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| Review of Market Implementation and Scheduling Models Considering the Flexibility Extraction of Differentiated Industrial Energy-Intensive Loads |
| Zhao Xudong1, Wang Yibo1, Wang Bowen2, Liu Chuang1, Zhao Yiru1 |
1. School of Electrical Engineering Northeast Electric Power University Jilin 132012 China;
2. Power Economic Research Institute of Jilin Electric Power Co. Ltd Northeast Electric Power University Changchun 130021 China |
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Abstract With the global rise in new energy generation, power systems increasingly face challenges in accommodating a high share of renewable sources. The intermittent nature of renewables like wind and solar presents significant difficulties for grid stability and reliability. Stimulating the demand response flexibility of industrial energy-intensive loads has become crucial for effectively integrating these renewables. By scheduling these loads based on new energy output and system supply-demand dynamics, the grid's capacity to absorb renewable energy and maintain operational stability is enhanced.
Differentiated industrial energy-intensive loads are defined, and a comprehensive flexibility analysis is conducted on three typical types: cement plants, aluminum electrolysis plants, and refineries. These industries consume substantial electrical energy and possess varying degrees of operational flexibility suitable for demand response. Their optimization scheduling objectives—such as minimizing operational costs, reducing peak demand, and improving energy efficiency—are explored, along with their market participation characteristics, including responsiveness to price signals and provision of ancillary services.
Scheduling models for these industrial loads are introduced, considering specific operational constraints, production requirements, and flexibility potentials. For example, the cement plant model accounts for kiln thermal characteristics and clinker storage capacity, while the aluminum electrolysis model addresses electrolytic cell sensitivity to power interruptions. The refinery model incorporates processing unit complexities and product demands. Existing solution algorithms and solvers are analyzed, discussing the pros and cons of traditional optimization methods, heuristic algorithms, and their applicability in solving these models.
The application challenges and future development directions of these loads in power systems are summarized. Emphasis is placed on designing effective incentive mechanisms—such as dynamic pricing and financial incentives—to motivate industry participation in demand response programs. Enhancing data management capabilities is crucial, as accurate and timely data on energy consumption and market prices are essential for effective scheduling. The dissemination of operational skills among industry personnel is also highlighted to facilitate the adoption of advanced scheduling practices.
This study fills a domestic gap in reviews on demand response flexibility for industrial energy-intensive loads, providing significant theoretical support for policymakers, researchers, and industry practitioners. Leveraging the demand response potential of these industries promotes the green and low-carbon transformation of the power system, contributing to sustainable energy development and environmental protection goals.
The findings enhance understanding of the role of industrial energy-intensive loads in demand response and offer practical insights into optimizing their participation in the electricity market. Integrating industrial load flexibility improves renewable energy accommodation, reduces operational costs, and enhances grid stability. Future research directions include developing advanced scheduling models that better capture industrial complexities and uncertainties, exploring novel incentive schemes aligning industry and power system operator interests, and integrating emerging technologies like big data analytics and artificial intelligence to enhance predictive capabilities.
By addressing these aspects, the study contributes to the transition toward a more flexible, efficient, and sustainable power system. Effective engagement of industrial energy-intensive loads in demand response benefits the power system and provides economic and environmental advantages for the industries, fostering a mutually beneficial relationship between energy providers and consumers.
In summary, stimulating industrial load flexibility is pivotal in transitioning to greener power systems. Addressing challenges and seizing opportunities can lead to significant advancements in energy efficiency, economic savings, and environmental protection. This analysis serves as a foundation for future efforts to integrate industrial demand response into power system planning and operation, ultimately contributing to global sustainability objectives.
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Received: 02 April 2024
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[1] 国家能源局. 多地用电破纪录: 能源求变, 风光装机崛起进行时[EB/OL]. [2023-07-31]. http://www.nea.gov.cn/2023-07/31/c_1310734804.htm.
[2] 姜云鹏, 任洲洋, 李秋燕, 等. 考虑多灵活性资源协调调度的配电网新能源消纳策略[J]. 电工技术学报, 2022, 37(7): 1820-1835.
Jiang Yunpeng, Ren Zhouyang, Li Qiuyan, et al.An accommodation strategy for renewable energy in distribution network considering coordinated dispatching of multi-flexible resources[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1820-1835.
[3] 潘超, 范宫博, 王锦鹏, 等. 灵活性资源参与的电热综合能源系统低碳优化[J]. 电工技术学报, 2023, 38(6): 1633-1647.
Pan Chao, Fan Gongbo, Wang Jinpeng, et al.Low-carbon optimization of electric and heating integrated energy system with flexible resource participation[J]. Transactions of China Electrotechnical Society, 2023, 38(6): 1633-1647.
