Look-Ahead SCOPF (LASCOPF) for Tracking Demand Variation via Auxiliary Proximal Message Passing (APMP) Algorithm

TL;DR

This paper introduces a novel distributed algorithm, APMP, for solving look-ahead security constrained optimal power flow problems considering demand forecasts and contingencies within a Model Predictive Control framework.

Contribution

The paper proposes the Auxiliary Proximal Message Passing (APMP) algorithm, a bi-layered distributed method combining decomposition and coordination for efficient power flow optimization over multiple dispatch intervals.

Findings

APMP effectively solves look-ahead SCOPF problems with demand variation.

The algorithm demonstrates good scalability and convergence in numerical simulations.

It enables distributed computation across devices and dispatch intervals.

Abstract

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.