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1.清华大学电机工程与应用电子技术系,北京市 100084
2.国网安徽省电力有限公司,安徽省合肥市 230061
3.国网安徽省电力有限公司电力科学研究院,安徽省合肥市 230022
Received:23 October 2024,
Revised:2024-11-23,
Online First:13 January 2025,
Published:25 January 2026
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SU Xiangbo,LYU Ruike,BAI Yunlong,et al.Modeling Method for Feasible Region of Industrial Production Processes Based on Optimal Adjustable Load Model[J].Automation of Electric Power Systems,2026,50(2):18-26.
苏湘波,吕睿可,白云龙等.基于最优可调负荷模型的工业生产过程用电可行域建模方法[J].电力系统自动化,2026,50(2):18-26. DOI: 10.7500/AEPS20241023003.
SU Xiangbo,LYU Ruike,BAI Yunlong,et al.Modeling Method for Feasible Region of Industrial Production Processes Based on Optimal Adjustable Load Model[J].Automation of Electric Power Systems,2026,50(2):18-26. DOI: 10.7500/AEPS20241023003.
工业用户具有通过需求侧调控为电力系统提供灵活性的巨大潜力。虽然工业生产过程具有复杂的用能机理,但目前已有包括状态-任务网络(STN)在内的通用模型可以有效建模一类工业过程的用能约束。然而,STN模型具有大量离散变量且所需参数较多,难以直接嵌入面向海量需求侧灵活性资源的经济调度,阻碍了工业用户灵活性的充分利用。为此,面向可用STN模型建模生产过程中的工业用户,提出了基于最优可调负荷模型(OALM)的可行域建模方法。首先,利用需求响应时间尺度消除STN模型的整数变量,实现模型线性化;然后,针对工业用户的用电特点,采用可调负荷模型近似线性化STN模型,利用鲁棒优化方法求解其参数以最大化可行域体积,并通过添加功率能量耦合约束来解决现行可行域参数优化的无界问题。算例表明,与高维、含整数变量的原始STN模型相比,低维、线性的OALM在不同的生产计划、时间尺度和电价场景下均能保持较高的准确度和稳定性,为解决工业用户大规模接入负荷侧调控所面临的维数灾难问题提供了新的思路。
Industrial users possess significant potential to provide flexibility for the power system through demand-side regulation. Although industrial production processes involve complex energy consumption mechanisms
existing general models such as the state-task network (STN) can effectively model the energy constraints of certain types of industrial processes. However
the STN model involves a large number of discrete variables and requires extensive parameters
making it difficult to directly embed into economic dispatch for massive demand-side flexibility resources. This hinders the full utilization of industrial user flexibility. To address this
a modeling method for the feasible region based on the optimal adjustable load model (OALM) is proposed for industrial users whose production processes can be modeled using STN. First
the integer variables of the STN model are eliminated by leveraging the demand response timescale
thereby linearizing the model. Then
considering the electricity consumption characteristics of industrial users
the adjustable load model is employed to approximate the linearized STN model. Robust optimization method is used to solve its parameters to maximize the volume of the feasible region
and power-energy coupling constraints are added to resolve the unbounded issue in current feasible region parameter optimization. Case studies demonstrate that
compared to the high-dimensional and integer-variable-based original STN model
the low-dimensional and linear OALM maintains high accuracy and stability under different production plans
timescales
and electricity price scenarios. This provides a new approach to addressing the curse of dimensionality faced by large-scale integration of industrial users into load-side regulation.
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