[4] 王雨晴, 王文诗, 徐心竹, 等. 面向低碳交通的含新能源汽车共享站电-氢微能源网区间-随机混合规划方法[J]. 电工技术学报, 2023, 38(23): 6373-6390.
Wang Yuqing, Wang Wenshi, Xu Xinzhu, et al.Hybrid interval/stochastic planning method for new energy vehicle sharing station-based electro-hydrogen micro-energy system for low-carbon transportation[J]. Transactions of China Electrotechnical Society, 2023, 38(23): 6373-6390.
[5] 张玉莹, 曾博, 周吟雨, 等. 碳减排驱动下的数据中心与配电网交互式集成规划研究[J]. 电工技术学报, 2023, 38(23): 6433-6450.
Zhang Yuying, Zeng Bo, Zhou Yinyu, et al.Research on interactive integration planning of data centers and distribution network driven by carbon emission reduction[J]. Transactions of China Electrotechnical Society, 2023, 38(23): 6433-6450.
[6] 王宇绅, 陈皓勇, 黄宇翔, 等. 多重不确定性下的虚拟电厂参与电能量和需求响应市场鲁棒优化调度策略[J]. 发电技术, 2024, 45(6): 1173-1185.
Wang Yushen, Chen Haoyong, Huang Yuxiang, et al.Robust optimal scheduling strategy for virtual power plant participation in electric energy and demand response markets under multiple uncertainties[J]. Power Generation Technology, 2024, 45(6): 1173-1185.
[7] Shoreh M H, Siano P, Shafie-khah M, et al. A survey of industrial applications of demand response[J]. Electric Power Systems Research, 2016, 141: 31-49.
[8] 张海亮, 王艺博, 蔡国伟, 等. 面向风电消纳与电熔镁高载能负荷调控的源荷协调优化策略[J]. 电工技术学报, 2022, 37(17): 4401-4410.
Zhang Hailiang, Wang Yibo, Cai Guowei, et al.Source-load coordination optimization strategy for wind power accommodation and high energy load regulation of electric fused magnesium[J]. Transa-ctions of China Electrotechnical Society, 2022, 37(17): 4401-4410.
[9] 邢文, 段斌. 基于负荷转移调度的高耗能企业电能需量控制[J]. 电气技术, 2016, 17(9): 70-76.
Xing Wen, Duan Bin.Power demanding control for energy-intensive enterprises based on load transfer dispatch[J]. Electrical Engineering, 2016, 17(9): 70-76.
[10] 晋宏杨, 孙宏斌, 牛涛, 等. 考虑风险约束的高载能负荷—风电协调调度方法[J]. 电力系统自动化, 2019, 43(16): 9-16.
Jin Hongyang, Sun Hongbin, Niu Tao, et al.Coordinated dispatch method of energy-extensive load and wind power considering risk constraints[J]. Automation of Electric Power Systems, 2019, 43(16): 9-16.
[11] 刘闯, 孙傲, 王艺博, 等. 计及电熔镁负荷与储能联合调峰的电力系统日前-日内联合经济调度方法[J]. 电力自动化设备, 2022, 42(2): 8-15.
Liu Chuang, Sun Ao, Wang Yibo, et al.Day-ahead and intra-day joint economic dispatching method of electric power system considering combined peak-shaving of fused magnesium load and energy storage[J]. Electric Power Automation Equipment, 2022, 42(2): 8-15.
[12] Xenos D P, Mohd Noor I, Matloubi M, et al.Demand-side management and optimal operation of industrial electricity consumers: an example of an energy-intensive chemical plant[J]. Applied Energy, 2016, 182: 418-433.
[13] 彭春华, 张金克, 陈露, 等. 计及差异化需求响应的微电网源荷储协调优化调度[J]. 电力自动化设备, 2020, 40(3): 1-7.
Peng Chunhua, Zhang Jinke, Chen Lu, et al.Source-load-storage coordinated optimal scheduling of micro-grid considering differential demand response[J]. Electric Power Automation Equipment, 2020, 40(3): 1-7.
[14] Paulus M, Borggrefe F.The potential of demand-side management in energy-intensive industries for electricity markets in Germany[J]. Applied Energy, 2011, 88(2): 432-441.
[15] Zhang Yingfeng, Ma Shuaiyin, Yang Haidong, et al.A big data driven analytical framework for energy-intensive manufacturing industries[J]. Journal of Cleaner Production, 2018, 197: 57-72.
