Two-layer Data-driven Robust Scheduling for Industrial Heat Loads  

作　　者：Chuanshen Wu Yue Zhou Jianzhong Wu 

机构地区：[1]School of Engineering,Cardiff University,Cardiff CF243AA,U.K.

出　　处：《Journal of Modern Power Systems and Clean Energy》2025年第1期265-275,共11页现代电力系统与清洁能源学报(英文)

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

摘　　要：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 Kernelbased 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.

关 键 词：Bitumen tank demand response industrial heat load robust optimization SCHEDULING UNCERTAINTY 

分 类 号：TM73[电气工程—电力系统及自动化]