Multi-Transmission Node DER Aggregation: Chance-Constrained Unit Commitment with Bounded Hetero-Dimensional Mixture Model for Uncertain Distribution Factors

TL;DR

This paper introduces a novel chance-constrained unit commitment model that effectively manages multi-transmission-node DER aggregations using a bounded hetero-dimensional mixture model for uncertain distribution factors, enhancing operational reliability and economic efficiency.

Contribution

It develops a new DF-based CCUC model incorporating BHMM for uncertain DFs, enabling better handling of multi-node DER aggregations in power system operations.

Findings

Successfully models uncertain DFs with BHMM.

Effectively manages M-DERA impacts on power flows.

Improves operational economics and reduces line overloads.

Abstract

To facilitate the integration of distributed energy resources (DERs) into the wholesale market while maintaining the tractability of associated market operation tools such as unit commitment (UC), existing DER aggregation (DERA) studies usually consider that each DERA is presented on a single node of the transmission network. Nevertheless, the increasing scale and geographical distribution of DERs spur the emergence of DERAs covering multiple transmission nodes, posing new challenges in modeling such multi-transmission-node DERAs (M-DERAs). Indeed, assessing the aggregated impact of an M-DERA on power flows is a non-trivial task, because the sensitivities of each transmission line to DERs at different transmission nodes are not identical. Inspired by the distribution factor (DF) based shift factor (SF) aggregation strategy in industry practice, this paper proposes a novel DF-based chance-constrained UC (CCUC) model to determine system optimal operation plans with M-DERAs. DFs, treated as uncertain parameters to describe possible responses of DERs against aggregated dispatch instructions from regional transmission organizations, are modeled via a bounded hetero-dimensional mixture model (BHMM) by leveraging historical DF records distributed on multiple hyperplanes in a bounded space. With this, power flow limits are modeled as chance constraints in CCUC, which is reformulated into a scenarios-based stochastic form and solved by Benders decomposition. The proposed method is tested on an IEEE 24-bus system to illustrate its effectiveness in managing M-DERA integration while ensuring operational economics and mitigating the overloading of transmission lines.