Multi-Time-Scale Coordinated Planning of Transmission System with Heterogeneous Flexibility and Renewable Uncertainty

The large-scale integration of renewable energy into transmission systems introduces significant variability and uncertainty, necessitating additional flexibility to ensure reliable and economic operation. Heterogeneous flexibility resources (e.g., storage, gas-fired units, demand-side response) distinct temporal and operational characteristics, complicating coordinated planning and operation. Therefore, this paper proposes a multi-timescale coordinated framework that jointly addresses long-term investment and short-term operation. At the upper level, investment siting and sizing of multiple flexibility resources are optimized to minimize total cost while enhancing renewable accommodation. At the lower level, stochastic operational subproblems are formulated under representative wind, solar, and load scenarios, incorporating unit commitment and network power flow constraints. To manage the computational complexity of large-scale systems with numerous scenarios, a Benders decomposition algorithm is developed. The master problem determines investment decisions, while subproblems evaluate operational feasibility and optimality, with cuts iteratively exchanged until convergence. Case studies on a benchmark transmission system demonstrate that the proposed approach improves renewable integration, reduces system cost, and achieves scalable computational performance.