Electric Power ›› 2026, Vol. 59 ›› Issue (3): 37-47.DOI: 10.11930/j.issn.1004-9649.202503003
• Key Technologies of Local Energy System Operation Under Electric-Carbon Coordination • Previous Articles Next Articles
KONG Xiangyu1(
), YANG Zhenyu1(
), LIU Ziyu1,3(
), GAO Bixuan1(
), ZHUANG Zhong2(
), DUAN Meimei2(
)
Received:2025-03-03
Revised:2025-04-23
Online:2026-03-16
Published:2026-03-28
Supported by: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.
| 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 |
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 |
| 行业 | 水泥 | 钢铁 | 铝 | 食品 | 制造 | ASU |
| 日前 | √ | √ | √ | √ | √ | √ |
| 日内 | √ | √ | √ | √ | √ | √ |
| 实时 | √ | — | — | — | — | √ |
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 | √ | — | — | — | — | √ |
| 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 |
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 |
| 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 |
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 |
| 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 |
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 |
| 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 | |||||||||||
| 文献[ | –8.72 | –12.77 | –9.26 | –10.26 | ||||||||
| 文献[ | –6.53 | –3.75 | –4.37 | –1.71 | ||||||||
| 文献[ | –10.08 | –7.81 | –12.54 | –14.02 | ||||||||
| 2 | 本文 | |||||||||||
| 文献[ | –7.80 | –14.12 | –8.33 | –11.45 | ||||||||
| 文献[ | –3.22 | +1.87 | –0.68 | –1.83 | ||||||||
| 文献[ | –10.84 | –9.18 | –12.19 | –12.02 | ||||||||
| 3 | 本文 | |||||||||||
| 文献[ | –9.82 | –9.87 | –10.37 | –13.26 | ||||||||
| 文献[ | –7.48 | –2.75 | –5.64 | –1.86 | ||||||||
| 文献[ | –6.31 | –8.49 | –9.71 | –9.14 | ||||||||
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 | |||||||||||
| 文献[ | –8.72 | –12.77 | –9.26 | –10.26 | ||||||||
| 文献[ | –6.53 | –3.75 | –4.37 | –1.71 | ||||||||
| 文献[ | –10.08 | –7.81 | –12.54 | –14.02 | ||||||||
| 2 | 本文 | |||||||||||
| 文献[ | –7.80 | –14.12 | –8.33 | –11.45 | ||||||||
| 文献[ | –3.22 | +1.87 | –0.68 | –1.83 | ||||||||
| 文献[ | –10.84 | –9.18 | –12.19 | –12.02 | ||||||||
| 3 | 本文 | |||||||||||
| 文献[ | –9.82 | –9.87 | –10.37 | –13.26 | ||||||||
| 文献[ | –7.48 | –2.75 | –5.64 | –1.86 | ||||||||
| 文献[ | –6.31 | –8.49 | –9.71 | –9.14 | ||||||||
| 调控方法 | 碳排放量/t | |||
| 场景1 | 场景2 | 场景3 | ||
| 响应前 | 方法1 | |||
| 响应后 | 方法2 | |||
| 方法3 | ||||
Table 7 Carbon emissions of industrial parks with different regulatory methods
| 调控方法 | 碳排放量/t | |||
| 场景1 | 场景2 | 场景3 | ||
| 响应前 | 方法1 | |||
| 响应后 | 方法2 | |||
| 方法3 | ||||
| 1 |
孔祥玉, 刘超, 陈宋宋, 等. 考虑动态过程的可调资源集群多时间节点响应潜力评估方法[J]. 电力系统自动化, 2022, 46 (18): 55- 64.
|
|
KONG Xiangyu, LIU Chao, CHEN Songsong, et al. Assessment method for multi-time-node response potential of adjustable resource cluster considering dynamic process[J]. Automation of Electric Power Systems, 2022, 46 (18): 55- 64.
|
|
| 2 | 林宇豪, 杨军, 王弘利, 等. 考虑决策依赖不确定性的光储充一体化电站优化运行策略[J]. 浙江电力, 2025, 44 (10): 91- 101. |
| LIN Yuhao, YANG Jun, WANG Hongli, et al. An optimal operation strategy for photovoltaic-storage-charging integrated stations considering decision-dependent uncertainty[J]. Zhejiang Electric Power, 2025, 44 (10): 91- 101. | |
| 3 |
赵航, 孙改平, 陈耿, 等. 计及源-荷联合出力特性的配电网多目标优化调度[J]. 浙江电力, 2025, 44 (11): 83- 92.
