Dual-Time-Scale Optimal Scheduling for High-Energy-Consuming Industrial Park Considering Uncertainty of Photovoltaic

doi:10.16183/j.cnki.jsjtu.2024.248

Abstract

Abstract:

To fully exploit the potential regulation capacity of high-energy-consuming industrial loads and alleviate the pressure on the supply-demand balance of power systems caused by the large-scale access of renewable energy resources, this paper proposes a dual-time-scale optimal scheduling method for high-energy-consuming industrial parks considering the uncertainty of photovoltaic (PV). First, typical scenarios of PV power outputs are generated based on the conditional generation adversarial network to characterize the uncertainty of PV during the optimal scheduling process of industrial parks. Then, a dual-time-scale optimal scheduling model is established for high-energy-consuming industrial parks considering the coupling mechanism and regulation characteristics of various regulation resources in the industrial park. In this scheme, the production plan of high-energy-consuming industrial users is optimized to maximize the operation economy of the industrial park in the day-ahead stage, while power fluctuations of the industrial park are further mitigated by coordinating the tap position of electric arc furnaces and the charging/discharging power of energy storage resources in the intra-day stage. Finally, the optimal scheduling of an industrial park containing two typical high-energy-consuming industries, namely, iron and steel plants and cement plants is conducted to verify the effectiveness of the proposed method.

Key words: high-energy-consuming industry, optimal scheduling, uncertainty of photovoltaic (PV), dual time scale

CLC Number:

TM731

WANG Jiaying, LU Chunguang, JIAO Wenshu, WU Qiuwei, CAO Yongji, YANG Jianli. Dual-Time-Scale Optimal Scheduling for High-Energy-Consuming Industrial Park Considering Uncertainty of Photovoltaic[J]. Journal of Shanghai Jiao Tong University, 2026, 60(6): 904-914.

