Research on source-load uncertainty optimal scheduling based on a hybrid robust multi-interval optimization method

Zhuang Zhao, Jiahui Wu, Bo Wang and Rui Wang

Renewable Energy, 2025, vol. 251, issue C

Abstract: The new power systems(NPS) play an important role in enabling the efficient use of clean energy. In order to improve the operation economy, reliability and efficient consumption of renewable energy of NPS, a hybrid multi-interval robust optimization model was proposed. First, the model takes into account the improved thermal power flexible conversion energy cost model, and designs the output efficiency interval model of wind farm and photovoltaic power station considering the impact of equipment maintenance and failure. Compared with traditional models, these models can more accurately reflect the energy consumption cost and actual output of power supply equipment. Secondly, a hybrid multi-interval robust optimization model is proposed to improve the conservatism of traditional interval optimization methods. In addition, in order to improve the solving efficiency, this paper introduces the adaptive compression particle swarm optimization algorithm to overcome the problem that the traditional optimization algorithm is easy to fall into the local optimal solution. Finally, the IEEE30-node system is taken as an example for simulation verification. The results show that the proposed method can effectively reduce the adverse effects caused by the uncertainty of source and load, and improve the absorption rate of wind power and photovoltaic.

Keywords: New power system; Flexible transformation; Multi-interval efficiency coefficient; Source load uncertainty; Multi-interval robust optimization; Improved particle swarm (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:251:y:2025:i:c:s0960148125009784

DOI: 10.1016/j.renene.2025.123316

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