[16] 文晶, 刘文颖, 谢昶, 等. 计及风电消纳效益的电力系统源荷协调二层优化模型[J]. 电工技术学报, 2015, 30(8): 247-256.
Wen Jing, Liu Wenying, Xie Chang, et al.Source-load coordination optimal model considering wind power consumptive benefits based on bi-level programming[J]. Transactions of China Electrotechnical Society, 2015, 30(8): 247-256.
[17] 周宁, 杨知方, 钟海旺, 等. 售电侧市场的工业园区能源代理模式设计[J]. 电力系统自动化, 2015, 39(17): 147-152, 191.
Zhou Ning, Yang Zhifang, Zhong Haiwang, et al.Energy agent mechanism design for industrial parks in retail electricity markets[J]. Automation of Electric Power Systems, 2015, 39(17): 147-152, 191.
[18] 胡殿刚, 齐晓琳, 李韶瑜, 等. 考虑最优风电投标量的高载能用户电价决策模型[J]. 电网技术, 2016, 40(8): 2265-2272.
Hu Diangang, Qi Xiaolin, Li Shaoyu, et al.Price decision model of high-load users considering optimal wind bidding strategy[J]. Power System Technology, 2016, 40(8): 2265-2272.
[19] 王健, 鲁宗相, 乔颖, 等. 高载能负荷提高风电就地消纳的需求响应模式研究[J]. 电网技术, 2017, 41(7): 2115-2124.
Wang Jian, Lu Zongxiang, Qiao Ying, et al.Research on demand response mechanism of wind power local accommodation utilizing energy-intensive loads[J]. Power System Technology, 2017, 41(7): 2115-2124.
[20] 张智刚, 康重庆. 碳中和目标下构建新型电力系统的挑战与展望[J]. 中国电机工程学报, 2022, 42(8): 2806-2819.
Zhang Zhigang, Kang Chongqing.Challenges and prospects for constructing the new-type power system towards a carbon neutrality future[J]. Proceedings of the CSEE, 2022, 42(8): 2806-2819.
[21] Golmohamadi H.Demand-side management in industrial sector: a review of heavy industries[J]. Renewable and Sustainable Energy Reviews, 2022, 156: 111963.
[22] 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.
[23] Heffron R, Körner M F, Wagner J, et al.Industrial demand-side flexibility: a key element of a just energy transition and industrial development[J]. Applied Energy, 2020, 269: 115026.
[24] Gao Tianming, Shen Lei, Shen Ming, et al.Analysis of material flow and consumption in cement production process[J]. Journal of Cleaner Production, 2016, 112: 553-565.
[25] Ishak S A, Hashim H.Low carbon measures for cement plant-a review[J]. Journal of Cleaner Production, 2015, 103: 260-274.
[26] Galitsky C, Worrell E.Energy efficiency improvement and cost saving opportunities for cement making[R]. Washington D. C.: Energy Star, 2008.
[27] Jamali D H, Noorpoor A.Optimization of a novel solar-based multi-generation system for waste heat recovery in a cement plant[J]. Journal of Cleaner Production, 2019, 240: 117825.
[28] Yao Mingtao, Hu Zhaoguang, Sifuentes F, et al.Integrated power management of conventional units and industrial loads in China’s ancillary services scheduling[J]. Energies, 2015, 8(5): 3955-3977.
[29] Cadavid-Giraldo N, Velez-Gallego M C, Restrepo-Boland A. Carbon emissions reduction and financial effects of a cap and tax system on an operating supply chain in the cement sector[J]. Journal of Cleaner Production, 2020, 275: 122583.
[30] Kermeli K, Edelenbosch O Y, Crijns-Graus W, et al.The scope for better industry representation in long-term energy models: modeling the cement industry[J]. Applied Energy, 2019, 240: 964-985.
[31] Hasanbeigi A, Price L, Lu Hongyou, et al.Analysis of energy-efficiency opportunities for the cement indus-try in Shandong Province, China: a case study of 16 cement plants[J]. Energy, 2010, 35(8): 3461-3473.
[32] Tesema G, Worrell E.Energy efficiency improvement potentials for the cement industry in Ethiopia[J]. Energy, 2015, 93: 2042-2052.
[33] Atmaca A, Kanoglu M.Reducing energy consumption of a raw mill in cement industry[J]. Energy, 2012, 42(1): 261-269.
[34] 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.
[35] Huang Y H, Chang Yilin, Fleiter T.A critical analysis of energy efficiency improvement potentials in Taiwan’s cement industry[J]. Energy Policy, 2016, 96: 14-26.
[36] Hasanbeigi A, Menke C, Therdyothin A.The use of conservation supply curves in energy policy and economic analysis: the case study of Thai cement industry[J]. Energy Policy, 2010, 38(1): 392-405.
