MODELING THE GRID INTEGRATION OF VARIABLE

RENEWABLE ENERGY (VRE) RESOURCES

23 November 2018

Bethany Frew

National Renewable Energy Laboratory (NREL)

NREL/PR-6A20-72957
NREL | 2

National economic impact

facilities, 

renowned 

technology 

experts

World-class

with industry, 

academia, and 

government

Partnerships

operates as a 

living 

laboratory

Campus National 

economic 

impact

NREL at a Glance

$872M

annually 1,800

Employees,

early-career 

researchers and 

visiting scientists

\

nearly

750 National economic impact

plus more than 

400
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

OUTLINE

TRADITIONAL (~30 min)

• Overview of modeling tools, methods, and datasets that we 

traditionally/currently use

FORWARD-LOOKING (~40 min)

• What are we working on now?

• Where are we heading?
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

OVERVIEW OF MODELING TOOLS, 

METHODS, AND DATASETS THAT WE

TRADITIONALLY/CURRENTLY USE
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

SOME NREL TOOLS
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

CAPACITY EXPANSION MODELS

• Regional Energy Deployment System (ReEDS)

• Resource Planning Model (RPM)
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

REGIONAL ENERGY DEPLOYMENT SYSTEM

(REEDS)

Simulates the expansion and operation of the North American

generation and transmission system given projections of load, fuel 

prices, technology costs and performance, and policies/regulations

https://www.nrel.gov/analysis/reeds
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

REEDS KEY ATTRIBUTES

• High spatial resolution to represent 

transmission, RE resources and load:

• 134 Balancing Areas (BAs), 356 renewable 

resource regions

• High temporal resolution to represent 

seasonal and diurnal variations in load and 

resources:

• 17 time-slices for each year

• Detailed representation of challenges 

associated with integration of variable 

resource renewables: 

• curtailment and capacity value

• Key outputs: annual generator and 

transmission capacity builds/retirements, 

dispatch, emissions, fuel consumption, 

electricity prices
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

RESOURCE PLANNING MODEL (RPM): MIXED

NODAL/ZONAL MODEL

http://www.nrel.gov/analysis/models_rpm.html
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

METRICS TO CAPTURE VARIABILITY AND

UNCERTAINTY

Capacity value (CV) and Curtailment

ReEDS/

RPM

CV and Curtailment 

Estimation Method(s)

(8760 Module,

Statistical Calculation,

Functional Fit, etc.)

•Load and VG 

8760 profiles

•Existing CV and Curtailment

•Marginal CV and Curtailment •Capacity and 

generation results 

from previous 

solve period
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

DISTRIBUTED RESOURCES AND DEMAND-SIDE

TOOLS

• dGen

• NREL Point of Contact (POC): Ben Sigrin

• Demand-side grid (dsgrid)

• NREL POC: Elaine Hale
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

Bottom-up modeling of 

buildings, industry, and electric 

vehicles to enable:

• Future projections and whatif scenarios for load shape in 

addition to magnitude

• Realistic estimates of 

potential load flexibility

(i.e., demand response)

• Understand interactions 

between energy efficiency 

and demand response 

potential (also renewables 

and DERs)

THE DEMAND-SIDE GRID (DSGRID) MODEL CREATES

HIGHLY RESOLVED TIME-SYNCHRONOUS LOAD DATA BY

LEVERAGING SECTOR-SPECIFIC MODELING EXPERTISE

https://www.nrel.gov/docs/fy18osti/71492.pdf
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

DISTRIBUTED TECHNOLOGY DIFFUSION

(DGEN) 

• Forecasts customer adoption of distributed generation technologies (solar, 

storage, wind, geothermal) for residential, commercial, and industrial 

entities, given assumptions about future electricity costs, technology cost 

and performance, policy and regulation, and customer behavior

• High geographic resolution enables state, utility, or city-specific analysis with 

overlay of multiple spatial layers

ReEDS and RPM do not estimate DG adoption. 

