| 引用本文: | 李翠萍,张世宁,李军徽,等.风储系统作为黑启动电源的容量配置策略[J].电力系统保护与控制,2021,49(3):88-95.[点击复制] |
| LI Cuiping,ZHANG Shining,LI Junhui,et al.Capacity configuration strategy of a wind power and energy storage system as a black-start source[J].Power System Protection and Control,2021,49(3):88-95[点击复制] |
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| 摘要: |
| 风储系统作为黑启动电源，对解决风多水少地区水电黑启动难题具有重要作用，但风电波动及储能高成本制约了风储黑启动的发展。为解决风电参与黑启动的功率波动问题，在考虑风电出力波动性和电池储能系统自身运行约束的基础上，提出了风储系统作为黑启动电源的储能容量优化配置策略。首先基于黑启动最小风速，定义最小风速概率密度以及最佳风速概率倾度制定双目标特征函数，从数值、时间及资源有效利用三个方面确定了参与黑启动的风电场。其次，基于自组织特征映射神经网络(SOFM)和最小二乘法支持向量机(LSSVM)结合的风电功率预测方法，确定了辅助风电参与黑启动的储能容量及最大充放电功率。最后，在一个并网点并入5个风电场的内蒙古某局域电网中验证了所提模型和方法的有效性。 |
| 关键词: 黑启动 多储能电站 容量配置 概率密度 |
| DOI：DOI: 10.19783/j.cnki.pspc.200488 |
| 投稿时间：2020-05-03修订日期：2020-09-08 |
| 基金项目:吉林省自然科学基金联合基金项目资助(2020122352JC) |
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| Capacity configuration strategy of a wind power and energy storage system as a black-start source |
| LI Cuiping,ZHANG Shining,LI Junhui,YOU Hongfei,ZHANG Jiaxing,KONG Ming |
| (1. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education,
Northeast Electric Power University, Jilin 132012, China; 2. Hohhot Power Supply Bureau, Inner Mongolia Power (Group)
Co., Ltd., Hohhot 010020, China; 3. Pingshan County Power Supply Company, State Grid Hebei Electric Power Co., Ltd.,
Shijiazhuang 050400, China; 4. NARI Technology Development Limited Company, Nanjing 210061, China;
5. Jinan Power Supply Company, State Grid Shandong Electric Power Co., Ltd., Jinan 250000, China) |
| Abstract: |
| As a black-start power source, a wind power and energy storage system plays an important role in solving the problem of hydroelectric generation in regions with more wind and less water. However, because of the fluctuation of wind power and the high cost of energy storage, their development as a black-start source is restricted. In order to solve the problem of power fluctuation caused by wind power and the operating constraints of the battery energy storage system itself, an optimal energy storage capacity configuration strategy is proposed. First, based on the minimum wind speed of a black start, the probability density of the minimum wind speed and the probability inclination of optimal wind speed are defined. The wind farms involved in a black start are determined from the three aspects of numerical value, time and effective utilization of resources. Secondly, based on a wind power prediction method combined with a Self-Organizing Feature Mapping (SOFM) neural network and the Least Squares Support Vector Machine (LSSVM), the capacity and maximum charge-discharge power of the energy storage assisting wind power in a black start are determined. Finally, the effectiveness of the proposed model and method is verified in the Inner Mongolia power grid which is connected to 5 wind farms.
This work is supported by the Joint Foundation of Natural Science Foundation of Jilin Province (No. 2020122352JC). |
| Key words: black start multi-storage power station capacity configuration probability density |
