Impact of Power System Partitioning on the Efficiency of Distributed Multi-Step Optimization

TL;DR

This paper enhances the efficiency of multi-step power system optimization by applying spectral clustering-based partitioning and distributed solution methods, enabling faster computation without frequent re-partitioning.

Contribution

It introduces a novel spectral clustering-based partitioning technique combined with an improved decomposition method for distributed MPC in power systems.

Findings

Significantly faster solution times with optimal partitions.

Partitioning remains effective across multiple time steps.

Applicable to IEEE 14-bus and 118-bus systems.

Abstract

Recent studies have shown that multi-step optimization based on Model Predictive Control (MPC) can effectively coordinate the increasing number of distributed renewable energy and storage resources in the power system. However, the computation complexity of MPC is usually high which limits its use in practical implementation. To improve the efficiency of MPC, in this paper, we apply a distributed optimization method to MPC. The approach consists of a partitioning technique based on spectral clustering that determines the best system partition and an improved Optimality Condition Decomposition method that solves the optimization problem in a distributed manner. Results of simulations conducted on the IEEE 14-bus and 118-bus systems show that the distributed MPC problem can be solved significantly faster by using a good partition of the system and this partition is applicable to multiple time steps without frequent changes.