计及生产特性的工业园区用户低碳调度决策方法

doi:10.11930/j.issn.1004-9649.202503003

中国电力 ›› 2026, Vol. 59 ›› Issue (3): 37-47.DOI: 10.11930/j.issn.1004-9649.202503003

• 电-碳协同下分布式能源系统运营关键技术 • 上一篇下一篇

计及生产特性的工业园区用户低碳调度决策方法

孔祥玉¹(), 杨振宇¹(), 刘子瑜¹^,³(), 高碧轩¹(), 庄重²(), 段梅梅²()

1. 天津大学智能电网教育部重点实验室，天津南开　300072
2. 国网江苏省电力有限公司营销服务中心，江苏南京　210019
3. 国网大连供电公司，辽宁大连　116001

收稿日期:2025-03-03 修回日期:2025-04-23 发布日期:2026-03-16 出版日期:2026-03-28
作者简介:
孔祥玉（1978），男，通信作者，博士，教授，博士生导师，主要从事电力系统优化运行、新能源发电、智能配用电方面的研究工作。E-mail：eekongxy@tju.edu.cn
杨振宇（2000），男，硕士研究生，从事新能源发电、低碳调度方面的研究工作。E-mail：zyyang@tju.edu.cn
基金资助:
国家电网有限公司科技项目（5400-202418217A-1-1-ZN）。

Low-carbon scheduling decision method for industrial park users considering production characteristics

KONG Xiangyu¹(), YANG Zhenyu¹(), LIU Ziyu¹^,³(), GAO Bixuan¹(), ZHUANG Zhong²(), DUAN Meimei²()

1. Key Laboratory of Smart Grid, Ministry of Education, Tianjin University, Tianjin 300072, China
2. Marketing Service Center, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210019, China
3. State Grid Dalian Electric Power Supply Co., Ltd., Dalian 116001, China

Received:2025-03-03 Revised:2025-04-23 Online:2026-03-16 Published:2026-03-28
Supported by:
This work is supported by the Science and Technology Project of State Grid Corporation of China (No.5400-202418217A-1-1-ZN).

摘要/Abstract

摘要：

在“双碳”目标和新型电力系统建设的背景下，工业园区作为高耗能企业的集中地，其低碳清洁的运行方式是目前中国实现双碳目标中面临的重大挑战。针对工业园区用户低碳调控需求，提出了一种计及生产特性的低碳调度决策方法。首先建立了工业园区需求响应调控典型场景与业务流程，并考虑不同工业行业的生产工艺，提出面向不同时间尺度响应调控的工业用户分类及筛选方法，随后将碳减排收益纳入考虑，并以负荷聚合商收益最大为目标，构建需求响应低碳调度决策模型，并采用改进鲸鱼优化算法进行模型求解。最后通过实际算例，验证了该方法能够有效支撑工业园区负荷聚合商制定响应调控激励方案，在保证聚合商收益的同时减少园区碳排放量约10%。

关键词: 工业园区, 生产特性, 低碳调度, 需求响应, 多时间尺度

Abstract:

Under the "dual-carbon" goals and the construction of new power systems, industrial parks-as concentrations of energy-intensive enterprises-face significant challenges in achieving low-carbon and clean operation, which is critical for China to meet its national carbon targets. To address the low-carbon regulation requirements of industrial park users, a low-carbon scheduling decision method for industrial park users considering production characteristics is proposed. Firstly, we establish typical demand response regulation scenarios and operational frameworks for industrial parks. Considering the production processes of different industrial sectors, we develop an industrial user classification and screening method for multi-timescale response regulation. Subsequently, incorporating carbon emission reduction benefits into the optimization objectives, we formulate a demand response low-carbon scheduling decision model aiming at maximizing load aggregators' revenues. The model is solved using a modified whale optimization algorithm. Case studies demonstrate that the proposed method can effectively support load aggregators in formulating response regulation incentive schemes for industrial parks, achieving approximately 10% carbon emission reduction while maintaining aggregators' profitability.

Key words: industrial park, production characteristics, low-carbon scheduling, demand response, multiple timescales

孔祥玉, 杨振宇, 刘子瑜, 高碧轩, 庄重, 段梅梅. 计及生产特性的工业园区用户低碳调度决策方法[J]. 中国电力, 2026, 59(3): 37-47.