Figures/Tables 14

Fig.1

Fig.2

Tab.1

Tab.2

Fig.3

Fig.4

Tab.3

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

References 24

[1]	朱继忠, 董瀚江, 李盛林, 等. 基于分布式新能源集群的微能源网优化调度综述[J]. 中国电机工程学报, 2024, 44(20): 7952-7970.
	ZHU Jizhong, DONG Hanjiang, LI Shenglin, et al. Review of optimal dispatching for the aggregation of micro-energy grids based on distributed new energy[J]. Proceedings of the CSEE, 2024, 44(20): 7952-7970.
[2]	徐箭, 余青芳, 廖思阳, 等. 考虑生产安全的工业园区联络线功率平滑策略[J]. 上海交通大学学报, 2024, 58(6): 941-953. doi: 10.16183/j.cnki.jsjtu.2022.516
	XU Jian, YU Qingfang, LIAO Siyang, et al. Power smoothing strategy for industrial park tie-line considering production safety[J]. Journal of Shanghai Jiao Tong University, 2024, 58(6): 941-953.
[3]	姚明涛, 韩雪, 郑雅楠, 等. 推动高耗能行业参与电力辅助服务市场的思考[J]. 中国能源, 2018, 40(2): 22-26.
	YAO Mingtao, HAN Xue, ZHENG Yanan, et al. Ponder on promoting the participation of high energy-intensive industries in power ancillary service market[J]. Energy of China, 2018, 40(2): 22-26.
[4]	晋宏杨, 孙宏斌, 郭庆来, 等. 基于能源互联网用户核心理念的高载能-风电协调调度策略[J]. 电网技术, 2016, 40(1): 139-145.
	JIN Hongyang, SUN Hongbin, GUO Qinglai, et al. Dispatch strategy based on energy Internet customer-centered concept for energy intensive enterprise and renewable generation to improve renewable integration[J]. Power System Technology, 2016, 40(1): 139-145.
[5]	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 Xingjie, XU Yonghai, HUANG Zitong, 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.
[6]	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 Haibo, ZHANG Li. Supply-demand interactive scheduling model considering short production process of steel enterprises[J]. Automation of Electric Power Systems, 2021, 45(15): 64-76.
[7]	MENG H, HUANG Y, ZHENG C N, et al. Research and system design of energy dynamic balance and optimal scheduling of iron and steel enterprises[J]. IOP Conference Series: Earth & Environmental Science, 2022, 983(1): 012037.
[8]	GOLMOHAMADI H, KEYPOUR R, BAK-JEN-SEN 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. doi: 10.1109/TIE.41 URL
[9]	文岚, 仇诗涛. 电力负荷管理在水泥行业中的应用分析[J]. 大众用电, 2023, 38(10): 31-32.
	WEN Lan, QIU Shitao. Application analysis of po-wer load management in cement industry[J]. Popular Utilization of Electricity, 2023, 38(10): 31-32.
[10]	桑乃云. 基于可调节负荷的水泥厂用电策略研究[J]. 技术与市场, 2018, 25(12): 191-192.
	SANG Naiyun. Study on power consumption strategy of cement plant based on adjustable load[J]. Techno-logy & Market, 2018, 25(12): 191-192.
[11]	李胜飞, 魏玲, 李志云, 等. 典型高耗能负荷参与系统需求侧管理的研究[J]. 自动化技术与应用, 2023, 42(9): 63-67.
	LI Shengfei, WEI Ling, LI Zhiyun, et al. Research on high energy consumption load participating in demand side management of power system[J]. Techniques of Automation & Applications, 2023, 42(9): 63-67.
[12]	马嘉晨, 袁满, 杨德友, 等. 用于提升风电消纳能力的高载能负荷调度策略研究[J]. 东北电力大学学报, 2024, 44(2): 79-87.
	MA Jiachen, YUAN Man, YANG Deyou, et al. Research on high-energy load dispatching strategy used to improve wind power ability[J]. Journal of Northeast Electric Power University, 2024, 44(2): 79-87.
[13]	朱丹丹, 赵静波, 李强, 等. 高载能负荷企业参与受阻风电消纳决策方法[J]. 电力工程技术, 2021, 40(2): 121-127.
	ZHU Dandan, ZHAO Jingbo, LI Qiang, et al. Decision making method for energy-intensive enterprise in consuming curtailed wind power[J]. Electric Power Engineering Technology, 2021, 40(2): 121-127.
[14]	智勇, 郭帅, 何欣, 等. 面向智慧工业园区的双层优化调度模型[J]. 电力系统自动化, 2017, 41(1): 31-38.
	ZHI Yong, GUO Shuai, HE Xin, et al. Bilevel optimal dispatch model for intelligent industrial park[J]. Automation of Electric Power Systems, 2017, 41(1): 31-38.
[15]	胡枭, 徐国栋, 尚策, 等. 工业园区参与调峰的电池储能-需求响应联合规划[J]. 电力系统自动化, 2019, 43(15): 116-123.
	HU Xiao, XU Guodong, SHANG Ce, et al. Joint planning of battery energy storage and demand response for industrial park participating in peak shaving[J]. Automation of Electric Power Systems, 2019, 43(15): 116-123.
[16]	李耐心, 李良杰, 杜鹏. 计及负荷灵活特性的工业园区综合能源系统日前优化调度研究[J]. 能源与环保, 2024, 46(1): 227-234.
	LI Naixin, LI Liangjie, DU Peng. Research on day-ahead optimal scheduling of integrated energy system in industrial parks based on flexible load characteristics[J]. China Energy & Environmental Protection, 2024, 46(1): 227-234.
[17]	王奖, 邓丰强, 张勇军, 等. 园区能源互联网的规划与运行研究综述[J]. 电力自动化设备, 2021, 41(2): 24-32.
	WANG Jiang, DENG Fengqiang, ZHANG Yongjun, et al. Review on planning and operation research of park energy Internet[J]. Electric Power Automation Equipment, 2021, 41(2): 24-32.
[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]	王强, 倪梦飞, 崔勇. 分布式光伏在高耗能工业园区中的应用[J]. 自动化应用, 2023(24): 198-199.
	WANG Qiang, NI Mengfei, CUI Yong. Application of distributed photovoltaics in high energy consumption industrial parks[J]. Automation Application, 2023(24): 198-199.
[20]	GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks[EB/OL]. (2014-06-10)[2024-06-27]. http://arxiv.org/abs/1406.2661v1.
[21]	黄越辉, 孙亚南, 李驰, 等. 基于条件生成对抗网络的多区域风电短期出力场景生成方法[J]. 电网技术, 2023, 47(1): 63-77.
	HUANG Yuehui, SUN Yanan, LI Chi, et al. Constructing method of short-term output scenarios for multi-regional wind power based on conditional gene-rative adversarial network[J]. Power System Technology, 2023, 47(1): 63-77.
[22]	董骁翀, 孙英云, 蒲天骄. 基于条件生成对抗网络的可再生能源日前场景生成方法[J]. 中国电机工程学报, 2020, 40(17): 5527-5536.
	DONG Xiaochong, SUN Yingyun, PU Tianjiao. Day-ahead scenario generation of renewable energy based on conditional GAN[J]. Proceedings of the CSEE, 2020, 40(17): 5527-5536.
[23]	徐文滨, 孙一凡, 王林, 等. 考虑风电时空相关性的电气互联系统分布式优化调度[J]. 浙江电力, 2023, 42(3): 9-17.
	XU Wenbin, SUN Yifan, WANG Lin, et al. Distributed optimal dispatching of the integrated electri-city and natural gas systems considering spatial-temporal correlation of wind power[J]. Zhejiang Electric Power, 2023, 42(3): 9-17.
[24]	靳小龙, 穆云飞, 贾宏杰, 等. 融合需求侧虚拟储能系统的冷热电联供楼宇微网优化调度方法[J]. 中国电机工程学报, 2017, 37(2): 581-591.
	JIN Xiaolong, MU Yunfei, JIA Hongjie, et al. Optimal scheduling method for a combined cooling, heating and power building microgrid considering virtual storage system at demand side[J]. Proceedings of the CSEE, 2017, 37(2): 581-591.

