Two-Stage Optimization for Dynamic Line Rating and Energy Storage Deployment

摘要

The increasing penetration of distributed energy resources (DER) and weather-driven variability has intensified congestion and reliability stress in transmission networks. Strategies that enhance the utilization of existing infrastructure, such as static line ratings (SLR) and energy storage systems (ESS), have therefore become necessary. SLRs rely on conservative ambient assumptions and often understate thermal limits, whereas dynamic line ratings (DLR) adjust capacity according to weather conditions and unlock additional transfer capability. Energy storage systems provide temporal flexibility, but their transmission-level effectiveness depends on proper siting and sizing. This paper proposes a two-stage optimization method for joint placement of DLR installations and utility-scale energy storage. In the first stage, a mixed-integer linear program selects DLR corridors and ESS buses by minimizing operating cost, DER curtailment, and load-shedding penalties subject to DC power flow and investment constraints. In the second stage, the model determines ESS energy capacity and operating schedules under ambient-driven line ratings. Ambient weather data is used to generate DLR profiles, and sequential Monte Carlo simulation is applied to assess system adequacy. The proposed method, when deployed on the modified IEEE RTS 24-bus system, shows that coordinated DLR and ESS planning improves transmission capability, mitigates congestion, and strengthens system adequacy under weather variability.