KONG Xiangyu, YANG Zhenyu, LIU Ziyu, GAO Bixuan, ZHUANG Zhong, DUAN Meimei. Low-carbon scheduling decision method for industrial park users considering production characteristics[J]. Electric Power, 2026, 59(3): 37-47.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202503003

https://www.electricpower.com.cn/CN/Y2026/V59/I3/37

图/表 16

图 1 工业园区需求响应调控业务流程

Fig.1 Demand response regulation business process for industrial parks

图 2 水泥行业生产工序流程

Fig.2 Flow of cement production processes

表 1 水泥行业不同工序环节的多时间尺度调节类型及潜力

Table 1 Multi-timescale regulation types and potential in cement production processes

生产环节	调节类型	日前调控潜力	日内调控潜力	实时调控潜力
破碎	可中断	高	高	高
生料制备	可中断	高	高	高
熟料烧成	不可中断	低	低	低
水泥粉磨	可中断	高	中	中

表 2 典型行业参与多时间尺度响应调控的可行性

Table 2 Feasibility of different industrial sectors participating in multi timescale response regulation

行业	水泥	钢铁	铝	食品	制造	ASU
日前	√	√	√	√	√	√
日内	√	√	√	√	√	√
实时	√	—	—	—	—	√

图 3 GWOA算法流程

Fig.3 GWOA algorithm flow

表 3 不同时间尺度业务参数设置

Table 3 Business parameters settings at different timescales

场景	响应目标/ MW	响应补贴/ (元∙(kW∙h)^–1)	惩罚价格/ (元∙(kW∙h)^–1)
1	80.00	2.00	4.00
2	40.00	3.00	4.50
3	10.00	4.50	5.00

图 4 工业用户分时电价及实时碳价

Fig.4 The time of use electricity price and real-time carbon price for industrial users

表 4 典型行业的综合耗电碳排放强度

Table 4 Comprehensive carbon emission intensity of electricity consumption in typical industries

行业	碳排放强度/(kg·(kW·h)^–1)
水泥	5.70
铝冶炼	0.90
钢铁	5.40
其他	2.00

表 5 GWOA的超参数设置

Table 5 Hyper-parameter settings for GWOA

参数	值
种群规模	30
问题维度	20
最大迭代次数	500
最长评估时间/s	900

图 5 场景1工业园区用户调控决策补贴价格及响应降负荷率结果

Fig.5 Scenario 1: Subsidy pricing and load-reduction rate in response for industrial park user regulation

图 6 场景2工业园区用户调控决策补贴价格及响应降负荷率结果

Fig.6 Scenario 2: Subsidy pricing and load reduction rate in response for industrial park user regulation

图 7 场景3工业园区用户调控决策补贴价格及响应降负荷率结果

Fig.7 Scenario 3: Subsidy pricing and load reduction rate in response for industrial park user regulation

表 6 不同场景和响应时段下采用不同方法的工业园区响应调控结果经济性对比

Table 6 Economic comparison of response regulation results of industrial parks using different methods for different scenarios and response periods

场景	方法	时段
		07:00—08:00		11:00—12:00		15:00—16:00		19:00—20:00
		收益	相对值/%	收益	相对值/%	收益	相对值/%	收益	相对值/%
1	本文	22054		10228		24621		10547
	文献[11]	20129	–8.72	8922	–12.77	22341	–9.26	9465	–10.26
	文献[29]	20614	–6.53	9843	–3.75	23545	–4.37	10367	–1.71
	文献[30]	19831	–10.08	9429	–7.81	21534	–12.54	9068	–14.02
2	本文	32362		23256		35009		19242
	文献[11]	29838	–7.80	19972	–14.12	32093	–8.33	17039	–11.45
	文献[29]	31320	–3.22	23691	+1.87	34771	–0.68	18890	–1.83
	文献[30]	28854	–10.84	21121	–9.18	30741	–12.19	16929	–12.02
3	本文	25593		13527		28300		11996
	文献[11]	23080	–9.82	12192	–9.87	25365	–10.37	10405	–13.26
	文献[29]	23678	–7.48	13155	–2.75	26704	–5.64	11773	–1.86
	文献[30]	23978	–6.31	12379	–8.49	25552	–9.71	10900	–9.14

表 7 不同调控方法下的工业园区碳排放情况

Table 7 Carbon emissions of industrial parks with different regulatory methods

调控方法		碳排放量/t
调控方法		场景1	场景2	场景3
响应前	方法1	8324.89	8324.89	8324.89
响应后	方法2	2853.48	5723.04	7695.26
响应后	方法3	2500.39	5056.71	6981.83

图 8 不同场景下采用不同调控方法对应的园区日碳排放量

Fig.8 Park daily carbon emissions corresponding to different regulation methods under different scenarios

图 9 3种算法求解得到的收益随迭代次数变化

Fig.9 Revenue vs. iterations using three algorithms

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计及生产特性的工业园区用户低碳调度决策方法

Low-carbon scheduling decision method for industrial park users considering production characteristics

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计及生产特性的工业园区用户低碳调度决策方法

Low-carbon scheduling decision method for industrial park users considering production characteristics

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