OPTIMAL CONTROL STRATEGY FOR SHORT-PROCESS STEEL ENTERPRISES PARTICIPATING IN PEAK REGULATION OF HIGH-PENETRATION RENEWABLE ENERGY SYSTEMS

Li Junhui; Zhang Jiong; Fan Tianzhen; Wang Shunjiang; Li Cuiping; Zhu Xingxu

doi:10.19912/j.0254-0096.tynxb.2025-0654

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (9) : 448-459. DOI: 10.19912/j.0254-0096.tynxb.2025-0654

OPTIMAL CONTROL STRATEGY FOR SHORT-PROCESS STEEL ENTERPRISES PARTICIPATING IN PEAK REGULATION OF HIGH-PENETRATION RENEWABLE ENERGY SYSTEMS

Li Junhui¹, Zhang Jiong¹, Fan Tianzhen¹, Wang Shunjiang², Li Cuiping¹, Zhu Xingxu¹

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Abstract

In order to solve the problem that the source-load interaction mechanism is unclear when short-process steel enterprises participate in peak regulation of power system with high proportion of renewable energy, resulting in the limited effect of system peak regulation, an optimal control method for short-process steel enterprises to participate in power system peak regulation is proposed. Firstly, the peak regulation architecture of the renewable energy system integrating the production load of short-process steel enterprises is designed, and a hierarchical response model is constructed according to the load regulation characteristics. Secondly, based on the non-cooperative game process of grid demand release, enterprise response solution and aggregator information interaction, the dual signal response mechanism of electricity price and incentive is formulated, taking into account power grid peak regulation demand and enterprise production electricity consumption in the high proportion of renewable energy power system. Finally, a two-layer economic scheduling model of source and load is constructed, and the model is solved through hierarchical iterative and the balance of source and load interests is achieved. The simulation results show that the proposed method can reduce the peak-to-valley difference of load by 2.74%, increase the renewable energy accommodation of the power grid by 5.53%, and reduce the comprehensive electricity cost of enterprises by 17.72%, which significantly improves the response enthusiasm of enterprises and improves the peak regulation effect of the power grid.

Key words

power systems / peak regulation / renewable energy / load model / game theory / demand response / real-time electricity price

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Li Junhui, Zhang Jiong, Fan Tianzhen, Wang Shunjiang, Li Cuiping, Zhu Xingxu. OPTIMAL CONTROL STRATEGY FOR SHORT-PROCESS STEEL ENTERPRISES PARTICIPATING IN PEAK REGULATION OF HIGH-PENETRATION RENEWABLE ENERGY SYSTEMS[J]. Acta Energiae Solaris Sinica. 2026, 47(9): 448-459 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0654

