电制氢协同的含高比例光伏配电网两阶段电压随机优化控制

doi:10.11930/j.issn.1004-9649.202307084

中国电力 ›› 2024, Vol. 57 ›› Issue (8): 23-35.DOI: 10.11930/j.issn.1004-9649.202307084

• 基于氢能的电力系统灵活性提升技术 • 上一篇下一篇

电制氢协同的含高比例光伏配电网两阶段电压随机优化控制

张亚健¹(), 陈茨¹(), 薛飞²(), 马丽³(), 郑敏¹()

1. 上海大学机电工程与自动化学院，上海　200444
2. 国网宁夏电力有限公司电力科学研究院，宁夏银川　750000
3. 国网天津市电力公司东丽供电分公司，天津　300000

收稿日期:2023-07-23 录用日期:2023-10-21 发布日期:2024-08-23 出版日期:2024-08-28
作者简介:张亚健（1991—），男，通信作者，博士，副教授，从事综合能源系统优化运行与控制研究，E-mail：zhang_ya_jian@shu.edu.cn
陈茨（2000—），男，硕士研究生，从事配电网优化运行与控制研究，E-mail：bandachen@shu.edu.cn
薛飞（1994—），男，硕士，工程师，从事综合能源系统优化调度研究，E-mail：tjuxf1010@126.com
马丽（1993—），女，硕士，工程师，从事配电网规划研究，E-mail：mali_9308@163.com
郑敏（1976—），男，博士，副教授，从事电池储能系统优化与控制研究，E-mail：zhengmin203@shu.edu.cn
基金资助:
国家自然科学基金青年项目（62103254）；国网江苏省电力有限公司科技项目（J2021203）。

Two-Stage Stochastic Optimal Voltage Control of High-Proportional Photovoltaic Distribution Networks Considering Auxiliary Power to Hydrogen

Yajian ZHANG¹(), Ci CHEN¹(), Fei XUE²(), Li MA³(), Min ZHENG¹()

1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
2. Electric Power Research Institute of State Grid Ningxia Electric Power Co., Ltd., Yinchuan 750000, China
3. Dongli Power Supply Branch of State Grid Tianjin Electric Power Company, Tianjin 300000, China

Received:2023-07-23 Accepted:2023-10-21 Online:2024-08-23 Published:2024-08-28
Supported by:
This work is supported by National Natural Science Foundation of China (No.62103254), State Grid Jiangsu Electric Power Co., Ltd. Technology Project (No.J2021203).

摘要/Abstract

摘要：

针对传统配电网电压控制方法存在的调节资源有限、调控成本较高、响应速度较慢等问题，提出一种利用电制氢（power to hydrogen，P2H）辅助的两阶段电压随机优化控制策略。首先，在建立P2H装置在内的调压设备运行约束以及配电网线路约束的基础上，建立了考虑电解制气收益的日前-日内两阶段电压优化控制模型。其次，针对分布式能源出力和负荷需求日内短时扰动引发的电压波动甚至越限问题，基于拉丁超立方抽样与Kantorovich距离削减技术构建了配电网典型运行场景，并以各场景下日内阶段目标函数期望最小为目标求解电压控制策略。算例结果表明，所提方法相比于不考虑P2H辅助的常规电压控制方案，有效避免了电压越限问题，并且总控制成本降低了26.43%。

关键词: 电制氢, 配电网, 协调电压控制, 随机优化, 不确定性

Abstract:

To address the problems such as limited regulation resources, high regulation costs, and slow response speed in traditional distribution network voltage control methods, a two-stage stochastic optimal control strategy with participations of power-to-hydrogen (P2H) devices is investigated. Firstly, voltage regulation device operation constraints and distribution network line constraints are modeled. Then a two-stage day-ahead and intra-day voltage optimal control model is developed considering electrolytic gas production revenue. Secondly, to deal with the voltage fluctuations or even over-limitation problem caused by short-term disturbances of renewables and load demands, typical operation scenarios of distribution networks are constructed by adopting Latin hypercube sampling and Kantorovich distance reduction techniques. Then the voltage control strategy is solved by minimizing the expectation of intraday-stage objective functions under all scenarios. Finally, case studies have shown that compared with the traditional voltage methods without considering P2H participations, the voltage over-limitation can be effectively avoided and the total regulation costs can be reduced by over 26.43% by using the proposed method.

