Representative Period Selection for Robust Capacity Expansion Planning in Low-carbon Grids

Amid the urgency to decarbonize power systems, while mitigating extreme weather events, capacity expansion models can play a vital role in reliably planning the expansion of power systems and facilitating the integration of renewable energy (RE) sources. Optimizing capacity expansion generally involves selecting surrogate representative days from forecasts of load and the generation profiles of variable RE resources. To properly select those representative days, we propose a novel input-based clustering approach that utilizes three unique operational characteristics: load shedding, renewable curtailment, and transmission congestion. The proposed method allows for more robust and cost-effective capacity planning. The method is validated using a capacity expansion model and a production cost model aligned with California Independent System Operator (CAISO)'s decarbonization goals, and results in significant cost reduction and substantial decreases in load shedding.