A separate model (dGen) is used.

https://www.nrel.gov/analysis/dgen/
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

REEDS (OR RPM)-DGEN LINKAGES

dGen - 2018

Rooftop PV Adoption

ReEDS - 2018

Bulk Power System

dGen- 2020

Rooftop PV Adoption

ReEDS - 2020

Bulk Power System

...

dGen - 2050

Rooftop PV Adoption

ReEDS - 2050

Bulk Power System

 Rooftop PV capacity by region

 Capacity factor by time slice

 Retail electricity prices of adopters

 Rooftop PV marginal curtailment rate











Iterative

dGen – 2018-2050

Rooftop PV Adoption

ReEDS – 2018-2050

Bulk Power System

Single Pass

https://doi.org/10.1016/j.apenergy.2016.02.004
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

REEDS (OR RPM)-DGEN LINKAGES

Calculate required 

revenues based on system 

cost and end use 

consumption from ReEDS

Iterative linkage for assessing retail rate impact

https://doi.org/10.1016/j.tej.2017.10.003

Design tariffs to 

collect required 

revenues from 

dGen customers

Estimate DPV 

adoption in 

dGen with 

tariff design

Repeat for each ReEDS solve year

Initial ReEDS

Year n

Final

ReEDS

Year n
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

PRODUCTION COST MODELS (PCMS)

PLEXOS

NREL POC: Greg Brinkman
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

PLEXOS PRODUCTION COST MODEL

Hourly or subhourly 

chronological

Commits and dispatches 

generating units based on:

• Electricity demand

• Operating parameters of 

generators

• Transmission grid 

parameters

Used for system generation 

and transmission planning

• Increasingly used for real- time operation

Dispatch 

information, 

curtailment, 

fuel usage

Locational prices, 

production cost

Transmission 

congestion
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

MODEL LINKAGES: REEDS-TO-PLEXOS

Take buildout from 

ReEDS (or RPM)...

...and translate into operational 

database for PLEXOS
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

RESOURCE ADEQUACY ASSESSMENT TOOL

• Probabilistic Resource 

Adequacy Suite (PRAS)

• Recently extended to 

include transmission 

network outages

• Currently developing 

capability to capture 

energy-limited 

resources, such as 

storage and responsive 

demand

• NREL POC: Gord Stephen
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

PRAS

Simple single-region, single-period analysis Large-scale, multi-region analysis

Shortfall probability = LOLP

Probability-weighted average shortfall magnitude = EUE
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

PRAS – CHOOSE YOUR OWN ADVENTURE

VG Distribution Extraction

☐ Backcasting

☐ REPRA windowing

☐ Other?

Adequacy Metric

☐ LOLE

☐ EUE

☐ Other?

Capacity Value 

Metric

☐ ELCC

☐ EFC

☐ Other?

Results

☐ Aggregated

☐ Network – shortfalls only

☐ Network – all cases

Simulation Method

☐ Non-sequential copper plate (convolution)

☐ Non-sequential network flow (local convolution + Monte Carlo)

☐ Sequential copper plate (incl. storage, Markov chain + Monte Carlo)

☐ Sequential network flow (incl. storage, Markov chain + Monte Carlo)

☐ Other?

System Import

(e.g. PLEXOS,

ReEDS, RPM)

PRAS provides a modular collection of data processing and system simulation tools 
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

PRAS IN NREL GRID INTEGRATION STUDIES

Capacity Expansion / Planning

ReEDS / RPM

Production Cost / Operations

PLEXOS

Resource Adequacy 

Assessment

PRAS

Consumer Technology Adoption

dGen

• Evaluates / validates resource 

adequacy of grid buildout 

scenarios from capacity 

expansion models

• Informs adjustments to internal 

resource adequacy heuristics in 

those models

• Calculates capacity value (e.g. 

EFC, ELCC) of individual resources 

(e.g. wind, solar, transmission) 

under different scenarios
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

SHORTER TIMESCALE TOOLS

• Transmission and stability analysis; Transmission + 

Distribution  other parts of NREL

• NREL POCs: Himanshu Jain, Yingchen (YC) Zhang, Bryan 

Palmintier

IGMS

PSLF/PSSE
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

DATASETS

• VRE profiles

• NSRDB (National Solar Radiation Database)

• Wind Toolkit 

• reV for enhanced user interface/data coordination

• SAM

• Load profiles

• Generator properties

• heat rate curves, emissions, 

costs, ramping, etc.