[37] Hossain S R, Ahmed I, Azad F S, et al.Empirical investigation of energy management practices in cement industries of Bangladesh[J]. Energy, 2020, 212: 118741.
[38] Tabereaux A T, Peterson R D.Aluminum production[M]//Seetharaman S, Mclean A, Sohn H Y, et al. Treatise on Process Metallurgy. Amsterdam: Elsevier, 2014: 839-917.
[39] 丁鑫, 徐箭, 孙元章, 等. 联网型高耗能电解铝工业电网源荷协调平抑风电功率波动控制策略[J]. 电力自动化设备, 2022, 42(11): 47-55.
Ding Xin, Xu Jian, Sun Yuanzhang, et al.Source-load coordinated control strategy for smoothing wind power fluctuation in grid-connected high energy consuming electrolytic aluminum industrial power grid[J]. Electric Power Automation Equipment, 2022, 42(11): 47-55.
[40] Chen Runze, Wang Jianhui, Sun Hongbin.Clearing and pricing for coordinated gas and electricity day-ahead markets considering wind power uncertainty[J]. IEEE Transactions on Power Systems, 2018, 33(3): 2496-2508.
[41] Nguyen H T, Le Long bao, Wang Zhaoyu. A bidding strategy for virtual power plants with the intraday demand response exchange market using the stochastic programming[J]. IEEE Transactions on Industry Applications, 2018, 54(4): 3044-3055.
[42] 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.
[43] Jiang Hao, Lin Jin, Song Yonghua, et al.Demand side frequency control scheme in an isolated wind power system for industrial aluminum smelting production[J]. IEEE Transactions on Power Systems, 2014, 29(2): 844-853.
[44] Alharbi H, Bhattacharya K.Participation of pumped hydro storage in energy and performance-based regulation markets[J]. IEEE Transactions on Power Systems, 2020, 35(6): 4307-4323.
[45] Du Y, Li F.A hierarchical real-time balancing market considering multimicrogrids with distributed sustainable resources[J]. IEEE Transactions on Sustainable Energy, 2020, 11(1): 72-83.
[46] Zhang Yanfang, Li Wangxing, Yang Jianhong, et al.A techno-economic optimization model for aluminium electrolysis production[M]//Suarez C E. Light Metals 2012. Cham: Springer International Publishing, 2012: 709-714.
[47] Haraldsson J, Johansson M T.Review of measures for improved energy efficiency in production-related processes in the aluminium industry-from electrolysis to recycling[J]. Renewable and Sustainable Energy Reviews, 2018, 93: 525-548.
[48] Royo P, Ferreira V J, López-Sabirón A M, et al. Retrofitting strategies for improving the energy and environmental efficiency in industrial furnaces: a case study in the aluminium sector[J]. Renewable and Sustainable Energy Reviews, 2018, 82: 1813-1822.
[49] Bogdanov D, Gulagi A, Fasihi M, et al.Full energy sector transition towards 100% renewable energy supply: integrating power, heat, transport and industry sectors including desalination[J]. Applied Energy, 2021, 283: 116273.
[50] Gong Feixiang, Ren Kaikai, Zhang Aiqun, et al.Review of electrolytic aluminum load participating in demand response to absorb new energy potential and methods[C]//2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Nanchang, China, 2021: 1015-1019.
[51] Hao Han, Geng Yong, Hang Wen.GHG emissions from primary aluminum production in China: regional disparity and policy implications[J]. Applied Energy, 2016, 166: 264-272.
[52] Golmohamadi H, Keypour R, Bak-Jensen B, et al.A multi-agent based optimization of residential and industrial demand response aggregators[J]. Interna-tional Journal of Electrical Power & Energy Systems, 2019, 107: 472-485.
[53] Bao Peng, Zhang Wen, Cheng Dingyi, et al.Hierarchical control of aluminum smelter loads for primary frequency support considering control cost[J]. International Journal of Electrical Power & Energy Systems, 2020, 122: 106202.
[54] Shen Xun, Chen Lingen, Xia Shaojun, et al.Burdening proportion and new energy-saving technologies analysis and optimization for iron and steel production system[J]. Journal of Cleaner Production, 2018, 172: 2153-2166.
[55] Kong Haining, Qi Ershi, Li Hui, et al.An MILP model for optimization of byproduct gases in the integrated iron and steel plant[J]. Applied Energy, 2010, 87(7): 2156-2163.
[56] Zhang Qi, Wei Ziqing, Ma Jialin, et al.Optimization of energy use with CO2 emission reducing in an integrated iron and steel plant[J]. Applied Thermal Engineering, 2019, 157: 113635.
