Carbon Footprint Analysis and Low-carbon Optimization Method Simulation Study of Power Transformer Based on Digital Twin Technology

doi:10.16182/j.issn1004731x.joss.23-0605

Abstract

Abstract:

Power transformers are the main energy-consuming equipment for substations. According to the goal of “carbon peak, carbon neutralization” in China, it is of great significance to accurately calculate the carbon footprint of transformers and seek low-carbon optimization methods. A method for constructing a digital twin model of power transformer magnetic characteristics is proposed. Based on the three-dimensional electromagnetic time-harmonic field finite element analysis method, a three-dimensional model of SZ11-31.5MVA/66kV power transformer is established. The transformer loss map is obtained under fluctuating load condition, and the transformer digital twin model is constructed. The carbon footprint of the transformer is analyzed, the economic load coefficient of the transformer is determined, and the optimal economic operation mode of the substation is established. The low carbon optimization is carried out by installing magnetic shunt, and the carbon emission is reduced by 4.75% under rated condition. Through experimental calculation, the relative error between the model prediction and the actual simulation is less than 5%. This method can provide a scientific basis for the construction of substation with digital, low carbon and energy saving.

Key words: power transformer, digital twin, loss, carbon footprint, load factor, low-carbon optimization

CLC Number:

TM 41

Li Dongxue, Liu Yan, Shen Boyao, Jing Yongteng, Ma Qiang, Liu Ran. Carbon Footprint Analysis and Low-carbon Optimization Method Simulation Study of Power Transformer Based on Digital Twin Technology[J]. Journal of System Simulation, 2024, 36(9): 2075-2085.

Figures/Tables 23

Table 1

Table 2

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Table 3

Table 4

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Fig. 13

Fig. 14

Table 5

Fig. 15

Fig. 16

Fig. 17

Table 6

References 15

1	贺兴, 艾芊, 朱天怡, 等. 数字孪生在电力系统应用中的机遇和挑战[J]. 电网技术, 2020, 44(6): 2009-2019.
	He Xing, Ai Qian, Zhu Tianyi, et al. Opportunities and Challenges of the Digital Twin in Power System Applications[J]. Power System Technology, 2020, 44(6): 2009-2019.
2	高扬, 贺兴, 艾芊. 基于数字孪生驱动的智慧微电网多智能体协调优化控制策略[J]. 电网技术, 2021, 45(7): 2483-2491.
	Gao Yang, He Xing, Ai Qian. Multi Agent Coordinated Optimal Control Strategy for Smart Microgrid Based on Digital Twin Drive[J]. Power System Technology, 2021, 45(7): 2483-2491.
3	王卓. 基于全生命周期的110kV变电站降碳方案研究[J]. 电工电气, 2022(1): 66-69.
	Wang Zhuo. Lifecycle-based Research on Carbon Reduction Measures of 110 kV Substation[J]. Electrotechnics Electric, 2022(1): 66-69.
4	王玎. 基于碳足迹分析的变压器低碳优化设计[D]. 天津: 天津科技大学, 2017.
	Wang Ding. Low Carbon Optimization Design of Transformer Based on Carbon Footprint Analysis[D]. Tianjin: Tianjin University of Science and Technology, 2017.
5	杨东, 刘晶茹, 杨建新, 等. 基于生命周期评价的风力发电机碳足迹分析[J]. 环境科学学报, 2015, 35(3): 927-934.
	Yang Dong, Liu Jingru, Yang Jianxin, et al. Carbon Footprint of Wind Turbine by Life Cycle Assessment[J]. Acta Scientiae Circumstantiae, 2015, 35(3): 927-934.
6	樊立安, 张东明. 我国能源领域二氧化碳排放研究[J]. 低碳世界, 2022, 12(4): 99-101.
7	张守明. 碳质量平衡法测算燃油消耗量的相关问题探讨[J]. 计量与测试技术, 2020, 47(8): 57-59, 63.
	Zhang Shouming. Discussion on the Related Problems of the Calculation of Fuel Consumption by Carbon Mass Balance Method[J]. Metrology & Measurement Technique, 2020, 47(8): 57-59, 63.
8	李猛, 聂铭, 和敬涵, 等. 基于数字孪生的柔性直流电网纵联保护原理[J]. 中国电机工程学报, 2022, 42(5): 1773-1782, 中插12.LiMeng, NieMing, HeJinghan, et al. Pilot Protection of Flexible DC Grid Based on Digital Twin[J]. Proceedings of the CSEE, 2022, 42(5): 1773-1782, S12.
9	张立静, 盛戈皞, 侯慧娟, 等. 基于电热特性融合分析的油浸式变压器匝间短路故障辨识方法[J]. 电网技术, 2021, 45(7): 2473-2482.
	Zhang Lijing, Sheng Gehao, Hou Huijuan, et al. Detection Method of Interturn Short-circuit Faults in Oil-immersed Transformers Based on Fusion Analysis of Electrothermal Characteristic[J]. Power System Technology, 2021, 45(7): 2473-2482.
10	Stulov Alexey, Tikhonov Andrey, Snitko Irina. Fundamentals of Artificial Intelligence in Power Transformers Smart Design[C]//2020 International Ural Conference on Electrical Power Engineering (UralCon). Piscataway, NJ, USA: IEEE, 2020: 34-38.
11	潘博, 张弛, 张华, 等. 数字孪生变电站在电网企业数智化转型的探索与应用[J]. 电力与能源, 2020, 41(5): 558-560, 590.
	Pan Bo, Zhang Chi, Zhang Hua, et al. Exploration and Application of Digital Twin Substation in Digital Intelligent Transformation of Power Grid Enterprises[J]. Power & Energy, 2020, 41(5): 558-560, 590.
12	Moutis Panayiotis, Alizadeh-Mousavi Omid. Digital Twin of Distribution Power Transformer for Real-time Monitoring of Medium Voltage from Low Voltage Measurements[J]. IEEE Transactions on Power Delivery, 2021, 36(4): 1952-1963.
13	江友华, 易罡, 黄荣昌, 等. 基于多源信息融合的变压器检测与评估技术[J]. 上海电力大学学报, 2020, 36(5): 481-485.
	Jiang Youhua, Yi Gang, Huang Rongchang, et al. Transformer State Detection and Evaluation Technology Based on Multi-source Information Fusion[J]. Journal of Shanghai University of Electric Power, 2020, 36(5): 481-485.
14	Ivan E Kolesnikov, Korzhov Anton V, Konstantin E Gorshkov. Digital Program for Diagnosing the Status of a Power Transformer[C]//2020 Global Smart Industry Conference (GloSIC). Piscataway, NJ, USA: IEEE, 2020: 315-321.
15	井永腾, 王宁, 李岩, 等. 电磁-热-流弱耦合的变压器绕组温升研究[J]. 电机与控制学报, 2019, 23(10): 41-48.
	Jing Yongteng, Wang Ning, Li Yan, et al. Research on Temperature Rise of Transformer Windings with Electromagnetic-thermal-flow Weak Coupling[J]. Electric Machines and Control, 2019, 23(10): 41-48.