• Network connection/mapping

• Outages and repair times

• Hydropower data
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

NREL RESOURCE DATA: THE AMERICAS

WIND Toolkit 

• Temporal Range: 2007-2013

• Temporal Interval: 5-minute

• Spatial Resolution: nominal 2 km

• Spatial Extent: North America

National Solar Radiation Database (NSRDB)

• Temporal Range: 1998-2016

• Temporal Interval: 30-minute

• Spatial Resolution: nominal 4 km

• Spatial Extent: Most of Western Hemisphere
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

NSRDB IN SOUTH AMERICA

https://nsrdb.nrel.gov/nsrdb-viewer
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

WIND TOOLKIT

https://maps.nrel.gov/wind-prospector/

https://www.nrel.gov/docs/fy15osti/61740.pdf
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

NREL’S EXPANDING PORTFOLIO OF

INTERNATIONAL WIND RESOURCE DATA

India:

- 2014

- 5 min, 3 km resolution

Kazakhstan:

- 2015

- 15 min, 3 km resolution

Greater Kazakhstan Area:

- 2015

- 15 min, 9 km resolution

Bangladesh:

- 2014-06 to 2017-12

- 1 hr, 3 km resolution

Philippines:

- TMY
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

SYSTEM ADVISOR MODEL (SAM)

NREL POC: Janine Freeman
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

SAM

SAM is free software for modeling the performance 

and economics of renewable energy projects.

Technologies

Photovoltaics

Wind

Concentrating solar power

Geothermal

Biomass

Solar water heating

Financial Models

Behind-the-meter 

residential

commercial

third-party ownership

Power purchase agreements

single owner

equity flips

sale-leaseback

Simple LCOE calculator
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

STEPS TO MODELING RENEWABLE ENERGY IN

SAM

Weather Data

System Specs

Electricity 

Production

Costs

System Losses

Compensation

Financing

Incentives

Results

Annual, Monthly, and Hourly Output, Capacity Factor, LCOE, NPV, Payback, Revenue
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

ENHANCED COORDINATION OF DATA USING

RENEWABLE ENERGY POTENTIAL MODEL (REV)

NREL POC: Galen Maclaurin
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

ENERGY-WATER NEXUS: HYDROPOWER DATA

FOR OUR MODELS

• Characterize hydropower resources

• Existing dams, expand/upgrade existing resources, power 

non-powered dams, and new stream reaches

• Separate into dispatchable and non-dispatchable

• Pumped storage hydropower

• Seasonal water availability as a long-term average, no interannual variability 

• Seasonal capacity variations are represented for the existing 

fleet

• Implemented in ReEDS at time-slice level

• PLEXOS does monthly energy limits and hourly/sub-hourly UC and dispatch (RT dispatch is typically fixed from DA)

NREL POCs: Stuart Cohen and Jordan Macknick

Hydropower modeling: https://www.nrel.gov/docs/fy17osti/68231.pdf

https://www.energy.gov/eere/water/articles/hydropower-vision-new-chapter-america-s-1st-renewable-electricity-source
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

ENERGY-WATER NEXUS: THERMAL COOLING

IN REEDS

• Characterize thermal cooling 

water supply and demand

• Water access supply curves, 

including regional availability and 

cost of multiple water classes

• Thermal generators have cooling 

technology mapping, each of 

which is characterized by 

seasonal water withdrawal and 

consumption rates (constrained 

by balancing area) and various 

operational constraints/costs
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

ENERGY-WATER NEXUS: LINK REEDS WITH

CLIMATE MODELS

• Assess impact of climate change on 

electricity system

• Load changes by season and region

• Thermal power plant performance 

changes due to air temperatureefficiency relationships

• Transmission line capacity is 

reduced at high temperatures

• Surface water 

availability/temperature for 

thermal cooling/hydropower 

changes with precipitation-runoff 

trends https://www.globalchange.gov/content/nca4-planning
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

CENTRAL DATA SET: ANNUAL TECHNOLOGY

BASELINE

Updated each year and released with suite of “Standard Scenarios”

https://www.nrel.gov/analysis/data-tech-baseline.html

https://atb.nrel.gov/

NREL POCs: 

