Implications of Model Structure and Detail for Utility Planning. Scenario Case Studies using the Resource Planning Model

Mai, Trieu¹  Barrows, Clayton¹  Lopez, Anthony¹  Hale, Elaine¹  Dyson, Mark¹  Eurek, Kelly¹ 

[1] National Renewable Energy Laboratory (NREL), Golden, CO (United States)

关键词: UTILITY PLANNING;    CAPACITY DEPLOYMENT;    CAPACITY EXPANSION MODELS;    CAPACITY EXPANSION MODELING;    MODEL CONFIGURATION;    MODEL STRUCTURE;    SCENARIOS;    RESOURCE PLANNING MODEL;    WESTERN INTERCONNECTION;    COLORADO;   

DOI  :  10.2172/1215187
RP-ID  :  NREL/TP--6A20-63972
PID  :  OSTI ID: 1215187

学科分类：电力

美国|英语

来源: SciTech Connect

【 摘 要 】

We examine how model investment decisions change under different model configurations and assumptions related to renewable capacity credit, the inclusion or exclusion of operating reserves, dispatch period sampling, transmission power flow modeling, renewable spur line costs, and the ability of a planning region to import and export power. For all modeled scenarios, we find that under market conditions where new renewable deployment is predominantly driven by renewable portfolio standards, model representations of wind and solar capacity credit and interactions between balancing areas are most influential in avoiding model investments in excess thermal capacity. We also compare computation time between configurations to evaluate tradeoffs between computational burden and model accuracy. From this analysis, we find that certain advanced dispatch representations (e.g., DC optimal power flow) can have dramatic adverse effects on computation time but can be largely inconsequential to model investment outcomes, at least at the renewable penetration levels modeled. Finally, we find that certain underappreciated aspects of new capacity investment decisions and model representations thereof, such as spur lines for new renewable capacity, can influence model outcomes particularly in the renewable technology and location chosen by the model. Though this analysis is not comprehensive and results are specific to the model region, input assumptions, and optimization-modeling framework employed, the findings are intended to provide a guide for model improvement opportunities.