Technical Assistance for States: Grid-Interactive Efficient Buildings

GEB Potential Working Group Call

July 17, 2020

Chioke Harris1, Sam Koebrich1, Joyce McLaren1

1National Renewable Energy Laboratory

Agenda1. Present regional level GEB potential

2. Collect feedback

3. Present ideas on exploring customer participation

4. Collect feedback & data source ideas

Discussion1. Would you benefit from seeing similar results, specific

to your region?

2. Is the granularity of the results sufficient for your use?

3. What other data or information would be particularly

useful? How can we provide more helpful information

that can be applied to your needs?

4. How would/will you use data on GEB potential? (Inform

goal-setting? Inform program design?)

Assessing the potential for energy flexibility from buildings at the regional level

Chioke Harris2

with Jared Langevin1, Handi Putra1, Andrew Speake2, Elaina Present2, Rajendra

Adhikari2, and Eric Wilson2

1 Lawrence Berkeley National Laboratory

2 National Renewable Energy Laboratory

Motivating question

How much can grid-interactive

efficient building technologies impact

system load shape in the SRVC

region?

What is the electric load “resource” available from buildings if

they can operate flexibly?

• Buildings comprise 75% of U.S. electricity demand.

• Demand-side flexibility can support variable renewable electricity penetration cost-effectively.

• The magnitude of the potential grid resource from flexible building technologies has not yet been quantified.

Comparison of the costs per MWh of shifting renewable energy from generation sources, and battery storage/distributed energy resources.

Aggregated demand-side flexibility resources are found to be cost-effective and frequently cheaper than the generation alternative. Source: McKinsey.

A bottoms-up stock-and-flow model of buildings is used to evaluate the time- and location-specific effect of efficiency, flexibility measures

1. Define individual measures, rolled up into three (3) measure portfolios:

2. Develop 8760 (hourly) fractions of annual baseline electricity demand by location, building type, and end use

3. Develop bottom-up EnergyPlus measure simulations and hourly savings fractions based on regional system needs

4. Translate measures to Scout and assess regional/national portfolio potential, annually and sub-annually (2015-2050)

* “Flexibility” measures can reduce load during peak hours (“shed”) or move electricity use out of the peak period (“shift”).

Further details on demand flexibility can be found in the Building Technologies Office Grid-interactive Efficient Buildings Overview.

• energy efficiency (EE)

• demand flexibility* (DF)

• EE + DF

Residential measures were modeled using ResStock

Scenario Measure Name End Use(s) Description

Energy Efficiency (EE)

Scout “Best Available” ECM portfolio

All major end usesCurrent best available residential efficiency ECMs, definitions posted on Scout GitHub repository

Programmable thermostat (PCT) setups and setbacks

HVACApply thermostat setups and setbacks while maintaining temperature setpoint diversity

Demand Flexibility (DF)

PCT + pre-cooling and heating HVAC Decrease/increase temperature set points during peak period

Grid-responsive water heater Water HeatingIncrease temperature setpoint at beginning of take period, decrease setpoint at beginning of peak period

Grid-responsive washer/dryer, variable-speed pool pump

AppliancesShift washer/dryer cycles and pool pump power to off-peak hours

Low priority plug load adjustments ElectronicsShift or switch off/unplug some low-priority electronics during peak hours (e.g., TVs, set top boxes, laptops/PCs)

EE + DF

PCT + pre-cool/heat + efficient envelope and HVAC equipment

HVAC, LightingCombine EE HVAC and envelope upgrades with DF HVAC controls

Grid-responsive cycling/control + efficient equipment

Appliances, WH, Electronics

Combine DF WH, appliance, and electronics strategies with most efficient equipment

All remaining EE ECMs Refrigeration Account for efficiency outside of other EE+DF measures

ResStock, a framework for simulating a statistically representative sample of residential buildings in OpenStudio

and EnergyPlus, was used to explore the effect of various measures on hourly residential building energy use.