Laura Vimmerstedt

and Wesley Cole 
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

MODEL OUTPUTS

• Standard output reports to visualize 

results and diagnose problems

Results!
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

REEDS OUTPUTS

Standard html reports 

plus interactive data 

viewer (example here 

for Standard 

Scenarios):

https://openei.org/apps/reeds/#
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

PLEXOS OUTPUTS: “MAGMA”

https://github.com/NREL/MAGMA

Scenario Name
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

ENHANCED VISUALIZATIONS: KALEIDOSCOPE

(HTTPS://GITHUB.COM/NREL/KALEIDOSCOPE)
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

OUTLINE

TRADITIONAL (~30 min)

• Overview of modeling tools, methods, and datasets that we 

traditionally/currently use

FORWARD-LOOKING (~40 min)

• What are we working on now?

• Where are we heading?
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

WHAT ARE WE WORKING ON NOW?
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

GREATER INTEROPERABILITY BETWEEN

MODELING TOOLS AND DATA SETS

• Energy systems integration

• Developing capabilities to model differing loads, electrification, 

integration of various energy systems 

• Greater emphasis on cross-sector, multi-timescale tools (and 

analysis)

• Better understanding of system interactions

• Ultra-high VRE penetration studies 

• Continental grid integration studies

• Advanced solving techniques utilizing HPC (high 

performance computing) facility

• Greater focus on flexibility

• Flexibility Inventory tool
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

POWER SYSTEM – NATURAL GAS MODELING

• Natural gas-fired generation is increasing, and increases in VRE penetration 

levels impact how gas fired power plants interact with the power

system, as well as how they interact with the gas network

• Objective: co-simulate power and natural gas network operations

Compare Business as 

Usual versus 

DA Coordination

Assess economic and 

reliability impacts

NREL POCs: Carlo Brancucci, Greg 

Stark, and Bri-Mathias Hodge
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

ELECTRIFICATION FUTURES STUDY (EFS):

HOW DO CHANGES TO ANNUAL ELECTRICITY DEMAND

IMPACT CAPACITY EXPANSION RESULTS?

1.6%/year CAGR (2016-2050)

1.2%/year

0.6%/year

NREL POCs: Trieu Mai and Caitlin Murphy

https://www.nrel.gov/docs/fy18osti/71500.pdf

2050 U.S. electricity 

consumption increases 

relative to Reference 

Medium +932 TWh (20%) 

High +1,782 TWh (38%) 

www.nrel.gov/efs
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

EFS: HOW DO CHANGES TO THE TIMING AND

MAGNITUDE OF PEAK DEMAND IMPACT CAPACITY

EXPANSION RESULTS? 

Summer = June-August, 

Fall = September-November, 

Winter = December-February, 

Spring = March-May

https://www.nrel.gov/docs/fy18osti/71500.pdf

2015 2050

• EFS added a winter peaking timeslice to capture interactions during this key period

• Demand side flexibility is modeled endogenously
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

BETTER UNDERSTANDING OF INCREASINGLY

COMPLEX SYSTEM INTERACTIONS

Synergistic relationship between VRE deployment and 

storage as a peaking resource 

Zero PV

10% PV

5% PV

With increased PV penetration, the capacity credit of PV decreases 

while the capacity credit of storage (generally...) increases

Source: Denholm 2018: https://www.nrel.gov/docs/fy18osti/70905.pdf

See application in ReEDS: https://www.nrel.gov/docs/fy18osti/71462.pdf
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

LA 100% RENEWABLE ENERGY STUDY

Use a suite of coordinated modeling and simulation tools to 

determine what investments could be made to achieve a reliable

100% renewable energy portfolio

Key considerations:

• Load (including flexible portions), resource, and network data 

at adequate spatial and temporal resolution to characterize 

system  projected to future and capturing price 

responsiveness of flexible loads and DR

• Distribution system hosting capacity and update costs

NREL POC: Paul Denholm
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

State-of-the-art analysis of the 

U.S., Canada, and Mexico power 

systems, from planning through 

operations

• Long-term pathways to a modern power system in North 

America

• Operational feasibility of very high-penetration scenarios

• Weather variability and uncertainty

• Value of enabling technologies: flexible hydropower, thermal 

generation, demand response, storage, transmission

• Value of operating practices: interchange, enhanced 

scheduling, local generation, reserve provisions

WHAT WE’RE STUDYING

Preliminary Findings, Not for Quotation or Distribution

THE NORTH AMERICAN

RENEWABLE INTEGRATION STUDY (NARIS)
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

