NREL’s Resource Planning Model

Elaine T. Hale, Ph.D.

Senior Engineer

Strategic Energy Analysis Center
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Contents

1 Model Overview

2 Example Analyses

3 Model Linkages
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Resource Planning Model (RPM)

Capacity expansion model for a regional electric system over a utility 

planning horizon (through 2035). 

Key features:

– Individual generation unit and transmission line representation

– Hourly chronological dispatch and detailed system operation 

representation

– High spatial resolution informs generator siting options, particularly for 

renewable resources

– Flexible data structure to develop models for customized regions

– New: Models the cost and value of storage and other enabling 

technologies

https://www.nrel.gov/analysis/models-rpm.html
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RPM is 

a Mixed 

Nodal

/ Zonal 

Model
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Examples from Colorado model

High spatial resolution modeling to accurately represent 

renewable resource potential and quality 

Clustering techniques are applied to develop renewable resource zones that have similar output 

characteristics. Each zone is characterized by: 

(1) resource potential, (2) hourly profiles, and (3) grid interconnection costs
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Temporal resolution 

and sampled dispatch • Clustering used to select representative weeks • Can model up to 3 weeks 

+ peak day at hourly 

resolution 

• Typical models include 4 

separate chronological 

24-hour periods, i.e. 96 

hours of the year

(Getman et al. 2015)
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Reduced 

Order 

Dispatch Does 

Not Fully 

Address the 

Variability 

Seen 

Throughout 

the Year

96 dispatch 

hours per year

Peak day does 

not fully capture 

capacity value

Low period does not fully 

capture conflict between 

wind, solar, and thermal fleet 

inflexibility

We therefore estimate some parameters using a full year’s worth of data ...
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Variable Generation (Wind and Solar) Capacity Value

• Capture shift in net peak load based on top 100 hours

• Values geospatial and technology diversity

• For each NERC region:

– Capacity value of existing VG = <LDC – NLDC>top 100 / (existing VG capacity · 100)

• For each VG resource region:

– Marginal capacity value of new VG = <NLDC(δ) – NLDC>top 100 / (δ · 100)

Fractional capacity values used in planning reserve constraints

Hour

LDC

NLDC

Demand

NLDC(δ)
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Minimum Curtailment of Variable Generation

• Curtailment of wind and solar arises when load, net of renewable 

generation (i.e., “net-load”), is below the system’s minimum generation 

level. This occurs because committed thermal generators can only be 

turned down to a specific minimum level.

• RPM estimates curtailment rates based on regression analysis of 

curtailment observed in numerous production cost model runs

incremental 

curtailment 

for new VG

Hour

Load Duration Curve (LDC)

Net Load Duration 

Curve (NLDC)

Demand

NLDC(δ)

(representing an 

incremental build)

system’s minimum 

generation level

curtailment
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Primary outcomes are capacity 

expansion and generation dispatch 

Year

Capacity (GW)

Illustrative high-solar 

scenario; Results for 

a focus region
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Model revenues show why resources are built

SDGE

PACE-UT

4h

0.25h 4h 8h

Model revenues from a low tech cost scenario
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Contents

1 Model Overview

2 Example Analyses

3 Model Linkages
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reduced exports

Navajo Generating 

Study, Phase 2

2030 Generation Differences with Reference

Western Interconnection

Arizona Focus Region

Related to (Hurlbut et al. 2016)
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Ownership of Lands Suitable for RE Development

Lands suitable for wind development

Wind

(Barrows et al. 2016)
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Value of Storage

PACE-UT

4h

value-stacking dispatch revenue outputs clarify why 

a resource was built
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Contents

1 Model Overview

2 Example Analyses

3 Model Linkages
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Integration with Distributed Generation Market 

Demand Model (dGen)

Calculate financial 

performance

Estimate maximum 

market share

Estimate rate of 

adoption
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Automated toolchain enables production cost modeling 

(PCM) of expansion plans
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References

Gagnon, Pieter, Brady Stoll, Ali Ehlen, Trieu Mai, Galen Barbose, Andrew Mills and Jarrett Zuboy. 

