摘要
基于中国国家电网公司的风电场功率变化率限制标准中定义的出力波动考核计算方法,建立描述风电场发电收益和波动损失的指标,提出一种新型的基于风电功率预测结果的自动发电控制(automatic generation control,AGC)序列最优规划策略。与传统的斜率控制方式不同,该策略将发电功率波动限制作为约束条件,为降低1 min和10 min两个时间尺度的出力变化进行罚函数与目标函数的构建,随后通过粒子群算法获最优解。算例结合沿海和内陆两种不同属性的风电场出力历史数据进行波动平抑的AGC序列规划仿真,并将其应用于实际的风电场,仿真结果和实测运行数据表明基于超短期功率预测结果的粒子群算法最优规划方案可有效降低出力波动,发电量损失较少,对降低风电场运营成本,提升收益具有很高的实用价值。
This paper defined evaluation method of wind power fluctuation based on power variation limit standard specified by SGCC, and built the indicators to describe power generating benefits and fluctuating loss of wind farms. Then a new optimum planning strategy for obtaining automatic generation control （AGC） sequence was presented based on power forecasting. Different from traditional gradient control way, the strategy took power fluctuation as constraint condition, and established penalty functions and objective functions for diminishing variation of wind power in 1 minute and 10 minutes. And then the optimization solution was obtained by particle swarm optimization algorithm. Finally, simulation studies using the presented method were given based on historical power data of both coastal and inland wind farms. Meanwhile, the optimum strategy was used to an actual wind farm for testing. Simulation and testing results show that the PSO strategy based on ultra-short term power forecasting can reduce the fluctuation of wind power and has smaller losses of energy production. It is valuable to reduce operating costs and improve earnings of wind farms.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2015年第10期2383-2391,共9页
Proceedings of the CSEE
关键词
风电场
自动发电控制
功率波动
粒子群算法
wind farm
automatic generation control（AGC）
power fluctuation
particle swarm optimization （PSO）
