Congestion-Sensitive Grid Aggregation for DC Optimal Power Flow

TL;DR

This paper introduces two new grid aggregation methods for DC optimal power flow that utilize a novel Network Congestion Price metric, outperforming existing methods in accuracy and speed by better capturing line congestion impacts.

Contribution

The paper proposes two innovative aggregation methods based on the Network Congestion Price metric, improving over traditional Locational Marginal Price-based approaches.

Findings

Proposed methods outperform existing approaches in objective function accuracy.

New methods reduce maximum line limit violations.

Aggregation is faster with the proposed Network Congestion Price-based methods.

Abstract

The vast spatial dimension of modern interconnected electricity grids challenges the tractability of the DC optimal power flow problem. Grid aggregation methods try to overcome this challenge by reducing the number of network elements. Many existing methods use Locational Marginal Prices as a distance metric to cluster nodes. In this paper, we show that prevalent methods adopting this distance metric fail to adequately capture the impact of individual lines when there is more than one line congested. This leads to suboptimal outcomes for the optimization of the aggregated model. To overcome those issues, we propose two methods based on the novel Network Congestion Price metric, which preserves the impact of nodal power injections on individual line congestions. The proposed methods are compared to several existing aggregation methods based on Locational Marginal Prices. We demonstrate all methods on adapted versions of the IEEE RTS 24- and 300-Bus systems. We show that the proposed methods outperform existing approaches both in terms of objective function value error and maximum line limit violation, while exhibiting faster node clustering. We conclude that aggregation methods based on the novel Network Congestion Price metric are better at preserving the essential physical characteristics of the network topology in the grid aggregation process than methods based on Locational Marginal Prices.