特征参数	数值
额定容量/MVA	31.5
额定电压/kV	66
联结组别	YNd11
绕组结构	低-高-调

部件	材料	电导率/(S/m)
铁心	30ZH120	各向异性电导率
油箱	Q345B	7.692×10⁶
拉板	Q345B	7.692×10⁶
夹件	Q345B	7.692×10⁶
绕组	电工铜	5.714 3×10⁷

损耗	实验推算/W	仿真计算/W	误差/%
拉板	－	2 585.8	－
夹件	－	8 938.0	－
油箱	－	1 509.6	－
结构件	12 612	12 833.4	1.86

损耗	实验推算/W	仿真计算/W	误差/%
总损耗	149 492	153 056.5	2.38
空载	21 380	22 298.8	4.30
负载	115 500	117 924.3	2.10
结构件	12 612	12 833.4	1.86

时刻	总损耗/kW	碳足迹 CO₂/kg	碳累积 CO₂/kg
01:00	59.83	72.70	72.70
02:00	48.13	58.33	131.03
03:00	48.13	58.33	189.36
04:00	48.13	58.33	247.69
05:00	59.83	72.70	320.39
06:00	59.83	72.70	393.09
07:00	73.82	89.67	482.76
08:00	73.82	89.67	572.43
09:00	89.81	108.82	681.25
10:00	107.90	130.23	811.48
11:00	89.81	108.82	920.30
12:00	202.53	249.38	1169.68
13:00	89.81	108.82	1278.50
14:00	107.90	130.23	1408.73
15:00	128.39	154.15	1562.88
16:00	128.39	154.15	1717.03
17:00	107.90	130.23	1847.26
18:00	107.90	130.23	1977.49
19:00	89.81	108.83	2086.32
20:00	73.82	89.67	2175.99
21:00	59.83	72.70	2248.69
22:00	59.83	72.70	2321.39
23:00	48.13	58.33	2379.72
24:00	48.13	58.33	2438.05