Two-stage voltage optimization control method for hydrogen-production-assisted distribution networks

doi:10.11930/j.issn.1004-9649.202601035

Abstract

Abstract:

The large-scale application of renewable energy in the distribution network poses a challenge to the voltage stability of the distribution network. This article proposes a two-stage voltage optimization control method considering the assistance of electric hydrogen production equipment. In the recent optimization phase, a global voltage regulation strategy was developed using an optimization model that includes dynamic constraints on hydrogen production equipment, with the goal of minimizing distribution network operating losses and hydrogen production costs. During the real-time control phase within the day, a bounded adaptive event triggered control method is adopted to dynamically adjust the triggering frequency of control instructions, in order to solve the voltage deviation problem caused by short-term power fluctuations with a lower response frequency of the hydrogen production equipment. Simulation examples show that the proposed method can reduce the voltage deviation amplitude of the distribution network by more than 10.24%, and the cost of voltage regulation can be reduced by more than 4.89%, demonstrating high voltage stability and good operational economy.

Key words: hydrogen production, distribution network, event-triggered control, voltage regulation

CLC Number:

TK513.5

LI Xutao, ZHOU Yang, CHANG Qicheng, Alexis P. ZHAO, GU Wenbo, MA Xin, LI Hongqiang, WANG Weijie, ZHANG Yajian, BAO Shuxin. Two-stage voltage optimization control method for hydrogen-production-assisted distribution networks[J]. Electric Power, 2026, 59(2): 81-89.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202601035

https://www.electricpower.com.cn/EN/Y2026/V59/I2/81

Figures/Tables 9

Fig.1 Overall architecture of two-stage distribution network voltage control

Fig.2 IEEE 33-bus distribution system topology

Fig.3 Voltage profiles system nodes under different scenarios

Fig.4 Power accommodated by P2H under different scenarios

Fig.5 SOC of the ESSs under different regulation scenarios

Table 1 Day-ahead optimization control costs 单位：元

场景	网损成本	调压成本	ESS 成本	光伏成本	P2H 成本	氧气收益	氢气收益	总成本
1	481.62	16.73	59.03	101.29	0	0	0	658.67
2	409.31	1.16	27.08	103.85	222.41	120.69	182.37	460.75

Fig.6 Maximum voltage deviation of the system under 100 scenarios

Fig.7 Average voltage deviation at various nodes under different control methods

Table 2 Real-time control performance of different methods under worst-case scenarios

控制方法	平均电压偏差(p.u)	最大电压偏差(p.u)	电压合格率/%	调压成本/元
1	0.0114	0.0283	100.00	482.63
2	0.0213	0.0413	93.19	762.46
3	0.0146	0.0327	97.42	637.78
4	0.0127	0.0314	98.21	507.45

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