Integrated Two-Stage Scheduling Framework for Compressor Units via a Hybrid Algorithm and Dynamic Programming.
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| Title: | Integrated Two-Stage Scheduling Framework for Compressor Units via a Hybrid Algorithm and Dynamic Programming. |
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| Authors: | Chen, Cheng1 (AUTHOR), Zhao, Chun2 (AUTHOR), Zhang, Yunpeng2,3 (AUTHOR), Gao, Xi1,2 (AUTHOR), Chen, Linying2 (AUTHOR), Wei, Qi3 (AUTHOR), Xing, Likai3 (AUTHOR), Song, Feng3 (AUTHOR), Chen, Xiaoming1 (AUTHOR) chen_xm@dlut.edu.cn |
| Source: | Energies (19961073). Jun2026, Vol. 19 Issue 11, p2566. 31p. |
| Subject Terms: | *Natural gas storage, *Dynamic programming, *Compressor performance, *Time-based pricing, *Swarm intelligence, *Resource allocation, *Metaheuristic algorithms, *Electric power management |
| Abstract: | Electrically driven compressors are a primary energy consumer in natural gas storage facilities. Formulating an optimal gas injection allocation strategy considering their nonlinear characteristics and time-of-use (TOU) electricity prices is crucial. However, single-model optimizations struggle with this due to high dimensionality and strongly coupled variables. To overcome these challenges, we propose a two-stage "instantaneous load allocation—day-ahead scheduling" framework. Stage I employs a hybrid algorithm (ICSA-WOA) to optimize load allocations across various flow rates, generating a lookup table that effectively decouples the underlying physical model. Stage II utilizes this table alongside TOU prices to perform rapid day-ahead scheduling via dynamic programming (DP). Results demonstrate that ICSA-WOA achieves superior comprehensive performance compared to seven classical swarm intelligence algorithms. Furthermore, joint optimization of the pressure ratio and load via ICSA-WOA reduces the total power consumption by 9.7–10.9% relative to traditional fixed-ratio modes. Most significantly, while rigorously ensuring daily injection targets and safety, the proposed method reduces daily electricity costs by 3.3–14.2% compared to single-model approaches, providing a reasonable strategy for economic gas storage operations. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | Electrically driven compressors are a primary energy consumer in natural gas storage facilities. Formulating an optimal gas injection allocation strategy considering their nonlinear characteristics and time-of-use (TOU) electricity prices is crucial. However, single-model optimizations struggle with this due to high dimensionality and strongly coupled variables. To overcome these challenges, we propose a two-stage "instantaneous load allocation—day-ahead scheduling" framework. Stage I employs a hybrid algorithm (ICSA-WOA) to optimize load allocations across various flow rates, generating a lookup table that effectively decouples the underlying physical model. Stage II utilizes this table alongside TOU prices to perform rapid day-ahead scheduling via dynamic programming (DP). Results demonstrate that ICSA-WOA achieves superior comprehensive performance compared to seven classical swarm intelligence algorithms. Furthermore, joint optimization of the pressure ratio and load via ICSA-WOA reduces the total power consumption by 9.7–10.9% relative to traditional fixed-ratio modes. Most significantly, while rigorously ensuring daily injection targets and safety, the proposed method reduces daily electricity costs by 3.3–14.2% compared to single-model approaches, providing a reasonable strategy for economic gas storage operations. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en19112566 |
