计及最大需量基于改进RTN模型的短流程钢铁企业双层优化调峰策略

doi:10.11930/j.issn.1004-9649.202503038

中国电力 ›› 2025, Vol. 58 ›› Issue (8): 118-129.DOI: 10.11930/j.issn.1004-9649.202503038

计及最大需量基于改进RTN模型的短流程钢铁企业双层优化调峰策略

刘航¹(), 申皓¹, 纪陵², 钟永洁², 陈嘉瑞¹, 余洋³()

1. 国网邯郸供电公司，河北邯郸　056000
2. 国电南京自动化股份有限公司，江苏南京　210032
3. 新能源电力系统全国重点实验室（华北电力大学），河北保定　071003

收稿日期:2025-03-14 发布日期:2025-08-26 出版日期:2025-08-28
作者简介:
刘航（1986），男，高级工程师，从事电力系统运行与控制研究，E-mail：1257547816@qq.com
余洋（1982），男，通信作者，博士，教授，博士生导师，从事电力储能技术、柔性负荷建模与调度研究，E-mail：yangyu@ncepu.edu.cn
基金资助:
国网河北省电力有限公司科技项目（工业负荷灵活资源动态聚合互动响应与协同调控关键技术研究与应用，kj2023-029）。

Bi-level Optimization Peak-shaving Strategy for Short-process Steel Enterprises Considering Maximum Demand Based on an Improved RTN Model

LIU hang¹(), SHEN hao¹, JI Ling², ZHONG Yongjie², CHEN Jiarui¹, YU Yang³()

1. State Grid Handan Electric Power Supply Company, Handan 056000, China
2. Guodian Nanjing Automation Co., Ltd., Nanjing 210032, China
3. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China

Received:2025-03-14 Online:2025-08-26 Published:2025-08-28
Supported by:
This work is supported by Science and Technology Project of State Grid Hebei Electric Power Co., Ltd. (Research and Application of Key Technologies for Dynamic Aggregation Interactive Response and Co-regulation of Industrial Load Flexible Resources, No.kj2023-029).

摘要/Abstract

摘要：

短流程钢铁企业作为用能大户，其可调潜力巨大，为改善电网调峰状况提供了重要资源。但其生产工序紧密关联、订单波动大，导致用电不规律，参与电网调峰面临诸多困难。为此，提出计及最大需量基于改进资源任务网（resource task network，RTN）模型的短流程钢铁企业双层优化调峰策略，助力短流程钢铁企业参与电网调峰。首先，设计基于时间窗节点的改进资源任务网络，准确刻画单条生产线在加工不同类型订单时设备间物料和时间资源的耦合关系，保证订单分配及调度策略的可行性。其次，结合企业多生产线实际情况对企业订单进行分配，并提出考虑最大需量的供需互动双层优化调峰模型，利用自适应粒子群和Cplex求解器的混合求解算法进行求解。最后，根据某实际短流程钢铁企业数据，设置3个仿真场景对调度策略进行验证。结果表明，所提策略有效平滑了负荷曲线，同时降低企业用电成本。

关键词: 短流程钢铁企业, 双层优化, 最大需量, 订单分配, 调峰策略, 时间窗节点

Abstract:

As large energy users, the short-process steel enterprises have great potential for peak-shaving, which provides an important resource for improving the peak-shaving state of the power grid. However, their production processes are closely linked and orders fluctuate greatly, resulting in irregular electricity consumption, which makes it difficult for steel enterprises to participate in power grid peak-shaving. To this end, this paper proposes a bi-level optimization peak-shaving strategy for short-process steel enterprises considering maximum demand based on the improved resource-task network (RTN) model, so as to help short-process steel enterprises participate in power grid peak shaving. Firstly, an improved RTN with time window nodes was designed to accurately characterize the coupling relationships of materials and temporal resources between devices within a single production line when processing different types of orders, ensuring the feasibility of order allocation and scheduling strategies. Secondly, enterprise orders were allocated based on the actual multi-production-line scenarios, and a supply-demand interaction bi-level optimization peak-shaving model considering maximum demand was proposed, which was solved using a hybrid algorithm combining adaptive particle swarm optimization (APSO) and the Cplex solver. Finally, according to the data from an actual short-process steel enterprise, three simulation scenarios were set up to verify the proposed scheduling strategy. The results show that the proposed strategy can effectively smooth the load curve while reducing the enterprise’s electricity costs.

