基于改进猫群算法的氢储能容量优化配置

doi:10.11930/j.issn.1004-9649.202209013

中国电力 ›› 2023, Vol. 56 ›› Issue (10): 33-42.DOI: 10.11930/j.issn.1004-9649.202209013

• 面向新型电力系统的氢能及其系统集成控制关键技术 • 上一篇下一篇

基于改进猫群算法的氢储能容量优化配置

胡臻达¹(), 姜文瑾², 张林垚¹, 杨晓东², 邹艺超¹(), 王凯³

1. 国网福建省电力有限公司经济技术研究院，福建福州　350012
2. 国网福建省电力有限公司，福建福州　350003
3. 福建永福电力设计股份有限公司，福建福州　350108

收稿日期:2022-09-05 出版日期:2023-10-28 发布日期:2023-10-31
作者简介:胡臻达（1986—），男，硕士，从事电力系统规划和运行研究，E-mail： huzhenda@163.com
邹艺超（1996—），女，通信作者，硕士，从事电力系统规划和运行研究，E-mail： Zouyichao0508@126.com
基金资助:
国网福建省电力有限公司科技项目（基于供需失衡概率风险评估的新型电力系统供需保障能力研究，B3130N22000R）

Optimal Allocation of Hydrogen Storage Capacity Based on Improved Cat Swarm Optimization

Zhenda HU¹(), Wenjin JIANG², Linyao ZHANG¹, Xiaodong YANG², Yichao ZOU¹(), Kai WANG³

1. Economic and Technical Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350012, China
2. State Grid Fujian Electric Power Co., Ltd., Fuzhou 350003, China
3. Fujian Yongfu Power Engineering Co., Ltd., Fuzhou 350108, China

Received:2022-09-05 Online:2023-10-28 Published:2023-10-31
Supported by:
This work is supported by Science and Technology Project of State Grid Fujian Electric Power Company Limited (Research on Supply and Demand Support Capability of New Power System Based on Probabilistic Risk Assessment of Supply and Demand Imbalance, No.B3130N22000R).

摘要/Abstract

摘要：

以氢储能系统为基础，构建了风力发电和氢储能系统为主的混合微电网系统。本文对氢储能系统中的电解槽、燃料电池和储氢罐的容量进行了优化配置。综合考虑微电网的经济成本、供电可靠性和弃风率3个指标，构建了氢储能系统的容量优化配置模型；分别采用典型日和场景集的风电出力和负荷，建立氢储能系统的容量优化模型，以反映风电出力和负荷的随机性对优化结果的影响；采用动态权重的改进猫群算法对所提氢储能系统的容量优化配置模型进行求解。通过算例验证了改进猫群算法的可行性，同时也证明了所提氢储能系统容量优化配置模型的合理性。

关键词: 氢储能系统, 容量优化配置, 改进猫群算法, 风电, 供电可靠性

Abstract:

Based on the hydrogen energy storage system, a hybrid microgrid system with wind power generation and hydrogen energy storage system as the main components has been constructed. This article optimizes the capacity configuration of electrolytic cells, fuel cells, and hydrogen storage tanks in hydrogen energy storage systems. A capacity optimization configuration model for hydrogen energy storage systems was constructed by comprehensively considering the economic cost, power supply reliability, and wind abandonment rate of microgrids; Establish a capacity optimization model for hydrogen storage systems using wind power output and load from typical day and scenario sets, to reflect the impact of the randomness of wind power output and load on the optimization results; Using an improved cat swarm algorithm with dynamic weights, the capacity optimization configuration model of the proposed hydrogen energy storage system is solved. The feasibility of using the improved cat swarm algorithm to solve the capacity optimization configuration model of hydrogen storage system was verified through numerical examples, and the rationality of the proposed hydrogen storage system capacity optimization configuration model was also demonstrated.

Key words: hydrogen storage system, optimal allocation of capacity, improved cat swarm algorithm, wind power, power supply reliability

胡臻达, 姜文瑾, 张林垚, 杨晓东, 邹艺超, 王凯. 基于改进猫群算法的氢储能容量优化配置[J]. 中国电力, 2023, 56(10): 33-42.

Zhenda HU, Wenjin JIANG, Linyao ZHANG, Xiaodong YANG, Yichao ZOU, Kai WANG. Optimal Allocation of Hydrogen Storage Capacity Based on Improved Cat Swarm Optimization[J]. Electric Power, 2023, 56(10): 33-42.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202209013

https://www.electricpower.com.cn/CN/Y2023/V56/I10/33

图/表 12

图 1 猫群算法的流程图

Fig.1 Flow chart of cat swarm algorithm

图 2 典型日的风电出力和负荷

Fig.2 Typical daily wind output power and load

图 3 场景集的风电输出功率

Fig.3 Output power of wind turbine in scenario set

图 4 场景集的负荷

Fig.4 Load in scenario set

表 1 削减后的场景集概率

Table 1 The probability of cutting back the scenario set

场景		1		2		3		4		5
概率/%		12		27		10		28		23

表 2 氢储能系统的参数

Table 2 Parameters of hydrogen storage system

参数		数值
电解槽的投资系数 ρ₁/(元·kW^–1)		200
燃料电池的投资系数 ρ₂/(元·kW^–1)		2300
储氢罐的投资系数 ρ₃/(元·kW^–1)		800
电解槽的使用年限 n₁/年		20
燃料电池的使用年限 n₂/年		10
储氢罐的使用年限 n₃/年		10
电氢转换效率 η₁		0.6
储氢罐的存储效率 η₂		0.6
年利率 r		0.067

表 3 猫群算法的参数

Table 3 Parameters of cat swarm optimization

参数		数值
分组率		0.75
维度变化数		0.65
维度变化域		0.25
记忆池		5.00
c₁		2.05

表 4 氢储能系统的优化结果

Table 4 Optimization results of hydrogen storage system

场景类型	案例	电解槽容量/ MW	燃料电池容量/ MW	储氢罐容量/ MW	f₁/元	f₂/%	f₃/%
典型日	1	1.16	0.78	2.16	2022	3.54	5.66
	2	1.66	0.90	3.02	2656	3.27	4.64
	3	1.72	0.94	4.59	3080	2.84	2.98
场景集	4	1.21	0.81	2.25	2111	3.50	5.53
	5	1.93	1.15	3.46	3202	3.04	3.90
	6	2.12	1.23	5.10	3750	2.58	2.23

图 5 案例4的微电网优化结果

Fig.5 Optimization results of microgrid in case4

图 6 案例5的微电网优化结果

Fig.6 Optimization results of microgrid in case5

图 7 案例6的微电网优化结果

Fig.7 Optimization results of microgrid in case6

图 8 优化迭代的曲线

Fig.8 The curve of optimization iterative

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基于改进猫群算法的氢储能容量优化配置

Optimal Allocation of Hydrogen Storage Capacity Based on Improved Cat Swarm Optimization

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