| 引用本文: | 余 洋,陆文韬,陈东阳,等.光伏波动平抑下改进K-means的电池储能动态分组控制策略[J].电力系统保护与控制,2024,52(7):1-11.[点击复制] |
| YU Yang,LU Wentao,CHEN Dongyang,et al.Dynamic grouping control strategy for battery energy storage based on improvedK-means under photovoltaic fluctuation suppression[J].Power System Protection and Control,2024,52(7):1-11[点击复制] |
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| 光伏波动平抑下改进K-means的电池储能动态分组控制策略 |
| 余洋1,2,陆文韬1,2,陈东阳1,2,刘霡1,2,夏雨星1,2,郑晓明3 |
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| (1.新能源电力系统国家重点实验室(华北电力大学)，河北 保定 071003;2.河北省分布式储能与微网重点实验室
(华北电力大学(保定))，河北 保定 071003;3.国网山西省电力公司经济技术研究院，山西 太原 030001) |
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| 摘要: |
| 针对电池储能系统(battery energy storage system, BESS)进行光伏波动平抑时寿命损耗高及荷电状态(state of charge, SOC)一致性差的问题，提出了光伏波动平抑下改进K-means的BESS动态分组控制策略。首先，采用最小-最大调度方法获取光伏并网指令。其次，设计了改进侏儒猫鼬优化算法(improved dwarf mongoose optimizer, IDMO)，并利用它对传统K-means聚类算法进行改进，加快了聚类速度。接着，制定了电池单元动态分组原则，并根据电池单元SOC利用改进K-means将其分为3个电池组。然后，设计了基于充放电函数的电池单元SOC一致性功率分配方法，并据此提出BESS双层功率分配策略，上层确定电池组充放电顺序及指令，下层计算电池单元充放电指令。对所提策略进行仿真验证，结果表明，所设计的IDMO具有更高的寻优精度及更快的寻优速度。所提BESS平抑光伏波动策略在有效平抑波动的同时，降低了BESS运行寿命损耗并提高了电池单元SOC的均衡性。 |
| 关键词: 电池储能系统 波动平抑 功率分配 改进侏儒猫鼬优化算法 改进K-means聚类算法 |
| DOI：10.19783/j.cnki.pspc.230897 |
| 投稿时间：2023-07-14修订日期：2023-09-15 |
| 基金项目:国家重点研发计划项目资助(2018YFE0122200)；国家自然科学基金项目资助(52077078) |
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| Dynamic grouping control strategy for battery energy storage based on improvedK-means under photovoltaic fluctuation suppression |
| YU Yang1,2,LU Wentao1,2,CHEN Dongyang1,2,LIU Mai1,2,XIA Yuxing1,2,ZHENG Xiaoming3 |
| (1. State Key Laboratory of New Energy and Electric Power Systems (North China Electric Power University), Baoding 071003,
China; 2. Key Laboratory of Distributed Energy Storage and Microgrid of Hebei Province (North China Electric Power University),
Baoding 071003, China; 3. State Grid Shanxi Economic and Technological Research Institute, Taiyuan 030001, China) |
| Abstract: |
| To address the issues of high lifespan loss and poor state of charge (SOC) consistency during photovoltaic fluctuation suppression in a battery energy storage system (BESS), an improved K-means dynamic grouping control strategy for BESS under photovoltaic fluctuation suppression is proposed. First, the minimum-maximum scheduling method is used to obtain photovoltaic grid connection instructions. Secondly, an improved dwarf mongoose optimizer (IDMO) is designed and used to improve the traditional K-means clustering algorithm, accelerating the clustering speed. Next, the principle of dynamic grouping of battery cells is formulated, and based on the improved K-means of SOC of battery cells, they are divided into three battery groups. Then, a battery cell SOC consistent power distribution method based on the charge and discharge function is designed, and a BESS two-layer power distribution strategy is proposed. The upper layer determines the charge and discharge order and instructions of the battery pack, and the lower layer calculates the charge and discharge instructions of the battery cell. The proposed strategy is simulated and verified, and the results show that the designed IDMO has higher optimization accuracy and faster optimization. The proposed BESS strategy for suppressing photovoltaic fluctuations effectively suppressed fluctuations while reducing BESS operating life loss and improving the balance of the battery cell SOC. |
| Key words: battery energy storage system fluctuation suppression power distribution improved dwarf mongoose optimization algorithm improved K-means clustering algorithm |
