平抑海上风电波动的混合储能系统容量优化配置

doi:10.3969/j.issn.2097-0706.2024.06.007

摘要/Abstract

摘要：

海上风电具有功率波动强的特点，接入电网时难以达到并网功率变化限制要求。为了平抑海上风电的输出功率波动，提出了一种考虑海上风电波动平抑的混合储能系统（HESS）容量配置方法。首先，利用加权移动平均滤波法得到HESS的输出功率信息指令;然后，利用能量谷优化算法（EVO）对变分模态分解（VMD）的模态分解个数 $N$ 和惩罚因子 $ϑ$ 进行优化，采用参数优化后的VMD算法对HESS的功率指令进行分解;最后，以HESS全寿命周期内平均日总成本最小为目标构建HESS成本优化模型，通过优化最优分频点对HESS进行功率分配，从而制定经济性最优的储能配置方案。仿真结果表明，该方法能有效提高配置HESS平抑海上风电输出功率波动的经济性，可为海上风电配置储能接入电网运行提供参考。

关键词: 海上风电, 混合储能系统, 容量优化配置, 能量谷优化算法, 变分模态分解

Abstract:

The strong fluctuation of offshore wind power makes it difficult to keep the variation within limitations when wind power is connected to the grid. In order to stabilize offshore wind outputs， a capacity allocation method for a hybrid energy storage system（HESS）considering offshore wind power fluctuation is proposed. Firstly， the weighted moving average filtering method is used to obtain the output information instructions for the HESS. Then， energy valley optimizer（EVO） is used to optimize the modal decomposition number N and penalty factor $ϑ$ of the variational mode decomposition（VMD）， and the VMD with optimized parameters is used to decompose the HESS power instructions. Finally， an optimized cost model for the HESS is constructed with the goal of minimizing the average daily cost of the HESS in its whole life cycle. The optimal energy storage allocation scheme is established by allocating the HESS power based on optimal frequency division points. The simulation results show that the method proposed can effectively improve the economy of the HESS installed to stabilize wind power outputs， providing a reference for the grid connection of an offshore wind power station integrated with an energy storage system.

Key words: offshore wind power, hybrid energy storage system, capacity optimization allocation, energy valley optimization, variational mode decomposition

中图分类号:

TK89

张勋祥, 吴杰康, 孙烨桦, 彭其坚. 平抑海上风电波动的混合储能系统容量优化配置[J]. 综合智慧能源, 2024, 46(6): 54-65.

ZHANG Xunxiang, WU Jiekang, SUN Yehua, PENG Qijian. Capacity allocation optimization of hybrid energy storage systems considering fluctuation control on offshore wind power[J]. Integrated Intelligent Energy, 2024, 46(6): 54-65.

图/表 17

图1

表1

风电场有功输出功率最大限值

装机容量/MW	有功输出功率最大限值
装机容量/MW	1 min	10 min
$< 30$	3	10
30~150	装机容量/10	装机容量/3
$> 150$	15	50

表1

图2

图3

图4

图5

图6

图7

表2

图8

图9

图10

表3

图11

图12

表4

图13

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项目	1 min		10 min		功率标准差/MW
项目	最大波动功率/MW	最大波动率/%	最大波动功率/MW	最大波动率/%	功率标准差/MW
滤波前	15.000 0	100.00	15.000 0	100.00	4.185 7
滤波后	2.867 3	19.12	7.979 1	53.19	1.505 3

参数	锂电池	超级电容
充放电效率	0.80	0.95
SOC范围	[0.15，0.85]	[0.10，0.90]
单位功率投资成本/(元·kW^-1)	5 800	1 860
单位容量投资成本/[元·(kW·h)^-1]	930	12 400
单位功率报废处理成本/(元·kW^-1)	470	95
单位容量报废处理成本/[元·(kW·h)^-1]	0	0
运行维护单价/[元·(kW·h)^-1]	65	140
折现率	0.05
寿命周期/a	10
回收残值率	0.03

分频点	项目	方案1	方案2		方案3		方案4
分频点	项目	锂电池	锂电池	超级电容	锂电池	超级电容	锂电池	超级电容
K=3	功率/MW	10.96	10.07	1.74	6.93	6.12	6.51	6.09
	容量/（MW·h）	0.53	0.48	0.07	0.34	0.25	0.31	0.25
	平均日总成本/元	31 863	24 964		21 624		20 625
K=4	功率/MW	10.96	10.17	0.79	7.07	5.35	6.73	5.35
	容量/（MW·h）	0.53	0.48	0.05	0.36	0.22	0.33	0.21
	平均日总成本/元	31 863	24 511		21 296		20 455
K=5	功率/MW	10.96	10.32	0.55	7.21	5.18	7.95	5.16
	容量/（MW·h）	0.53	0.48	0.03	0.37	0.21	0.37	0.21
	平均日总成本/元	31 863	24 571		21 249		22 937
K=6	功率/MW	10.96	10.45	0.26	8.70	4.69	8.65	4.65
	容量/（MW·h）	0.53	0.51	0.01	0.45	0.19	0.42	0.19
	平均日总成本/元	31 863	24 586		24 469		24 324