基于现代内点法的风储系统多模式优化

Multi-mode Optimization for Hybrid Wind and Energy Storage System Based on Modern Interior-point Method

由于风电随机性、间歇性等特点,大型风电场并网时会对电网稳定性等诸多方面造成负面影响,通过给风电场配置电池储能系统(BESS)可改善这些问题。针对削峰填谷、功率平滑和功率跟踪3种BESS运行模式,建立了考虑电池寿命和风储系统功率波动约束的优化模型,采用改进后的两阶段现代内点法,根据风电功率预测结果对最优BESS充放电策略进行求解。算法中两阶段求解的引入解决了由表达式不确定的放电能量约束引起的不收敛问题。对风电功率预测结果进行插值后再次优化,相应地改变BESS功率的调整间隔,可限制不同时间级别的风电功率波动。算例结果表明,该算法有很好的收敛性和优化效果,且具有多项式时间复杂性,有利于实现在线实时控制。

The integration of large-scale wind farm has undesirable impacts on the grid due to the randomness and intermittency of the wind power. An effective way to solve this problem is to deploy battery energy storage systems （BESS） in a wind farm. An optimization model is developed considering constraints like battery lifecycle and fluctuation of wind power. A two-stage modern interior-point method is proposed for an optimal control strategy for BESS, using the results of wind power prediction. Constraints like discharge capacity of battery without a specific expression may cause divergence. By decoupling the model into two stages and adjusting the unsolved constraints in the second stage, divergence can be avoided successfully. The adjust time of BESS output can be shortened by using interpolated wind power forecast results, thus limiting the fluctuation of wind power under different time scales. The simulation results show that this algorithm has a good convergence and a satisfactory control effect. It also has characteristics of polynomial time, which is an advantage for online real-time control.