蓄电‒氢储混合储能系统的配电网双层优化

doi:10.16182/j.issn1004731x.joss.22-0139

摘要/Abstract

摘要：

在“双碳”目标与清洁能源氢能利用的背景下，针对配电网配置电化学储能和氢储能系统构成混合储能系统提升电能质量的需求，建立了混合储能系统双层优化模型，上层选址定容模型综合考虑投资成本、网损成本和电压偏移，下层优化运行模型考虑混合储能系统的运行成本，并引入电压稳定性指标进行评价。求解过程中利用灵敏度分析对选址可行域进行了降维，并提出一种改进的小生境多目标粒子群算法，将小生境处理机制与外部档案选取技术、混沌变异技术相结合。利用接入新能源的IEEE33节点系统进行算例仿真。结果表明：混合储能系统容量与接入点的优化配置，可以提高系统经济性、降低全网有功网损、减小电压偏移和提高电压稳定性。

关键词: 混合储能, 双层优化, 多目标粒子群算法, 小生境技术, 灵敏度分析

Abstract:

Under the background of carbon neutralization and emission peaking goals and the utilization of clean hydrogen energy, aiming at the demand of distribution network configuring electrochemical energy storage and hydrogen energy storage system to form a hybrid energy storage system to improve power quality, a bi-level optimization model of the hybrid energy storage system is established. The upper level location and capacity model comprehensively considers the investment cost, network loss cost and voltage offset, while the lower level optimization operation model considers the operation cost of hybrid energy storage system, and the voltage stability index is introduced for evaluation. In the solution process, the dimension of the feasible region of location is reduced by sensitivity analysis, and an improved niche multi-objective particle swarm optimization algorithm is proposed, which combines the niche processing mechanism with external file selection technology and chaotic mutation technology. Using the IEEE33 node system connected to new energy to conduct numerical example simulation, the results show that the optimal configuration of hybrid energy storage system capacity and access points can improve the economy of the system, reduce the active power loss of the whole network, reduce the voltage offset and improve the voltage stability.

Key words: hybrid energy storage, bi-level optimization, multi-objective particle swarm optimization algorithm, niche mirror technology, sensitivity test

中图分类号:

TP391.9

冯飞波, 闫兴德, 郑宝强, 尹晓峰, 周梦真, 蒋鑫. 蓄电‒氢储混合储能系统的配电网双层优化[J]. 系统仿真学报, 2022, 34(7): 1405-1416.

Feibo Feng, Xingde Yan, Baoqiang Zheng, Xiaofeng Yin, Mengzhen Zhou, Xin Jiang. Bi-Level Optimization of Distribution Network for Hybrid Energy Storage System of Storage Battery and Hydrogen Storage[J]. Journal of System Simulation, 2022, 34(7): 1405-1416.

图/表 11

图1

图2

图3

图4

表1

储能设备基本参数

储能类型	参数	数值
蓄电池	储能寿命/a	2
	充放电效率/%	85
	单位功率成本/(元/kW)	80
	单位容量成本/(元/kWh)	100
	单位功率运维费用/(元/kW)	0.2
	单位容量运维费用/(元/kWh)	0.2
氢储能	储能寿命/a	10
	电解槽制氢效率/%	80
	燃料电池转换效率/%	70
	电解槽单位功率成本/(元/kW)	250
	燃料电池单位功率成本/(元/kW)	600
	储氢罐单位体积成本/(元/ $m 3$ )	400
	电解槽单位功率运维费用/(元/kW)	0.3
	燃料电池单位功率运维费用/(元/kW)	0.3
	储氢罐单位体积运维费用/(元/ $m 3$ )	0.1

表1

图5

图6

图7

图8

图9

表2

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算法	测试次数	平均收敛时间/s	平均综合成本/万元	平均网损值/kW	平均电压偏移/p.u.
灵敏度分析简化后的IMOPSO	50	18.2	32.1	96	5.94
未简化IMOPSO	50	38.7	32.5	101	6.15
MOPSO	50	50.4	33.9	142	6.45

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