Optimal Network Expansion Planning Considering Uncertain Dynamic Thermal Line Rating

TL;DR

This paper presents a comprehensive optimization framework for network expansion planning that accounts for uncertainties in demand, renewable energy, EV charging, and dynamic thermal line ratings, enhancing grid efficiency and resilience.

Contribution

It introduces a hybrid stochastic-robust optimization model that integrates Dynamic Thermal Line Rating and a heuristic linearization to manage uncertainties and improve transmission planning.

Findings

Effective handling of uncertainties in demand, RES, and EV loads.

Improved transmission line utilization through DTLR integration.

Validated results on IEEE test systems demonstrate enhanced planning outcomes.

Abstract

This paper examines the integrated generation and transmission expansion planning problem to address the growing challenges associated with increasing power network loads. The proposed approach optimizes the operation and investment costs for new generation units and transmission lines, while also considering the environmental benefits of integrating renewable energy sources (RES) and the impact of electric vehicle (EV) charging on the grid. The inherent uncertainties in demand, EV charging loads, and RES generation are managed using a hybrid stochastic-robust optimization approach. Additionally, the model integrates Dynamic Thermal Line Rating (DTLR) to improve the efficiency and resilience of transmission lines. The framework also tackles the uncertainty related to DTLR, incorporating a heuristic linearization technique to reduce model complexity. The effectiveness of the proposed model and techniques is evaluated through simulations conducted on two case studies: the modified IEEE 6-bus system and the IEEE 24-bus Reliability Test System.