[57] Zhao Xiancong, Bai Hao, Shi Qi, et al.Optimal scheduling of a byproduct gas system in a steel plant considering time-of-use electricity pricing[J]. Applied Energy, 2017, 195: 100-113.
[58] Liu Changxin, Xie Zhihui, Sun Fengrui, et al.Exergy analysis and optimization of coking process[J]. Energy, 2017, 139: 694-705.
[59] Xiong Bing, Chen Lingen, Meng Fankai, et al.Modeling and performance analysis of a two-stage thermoelectric energy harvesting system from blast furnace slag water waste heat[J]. Energy, 2014, 77: 562-569.
[60] Meng Fankai, Chen Lingen, Sun Fengrui, et al.Thermoelectric power generation driven by blast furnace slag flushing water[J]. Energy, 2014, 66: 965-972.
[61] Chen Lingen, Yang Bo, Shen Xun, et al.Thermodynamic optimization opportunities for the recovery and utilization of residual energy and heat in China’s iron and steel industry: a case study[J]. Applied Thermal Engineering, 2015, 86: 151-160.
[62] Zhang Zelong, Chen Lingen, Yang Bo, et al.Thermodynamic analysis and optimization of an air Brayton cycle for recovering waste heat of blast furnace slag[J]. Applied Thermal Engineering, 2015, 90: 742-748.
[63] Liu Changxin, Xie Zhihui, Sun Fengrui, et al.Optimization for sintering proportioning based on energy value[J]. Applied Thermal Engineering, 2016, 103: 1087-1094.
[64] Rossi M, Comodi G, Piacente N, et al.Energy recovery in oil refineries by means of a hydraulic power recovery turbine (HPRT) handling viscous liquids[J]. Applied Energy, 2020, 270: 115097.
[65] Nezhadfard M, Khalili-Garakani A.Power generation as a useful option for flare gas recovery: enviro-economic evaluation of different scenarios[J]. Energy, 2020, 204: 117940.
[66] Aminmahalati A, Fazlali A, Safikhani H.Multi-objective optimization of CO boiler combustion chamber in the RFCC unit using NSGA II algorithm[J]. Energy, 2021, 221: 119859.
[67] Gholian A, Mohsenian-Rad H, Hua Yingbo, et al.Optimal industrial load control in smart grid: a case study for oil refineries[C]//2013 IEEE Power & Energy Society General Meeting, Vancouver, BC, 2013: 1-5.
[68] Alarfaj O, Bhattacharya K.Material flow based power demand modeling of an oil refinery process for optimal energy management[J]. IEEE Transactions on Power Systems, 2019, 34(3): 2312-2321.
[69] Golmohamadi H, Asadi A.Integration of joint power-heat flexibility of oil refinery industries to uncertain energy markets[J]. Energies, 2020, 13(18): 4874.
[70] Madlool N A, Saidur R, Rahim N A, et al.An overview of energy savings measures for cement industries[J]. Renewable and Sustainable Energy Reviews, 2013, 19: 18-29.
[71] Nejad M A, Golmohamadi H, Bashian A, et al.Application of demand response programs to heavy industries: a case study on a regional electric company[J]. International Journal of Smart Electrical Engineering, 2017, 6: 93-99.
[72] Eid C, Koliou E, Valles M, et al.Time-based pricing and electricity demand response: existing barriers and next steps[J]. Utilities Policy, 2016, 40: 15-25.
[73] 姚明涛, 胡兆光, 张宁, 等. 工业负荷提供辅助服务的多智能体响应模拟[J]. 中国电机工程学报, 2014, 34(25): 4219-4226.
Yao Mingtao, Hu Zhaoguang, Zhang Ning, et al.Multi-agent response simulation of industrial loads for ancillary services[J]. Proceedings of the CSEE, 2014, 34(25): 4219-4226.
[74] 周竞, 耿建, 唐律, 等. 可调节负荷资源参与电力辅助服务市场规则分析与思考[J]. 电力自动化设备, 2022, 42(7): 120-127.
Zhou Jing, Geng Jian, Tang Lü, et al.Rule analysis and cogitation for adjustable load resources participating in ancillary service market[J]. Electric Power Automation Equipment, 2022, 42(7): 120-127.
[75] Roesch M, Bauer D, Haupt L, et al.Harnessing the full potential of industrial demand-side flexibility: an end-to-end approach connecting machines with markets through service-oriented IT platforms[J]. Applied Sciences, 2019, 9(18): 3796.
