The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting obj...The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.展开更多
电力系统中包含大量敏感数据,保护这些数据的隐私安全对用户至关重要。针对在分布式最优潮流(optimal power flow,OPF)算法中,由于迭代过程中信息交换频繁导致的隐私泄露问题,提出一种面向分布式最优潮流的隐私保护方法。该算法采用完...电力系统中包含大量敏感数据,保护这些数据的隐私安全对用户至关重要。针对在分布式最优潮流(optimal power flow,OPF)算法中,由于迭代过程中信息交换频繁导致的隐私泄露问题,提出一种面向分布式最优潮流的隐私保护方法。该算法采用完全分布式计算方法来进一步增强隐私性,并引入了自适应惩罚参数方法以提高计算效率。在算法的迭代过程中对各节点间交流的传输变量添加差分隐私噪声,从而阻止攻击者通过窃听传输变量真实值而推测算法中的关键参量,实现了模糊关键参数的OPF问题的分布式求解框架。此外,对于所提算法的收敛性和最优性进行了理论证明,并在IEEE 9-总线系统中进行仿真验证。仿真结果验证了该算法具有收敛性与准确性,隐私保护性能也优于对比算法。该算法有效地解决了在迭代过程中由于信息交换导致的隐私泄露问题,在保持计算效率的同时,显著提高了数据隐私的安全性。展开更多
基金Projects(61105067,61174164)supported by the National Natural Science Foundation of China
文摘The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.
文摘电力系统中包含大量敏感数据,保护这些数据的隐私安全对用户至关重要。针对在分布式最优潮流(optimal power flow,OPF)算法中,由于迭代过程中信息交换频繁导致的隐私泄露问题,提出一种面向分布式最优潮流的隐私保护方法。该算法采用完全分布式计算方法来进一步增强隐私性,并引入了自适应惩罚参数方法以提高计算效率。在算法的迭代过程中对各节点间交流的传输变量添加差分隐私噪声,从而阻止攻击者通过窃听传输变量真实值而推测算法中的关键参量,实现了模糊关键参数的OPF问题的分布式求解框架。此外,对于所提算法的收敛性和最优性进行了理论证明,并在IEEE 9-总线系统中进行仿真验证。仿真结果验证了该算法具有收敛性与准确性,隐私保护性能也优于对比算法。该算法有效地解决了在迭代过程中由于信息交换导致的隐私泄露问题,在保持计算效率的同时,显著提高了数据隐私的安全性。
