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and Renewable Energy, operated by the Alliance f

Evaluating the Value of High Spatial Resolution in National Ca

Expansion Models using ReEDS 

Venkat Krishnan, Ph.D., and Wesley Cole, Ph.D., National Renewable Energy Labora

• Power sector capacity expansion models (CEMs) have a broad range of spatial resolutions- e.g., NEMS (22 regions), IPM (64 regions), ReEDS (134 regions), PLEXO• We use NREL’s Regional Energy Deployment System (ReEDS) model, a long-term national-scale electric sector CEM for the United States, to evaluate the value o

• We perform planning at three different spatial resolutions– 1) REF: 134 balancing areas (native ReEDS resolution), 2) STATE: 48 contiguous states, and 3) NERC: 13 

• We evaluate the impact of spatial resolution on renewable capacity deployment and location, associated transmission builds, and system costs. 

• Spatial aggregation impacts the relative competitiveness of renewables, and higher levels of aggregation led to less solar PV deployment while wind depl

ReEDS finds the regional mix of generation 

technologies that meet the electric sector 

requirements at least cost. 

ReEDS output includes: 

• Capacity and generation evolution of all generator types by region at 

high geospatial resolution 

• Impact of policies on clean energy deployment • Transmission expansion and inter-regional energy flows • Emissions, fuel consumption and water consumption • System costs and electricity prices 

• REF scenario: Renewable (RE) reso

performance is represented at ReEDregions for wind and CSP, 134 region

• STATE scenario: RE costs and capa

(capacity-weighted) across all the 13within each state, resulting in a commwithin each state 

• NERC scenario: RE costs and capa

(capacity-weighted) across all the 13within each NERC region, resulting in capacity factor within each NERC reg

• In all the scenarios, inter-BA transmis

were respected. 

• All three scenarios used the NREL’s 

(ATB) 2015 mid-case cost and perforATB is a collection of current and futuprojections for generating units for th

Biopower 

CSP 

Geothermal 

Hydropower 

PV 

Wind Inter-BA 

transmission 

• 134 Balancing Areas (BAs) • Highly resolved RE resource 

representations 

(356 wind/ CSP regions) 

• RE resource accessibility & other 

development costs 

• Transmission (~300 lines) 

WECC: 35 nodes, 72 AC, 2 DC 

East: 92 nodes, 213 AC lines ERCOT: 7 nodes, 12 AC lines 9 AC-DC-AC interties 

Model Constraints • Regional load, planning reserves & operating reserve requirements • Federal & state policies (e.g. RPS, CAIR, CSAPR, CPP, RGGI, MATS, AB32) • Inter-BA transmission constraints • 17 time-slices (4 seasons x 4 diurnal + 1 super peak) • Impact of variable resources on capacity value, reserves & curtailments 

Study De

Regional Energy Deployment System (ReEDS) Model - Introductio

Objective & Key Conslusion 

Scenarios revealed appreciable changes primarily in three technologies with lower resolution: • Solar PV capacity decreases • Wind capacity increases in response to 

the decrease in PV capacity 

• Gas-CC capacity increases to 

compensate for PV decrease 

• Additionally, slight changes in gas-CT 

and storage capacities in some yearsdriven by varying degrees of variable renewable curtailments 

Effect of spatial aggregation of RE resource parameters: • As in the case of PV resources, low cost high performance (high capacity factors 

and capacity value during peak periods) resources may be “missed” by the model as a result of averaging with other higher cost lower performance resources 

• On the other hand, as in the case of wind resources, averaging the parameters of a 

remote “good” resource that needs transmission with a local “poor” resource that does not need transmission may improve the “poor” resource’s attractiveness 

REF scenario – cumulative installed capacity 

Contact: Venkat Krishnan, Venkat.Krishnan@nrel.gov Power System Engineering Center, National Renewable Energy Laboratory 

• An overview of the ReEDS electricity s

model that optimizes the generation poexpansions from 2010 to 2050. 

• ReEDS has high spatial resolution in te

electric sector using 134 BAs, with genperformance data at the same or highewind and PV). 

• The paper assessed the impact of two 

by averaging the cost and performance and 13 NERC region levels. 

• Results revealed that spatial aggregat

the competitiveness of RE resources access). 

• Lower quality resources may appear be

higher-quality resources, and vice versrelative competitiveness of renewable tconsequent investment in capacity. 

• The scenarios indicated that solar PV 

level of aggregation. Higher levels of deployment while wind deployment inc

Summary & Conc

Impact of Spatial Resolution on Renewable Deployment & Planning C

The information contai

Impact on System Planning Cost (2015$): • With anticipated lower cost solar PV resources available to the high-resolution 

CEM models (this study assumed hitting $1/W SunShot target in 2030s), lower resolution CEM models which misses these low cost PV sites due to aggregation will over estimate the system planning costs. 

• This is because lower resolution CEM models under-invest in low cost PV 

resources and instead invest in other expensive generating options. 

• Relative to REF scenario, in the table we observe a ~$25B and ~$50B increase 

under STATE and NERC resolution scenarios. 

Impact on regional PV capacities: • Aggregation of the cost and performance metrics at the state-level has a relatively 

limited overall effect on solar PV deployments, while aggregation at the NERC level has a substantial impact, especially in SERC and ECAR (see right side figure on capacity difference). 

Impact on system planning cost & regional PV deployment 

Note: planning horizon – 2010 to 2050; discount rate – 3%