[76] Emir T, Güler M G.Production planning using day-ahead prices in a cement plant[M]//Dincer I, Ozgur Colpan C, Kizilkan O. Exergetic, Energetic and Environmental Dimensions. Amsterdam: Elsevier, 2018: 149-166.
[77] Yao Mingtao, Hu Zhaoguang, Zhang Ning, et al.Low-carbon benefits analysis of energy-intensive industrial demand response resources for ancillary services[J]. Journal of Modern Power Systems and Clean Energy, 2015, 3(1): 131-138.
[78] Lund P D, Lindgren J, Mikkola J, et al.Review of energy system flexibility measures to enable high levels of variable renewable electricity[J]. Renewable and Sustainable Energy Reviews, 2015, 45(1): 785-807.
[79] Nelson J R, Johnson N G.Model predictive control of microgrids for real-time ancillary service market participation[J]. Applied Energy, 2020, 269: 114963.
[80] Blomberg J, Söderholm P.Factor demand flexibility in the primary aluminium industry: evidence from stagnating and expanding regions[J]. Resources Policy, 2011, 36(3): 238-248.
[81] Castro P M, Sun Lige, Harjunkoski I.Resource-task network formulations for industrial demand side management of a steel plant[J]. Industrial & Engi-neering Chemistry Research, 2013, 52(36): 13046-13058.
[82] Xu Tengfang, Karali N, Sathaye J.Undertaking high impact strategies: the role of national efficiency measures in long-term energy and emission reduction in steel making[J]. Applied Energy, 2014, 122: 179-188.
[83] Gholian A, Mohsenian-Rad H, Hua Yingbo.Optimal industrial load control in smart grid[J]. IEEE Transactions on Smart Grid, 2016, 7(5): 2305-2316.
[84] Zhao Ruijie, Nowicki C, Gosselin L, et al.Energy and exergy inventory in aluminum smelter from a thermal integration point-of-view[J]. International Journal of Energy Research, 2016, 40(10): 1321-1338.
[85] Zhang Xiao, Hug G, Harjunkoski I.Cost-effective scheduling of steel plants with flexible EAFs[J]. IEEE Transactions on Smart Grid, 2017, 8(1): 239-249.
[86] Ramin D, Spinelli S, Brusaferri A.Demand-side management via optimal production scheduling in power-intensive industries: the case of metal casting process[J]. Applied Energy, 2018, 225: 622-636.
[87] Dranka G G, Ferreira P.Load flexibility potential across residential, commercial and industrial sectors in Brazil[J]. Energy, 2020, 201: 117483.
[88] Huang Chao, Zhang Hongcai, Song Yonghua, et al.Demand response for industrial micro-grid consi-dering photovoltaic power uncertainty and battery operational cost[J]. IEEE Transactions on Smart Grid, 2021, 12(4): 3043-3055.
[89] 高倩, 杨知方, 李文沅. 电力系统混合整数线性规划问题的运筹决策关键技术综述与展望[J]. 电工技术学报, 2024, 39(11): 3291-3307.
Gao Qian, Yang Zhifang, Li Wenyuan.Prospect on operations research for mixed-integer linear programming problems in power systems[J]. Transactions of China Electrotechnical Society, 2024, 39(11): 3291-3307.
[90] Gerami N, Ghasemi A, Lotfi A, et al.Energy consumption modeling of production process for industrial factories in a day ahead scheduling with demand response[J]. Sustainable Energy, Grids and Networks, 2021, 25: 100420.
[91] Li Baodong.Modeling energy consumption in the production processes of industrial units based on load response programs in the energy market[J]. Energy Engineering, 2023, 120(2): 461-481.
[92] 王越, 谢海鹏, 祝昊. 电解铝工业园区的源荷协同优化配置方法[J]. 电力自动化设备, 2024, 44(7): 132-140.
Wang Yue, Xie Haipeng, Zhu Hao.Source-load coordinated optimal planning method of electrolytic aluminum industrial park[J]. Electric Power Automation Equipment, 2024, 44(7): 132-140.
[93] Yue Xiaoyu, Liao Siyang, Xu Jian, et al.Collaborative optimization of renewable energy power systems integrating electrolytic aluminum load regulation and thermal power deep peak shaving[J]. Applied Energy, 2024, 373: 123869.
[94] Lin Shunfu, He Tianhang, Shen Yunwei, 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.
[95] Chen Yuandong, Ding Jinliang, Chen Qingda.A reinforcement learning based large-scale refinery production scheduling algorithm[J]. IEEE Transactions on Automation Science and Engineering, 2024, 21(4): 6041-6055.