Key words: power to hydrogen, distribution network, coordinated voltage control, stochastic optimization, uncertainties

张亚健, 陈茨, 薛飞, 马丽, 郑敏. 电制氢协同的含高比例光伏配电网两阶段电压随机优化控制[J]. 中国电力, 2024, 57(8): 23-35.

Yajian ZHANG, Ci CHEN, Fei XUE, Li MA, Min ZHENG. Two-Stage Stochastic Optimal Voltage Control of High-Proportional Photovoltaic Distribution Networks Considering Auxiliary Power to Hydrogen[J]. Electric Power, 2024, 57(8): 23-35.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202307084

https://www.electricpower.com.cn/CN/Y2024/V57/I8/23

图/表 17

图 1 电制氢原理示意

Fig.1 Power to hydrogen schematic

图 2 两阶段配电网电压控制总体架构

Fig.2 Overall architecture of two-stage distribution network voltage control

图 3 两阶段电压控制策略求解流程图

Fig.3 Flowchart of proposed two-stage voltage control strategy

图 4 IEEE 33节点配电网拓扑

Fig.4 IEEE 33-node distribution network topology

图 5 日前预测数据

Fig.5 Day-ahead forecasted data

图 6 不同场景下的电压曲线

Fig.6 Voltage profiles in different scenarios

图 7 不同场景下有载调压变压器档位

Fig.7 Positions of OLTC in different scenarios

图 8 不同场景下并联电容器投切组数

Fig.8 Switching number of CB in different scenarios

图 9 不同场景下P2H装置消纳功率

Fig.9 Power consumptions of P2H in different scenarios

图 10 不同场景下储能系统荷电状态

Fig.10 SOC performances of ESSs in different scenarios

表 1 日前优化控制成本

Table 1 Control costs for day-ahead optimization 单位：元

场景	总成本	网损成本	动作成本	电压越限成本	ESS调节成本	PV调节成本	弃光成本	P2H成本	氢气收益	氧气收益
1	648.2304	474.7340	11.96	3.9781	57.9824	30.5732	69.0027	0	0	0
2	571.3278	404.1624	6.46	0	34.5929	30.0528	69.0027	125.0945	98.0375	0
3	476.8993	415.8795	0.96	0	28.2351	30.0528	69.0027	219.5796	172.0863	114.7242

图 11 不含P2H的单阶段控制方法[8]电压

Fig.11 Voltage profiles using single-stage control method without considering P2H[8]

图 12 不含P2H的两阶段协调控制方法[9]电压

Fig.12 Voltage profiles using two-stage control method without considering P2H[9]

图 13 本文所提方法的电压（考虑P2H参与）

Fig.13 Voltage profiles using proposed method in this paper considering P2H

图 14 光伏渗透率40%时的电压曲线

Fig.14 Voltage profiles under 40% PV penetrations

表 2 不同方法控制成本

Table 2 Control costs of different methods 单位：元

控制方法	总成本	网损成本	动作成本	电压越限成本	ESS调节成本	PV调节成本	弃光成本	P2H成本	氢气收益	氧气收益
1	1997.7420	1855.6314	17.46	0	131.2100	40.7963	305.7374	1153.2174	903.7681	602.5121
2	2469.3280	1817.6913	17.46	0	142.3366	40.7963	305.7374	671.8061	526.4997	0
3	2827.6092	2270.4256	11.96	1.3876	186.3669	51.7318	305.7374	0	0	0

图 15 考虑弃风成本的电压曲线

Fig.15 Voltage profiles considering wind curtailment costs

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电制氢协同的含高比例光伏配电网两阶段电压随机优化控制

Two-Stage Stochastic Optimal Voltage Control of High-Proportional Photovoltaic Distribution Networks Considering Auxiliary Power to Hydrogen

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电制氢协同的含高比例光伏配电网两阶段电压随机优化控制

Two-Stage Stochastic Optimal Voltage Control of High-Proportional Photovoltaic Distribution Networks Considering Auxiliary Power to Hydrogen

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图/表 17

参考文献 36

相关文章 15

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