NARIS MODELING FLOW

Preliminary Findings, Not for Quotation or Distribution
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

HPC AND ADVANCED SOLVING TECHNIQUES

Decomposition techniques with 

parallel computing

Some NREL POCs: Devon Sigler, Josh 

Novacheck, Clayton Barrows
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

EXAMPLE: DOMAIN DECOMPOSITION

Domain 

Decomposition

PCM

Solution

Analysis

Partitioning

PCM PCM PCM

Solution Solution

Reassembly

PLEXOS PLEXOS PLEXOS
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

EXAMPLE: GEOGRAPHIC DECOMPOSITION

Step 1: 

Interchange 

Forecast 

(run DA as LP)

Step 2: Geographically 

Decompose UC

(solve each “focus 

region” with outputs 

from #1 in rest-ofsystem)

Step 3: 

Power Flow Reconciliation 

(full system using each 

region from Step 2)

Traditionally, one 

optimization for the 

entire system

New approach uses 

decomposition to 

reduce solve time
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

FLEXIBILITY INVENTORY: QUANTIFICATION OF

FLEXIBILITY

• Logistics: Current flexibility inventory can 

process the results of PLEXOS runs

• Requires both input and output

• Takes additional inputs, such as:

• Flexibility inventory timeframes

• Information about reserves

• Flexibility inventory results are produced 

as an html file, and plots in png format

• Publicly available in the “flexibility- inventory” branch of the MAGMA 

repository: 

https://github.com/NREL/MAGMA

• Report coming soon: Jorgenson, J. et al, 

"Power System Flexibility Requirement and Supply in High Solar PV Scenarios in 

the Western U.S." NREL/TP-6A20-72471

• NREL POC: Jennie Jorgenson
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

STEP 1: QUANTIFY FLEXIBILITY NEEDS

Net Load Analysis Ramping Needs

Largest ramps over various timescales
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

STEP 2: QUANTIFY FLEXIBILITY SUPPLY

Analyze commitment and dispatch of the generator fleet, and determine upward and downward 

flexibility, incorporating:

• Ramp rate

• Max Capacity

• Min Gen level

• Min up time

• Min down time

• Reserve provision
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

STEP 3: IDENTIFY POTENTIAL SHORTAGES

• Determine inventory 

at “low” levels

• Determine date and 

time

• Look for systematic 

reoccurrences of low 

inventory levels (i.e., 

time of day, season)

• Identify available 

resources during 

these periods
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

WHERE ARE WE HEADING?
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

A VISION FOR NEXT-GENERATION OF

MODELING TOOLS

• Improved versions of ReEDS

• Greater emphasis on reliability and resiliency

• Open source!

• PRAS

• ReEDS 2.0

• ReEDS India  flexible “abstractions” enable adaptation to other 

countries/systems

• Enhanced visualizations

• Flexible framework for coordinated suite of modeling tools

• Integrated, flexible framework with consistent structure and 

database across multiple timescales
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

IMPROVED REEDS: “REEDS 2.0”

• Demand-side module: developed a representation of consumer decision-making around energy service consumption, device adoption, and electricity consumption

• Dynamic foresight and solve-year flexibility: developed the capability to solve using one of three different types of foresight into future 

years: sequential (myopic), intertemporal (perfect foresight), and 

sliding-window (perfect foresight over a user specified interval) 

• Modular structure and iteration: to address non-linearities 

introduced by the demand-side module and intertemporal optimization capability, ReEDS 2.0 was restructured to consist of three 

stand-alone modules (supply, demand, & VRR); solutions are obtained through an iterative process

NREL POC: Daniel Steinberg 
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