高耗能可调设备	P_N,l,_i/MW	${\mathit{\alpha }}_{\mathit{i}}^{\mathrm{m}\mathrm{a}\mathrm{x}}$	${\mathrm{\tau }}_{\mathrm{i}}^{\mathrm{m}\mathrm{a}\mathrm{x}}$	${\stackrel{-}{\mathit{T}}}_{\mathit{\tau },\mathit{i}}$/h	E_w,_i_,min/(MW·h)	${\stackrel{-}{\mathit{n}}}_{\mathrm{t}\mathrm{a},\mathit{i}}$	c_i	可调功率范围/MW
钢铁1	20	2	2	2	300	9	0.022	20~24
钢铁2	18	2	2	2	270	9	0.025	18~22
钢铁3	24	2	2	2	312	10	0.025	24~30
钢铁4	6	4	4	2	66
钢铁5	5	4	4	1	50
钢铁6	3	4	4	1	33
钢铁7	4.5	3	3	2	45
钢铁8	5.5	3	3	2	55
钢铁9	3	4	4	1	30
钢铁10	3	4	4	1	30
水泥1	2	2	2	10	32
水泥2	2	2	2	10	32
水泥3	2.4	2	2	9	36
水泥4	2.6	2	2	10	41.6
水泥5	1	2	2	10	16
水泥6	8	2	2	4	120
水泥7	10	2	2	3	140
水泥8	7	2	2	4	105

园区储能类型	${\underset{\_}{\mathit{P}}}_{\mathrm{E}\mathrm{S}\mathrm{S},\mathit{i}}$/MW	${\stackrel{-}{\mathit{P}}}_{\mathrm{E}\mathrm{S}\mathrm{S},\mathit{i}}$/MW	η_ch,_i	η_dis_,i	${\underset{\_}{\mathit{E}}}_{\mathrm{E}\mathrm{S}\mathrm{S},\mathit{i}}$/(MW·h)	${\stackrel{-}{\mathit{E}}}_{\mathrm{E}\mathrm{S}\mathrm{S},\mathit{i}}$/(MW·h)
钢铁储能	10	10	0.95	0.95	2	18
水泥储能	2	2	0.95	0.95	0.4	3.6
光伏配储	11	11	0.95	0.95	2.2	19.8

策略	购电成本	售电收益	高耗能负荷运维成本	储能运维成本	总成本
1	1210910.87	6506.31	16632.18	0	1221036.74
2	1164543.15	14681.49	16632.18	1609.73	1168103.57
3	1176462.49	22272.10	16632.18	1606.97	1172429.54
4	1164543.15	14681.49	16632.18	1609.73	1168103.57