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Two-layer Data-driven Robust Scheduling for Industrial Heat Loads
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DOI：10.35833/MPCE.2024.000105.png)
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Abstract
En 中文
This paper establishes a two-layer data-driven robust scheduling method to deal with the significant computational complexity and uncertainties in scheduling industrial heat loads. First, a two-layer deterministic scheduling model is proposed to address the computational burden of utilizing flexibility from a large number of bitumen tanks (BTs). The key feature of this model is the capability to reduce the number of control variables through analyzing and modeling the clustered temperature transfer of BTs. Second, to tackle the uncertainties in the scheduling problem, historical data regarding BTs are collected and analyzed, and a data-driven piecewise linear Kernel-based support vector clustering technique is employed to construct the uncertainty set with convex boundaries and adjustable conservatism, based on which robust optimization can be conducted. The case results indicate that the proposed method enables the utilization of flexibility in BTs, improving the level of onsite photovoltaic consumption and reducing the aggregated load fluctuation.
Keywords:
Uncertainty
Asphalt
Job shop scheduling
Temperature distribution
Heating systems
Optimal scheduling
Resistance heating
Bitumen tank
demand response
industrial heat load
robust optimization
scheduling
uncertainty
Journal
IF:
6.1
Papers:
1.6K
Citations:
6.0K