REEDS 2.0 STRUCTURE

Demand-Side 

Module

Supply-Side 

Module

Cap, Trans, Gen Service Demand

Efficiency

Electricity Prices by Region

Year, & Time-slice

Electricity Consumption by

Region, Year, and Time-slice

Converges on Electricity Consumption

Cap, Gen,

Trans & Load 

Curtailment & 

Capacity Value

VRR 

Module

Load, Capacity Values,

Curtailment Rates

Initialization
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

REEDS 2.0 STRUCTURE

Demand-Side 

Module

Supply-Side 

Module

Cap, Trans, Gen Service Demand

Efficiency

Electricity Prices by Region

Year, & Time-slice

Electricity Consumption by

Region, Year, and Time-slice

Converges on Electricity Consumption

Cap, Gen,

Trans & Load 

Curtailment & 

Capacity Value

VRR 

Module

Load, Capacity Values,

Curtailment Rates

Initialization Optimization Optimization

Simulation
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

IMPROVED REEDS: “REEDS-INDIA”

• Adapt ReEDS to the Indian power 

system

• First application of ReEDS 

outside of North America

• Target audience is Central 

Electricity Authority (CEA)

• 34 balancing areas, 146 resource 

regions, 35 time slices

• Emphasis on making model 

structure generic/adaptable to 

any country or region

• Will be open sourced, 

including user-friendly interactive results viewer 

• NREL POC: Amy Rose 
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

KEY CONSIDERATIONS FOR ADAPTING A CAPACITY

EXPANSION MODEL TO A DIFFERENT SYSTEM

1. What type of data are available?

2. Do the current capabilities capture all the relevant 

features of the power system?

• May require different structures for certain parameters 

based on available data

• Also could require modified or additional constraints

These two considerations make adapting a model for any 

new power system a challenge
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

RESOURCE ADEQUACY-AWARE CAPACITY

EXPANSION MODEL

Iterative linking of 

resource adequacy 

assessment with capacity 

expansion model

Directly embed resource adequacy 

assessment scenarios into capacity expansion 

model; eliminate use of PRM and CV
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

MODELING RESILIENCY

• Joint probabilities for common-mode outages

• Capture corelated, extreme events

• New metrics?
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

OPEN SOURCE!

• PRAS: 

https://github.nrel.gov/PRAS

Coming in 2019 (hopefully)

• dGen

Coming in Sept 2020

• ReEDS 2.0

Coming in late 2019

• ReEDS India

Coming spring 2019

• and many more!
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

ENHANCED VISUALIZATIONS

Aids in diagnostic, validation, and analysis efforts

• Kaleidoscope

• Various online data “viewers”

• 3D visualization room
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

AN INTEGRATED MODELING VISION
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

SCALABLE INTEGRATED INFRASTRUCTURE

PLANNING (SIIP) TOOLKIT

Data driven system simulations

• Simulations define a set of 

problems that can be solved 

using numerical techniques

• Problems are generated by 

expressing model formulations 

against system data

* Infrastructure System: Power, Water, Gas, Buildings, Thermal, 

Transportation, Liquid Fuels

SIIP Framework 

Anatomy

System Data

Systems

Simulations

::*OptimizationProblem

::*OptimizationModel

::*Simulation

::*System

::*DynamicsProblem

::*DynamicsModel

NREL POC: Clayton Barrows
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

SIIP FRAMEWORK:

AN EXAMPLE FOR ELECTRICITY SYSTEMS

Modular, interoperable, modeling components that define 

infrastructure modeling problems informed by system data

Rigorous data model that defines infrastructure systems

• Collects information required for device level modeling

• Includes parsing capabilities

• Exploits Julia’s parametric dispatch for efficient code 

development

• Agnostic to simulations that will be performed

PowerSystems.jl

Mathematical formulations and simulation assemblies

• Support for optimization and dynamic simulation 

models

• Modular problem assembly to enable rapid 

development and extension

• Includes standard simulations (e.g. UC/ED)

PowerSimulations.jl

SIIP::Power
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

SIIP PROVIDES PLATFORM FOR ADDITIONAL

CAPABILITIES

SIIP

Markets

Distribution

Transportation Buildings

Etc.
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

MOVING TO JULIA: A NEW SCIENTIFIC

PROGRAMMING LANGUAGE

• Provides high-level 

language 

abstractions

• Makes efficient 

use of researcher 

time

• Access to low-level 

functionality

• Enables 

computational 

performance
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

GITHUB POWERSYSTEMS.JL RECENTLY

RELEASED
India 

(co-lead)

Denmark Finland Mexico

(co-lead)

South Africa Spain United States

(co-lead, under review) Brazil China

THANK YOU!

bethany.frew@nrel.gov