Momentum extrapolation prediction-based asynchronous distributed optimization for power systems
Momentum extrapolation prediction-based asynchronous distributed optimization for power systems
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DOI：
10.1016/j.epsr.2021.107193
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发表时间：
2021-07
影响因子：
3.9
通讯作者：
A. Mohammadi;A. Kargarian
中科院分区：
文献类型：
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作者：
A. Mohammadi;A. Kargarian
迭代分布式优化算法通常需要在每次迭代时同步子问题。定义了迭代索引，每次迭代求解一次子问题。这降低了分布式优化的可伸缩性和计算资源的利用率，特别是在子问题是异构的情况下。为了解决求解最优潮流的这些局限性，本文提出了一种基于预测校正的异步交变方向乘法器方法（a - admm）。在每次迭代结束时，OPF子问题不再需要等待其邻居最新的共享变量信息。提出了一种基于动量的外推方法来预测共享变量值。利用动量设计了一个校正步骤，以防止预测值远离可能的解并避免偏离。这些预测被集成到分布式优化中，以允许连续地解决子问题，而不需要在每次迭代中进行同步。提出的A-ADMM减少了子问题计算异构时的非生产性时间和计算资源利用率不足。即使子问题是齐次的，a - admm也可能提高求解速度，因为每次迭代k都是在迭代k+ 1中使用对共享变量值的良好预测进行的。数值结果表明，该算法具有良好的性能。
Iterative distributed optimization algorithms usually need synchronization of subproblems at each iteration. An iteration index is defined, and subproblems are solved once at each iteration. This degrades distributed optimization scalability and computational resource under-utilization, particularly if subproblems are heterogeneous. To address these limitations for solving optimal power flow, this paper proposes a prediction-correction-based asynchronous alternating direction method of multipliers (A-ADMM). At the end of each iteration, an OPF subproblem no longer needs to wait for its neighbors’ most updated shared variable information. A momentum-based extrapolation method is developed to predict shared variable values. A correction step is designed using momentum to prevent predicted values from becoming far from the possible solution and avoid divergence. These predictions are integrated into distributed optimization to allow subproblems to be solved continuously with no need for synchronization at each iteration. The proposed A-ADMM reduces the unproductive time and computational resource under-utilization if subproblems are computationally heterogeneous. A-ADMM potentially enhances the solution speed even if subproblems are homogeneous as every iteration k is carried out using a good forecast of shared variable values in iteration k+ 1. Numerical results show the promising performance of the proposed algorithm.