计及风电不确定性和弃风率约束的风电场储能容量配置方法

风电的大规模开发造成了严重的弃风问题,在风电场内配置储能装置可显著减少弃风,文中基于分布鲁棒优化方法研究了考虑风电不确定性和弃风率约束的风电场储能容量配置问题。首先根据历史数据构造风电出力的经验分布函数,为考虑风电出力的不确定性,以Kullback-Leibler(KL)散度作为分布函数距离测度建立了风电出力的概率分布函数集合。随后,将弃风率要求建模为概率分布函数集合中最坏分布下的鲁棒机会约束,进一步建立了以储能投资成本最小为目标、以弃风率为约束的鲁棒机会约束规划模型。最后,通过矫正机会约束中的风险阈值,将鲁棒机会约束转化为传统机会约束,并采用凸近似和抽样平均构建线性规划进行高效求解。基于IEEE 30节点电网进行了算例分析,并与传统的随机规划和鲁棒优化模型进行对比,验证了所提模型在处理风电不确定性时能有效兼顾保守性和鲁棒性。

Configuration Method of Energy Storage for Wind Farms Considering Wind Power Uncertainty and Wind Curtailment Constraint

The large-scale integration of wind power has caused serious curtailment problems and the configuration of energy storage in wind farms can significantly reduce the curtailment. Considering the uncertainty and curtailment rate constraint of wind power, this paper focuses on the energy storage configuration in wind farms based on distributionally robust optimization method. Firstly, an empirical distribution function for wind power is estimated based on historical data. Considering the wind power uncertainties, a set of probability distribution functions for wind power output is established, which takes the Kullback-Leibler (KL) divergence as distance measurement of distribution function. Secondly, the requirement of wind power curtailment rate is modelled as a robust chance constraint with respect to the worst distribution in the probability distribution function set. And a distributionally robust optimization model is proposed with the objective of minimizing the investment cost of energy storage and curtailment rate constraints. Finally, the robust chance constraint is transformed to a traditional one by correcting risk thresholds in chance constraint. Through convex approximation and sampling average, a linear program is constructed for highly efficient solution. A case study of the proposed model is conducted on IEEE 30-bus system, and the results are compared with the traditional stochastic programming and robust optimization models, which demonstrate the advantage of the proposed model in dealing with wind power uncertainties by balancing the conservatism and robustness effectively.