Estimating the Value of Improved Distributed Photovoltaic Adoption Forecasts for Utility 

Resource Planning, NREL Technical Report (2018)

Hurlbut, David et al. Navajo Generating Station & Federal Resource Planning. Volume 1: 

Sectoral, Technical, and Economic Trends, NREL Technical Report (2016)

Barrows, Clayton, Trieu Mai, Scott Haase, Jennifer Melius, and Meghan Mooney. Renewable 

Energy Deployment in Colorado and the West: A Modeling Sensitivity and GIS Analysis, NREL 

Technical Report (2016)

Hale, Elaine, Brady Stoll and Trieu Mai. Capturing the Impact of Storage and Other Flexible 

Technologies on Electric System Planning, NREL Technical Report (2016)

Getman, Dan, Anthony Lopez, Trieu Mai and Mark Dyson. Methodology for Clustering High- Resolution Spatiotemporal Solar Resource Data, NREL Technical Report (2015) 

Mai, Trieu, Clayton Barrows, Anthony Lopez, Elaine Hale, Mark Dyson and Kelly Eurek. 

Implications of Model Structure and Detail for Utility Planning: Scenario Case Studies Using 

the Resource Planning Model, NREL Technical Report (2015) 

Mai, Trieu, Easan Drury, Kelly Eurek, Natalie Bodington, Anthony Lopez, and Andrew Perry. 

Resource Planning Model: An Integrated Resource Planning and Dispatch Tool for Regional 

Electric Systems, NREL Technical Report (2013)
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Who are we?

Clayton Barrows

Ph.D. Energy and Mineral Engineering

Pennsylvania State University

Anthony Lopez

M.S. Geographic Information Science

University of Denver

Elaine Hale

Ph.D. Chemical Engineering

University of Texas, Austin

Trieu Mai

Ph.D. Theoretical Physics

University of California, Santa Cruz

Dylan Hettinger

B.S. Geography

Appalachian State University

Daniel Steinberg

M.E.Sc Environmental Economics and Energy Analysis

Yale School of Forestry and Environmental Studies

Jennie Jorgenson

M.S. Mechanical Engineering

University of Colorado, Boulder

Brady Stoll

Ph.D. Mechanical Engineering

University of Texas, Austin
www.nrel.gov

Thank you

NREL/PR-6A20-71664

This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, 

for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. Department 

of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office. The views expressed in 

the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and 

the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paidup, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. 

Government purposes.
Backmatter
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Algorithmic Structure

• Sequentially solves for resources that meet system needs at least cost

• 8760 methods adjust reduced-order co-optimization to dynamically account 

for VG & storage technology capacity value and curtailment impacts
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Capacity Expansion Models Have to Balance 

Operational Detail with Computational Complexity

Zero capacity credit

Reference

No operating reserves

Dispatch duration

Must run

Transmission

constraints

Grid connections

Islanded region

Computation Time as a Function of Model Configuration

These have generally been improved, but the relative times are still illustrative.

(Mai et al. 2015)
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Storage Capacity Value

• Capture shift in net peak load 

based on top 100 hours

• Values geospatial and technology 

diversity

• At the NERC level and by storage 

technology:

– capacity value of existing storage = 

<NLDC – SLDC>top 100 / existing capacity

– marginal capacity value of new storage =

<SLDC(δ)>top 100 / δ
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Capacity value of storage resource and required spillage (CSP 

with TES) is estimated using a heuristic dispatch algorithm 

applied to the NERC sub-region net load curve

Concentrating Solar 

Power with Thermal 

Energy Storage 

(CSP with TES) 

Example
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What is the cost of misforecasting distributed PV adoption 

(and what is the value of improving it)?

Example result: A large utility with 10 TWh/year of 

retail sales that is planning for DPV growth of 3.5% of 

total generation over 15 years could expect presentvalue savings of $4.0 million by reducing its DPV 

forecast uncertainty from roughly +75%/-55% to ±25%. (Gagnon et al. 2018)