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Implementing demand response in day-ahead economic dispatch using stochastic segmentation
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DOI：10.1016/j.epsr.2024.111233.png)
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Abstract
En 中文
Large-scale integration of wind power imposes heightened requirements on system flexibility, whereas existing methods struggle to meet the needs of flexible optimization. This paper proposes an economic dispatch method based on stochastic segmentation (SS), which considers the incentive-based demand response (IBDR) to promote the consumption of wind power. During the simulation, the Karhunen-Lo & egrave;ve (KL) transform and R-vine copula are used to construct the spatiotemporal correlation structure of wind speeds, and the scenarios with the same statistical characteristics as historical data are generated. In order to narrow the fluctuation range of random variables, the improved K-means clustering algorithm is utilized for cluster analysis. Thereafter, the scenario set corresponding to each clustering cluster is used as the input to the economic dispatch model, so as to obtain the dispatch strategies under different types of wind conditions. Simulation studies are carried out on the modified IEEE 30-bus system and IEEE 118-bus system. The research results show that, compared to the conventional method, the proposed method reduces the fuel costs of these two systems by 10.96% and 12.33%, and the wind curtailment rates by 32.82% and 36.21%, respectively. This indicates that the proposed method is an effective measure to cope with the stochastic economic dispatch problem.
Keywords:
Wind power
Incentive-based demand response
Spatiotemporal correlation
Stochastic segmentation
Economic dispatch
Journal
IF:
4.2
Papers:
1.1W
Citations:
2.2W