摘要
目前面向min级风电柔性并网的储能系统一般由蓄电池组成,因蓄电池配置容量有限,在应对风电场长过程连续下调峰导致的风电功率波动越限问题时,其控制效果将大打折扣。为此,引入容量在日内调度可近似视为不受限的氢储能系统,与蓄电池共同组成混合储能系统,并基于其各自特点,设计了最大发挥2种储能优势的混合储能系统,建立混合储能协助下的风电场并网状态空间模型,并求解可实现风电场柔性并网最小化能量转换损耗的混合储能系统最优充放电功率决策,根据决策执行结果分析混合储能系统的能量转换特点。仿真结果表明,与采用单独蓄电池储能的控制策略相比,在相同的储能系统配置成本下,提出的优化模型可以结合氢储能系统的能量容量优势来提高储能系统的供电持续性,并且能够合理安排蓄电池和氢储能系统的工作顺序来尽量减小风电场能量损耗,由此实现相对最优的风电场柔性并网功能。
Present energy storage system assisting flexible wind farm grid-connection in minute timescale is generally composed of battery. Restrained by capacity, this kind of energy storage system topology is unable to continuously operate when wind power fluctuation is persistently over-limit.In order to solve this problem, this paper introduced a hybrid energy storage system(HESS) topology composed of battery and hydrogen conversion system(HCS). In order to achieve flexible wind farm grid-connection with the least energy loss,an HESS control strategy was proposed to make full use of advantages of HCS capacity and battery energy conversion efficiency. The optimization goal was to minimize power fluctuation, battery life consumption and energy loss. Energy conversion characteristics were also analyzed. Simulation results showed that, compared with other strategies only using battery, the operation strategy using HESS under the same economy cost could combine advantages of battery efficiency and HCS power supply continuity and achieve balance of induced energy loss and power fluctuation.
作者
张哲原
李凌
丁苏阳
林湘宁
卓毅鑫
李正天
陈冲
汪致洵
ZHANG Zheyuan;LI Ling;DING Suyang;LIN Xiangning;ZHUO Yixin;LI Zhengtian;CHEN Chong;WANG Zhixun(School of Electrical and Electronic Engineering,Huazhong University of Science and Technology,Wuhan 430074,Hubei Province,China;State Grid Guangxi Electric Power Dispatching Center,Nanning 530000,Guangxi Zhuang Autonomous Region,China)
出处
《电网技术》
EI
CSCD
北大核心
2019年第4期1220-1226,共7页
Power System Technology
基金
国家自然科学基金项目(51537003)
南方电网广西电网公司科技项目(GXKJXM20170244)~~
关键词
风电场柔性并网
混合储能系统
供电持续性
能量损耗
能量转换效率
flexible wind farm grid-connection
hybrid energy storage system
model predictive control
power supply continuity
energy loss