From Zonal to Nodal Capacity Expansion Planning: Spatial Aggregation Impacts on a Realistic Test-Case

TL;DR

This paper investigates how spatial aggregation affects capacity expansion planning accuracy in power systems, showing that coarse zonal models can distort investment decisions despite computational advantages.

Contribution

It is the first systematic study to evaluate the validity of using zonal models for large-scale CEP based on a realistic California network.

Findings

Well-designed small aggregations can approximate nodal results effectively.

Coarser zonal models lead to significant distortions in investment decisions.

Advances in solving large-scale stochastic programs reduce the need for coarse aggregation.

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- and interconnect-scale CEP. By considering a realistic, large-scale test network based on the state of California with over 8,000 buses and 10,000 transmission lines, we demonstrate that well-designed small spatial aggregations can yield good approximations but that coarser zonal models result in large distortions of investment decisions.