基于两阶段随机优化的电氢耦合微电网周运行策略

doi:10.11930/j.issn.1004-9649.202408035

摘要/Abstract

摘要：

为充分发挥氢能的中长期存储优势，提出了一种基于场景法随机优化模型的电氢耦合微电网两阶段周优化调度策略。首先，建立微电网中电氢耦合设备的数学模型；其次，以最小化周运行成本为目标，分别设置电、氢储能的周期为周和日，建立基于周预测数据的微电网第1阶段周调度模型。然后，利用预测误差的典型场景衡量风电不确定性，以日运行期望成本与两阶段储氢罐状态偏差惩罚之和最小为目标，构建考虑不确定性的第2阶段日前调度模型，并通过滚动求解，得到最终周运行方案。最后，算例表明，所提策略能降低微电网运行成本并提高能量利用率。

关键词: 电氢耦合, 氢储能, 多时间尺度, 随机优化, 场景法

Abstract:

To fully capitalize on the medium- and long-term storage benefits of hydrogen energy, a two-stage weekly optimization scheduling strategy for electric-hydrogen coupled microgrids is proposed, utilizing a scenario-based stochastic optimization model. Firstly, the mathematical models for the electric-hydrogen coupled equipment within the microgrid are developed. Secondly, aiming to minimize the weekly operation cost, the cycles of electric and hydrogen energy storage are set as weekly and daily, respectively, and the first-stage weekly scheduling model of the microgrid based on weekly prediction data is established. Then, to account for the uncertainty in wind power generation, typical scenarios of prediction errors are introduced. A day-ahead scheduling model considering uncertainty is constructed, aiming to minimize the sum of the daily expected operational cost and the deviation penalty for the two-stage hydrogen storage tank's state of charge. The final weekly operation scheme is determined through rolling optimization. Finally, case studies demonstrate that the proposed strategy effectively reduces the microgrid's operating costs and enhances energy utilization.

Key words: electric-hydrogen coupling, hydrogen energy storage, multi-time scale, stochastic optimization, scenario-based method

陈铭宏天, 耿江海, 赵雨泽, 许鹏, 韩雨珊, 张育铭, 张子沫. 基于两阶段随机优化的电氢耦合微电网周运行策略[J]. 中国电力, 2025, 58(5): 82-90.

CHEN Minghongtian, GENG Jianghai, ZHAO Yuze, XU Peng, HAN Yushan, ZHANG Yuming, ZHANG Zimo. Two-Stage Stochastic Optimization Based Weekly Operation Strategy for Electric-Hydrogen Coupled Microgrid[J]. Electric Power, 2025, 58(5): 82-90.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202408035

https://www.electricpower.com.cn/CN/Y2025/V58/I5/82

图/表 13

图 1 电氢耦合微电网框架

Fig.1 Framework of electric-hydrogen coupling microgrid

图 2 模型求解流程

Fig.2 Model solving process

图 3 负荷及风电出力预测曲线

Fig.3 Load and wind power output forecast curves

表 1 设备参数

Table 1 Equipment parameters

参数	取值	参数	取值
电解槽容量/kW	600	储氢罐SOC初始值	0.2
电解槽效率/%	70	储氢罐SOC上、下限	0.9、0.1
燃料电池容量/kW	500	蓄电池容量/(kW·h)	300
燃料电池效率/%	60	蓄电池充、放效率/%	95、95
储氢罐容量/kg	250	蓄电池SOC初始值	0.3
储氢罐充、放效率/%	97、97	蓄电池SOC上、下限	0.9、0.1

表 2 成本参数

Table 2 Cost parameters

时段	电价/ (元·(kW·h)^–1)	弃风惩罚系数/ (元·(kW·h)^–1)
01:00—07:00、22:00—24:00	0.38	0.4
07:00—11:00、14:00—18:00	0.68	0.4
11:00—14:00、18:00—22:00	1.20	0.4

图 4 典型风电预测误差场景

Fig.4 Typical wind power prediction error scenarios

表 3 典型风电预测误差场景的概率

Table 3 Probabilities of typical wind power prediction error scenarios

场景		概率/%
1		35.7
2		31.6
3		32.7

图 5 不同偏差惩罚系数下策略2的运行结果

Fig.5 Operation results of Strategy 2 under different deviation penalty coefficients

表 4 不同策略的结果对比

Table 4 Comparison of results between different strategies

运行策略	总成本/ 元	购电量/ (kW·h)	风电消纳量/(kW·h)	能量利用率/%	制氢量/ kg
1	12 593	24 576	39 544	83.79	821.84
2	11 874	22 466	39 553	86.63	676.49

图 6 不同策略下的电功率平衡

Fig.6 Electrical power balance under different strategies

图 7 2种策略各日的购电量和制氢量对比

Fig.7 Comparison of daily electricity purchase and hydrogen production between the two strategies

图 8 2种策略的储氢罐SOC波动曲线

Fig.8 SOC fluctuation curves of the hydrogen storage tank for the two strategies

表 5 策略2和策略3的结果对比

Table 5 Comparison of results between strategy 2 and strategy 3

运行策略	总成本/ 元	购电量/ (kW·h)	风电消纳量/(kW·h)	能量利用率/%	制氢量/ kg
2	11 874	22 466	39 553	86.63	676.49
3	12 203	23 026	39 567	85.84	715.54

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