消纳弃风的风电混合储能供热系统容量配置优化
Capacity allocation optimization of wind power hybrid energy storage and heating system to absorb abandoned wind
摘要：
为解决“三北”地区冬季建筑能耗较高且弃风严重的问题,设计了由锂电池、固体蓄热装置和热泵设备组成的风电混合储能供热系统。首先基于BP神经网络预测了风电机组输出功率,采用k-means聚类分析得到了供热典型日负荷曲线;然后提出了一种基于粒子群优化算法的风电混合储能供热系统容量配置优化方法,以系统总成本最小和弃风量最低为约束条件构建了目标函数;最后比较了考虑和不考虑弃风条件下,风电混合储能供热系统的容量配置优化结果。研究表明,所提出的优化方法不但可以有效降低弃风率,场景适用性强,还能够满足严寒地区冬季清洁供热需求,为可再生能源高效利用提供参考。
Abstract：
In order to solve the problem of high energy consumption of buildings and serious wind abandonment in the “three northern” area in winter, a wind power hybrid energy storage and heating system composed of lithium battery storage units, solid heat storage devices and heat pump equipment is designed. Firstly, the output power of wind turbine is predicted based on the BP neural network, and the typical daily load curve of heating is obtained by the k-means cluster analysis. Then, a capacity allocation optimization method of the wind power hybrid energy storage and heating system based on the particle swarm optimization algorithm is proposed, which takes the least total cost of the system and the lowest wind abandonment as the constraints to construct the objective function. Finally, the capacity allocation optimization results of the wind power hybrid energy storage and heating system with and without considering wind abandonment are compared. The research shows that the proposed optimization method can not only effectively reduce the wind abandonment rate with strong scenario applicability, but also meet the demand for clean heating in severe cold zone in winter, which provides a reference for the efficient utilization of renewable energy.
Keywords:wind power hybrid energy storage and heating system; wind abandonment; capacity allocation optimization; particle swarm optimization algorithm; cluster analysis