| 引用本文: | 黄冬梅,吴 冰,时 帅,等.基于CEEMDAN-SAOA的平抑风电波动混合储能系统定容优化配置[J].电力系统保护与控制,2025,53(15):59-70.[点击复制] |
| HUANG Dongmei,WU Bing,SHI Shuai,et al.Capacity optimization of a hybrid energy storage system for wind power fluctuation suppression based on CEEMDAN-SAOA[J].Power System Protection and Control,2025,53(15):59-70[点击复制] |
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| 摘要: |
| 为解决风力发电直接并网所产生的功率波动问题，提出了一种基于改进阿基米德优化算法融合自适应噪声完全集合经验模态分解(complete ensemble EMD with adaptive noise, CEEMDAN)的容量配置方法。采用由限幅与滑动平均结合的加权滤波算法平滑风电出力，同时减小平滑结果的滞后性，得到风电并网功率和混合储能系统(hybrid energy storage system, HESS)参考功率。为了合理分配HESS的内部功率，借助CEEMDAN分解HESS的参考功率，得到高低频分量。综合考虑HESS功率和容量、荷电状态(state of charge, SOC)与负荷缺点率等因素，构建以年综合成本最小为目标的容量优化配置模型并采用改进阿基米德优化算法求解。基于实际算例进行仿真分析，结果表明，与原始风电并网相比，HESS配置方案将波动率减少了13.538%，平滑度提高了16.057%。相较于传统单一储能平抑效果更加明显，减少了容量配置。同时，对比传统阿基米德优化算法节省了15.325%的投资成本。 |
| 关键词: 改进阿基米德算法 自适应噪声完全集合经验模态分解 风力发电 平抑功率波动 混合储能 容量配置 |
| DOI：10.19783/j.cnki.pspc.241419 |
| 投稿时间：2024-10-23修订日期：2025-01-27 |
| 基金项目:国家重点研发计划项目资助(2021YFC3101602)；华能集团总部科技项目资助(HNKJ20-H66) |
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| Capacity optimization of a hybrid energy storage system for wind power fluctuation suppression based on CEEMDAN-SAOA |
| HUANG Dongmei1,WU Bing1,SHI Shuai1,LI Yuanyuan1,SONG Wei2,WANG Xiaoliang3 |
| (1. School of Electric Power Engineering, Shanghai University of Electric Power, Shanghai 200090, China;
2. College of Information Technology, Shanghai Ocean University, Shanghai 201306, China;
3. East China Sea Area and Island Center, MNR, Shanghai 200136, China) |
| Abstract: |
| To address the problem of power fluctuations caused by direct grid connection of wind power, this paper proposes a capacity optimization algorithm based on improved Archimedes optimization algorithm with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). A weighted filtering algorithm, combining amplitude limiting and moving average, is used to smooth wind power output and reduce the lag in the smoothed signal, thus generating the reference power for grid-connected wind power and the hybrid energy storage system (HESS). To allocate HESS’s internal power, the reference power is decomposed into high- and low-frequency components using CEEMDAN. Taking into account factors such as HESS power and capacity, state of charge (SOC), and load defect rate, a capacity optimization model aiming at the minimum annual comprehensive cost is constructed and solved by improved Archimedes optimization algorithm. Simulation analysis based on real case data shows that, compared with the original grid-connected wind power, the proposed HESS configuration scheme reduces power fluctuation by 13.538% and increases smoothness by 16.057%. Compared with the traditional single energy storage, the proposed method achieves better fluctuation mitigation and reduces the required capacity. Moreover, it lowers investment cost by 15.325% compared to the conventional Archimedes optimization algorithm. |
| Key words: improved Archimedes algorithm complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) wind power power fluctuation suppression hybrid energy storage capacity allocation |