References

[1] 国家发展改革委. 电力发展“十三五”规划[EB/OL].https://www.gov.cn/xinwen/2016-12/22/content_5151549.htm.
National Development and Reform Commission. The 13th Five-Year Plan for Electric Power Development[EB/OL].https://www.gov.cn/xinwen/2016-12/22/content_5151549.htm.
[2] 国家能源局. 国家能源局发布2025年全国电力工业统计数据[EB/OL].https://www.gov.cn/lianbo/bumen/202506/content_7029138.htm.
National Energy Administration. National Energy Administration releases2025 national power industry statistics[EB/OL]. https://www.gov.cn/lianbo/bumen/202506/content_7029138.htm.
[3] 姜婷玉, 陶劲宇, 王珂, 等. 基于非合作博弈的高能耗工业负荷参与调峰策略[J]. 电力系统自动化, 2025, 49(3): 13-21.
Jiang T Y, Tao J Y, Wang K, et al.High energy consumption industrial load participation peak shaving strategy based on non-cooperative game[J]. Automation of Electric Power System, 2025, 49(3): 13-21.
[4] 冉博路, 张姝, 黄河, 等. 基于二次聚类的工业用户负荷可调节潜力特征提取与综合评估方法[J]. 电力系统保护与控制, 2023, 51(18): 157-168.
Ran B L, Zhang S, Huang H, et al.Feature extraction and comprehensive evaluation method of load adjustable potential for industrial users based on secondary clustering[J]. Power System Protection and Control, 2023, 51(18): 157-168.
[5] 梁海平, 谢鑫, 李世航. 考虑空气源热泵负荷聚合参与的需求响应[J]. 太阳能学报, 2024, 45(8): 273-280.
Liang H P, Xie X, Li S H.Demand response considering air source heat pump load aggregated participation[J]. Acta Energiae Solaris Sinica, 2024, 45(8): 273-280.
[6] 范宇辉, 姜婷玉, 黄奇峰, 等. 基于画像的工业园区需求响应潜力评估[J]. 电力系统自动化, 2024, 48(1): 41-49.
Fan Y H, Jiang T Y, Huang Q F, et al.Portrait-based assessment on demand response potential of industrial parks[J]. Automation of Electric Power Systems, 2024, 48(1): 41-49.
[7] 刘子屹, 谢俊, 刘雨菁, 等. 电力需求侧灵活性系列: 钢铁行业灵活性潜力概述[EB/OL]. http://rmi.org.cn/insights/dsf-steel/.
Liu Z Y, Xie J, Liu Y J, et al.Electricity demand-side flexibility series: an overview of the flexibility potential of the steel industry[EB/OL]. http://rmi.org.cn/insights/dsf-steel/.
[8] 王海博, 张利. 计及短流程钢铁企业生产过程的供需互动调度模型[J]. 电力系统自动化, 2021, 45(15): 64-76.
Wang H B, Zhang L.Supply-demand interactive scheduling model considering short production process of steel enterprises[J]. Automation of Electric Power Systems, 2021, 45(15): 64-76.
[9] 徐峰达, 王铖, 潘远林. 响应可再生能源波动的灵活电炉负荷调控模式[J]. 太阳能学报, 2025, 46(3): 225-234.
Xu F D, Wang C, Pan Y L.Adaptive electric furnace load management strategy addressing renewable energy intermittency[J]. Acta Energiae Solaris Sinica, 2025, 46(3): 225-234.
[10] Ding Y M, Hong S H, Li X H.A demand response energy management scheme for industrial facilities in smart grid[J]. IEEE Transactions on Industrial Informatics, 2014, 10(4): 2257-2269.
[11] 张儒峰, 毛天宇, 曲锐, 等. 基于通用化生产流程模型的工业园区微电网能量管理与生产流程协同优化[J]. 电力系统保护与控制, 2024, 52(22): 59-72.
Zhang R F, Mao T Y, Qu R, et al.Collaborative optimization of energy management and production process of an industrial park microgrid based on a generic production process model[J]. Power System Protection and Control, 2024, 52(22): 59-72.
[12] 廖思阳, 肖雨朵, 徐箭, 等. 需求响应下计及高耗能工业负荷生产流程的经济调度模型[J]. 电力系统自动化, 2025, 49(3): 22-30.
Liao S Y, Xiao Y D, Xu J, et al.Economic dispatch model considering production process of energy-intensive industrial load under demand response[J]. Automation of Electric Power Systems, 2025, 49(3): 22-30.
[13] 韩晓娟, 王祖冉, 李浩源, 等. 计及多场景协同降碳的园区综合能源系统主从博弈-鲁棒优化调度[J]. 太阳能学报, 2026, 47(5): 175-189.
Han X J, Wang Z R, Li H Y, et al.Master-slave game of integrated energy system in park considering multi-scenario collaborative carbon reduction-robust optimal scheduling[J]. Acta Energiae Solaris Sinica, 2026, 47(5): 175-189.
[14] Huang X F, Hong S H, Li Y T.Hour-ahead price based energy management scheme for industrial facilities[J]. IEEE Transactions on Industrial Informatics, 2017, 13(6): 2886-2898.
[15] 李明轩, 齐步洋, 贺大玮. 工业园区需求响应资源聚合优化配置方法[J]. 电网技术, 2022, 46(9): 3543-3549.
Li M X, Qi B Y, He D W.Optimization of aggregation of demand response resources in industrial park[J]. Power System Technology, 2022, 46(9): 3543-3549.
[16] 张青苗, 陈来军, 李笑竹, 等. 基于主从博弈的工业园区运营商与分布式光储用户购售电优化决策[J]. 太阳能学报, 2024, 45(1): 450-456.
Zhang Q M, Chen L J, Li X Z, et al.Optimization decision of purchasing and selling power between industrial park operators and distributed optical storage users based on Stackelberg game[J]. Acta Energiae Solaris Sinica, 2024, 45(1): 450-456.
[17] 胡鹏, 艾欣, 张朔, 等. 基于需求响应的分时电价主从博弈建模与仿真研究[J]. 电网技术, 2020, 44(2): 585-592.
Hu P, Ai X, Zhang S, et al.Modelling and simulation study of TOU Stackelberg game based on demand response[J]. Power System Technology, 2020, 44(2): 585-592.
[18] 颜湘武, 王庆澳, 卜劲勇, 等. 计及不确定性的基于主从博弈的社区微网运营商电价优化方法[J]. 太阳能学报, 2024, 45(8): 78-85.
Yan X W, Wang Q A, Bu J Y, et al.Electricity price optimization method for community microgrid operators based on leader-follower game considering uncertainty[J]. Acta Energiae Solaris Sinica, 2024, 45(8): 78-85.
[19] Yue X Y, Fu L J, Liao S Y, et al.A source-load collaborative stochastic optimization method considering the electricity price uncertainty and industrial load peak regulation compensation benefit[J]. International Journal of Electrical Power & Energy systems, 2025, 167: 110630.
[20] Wang X H, Cui S J, Dong Q W.A two-layer cooperative optimization approach for coordinated photovoltaic-energy storage system sizing and factory energy dispatch under industrial load profiles[J]. Sustainability, 2025, 17(6): 2713.
[21] 叶兴杰, 徐永海, 黄子桐, 等. 钢铁企业参与电力系统调度及风电消纳[J]. 电力自动化设备, 2023, 43(8): 112-118.
Ye X J, Xu Y H, Huang Z T, et al.Iron and steel enterprises participating in power system scheduling and wind power consumption[J]. Electric Power Automation Equipment, 2023, 43(8): 112-118.
[22] 涂夏哲, 徐箭, 廖思阳, 等. 考虑过程控制的钢铁工业负荷用能行为分析与功率特性建模[J]. 电力系统自动化, 2018, 42(2): 114-120.
Tu X Z, Xu J, Liao S Y, et al.Process controlling based energy consumption behavior analysis and power characteristic modeling for iron and steel industry[J]. Automation of Electric Power Systems, 2018, 42(2): 114-120.