基于辅助邻近消息传递（APMP）算法的超前SCOPF（LASCOPF）需求变化跟踪
Look-Ahead SCOPF (LASCOPF) for Tracking Demand Variation via Auxiliary Proximal Message Passing (APMP) Algorithm
摘要：
本文研究了具有多个调度区间的前瞻安全约束最优潮流（LASCOPF）问题，其中负荷需求在调度区间内根据预测而变化。我们将在即将到来的调度间隔以及随后的调度间隔中考虑基本情况和几种应急情况。我们将在模型预测控制（MPC）范例中制定和解决问题。我们将提出的辅助近端消息传递（APMP）算法来解决这个问题，这是一个双层的分解协调型分布式算法，由外部的辅助问题原理（APP）层和内部的近端消息传递（PMP）层。该算法的APP部分将计算分布在几个调度间隔上，PMP部分在不同设备（即发电机、输电线路、负载）和节点或网络的每个调度间隔内执行分布式计算。我们将证明我们的方法的有效性与一系列的数值模拟。
英文摘要：
In this paper, we will consider the Look-Ahead Security Constrained Optimal Power Flow (LASCOPF) problem looking forward multiple dispatch intervals, in which the load demand varies over dispatch intervals according to some forecast. We will consider the base-case and several contingency scenarios in the upcoming as well as in the subsequent dispatch intervals. We will formulate and solve the problem in a Model Predictive Control (MPC) paradigm. We will present the Auxiliary Proximal Message Passing (APMP) algorithm to solve this problem, which is a bi-layered decomposition-coordination type distributed algorithm, consisting of an outer Auxiliary Problem Principle (APP) layer and an inner Proximal Message Passing (PMP) layer. The APP part of the algorithm distributes the computation across several dispatch intervals and the PMP part performs the distributed computation within each of the dispatch interval across different devices (i.e. generators, transmission lines, loads) and nodes or nets. We will demonstrate the effectiveness of our method with a series of numerical simulations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