Key words: short-process steel enterprises, bi-level optimization, maximum demand, order allocation, peak-shaving strategy, time window node

中图分类号:

TM732

刘航, 申皓, 纪陵, 钟永洁, 陈嘉瑞, 余洋. 计及最大需量基于改进RTN模型的短流程钢铁企业双层优化调峰策略[J]. 中国电力, 2025, 58(8): 118-129.

LIU hang, SHEN hao, JI Ling, ZHONG Yongjie, CHEN Jiarui, YU Yang. Bi-level Optimization Peak-shaving Strategy for Short-process Steel Enterprises Considering Maximum Demand Based on an Improved RTN Model[J]. Electric Power, 2025, 58(8): 118-129.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202503038

https://www.electricpower.com.cn/CN/Y2025/V58/I8/118

图/表 14

图 1 短流程钢铁企业生产流程

Fig.1 Production processes of short-process steel enterprises

图 2 短流程钢铁企业的RTN模型

Fig.2 RTN model of short-process steel enterprises

图 3 算法求解流程

Fig.3 Algorithm solving flow

表 1 短流程钢铁企业设备参数

Table 1 Equipment parameters of short-process steel enterprises

设备		额定功率/MW
EAF		85
LF		2
CC		7

表 2 各生产线生产时间窗（15 min）

Table 2 Production time window for each production line (15 min)

生产线编号	加工时间窗	运输时间窗	冷却时间
1	[16，36]	[2，4]	2
2	[24，40]	[3，6]	2
3	[24，48]	[4，8]	2

表 3 短流程钢铁企业订单数据

Table 3 Order data for short-process steel enterprises

订单编号	平均日钢材量/吨	平均日电力需求/(MW·h)
1	3452.66	433.4
2	2753.36	362.4
3	2660.91	355.6
4	1997.12	265.3
5	1978.52	245.3
6	1854.25	242.7
7	1824.36	242.6
8	1771.54	237.5
9	1764.34	216.3

表 4 3种场景

Table 4 Three scheduling scenarios

场景		场景说明
1		仅考虑EAF功率特性，以用电成本最小为目标安排生产
2		在场景1基础上，基于本文RTN模型考虑整体生产流程，以用电成本最小为目标安排生产
3		在场景2基础上，基于改进的RTN模型考虑多生产线订单分配策略的调度方式，以用电成本最小为目标安排生产

表 5 电价实际数据

Table 5 Actual two-part tariff data

电价构成	计费方法	价格
分时电价/(元·(kW·h)⁻¹)	峰	1.00
	平	0.60
	谷	0.30
基本电价/(元·(kW·月)⁻¹)	按最大需量	35

图 4 成本对比

Fig.4 Cost comparison

图 5 多生产线订单分配情况

Fig.5 Multi-line order allocation

图 6 企业用电成本对比

Fig.6 Comparison of electricity costs for enterprises

图 7 不同场景下用电需量变化

Fig.7 Electricity costs under different scenarios

图 8 优化算法对比

Fig.8 Comparison of optimization algorithms

图 9 不同场景负荷对比

Fig.9 Comparison of load in different scenarios

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计及最大需量基于改进RTN模型的短流程钢铁企业双层优化调峰策略

Bi-level Optimization Peak-shaving Strategy for Short-process Steel Enterprises Considering Maximum Demand Based on an Improved RTN Model

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计及最大需量基于改进RTN模型的短流程钢铁企业双层优化调峰策略

Bi-level Optimization Peak-shaving Strategy for Short-process Steel Enterprises Considering Maximum Demand Based on an Improved RTN Model

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