|
|
ZHAO Hang, SUN Gaiping, CHEN Geng, et al. Multi-objective optimal scheduling of distribution networks considering joint source-load output characteristics[J]. Zhejiang Electric Power, 2025, 44 (11): 83- 92.
|
|
| 4 | 王仕龙, 张汉雄, 卢嘉琛, 等. 基于梯级水电调节的风—光—水联合跨区消纳优化调度[J]. 智慧电力, 2025, 53 (7): 28- 35. |
| WANG Shilong, ZHANG Hanxiong, LU Jiachen, et al. Optimal dispatch for cross-regional integration of wind: PV: hydropower hybrid systems based on cascade hydropower regulation[J]. Smart Power, 2025, 53 (7): 28- 35. | |
| 5 | 于雷, 姚俊伟, 杨金龙. 小样本下风光储耦合系统的新能源消纳能力概率评估方法[J]. 智慧电力, 2024, 52 (10): 9- 15. |
| YU Lei, YAO Junwei, YANG Jinlong. Probabilistic evaluation method for renewable energy integration capability for wind-photovoltaic-storage coupling system with small sample[J]. Smart Power, 2024, 52 (10): 9- 15. | |
| 6 |
DOS SANTOS S A B, SOARES J M, BARROSO G C, et al. Demand response application in industrial scenarios: a systematic mapping of practical implementation[J]. Expert Systems with Applications, 2023, 215, 119393.
|
| 7 | 韩刚, 黎雄, 徐箭, 等. 计及需求响应下典型工业负荷排放特性的环境经济调度[J]. 电力系统自动化, 2023, 47 (8): 109- 119. |
| HAN Gang, LI Xiong, XU Jian, et al. Environmental economic dispatch considering emission characteristics of typical industrial loads under demand response[J]. Automation of Electric Power Systems, 2023, 47 (8): 109- 119. | |
| 8 |
ZHOU X F, CAI C Y, LI Y J, et al. A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power[J]. Global Energy Interconnection, 2023, 6 (6): 738- 750.
|
| 9 | 甘磊, 杨天禹, 陈星莺, 等. 基于低碳工艺与流程控制的钢铁工业园区综合能源系统低碳调度方法[J]. 电网技术, 2023, 47 (8): 3099- 3113. |
| GAN Lei, YANG Tianyu, CHEN Xingying, et al. Low-carbon scheduling of integrated energy system in iron & steel industrial park considering low-carbon techniques and process control[J]. Power System Technology, 2023, 47 (8): 3099- 3113. | |
| 10 | 吴林林, 陈璨, 胡俊杰, 等. 支撑新能源电力系统灵活性需求的用户侧资源应用与关键技术[J]. 电网技术, 2024, 48 (4): 1435- 1450. |
| WU Linlin, CHEN Can, HU Junjie, et al. User side resource application and key technologies for flexibility demand of renewable energy power system[J]. Power System Technology, 2024, 48 (4): 1435- 1450. | |
| 11 |
KWAC J, KIM J I, RAJAGOPAL R. Efficient customer selection process for various DR objectives[J]. IEEE Transactions on Smart Grid, 2019, 10 (2): 1501- 1508.
|
| 12 |
CHEN X, NIE Y T, LI N. Online residential demand response via contextual multi-armed bandits[J]. IEEE Control Systems Letters, 2021, 5 (2): 433- 438.
|
| 13 | LI Y Y, HU Q R, LI N. Learning and selecting the right customers for reliability: a multi-armed bandit approach[C]//2018 IEEE Conference on Decision and Control (CDC). Miami, FL, USA. IEEE, 2019: 4869-4874. |
| 14 |
HEYDARIAN-FORUSHANI E, GOLSHAN M E H, MOGHADDAM M P, et al. Robust scheduling of variable wind generation by coordination of bulk energy storages and demand response[J]. Energy Conversion and Management, 2015, 106, 941- 950.