[96] Wu Naiqi, Li Zhiwu, Qu Ting.Energy efficiency optimization in scheduling crude oil operations of refinery based on linear programming[J]. Journal of Cleaner Production, 2017, 166: 49-57.
[97] Reka S S, Ramesh V.Industrial demand side response modelling in smart grid using stochastic optimisation considering refinery process[J]. Energy and Buildings, 2016, 127: 84-94.
[98] Xu Tiantian, Long Jian, Zhao Liang, et al.Material and energy coupling systems optimization for large-scale industrial refinery with sustainable energy penetration under multiple uncertainties using two-stage stochastic programming[J]. Applied Energy, 2024, 371: 123525.
[99] Alves M J, Henggeler Antunes C.A new exact method for linear bilevel problems with multiple objective functions at the lower level[J]. European Journal of Operational Research, 2022, 303(1): 312-327.
[100] 曾丹, 姚建国, 杨胜春, 等. 应对风电消纳中基于安全约束的价格型需求响应优化调度建模[J]. 中国电机工程学报, 2014, 34(31): 5571-5578.
Zeng Dan, Yao Jianguo, Yang Shengchun, et al.Optimization dispatch modeling for price-based demand response considering security constraints to accommodate the wind power[J]. Proceedings of the CSEE, 2014, 34(31): 5571-5578.
[101] 高倩, 杨知方, 李文沅, 等. 分支定界搜索信息深度引导的电-气互联系统调度决策加速求解方法[J]. 电工技术学报, 2024, 39(13): 3990-4002.
Gao Qian, Yang Zhifang, Li Wenyuan, et al.Dispatch acceleration of integrated electricity and gas system using branch-and-bound search information[J]. Tran-sactions of China Electrotechnical Society, 2024, 39(13): 3990-4002.
[102] 汪恭书, 刘静宜, 唐立新. 连铸-轧制混流生产模式下轧批调度问题的分支-定价算法[J]. 自动化学报, 2017, 43(7): 1178-1189.
Wang Gongshu, Liu Jingyi, Tang Lixin.Branch-and-price algorithm for rolling batch scheduling problem in continuous-casting and rolling processes with hybrid production mode[J]. Acta Automatica Sinica, 2017, 43(7): 1178-1189.
[103] 江岳文, 罗泽宇, 程诺. 基于线性化方法的交直流混合配电系统网架规划[J]. 电工技术学报, 2024, 39(5): 1404-1418.
Jiang Yuewen, Luo Zeyu, Cheng Nuo.Network planning of AC/DC hybrid distribution system based on linearization method[J]. Transactions of China Electrotechnical Society, 2024, 39(5): 1404-1418.
[104] Wang Yihan, Chen Chen, Tao Yuan, et al.A many-objective optimization of industrial environmental management using NSGA-III: a case of China’s iron and steel industry[J]. Applied Energy, 2019, 242: 46-56.
[105] Zeng Zhiqiang, Hong Mengna, Li Jigeng, et al.Integrating process optimization with energy-efficiency scheduling to save energy for paper mills[J]. Applied Energy, 2018, 225: 542-558.
[106] Roesch M, Linder C, Zimmermann R, et al.Smart grid for industry using multi-agent reinforcement learning[J]. Applied Sciences, 2020, 10(19): 6900.
[107] Talebjedi B, Khosravi A, Laukkanen T, et al.Energy modeling of a refiner in thermo-mechanical pulping process using ANFIS method[J]. Energies, 2020, 13(19): 5113.
[108] Rajalakshmi M, Jeyadevi S, Karthik C.An application of hybrid firefly and pso with support vector regression for modeling a clarifier process in sugar industry[C]//2018 National Power Engineering Con-ference (NPEC), Madurai, India, 2018: 1-10.
[109] Ji Xiaoyuan, Ye Hu, Zhou Jianxin, et al.An improved teaching-learning-based optimization algorithm and its application to a combinatorial optimization problem in foundry industry[J]. Applied Soft Computing, 2017, 57: 504-516.
[110] Owolabi T O, Suleiman M A, Adeyemo H B, et al.Estimation of minimum ignition energy of explosive chemicals using gravitational search algorithm based support vector regression[J]. Journal of Loss Prevention in the Process Industries, 2019, 57: 156-163.
[111] Ullah I, Hussain I, Singh M.Exploiting grasshopper and cuckoo search bio-inspired optimization algorithms for industrial energy management system: smart industries[J]. Electronics, 2020, 9(1): 105.
[112] Qadeer K, Qyyum M A, Lee M.Krill-herd-based investigation for energy saving opportunities in offshore liquefied natural gas processes[J]. Industrial & Engineering Chemistry Research, 2018, 57(42): 14162-14172.
