【摘要】 在全球能源互联网的大背景下,风力发电作为一种清洁能源受到重视。由于风电的波动性对电力系统的经济、稳定运行造成强烈的冲击,弃风情况日益严峻。文章提出一种新的风电消纳策略,采用极点对称模态分解(extremepoint symmetric mode decomposition,ESM D)把原始风电出力分解为光滑的出力曲线和波动分量,使用混合储能吸收其波动分量,同时在系统运行中通过需求响应消纳更多的风电。针对粒子群算法容易陷入局部最优的缺点,将莱维飞行引入粒子群算法以增强粒子跳出"早熟"的能力。算例结果证明了这种风电消纳策略的有效性,即在维持一定的运行费用的同时,利用混合储能和需求响应,提高系统的风电消纳能力。更多还原

【Abstract】 In the background of global Energy Internet,wind power as a clean energy has attracted much attention. But the volatility of wind power causes a strong impact on the security and stable operation of the power system,abandoned wind becomes more and more serious. Therefore,this paper proposes a new wind power consumptive strategy,which adopts the extreme-point symmetric mode decomposition( ESM D) to decompose the original wind power output into the smooth output curve and the fluctuating component,uses hybrid energy storage to absorb its fluctuating component,at the same time in the operation of the system accommodates more wind power through demand response. According to the shortcoming that the particle swarm optimization( PSO) algorithm is easy to fall into local optimum,we introduce the Levy flight PSO algorithm to enhance the ability of the particles to jump out of the"premature". The numerical example proves the effectiveness of this wind power consumptive strategy,that the use of hybrid energy storage and demand response can improve the ability of accommodating wind power system,while maintaining a certain running cost.更多还原