|
| 15 | 姜婷玉, 陶劲宇, 王珂, 等. 基于非合作博弈的高耗能工业负荷参与调峰策略[J]. 电力系统自动化, 2025, 49 (3): 13- 21. |
| 16 |
LIN S F, HE T H, SHEN Y W, et al. Bilevel optimal dispatch model for a peak regulation ancillary service in an industrial park of energy-intensive loads[J]. Electric Power Systems Research, 2024, 230, 110272.
|
| 17 |
CAO Z X, ZHANG M H, ZHAI C, et al. Scheduling optimization of shared energy storage station in industrial park based on reputation factor[J]. Energy and Buildings, 2023, 299, 113596.
|
| 18 | 陈光宇, 杨锡勇, 江海洋, 等. 高比例新能源接入下计及工业负荷特性的电网需求响应调控策略[J]. 电力自动化设备, 2023, 43 (4): 177- 184. |
| CHEN Guangyu, YANG Xiyong, JIANG Haiyang, et al. Demand response regulation strategy for power grid accessed with high proportion of renewable energy considering industrial load characteristics[J]. Electric Power Automation Equipment, 2023, 43 (4): 177- 184. | |
| 19 |
KANSAL G, TIWARI R. Elasticity modelling of price-based demand response programs considering customer's different behavioural patterns[J]. Sustainable Energy, Grids and Networks, 2023, 36, 101244.
|
| 20 | 李思维, 孔祥玉, 刘畅, 等. 考虑多元用户行为特征的需求侧管理决策方法[J]. 电网技术, 2023, 47 (5): 1942- 1950. |
| LI Siwei, KONG Xiangyu, LIU Chang, et al. Demand side management pricing method considering multi-user behavior characteristics[J]. Power System Technology, 2023, 47 (5): 1942- 1950. | |
| 21 |
WANG J Y, WANG Q, SUN W Q. Quantifying flexibility provisions of the ladle furnace refining process as cuttable loads in the iron and steel industry[J]. Applied Energy, 2023, 342, 121178.
|
| 22 |
YU Q F, XU J, LIAO S Y, et al. Adaptive load control of electrolytic aluminum for power system frequency regulation based on the aluminum production operation state[J]. Energy Reports, 2022, 8, 1259- 1269.
|
| 23 | 代心芸, 陈皓勇, 肖东亮, 等. 电力市场环境下工业需求响应技术的应用与研究综述[J]. 电网技术, 2022, 46 (11): 4169- 4186. |
| DAI Xinyun, CHEN Haoyong, XIAO Dongliang, et al. Review of applications and researches of industrial demand response technology under electricity market environment[J]. Power System Technology, 2022, 46 (11): 4169- 4186. | |
| 24 |
YUN L X, XIAO M K, LI L. Vehicle-to-manufacturing (V2M) system: a novel approach to improve energy demand flexibility for demand response towards sustainable manufacturing[J]. Applied Energy, 2022, 323, 119552.
|
| 25 |
KELLEY M T, TSAY C, CAO Y N, et al. A data-driven linear formulation of the optimal demand response scheduling problem for an industrial air separation unit[J]. Chemical Engineering Science, 2022, 252, 117468.
|
| 26 |
WANG K W, TONG L G, YIN S W, et al. Novel ASU–LAES system with flexible energy release: Analysis of cycle performance, economics, and peak shaving advantages[J]. Energy, 2024, 288, 129720.
|
| 27 | 吴茵茵, 齐杰, 鲜琴, 等. 中国碳市场的碳减排效应研究: 基于市场机制与行政干预的协同作用视角[J]. 中国工业经济, 2021 (8): 114- 132. |
| WU Yinyin, QI Jie, XIAN Qin, et al. The carbon emission reduction effect of China's carbon market: from the perspective of the coordination between market mechanism and administrative intervention[J]. China Industrial Economics, 2021 (8): 114- 132. | |
| 28 |
GOLMOHAMADI H, KEYPOUR R, BAK-JENSEN B, et al. Robust self-scheduling of operational processes for industrial demand response aggregators[J]. IEEE Transactions on Industrial Electronics, 2020, 67 (2): 1387- 1395.
|
| 29 |
WANG L Y, LIN J L, DONG H Q, et al. Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system[J]. Energy, 2023, 270, 126893.
|
| 30 |
KONG X Y, WANG Z T, LIU C, et al. Refined peak shaving potential assessment and differentiated decision-making method for user load in virtual power plants[J]. Applied Energy, 2023, 334, 120609.