[113] Xu Lei, Hou Lei, Zhu Zhenyu, et al.Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm[J]. Energy, 2021, 222: 119955.
[114] 赵曰浩, 鞠平. 考虑供气约束与净负荷预测误差的电-气综合能源系统调度策略[J]. 电力自动化设备, 2022, 42(7): 221-228.
Zhao Yuehao, Ju Ping.Scheduling strategy of integrated electricity and gas system considering constraint of gas supplying and prediction error of net power load[J]. Electric Power Automation Equipment, 2022, 42(7): 221-228.
[115] de Souza Amorim F M, da Silva Arantes M, de Souza Ferreira M P, et al. MILP formulation and hybrid evolutionary algorithms for the glass container industry problem with multiple furnaces[J]. Computers & Industrial Engineering, 2021, 158: 107398.
[116] Pan Ruilin, Wang Qiong, Li Zhenghong, et al.Steelmaking-continuous casting scheduling problem with multi-position refining furnaces under time-of-use tariffs[J]. Annals of Operations Research, 2022, 310(1): 119-151.
[117] Kumar V, Naresh R, Sharma V.GAMS environment based solution methodologies for ramp rate constrained profit based unit commitment problem[J]. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2021, 45(4): 1325-1342.
[118] Zondervan E, Grossmann I E, de Haan A B. Energy optimization in the process industries: unit com-mitment at systems level[J]. Computer Aided Chemical Engineering, 2010, 28: 931-936.
[119] Ianda T F, Sales E B, Nascimento A N, et al.Optimizing the cooperated ‘multi-countries’ biodiesel production and consumption in sub-saharan africa[J]. Energies, 2020, 13(18): 4717.
[120] Jabari F, Mohammadi-ivatloo B, Bannae Sharifian M B, et al. Design and robust optimization of a novel industrial continuous heat treatment furnace[J]. Energy, 2018, 142: 896-910.
[121] Clerget C H, Petit N.Dynamic optimization of processes with time varying hydraulic delays[J]. Journal of Process Control, 2019, 83: 20-29.
[122] Diekerhof M, Monti A, Lebedeva E, et al.Production and demand management[M]//AIRO Springer Series. Cham: Springer International Publishing, 2020: 3-25.
[123] Ishaq H, Dincer I.A new energy efficient single-stage flash drying system integrated with heat recovery applications in industry[J]. Drying Technology, 2020, 38(5/6): 735-746.
[124] Jiang Hao, Lin Jin, Song Yonghua, et al.MPC-based frequency control with demand-side participation: a case study in an isolated wind-aluminum power system[J]. IEEE Transactions on Power Systems, 2015, 30(6): 3327-3337.
[125] 柯德平, 冯帅帅, 刘福锁, 等. 新能源发电调控参与的送端电网直流闭锁紧急频率控制策略快速优化[J]. 电工技术学报, 2022, 37(5): 1204-1218.
Ke Deping, Feng Shuaishuai, Liu Fusuo, et al.Rapid optimization for emergent frequency control strategy with the power regulation of renewable energy during the loss of DC connection[J]. Transactions of China Electrotechnical Society, 2022, 37(5): 1204-1218.
[126] 黎博, 陈民铀, 钟海旺, 等. 高比例可再生能源新型电力系统长期规划综述[J]. 中国电机工程学报, 2023, 43(2): 555-581.
Li Bo, Chen Minyou, Zhong Haiwang, et al.A review of long-term planning of new power systems with large share of renewable energy[J]. Proceedings of the CSEE, 2023, 43(2): 555-581.
[127] 徐成司, 董树锋, 张舒鹏, 等. 面向工业园区的集中-分布式综合需求响应方法[J]. 电网技术, 2021, 45(2): 489-500.
Xu Chengsi, Dong Shufeng, Zhang Shupeng, et al.Centralized-distributed integrated demand response method for industrial park[J]. Power System Technology, 2021, 45(2): 489-500.
[128] 吴珊, 边晓燕, 张菁娴, 等. 面向新型电力系统灵活性提升的国内外辅助服务市场研究综述[J]. 电工技术学报, 2023, 38(6): 1662-1677.
Wu Shan, Bian Xiaoyan, Zhang Jingxian, et al.A review of domestic and foreign ancillary services market for improving flexibility of new power system[J]. Transactions of China Electrotechnical Society, 2023, 38(6): 1662-1677.
[129] Annala S, Viljainen S, Tuunanen J, et al.Does know-ledge contribute to the acceptance of demand response?[J]. Journal of Sustainable Development of Energy, Water and Environment Systems, 2014, 2(1): 51-60. |
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2025, Vol. 40 
