Multi-period grid capacity expansion model with new spatial demand modeling for data centers and manufacturing loads, applied to synthetic ERCOT-like grid showing 83.6% generation capacity increase over seven years.
From Zonal to Nodal Capacity Expansion Planning: Spatial Aggregation Impacts on a Realistic Test-Case
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abstract
Solving power system capacity expansion planning (CEP) problems at realistic spatial resolutions is computationally challenging. Thus, a common practice is to solve CEP over zonal models with low spatial resolution rather than over full-scale nodal power networks. Due to improvements in solving large-scale stochastic mixed integer programs, these computational limitations are becoming less relevant, and the assumption that zonal models are realistic and useful approximations of nodal CEP is worth revisiting. This work is the first to conduct a systematic computational study on the assumption that spatial aggregation can reasonably be used for ISO-scale CEP. By considering a realistic, large-scale test network based on the state of California with over 8,000 buses, we find that well-designed small spatial aggregations can yield good approximations but that coarser zonal models may result in large distortions of investment decisions, e.g., capacity under-investment of up to 41% for the lowest resolution model considered.
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Grid Capacity Expansion under Data Centers and Electrified Manufacturing Large Loads
Multi-period grid capacity expansion model with new spatial demand modeling for data centers and manufacturing loads, applied to synthetic ERCOT-like grid showing 83.6% generation capacity increase over seven years.