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

Conference · Mon Jul 08 04:00:00 EDT 2024

DOI:https://doi.org/10.1109/TD47997.2024.10556202· OSTI ID:2478124

Anderson, Osten P. ^[1]; Yu, Nanpeng ^[2]; Oikonomou, Konstantinos ^[1]; Maloney, Patrick R. ^[1]; Wu, Di ^[1]

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1. BATTELLE (PACIFIC NW LAB)
2. University of California, Riverside

With the increasing urgency to decarbonize power systems, while mitigating extreme events, capacity expansion models can play a vital role in reliably planning the expansion of power systems and facilitating the integration of renewable energy sources. Optimizing capacity expansion generally involves selecting surrogate representative days from forecasts of load and the generation profiles of variable renewable energy resources. To properly select those representative days, we propose a novel input-based approach in combination with the k-means clustering algorithm that utilize three unique operational inputs: 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 based on California Independent System Operator (CAISO)'s decarbonization goals, and results in reduced costs and drastically lower load shedding.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 2478124
• Report Number(s): PNNL-SA-189191
• Country of Publication: United States
• Language: English