基于均值-方差-偏度的配电网有功-无功随机模型预测控制

Mean-variance-skewness-based Stochastic Active-reactive Model Predictive Control for Distribution Network

针对高比例具有不确定性的可再生能源接入对配电网安全经济运行带来的挑战,提出一种基于均值-方差-偏度的有功-无功随机模型预测控制协调优化策略。首先,在综合考虑有功和无功成本的基础上,基于Fish⁃er-z变换的拉丁超立方采样生成可再生能源出力场景集,进而构建表征不确定性下收益的期望-偏度模型和表征不确定性风险的方差模型。然后,建立权衡收益和风险的三阶近似矩效用函数,提出基于均值-方差-偏度的有功-无功随机模型预测控制协调优化框架。最后,基于线性化潮流方法对所提模型进行线性化处理,在改进的IEEE-37节点系统进行仿真验证。结果表明,所提策略能够有效应对可再生能源不确定性对配电网经济运行与电压质量的影响。

To address the challenge to the safe and economic operation of distribution network which is posed by the in⁃tegration of high proportion of renewable energy with uncertainties,a mean-variance-skewness(MVS)based coordinat⁃ed optimization strategy of stochastic active-reactive model predictive control is proposed in this paper.First,on the ba⁃sis of comprehensively considering both the active and reactive power costs,a renewable energy output scenario set is generated by Latin hypercube sampling based on the Fisher-z transform,and an expectation-skewness model that char⁃acterizes uncertain benefits and a variance model that characterizes uncertain risks are constructed accordingly.Then,a third-order approximate moment utility function is established to balance the benefits and risks,and a coordinated op⁃timization framework for stochastic active-reactive model predictive control based on MVS is put forward.Finally,the proposed model is linearized based on the linearized power flow method,and an improved IEEE 37-node system is used for simulation verification.Results show that the proposed strategy can effectively deal with the impact of uncertainties in renewable energy on the economic operation of distribution network as well as voltage quality.