thermal scheduling. This scheduling problem involves strategically managing the limited stored hydro energy in coordination with the electricity system. The scenario fan problem, selected as a long‐term hydro‐thermal scheduling approach, can effectively represent the individual hydropower plants and their short‐term flexibility within the long‐term scheduling process, making it well‐suited for capturing the disaggregated physical characteristics of hydro stations. However, the computational burden of scenario fan problem increases due to the complexity of the cascaded system and the large number of inflow scenarios. To tackle these challenges, a machine learning clustering method is developed for extracting shape‐based features to capture the energy and shape attributes of hydro inflows. A comparative analysis was conducted to evaluate the effectiveness of the proposed method, comparing it with four established clustering methods. This evaluation was carried out on a hydro‐thermal test case, utilizing both in‐sample and out‐of‐sample data analysis. The findings demonstrate that the proposed framework outperforms other methods in terms of computational time by 42.6% and optimal reservoir trajectories by 12.1% considering K‐means using Euclidean distance as the benchmark. Additionally, this study showcases the feasibility of performing long‐term hydro‐thermal scheduling at a granular level, with detailed topological information and a shorter time scale. This expanded approach opens up new possibilities for addressing the integration of renewable energy resources within the realm of long‐term hydro‐thermal scheduling.

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Approximating hydropower systems by feasibility spaces in stochastic dual dynamic programming

Helseth

2024

Electric Power Systems Research

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Hydropower Aggregation by Spatial Decomposition—An SDDP Approach

Cited by 8 publications

References 41 publications

Scenario Clustering for Long-term Hydropower-Thermal Scheduling using Shape Feature Extraction

Scenario Clustering for Long-term Hydropower-Thermal Scheduling using Shape Feature Extraction

Exploring the application of machine‐learning techniques in the next generation of long‐term hydropower‐thermal scheduling

Approximating hydropower systems by feasibility spaces in stochastic dual dynamic programming

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