High-Resolution PTDF-Based Planning of Storage and Transmission Under High Renewables
For power system planners, it provides a computationally efficient method to integrate large-scale storage into transmission expansion under high renewable penetration.
The paper develops a scalable PTDF-based co-optimization of transmission and storage planning, achieving optimality gaps below 2% on a 2,000-bus system with storage at 167 nodes (32% of peak renewable capacity).
Transmission Expansion Planning (TEP) optimizes power grid upgrades and investments to ensure reliable, efficient, and cost-effective electricity delivery while addressing grid constraints. To support growing demand and renewable energy integration, energy storage is emerging as a pivotal asset that provides temporal flexibility and alleviates congestion. This paper develops a multiperiod, two-stage PTDF formulation that co-optimizes transmission upgrades and storage siting/sizing. To ensure scalability, a trust-region, multicut Benders scheme warm-started from per-representative-day optima is proposed. Applied to a 2,000-bus synthetic Texas system under high-renewable projections, the method attains final optimality gaps below 2% and yields a plan with storage at 167 nodes (32% of peak renewable capacity). These results demonstrate that the proposed PTDF-based methodology efficiently handles large distributed storage fleets, demonstrating scalability at high spatial resolution.