基于KL散度的含储能机组组合分布鲁棒优化方法

为应对风电场出力的波动性和随机性给机组组合带来的问题,提出了基于Kullback-Leibler(KL)散度的含储能机组组合的两阶段分布鲁棒优化模型。在电池储能的运行模型的基础,将电池储能模型嵌入到传统的火电机组组合模型中,建立了含储能的机组组合两阶段优化模型;基于KL散度构建了风电场出力的模糊集,形成了含储能机组组合的两阶段分布鲁棒优化模型,通过对偶变换和广义Benders分解将其转化成易于求解的混合整数凸优化模型进行求解。通过IEEE RTS 24节点系统仿真结果表明,所提出的分布鲁棒优化方法保守性优于鲁棒优化方法,经济性接近随机优化方法,且随着KL散度增大,机组组合成本缓慢增加。

Distributionally robust optimization method for unit commitment with energy storage based on KL divergence

In order to solve the problem of unit commitment caused by the fluctuation and randomness of wind farm output, a two-stage distributionally robust optimization model of unit commitment with energy storage based on KL divergence is proposed. Firstly, based on the operation model of battery energy storage system, the battery energy storage system model is embedded into the traditional thermal power unit commitment model, and a two-stage optimization model of unit commitment with battery energy storage system is established; Then, the fuzzy set of wind farm output is constructed based on KL divergence, and a two-stage distributionally robust optimization model of unit commitment with battery energy storage system is formed. It is transformed into a mixed integer convex optimization model which is easy to solve by dual transformation and generalized Benders decomposition. Finally, the simulation results of IEEE RTS 24 bus system show that the proposed distributionally robust optimization method is more conservative than the robust optimization method, and its economy is close to the stochastic optimization method. With the increase of KL divergence, the unit commitment cost increases slowly.