A Scenario-based Stochastic Model of using BESS-based Virtual Transmission Lines in Day-Ahead Unit Commitment

TL;DR

This paper introduces a scenario-based stochastic model for using BESS-based Virtual Transmission Lines in day-ahead unit commitment, demonstrating significant cost savings and congestion relief compared to traditional methods.

Contribution

It proposes a novel stochastic security-constrained unit commitment model incorporating VTL and RES uncertainty, showing its advantages over physical lines and standalone BESS.

Findings

VTL achieves 23% more operational cost reduction than physical lines.

VTL provides up to 67% more congestion relief than standalone BESS.

The model effectively manages RES forecast errors in system operation.

Abstract

The rapid increase in renewable energy sources (RES) implementation in the power system creates more severe network congestion, which may reduce grid operation efficiency and cause renewable curtailment. Deterministic optimization for the unit commitment shows that battery energy storage system (BESS)-based Virtual Transmission Line (VTL), as an alternative to physical transmission lines, can offer a quick solution for congestion relief, reduced operational costs, and lowered renewable curtailment. This paper aims to evaluate the benefits of VTL when considering Renewable Energy Sources uncertainty. Particularly, this work proposes a scenario-based stochastic security-constrained unit commitment model considering VTL, referred to as SSCUC-VTL. It incorporates the forecast error of RES into the commitment decision for systems with VTL. The performance of applying the VTL strategy is compared to that of adding a new physical transmission line and a standalone BESS. A case study has been conducted on an enhanced IEEE 24-bus test system. The simulation results demonstrate that VTL provides 23% more operational cost reduction than the physical transmission line, and up to 67% more congestion relief than the standalone BESS in a power system with solar and wind generation.