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A Newton Method-Based Distributed Algorithm for Multi-Area Economic Dispatch
IEEE Transactions on Power Systems ( IF 6.5 ) Pub Date : 2020-03-01 , DOI: 10.1109/tpwrs.2019.2943344
Jiahu Qin , Yanni Wan , Xinghuo Yu , Yu Kang

In this paper, we propose a novel Newton method-based distributed algorithm (NMDA), which is also effective in solving the general single-area EDP (SAEDP), to deal with the multi-area economic dispatch problem (MAEDP), of which the focus is to minimize the total generation cost in the presence of system and generator constraints. To develop the NMDA, we first introduce a virtual SAEDP formulation to fit the framework of Newton method (NM), and then employ the average consensus protocol to obtain the global information needed to execute the NM and backtracking line search algorithm in a distributed manner. Compared with the centralized methods that can yield the optimal solution, the proposed NMDA provides a suboptimal solution with a very small relative error. The NMDA ensures the instantaneous system power balance throughout the iteration process while the centralized methods compared in this paper cannot do so. We also provide a rigorous theoretical analysis for the convergence of NMDA. Moreover, the advantage of NMDA in terms of the convergence speed is validated by comparing with other distributed methods such as the gradient-based ADMM (G-ADMM) and quasi Newton-based primal dual interior point (QN-PDIP) method. Finally, case studies demonstrate the effectiveness and scalability of the proposed distributed algorithm.

中文翻译:

一种基于牛顿法的多区域经济调度分布式算法

在本文中,我们提出了一种新的基于牛顿法的分布式算法(NMDA),它也可以有效地解决一般的单区域 EDP(SAEDP),以处理多区域经济调度问题(MAEDP),其中重点是在存在系统和发电机限制的情况下最小化总发电成本。为了开发 NMDA,我们首先引入虚拟 SAEDP 公式以适应牛顿法 (NM) 的框架,然后采用平均共识协议以分布式方式获取执行 NM 和回溯线搜索算法所需的全局信息。与可以产生最优解的集中式方法相比,所提出的 NMDA 提供了一个相对误差非常小的次优解。NMDA 确保了整个迭代过程中的瞬时系统功率平衡,而本文中比较的集中式方法无法做到这一点。我们还为 NMDA 的收敛提供了严格的理论分析。此外,通过与基于梯度的 ADMM (G-ADMM) 和基于拟牛顿的原始对偶内点 (QN-PDIP) 方法等其他分布式方法进行比较,验证了 NMDA 在收敛速度方面的优势。最后,案例研究证明了所提出的分布式算法的有效性和可扩展性。通过与基于梯度的ADMM(G-ADMM)和基于拟牛顿的原始对偶内点(QN-PDIP)方法等其他分布式方法进行比较,验证了NMDA在收敛速度方面的优势。最后,案例研究证明了所提出的分布式算法的有效性和可扩展性。通过与基于梯度的ADMM(G-ADMM)和基于拟牛顿的原始对偶内点(QN-PDIP)方法等其他分布式方法进行比较,验证了NMDA在收敛速度方面的优势。最后,案例研究证明了所提出的分布式算法的有效性和可扩展性。
更新日期:2020-03-01
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