|
| [1] | QIN Yuming, ZHU Yun. Optimal scheduling of integrated energy system in industrial parks considering oxy-fuel combustion technology and demand response [J]. Electric Power, 2026, 59(3): 48-63. |
| [2] | WANG Shiqian, HUA Yuanpeng, LI Qiuyan, LIU Bo, YANG Jianping, XIANG Yue. Bidding operation strategies for charging stations considering various vehicle-grid interactive scenarios [J]. Electric Power, 2026, 59(3): 64-73. |
| [3] | YU Junyi, LIAO Siyang, KE Deping. District cooling demand response strategy based on tripartite Stackelberg game [J]. Electric Power, 2026, 59(2): 24-36. |
| [4] | WANG Shiqian, HAN Ding, WANG Nan, BAI Hongkun, SONG Dawei, HU Caihong. Cooperative Scheduling of Active Distribution Network Based on Two Layer Master Slave Game [J]. Electric Power, 2025, 58(9): 105-114. |
| [5] | RU Chuanhong, LU Ji, QIN Jian, ZHANG Junda, CHANG Junxiao, JIANG Beini. Stochastic Optimization Strategy for Load Management of Industrial Park Under Energy Constraints [J]. Electric Power, 2025, 58(8): 130-138. |
| [6] | GAO Fangjie, SUN Yujie, LI Yi, LE Ying, ZHANG Jiguang, XU Chuanbo, LIU Dunnan. Robust Optimization Scheduling of Island Multi-energy Microgrid Considering Offshore Wind Power to Hydrogen [J]. Electric Power, 2025, 58(7): 68-79. |
| [7] | ZHANG Bohang, QI Jun, XIE Luyao, ZHANG Youbing, ZHANG Boyang. Distributed Model Predictive Frequency Control of Interconnected Power Systems Considering Demand Response [J]. Electric Power, 2025, 58(7): 105-114. |
| [8] | ZHANG Jie, HUA Yufei, WANG Chen. A Demand Side Adjustment Capacity Sharing Model Based on Cooperative Game [J]. Electric Power, 2025, 58(6): 45-55. |
| [9] | WEI Chunhui, SHAN Linsen, HU Dadong, GAO Qianheng, ZHANG Xinsong, XUE Xiaocen. Optimal Scheduling Strategy of Park-level Virtual Power Plant for Demand Response [J]. Electric Power, 2025, 58(6): 112-121. |
| [10] | XU Shijie, HU Bangjie, ZHAO Liang, WANG Pei. Research on Optimal Dispatch with Source-Load Coordination for Micro-energy Grid Based on Energy-Carbon Coupling Model [J]. Electric Power, 2025, 58(4): 1-12. |
| [11] | XIANG Shilin, XIANG Yue, WANG Yanliang, LU Yu. Optimization Strategy for Spatiotemporal Cooperative Operation of Multiple Data Centers Considering Load Response Characteristics [J]. Electric Power, 2025, 58(4): 170-181. |
| [12] | Wenjun XU, Gang MA, Yunting YAO, Yuxiang MENG, Weikang LI. Multi-energy Optimal Scheduling of Industrial Parks Considering Green Certificate - Carbon Trading Mechanism and Hydrogen Compressed Natural Gas [J]. Electric Power, 2025, 58(2): 154-163. |
| [13] | YU Wanshui, YI Jun, YANG Wenli, MIAO Bo, ZHANG Haotian, CHEN Wenjing, BAO Jixiu, JIN Xianglong. User-Side Dynamic Carbon Responsibility Accounting Method Considering Marginal Carbon Emissions and Demand Response [J]. Electric Power, 2025, 58(12): 86-95. |
| [14] | PAN Tingzhe, JIN Fengyuan, LU Yonghao, CAO Wangzhang, YANG Hao, YU Heyang, ZHAO Boyang. Design of Dynamic Pricing Strategy for Electric Vehicles Charging in Smart Communities [J]. Electric Power, 2025, 58(11): 14-24, 37. |
| [15] | WEN Sihai, XIAN Yuesheng, HAN Yang, LIU Qunying, CHEN Shuheng. A Stackelberg Game-based Optimization Strategy for Integrated Energy Systems Incorporating Wind-solar Power Scenario Generation [J]. Electric Power, 2025, 58(11): 72-87. |
| Viewed | ||||||
|
Full text |
|
|||||
|
Abstract |
|
|||||
