考虑主动管理与需求侧管理的主动配电网分布式光伏最大准入容量计算方法

为了在确保配电网安全、稳定运行的同时,促进配电网分布式光伏消纳,文章提出了一种主动配电网分布式光伏最大准入容量计算方法。以最大化分布式光伏准入容量为目标,计及系统潮流方程、节点电压、支路电流等多个电气约束,采用有载调压、无功补偿、储能充放电、网络重构、负荷削减等主动管理措施和需求侧管理措施,提升了“源荷”不确定性最不利条件下分布式光伏最大准入容量,建立了二阶段鲁棒优化模型。将原模型分解为主问题与子问题后,转化为混合整数二阶锥形式,使用列与约束生成算法求解。在改进的IEEE 33节点算例上对模型与算法的有效性进行检验,得到了主动配电网分布式光伏最大准入容量,并验证了主动管理与需求侧管理对其的提升作用。     

含智能软开关的主动配电网分布式光伏准入容量鲁棒优化

考虑分布式光伏和负荷功率的不确定性,建立了二阶段鲁棒优化模型,用于评估主动配电网(ADN)的分布式光伏的最大准入容量。若干主动管理(AM)措施被应用于ADN中,包括带储能的多端智能软开关(E-SOP)调节、有载调压变压器(OLTC)调节、无功补偿、分布式光伏无功调节和网络重构。基于凸松弛技术,原问题可以分解为主问题和子问题。主问题优化典型日AM措施模拟运行策略,以计算待选节点上分布式光伏最大准入容量;子问题寻找不确定性的最恶劣场景。针对凸松弛引入的间隙,提出了一种将渐紧线切割算法和列与约束生成算法相结合的求解策略,以获得间隙足够小的原问题最优解。在改进的IEEE 33节点ADN上验证了模型和算法的有效性,分析了不确定性和AM措施对ADN中分布式光伏最大准入容量的影响。     

动态聚合权重的隐私保护联邦学习框架

There are two problems with the privacy-preserving federal learning framework under an unreliable central server. ① A fixed weight, typically the size of each participant’s dataset, is used when aggregating distributed learning models on the central server. However, different participants have non-independent and homogeneously distributed data, then setting fixed aggregation weights would prevent the global model from achieving optimal utility. ②Existing frameworks are built on the assumption that the central server is honest, and do not consider the problem of data privacy leakage of participants due to the untrustworthiness of the central server. To address the above issues, based on the popular DP-FedAvg algorithm, a privacy-preserving federated learning DP-DFL algorithm for dynamic weight aggregation under a non-trusted central server was proposed which set a dynamic model aggregation weight. The proposed algorithm learned the model aggregation weight in federated learning directly from the data of different participants, and thus it is applicable to non-independent homogeneously distributed data environment. In addition, the privacy of model parameters was protected using noise in the local model privacy protection phase, which satisfied the untrustworthy central server setting and thus reduced the risk of privacy leakage in the upload of model parameters from local participants. Experiments on dataset CIFAR-10 demonstrate that the DP-DFL algorithm not only provides local privacy guarantees, but also achieves higher accuracy rates with an average accuracy improvement of 2.09% compared to the DP-FedAvg algorithm models. © 2022, Beijing Xintong Media Co., Ltd.. All rights reserved.     

考虑智能软开关接入的主动配电网扩展规划方法EI北大核心CSCD

柔性互联装置的广泛应用给主动配电网(active distribution network,ADN)规划带来巨大挑战。该文提出一种考虑智能软开关(soft open point,SOP)接入的ADN扩展规划方法,对变电站新建及扩容,线路新建,智能软开关、分布式电源、储能系统以及无功补偿等设备的选址定容进行协同规划。首先,考虑分布式电源出力和负荷功率不确定性,采用基于改进高斯混合模型的聚类方法构建典型日场景。在此基础上,以年综合费用最小为目标函数,建立了考虑SOP接入的ADN扩展规划模型。然后,通过线性化和二阶锥松弛技术,将原始非凸非线性规划模型转化为混合整数二阶锥规划(mixed-integer second-order cone programming,MISOCP)模型,并提出逐次收缩凸松弛算法以获得凸松弛间隙足够小的原问题最优解。最后,在54节点主动配电网算例上验证了所提规划模型和求解算法的可行性与有效性。