Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Two-layer Data-driven Robust Scheduling for Industrial Heat Loads
Author:
Affiliation:

School of Engineering, Cardiff University, Cardiff CF24 3AA, U.K.

Fund Project:

This work was supported in part by the European Regional Development Fund through the Welsh Government (No. 80835) (Flexible Integrated Energy Systems West) and by the Engineering and Physical Sciences Research Council (No. EP/W028573/1, No. EP/T022795/1).

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
    Abstract:

    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.

    Reference
    Related
    Cited by
Get Citation
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:January 26,2024
  • Revised:April 02,2024
  • Adopted:
  • Online: January 24,2025
  • Published: