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

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 ac...

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Veröffentlicht in:Renewable energy Jg. 251; S. 123316
Hauptverfasser: Zhao, Zhuang, Wu, Jiahui, Wang, Bo, Wang, Rui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.10.2025
Schlagworte:
absorption
algorithms
clean energy
energy
energy costs
equipment maintenance and repair
Flexible transformation
hybrids
Improved particle swarm
Multi-interval efficiency coefficient
Multi-interval robust optimization
New power system
power plants
solar energy
Source load uncertainty
system optimization
uncertainty
wind farms
wind power
New power system
Multi-interval efficiency coefficient
Improved particle swarm
Flexible transformation
Source load uncertainty
Multi-interval robust optimization
ISSN:0960-1481
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Zusammenfassung: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.
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ISSN:0960-1481
DOI:10.1016/j.renene.2025.123316