Multi-time-scale robust scheduling optimization of industrial integrated energy system under multiple uncertainties

LIU Lin; LIU Yurong; QIAN Feng

doi:10.11949/0438-1157.20260321

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Multi-time-scale robust scheduling optimization of industrial integrated energy system under multiple uncertainties

更新时间：2026-05-18

- Multi-time-scale robust scheduling optimization of industrial integrated energy system under multiple uncertainties
- CIESC Journal (2026)
- 作者机构：
  
  1.华东理工大学信息科学与工程学院，上海 200237
  2.能源化工过程智能制造教育部重点实验室，上海 200237
- 作者简介：
- 基金信息：
- DOI：10.11949/0438-1157.20260321
  CLC： TQ021.8;TM73
- Received：16 March 2026，
  
  Revised：2026-05-17，
  
  Accepted：18 May 2026，
- 稿件说明：
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LIU Lin, LIU Yurong, QIAN Feng. Multi-time-scale robust scheduling optimization of industrial integrated energy system under multiple uncertainties[J/OL]. CIESC Journal, 2026.
DOI：

LIU Lin, LIU Yurong, QIAN Feng. Multi-time-scale robust scheduling optimization of industrial integrated energy system under multiple uncertainties[J/OL]. CIESC Journal, 2026. DOI： 10.11949/0438-1157.20260321.

摘要

针对流程工业综合能源系统碳排放密集、多能耦合复杂，且单一时间尺度调度难以兼顾鲁棒与经济性的问题，提出一种考虑多重不确定性的工业综合能源系统日前-日内多时间尺度协同调度方法。首先，构建集成可再生能源的工业热电联产模型，引入阶梯碳交易与电热柔性需求响应，提升系统低碳灵活性。其次，在日前阶段，针对源荷预测误差与电价波动的多重不确定性，建立两阶段分布鲁棒优化模型，采用基于历史样本的模糊集及多面体集表征源荷和电价的不确定性，利用强对偶理论和列与约束生成算法求解日前鲁棒最优机组出力计划；日内阶段基于模型预测控制方法设计滚动优化策略，实现对日前计划的动态跟踪与偏差修正。算例结果表明，所提方法能够有效提升可再生能源消纳能力，降低系统运行成本与碳排放，并在多重不确定性扰动下减少日内修正代价，实现经济性、低碳性与鲁棒性的平衡。

Abstract

To address the problems of carbon-intensive operation

complex multi-energy coupling

and the difficulty of achieving both robustness and economic performance under a single time-scale scheduling framework in industrial integrated energy systems

a day-ahead and intra-day multi-time-scale coordinated scheduling method considering multiple uncertainties is proposed. First

an industrial combined heat and power model integrated with renewable energy is established

and ladder-type carbon trading and integrated electricity-heat flexible demand response are introduced to enhance the low-carbon and operational flexibility of the system. Second

in the day-ahead stage

a two-stage distributionally robust optimization model is developed to address multiple uncertainties arising from source-load forecast errors and electricity price fluctuations. Historical-sample-based ambiguity sets and polyhedral sets are employed to characterize the uncertainties of source-load and electricity prices

respectively

and the day-ahead robust optimal unit output schedule is obtained by using strong duality theory and a column-and-constraint generation algorithm. In the intra-day stage

a rolling optimization strategy based on model predictive control is designed to dynamically track the day-ahead schedule and correct real-time deviations. Case study results demonstrate that the proposed method can effectively improve renewable energy accommodation

reduce operating cost and carbon emissions

and lower intra-day adjustment costs under multiple uncertainty disturbances

thereby achieving a balance among economy

low-carbon performance

and robustness.

关键词

Keywords

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