Commercial measures were modeled with prototype buildings

Scenario Measure Name End Use(s) Description

Energy Efficiency (EE)

Scout “Best Available” ECM portfolio

All major end uses

Current best available commercial ECMs, definitions posted on Scout GitHub repository

Demand Flexibility (DF)

Global temperature adjustment (GTA)

Increase zone temperature set points for one or more peak hours

GTA + pre-cooling Decrease zone set points prior to peak period

GTA + pre-cooling + ice storage Charge ice storage overnight and discharge during peak period

Continuous dimming LightingDim lighting, and shut off lighting in unoccupied spaces, for one or more peak hours

Low priority device switching ElectronicsSwitch off low-priority devices (e.g., unused PCs, equipment) for one or more peak hours

EE + DF

GTA + pre-cool/heat + efficient envelope and HVAC equip.; daylighting controls + dimming

HVAC, LightingCombine DF HVAC/lighting strategies with more efficient envelope/equipment, daylighting, and controls to maximize EE and DF

Device switching + efficient electronics

ElectronicsCombine DF electronics strategy with the most efficient electronic equipment

All remaining EE ECMsRefrigeration,

WHAccount for efficiency outside of combined EE+DF measures above

The Commercial Prototype Reference Models were used with OpenStudio and EnergyPlus to explore the effect

of various measures on hourly commercial building energy use.

County-level

mapping

Results are generated across multiple scales, each with specific

geographic resolution, and aligned by county

Building-scale simulation

Tools: ResStock, DOE Commercial Prototype Models

Boundaries: Representative cities for 14 mainland ASHRAE 90.1-2016 climate zones

December 2018

U.S. Energy Information Administration | Electricity Market Module of the National Energy Modeling System: Model Documentation 2018 6

Figure 3. Market model supply regions

1 ‐ Texas Reliability Entity (ERCT) 2 ‐ Florida Reliability Coordinating Council (FRCC) 3 ‐ Midwest Reliability Organization / East (MROE) 4 ‐ Midwest Reliability Organization / West (MROW) 5 ‐ Northeast Power Coordinating Council / New England (NEWE) 6 ‐ Northeast Power Coordinating Council / NYC‐Westchester (NYCW) 7 ‐ Northeast Power Coordinating Council / Long Island (NYLI) 8 ‐ Northeast Power Coordinating Council / Upstate New York (NYUP) 9 ‐ ReliabilityFirst Corporation / East (RFCE) 10 ‐ ReliabilityFirst Corporation / Michigan (RFCM) 11 ‐ ReliabilityFirst Corporation / West (RFCW) 12 ‐ SERC Reliability Corporation / Delta (SRDA) 13 ‐ SERC Reliability Corporation / Gateway (SRGW) 14 ‐ SERC Reliability Corporation / Southeastern (SRSE) 15 ‐ SERC Reliability Corporation / Central (SRCE) 16 ‐ SERC Reliability Corporation / Virginia‐Carolina (SRVC) 17 ‐ Southwest Power Pool Regional Entity / North (SPNO) 18 ‐ Southwest Power Pool Regional Entity / South (SPSO) 19 ‐ Western Electricity Coordinating Council / Southwest (AZNM) 20 ‐ Western Electricity Coordinating Council / California (CAMX) 21 ‐ Western Electricity Coordinating Council / Northwest Power Pool Area (NWPP) 22 ‐ Western Electricity Coordinating Council / Rockies (RMPA)

Region-scale simulation

Tools: Scout

Boundaries: 22 U.S. EIA Electricity Market Module (EMM) regions

2A (FRCC)

Hour Ending (Local Standard Time)

1 3 5 7 9 11 13 15 17 19 21 23

Winter Month

Peak DayTypical WeekdayTypical Weekend

Avg. Peak Load Hr./RangeAvg. Low Load Hr./Range

2B, 4B (AZNM)

1 3 5 7 9 11 13 15 17 19 21 23

Winter Month

Avg. Peak Load Hr./RangeAvg. Low Load Hr./RangePeak DayTypical WeekdayTypical Weekend

3A−1 (SRVC)

Hour Ending (Local Standard Time)Fr

1 3 5 7 9 11 13 15 17 19 21 23

Winter Month

3A−2 (SPSO)

1 3 5 7 9 11 13 15 17 19 21 23

−0.2

Winter Month

3B (ERCOT)

1 3 5 7 9 11 13 15 17 19 21 23

Winter Month

3C (CAMX)

1 3 5 7 9 11 13 15 17 19 21 23

Winter Month

4A−1 (NYCW)

1 3 5 7 9 11 13 15 17 19 21 23

Winter Month

4A−2 (SPNO)

1 3 5 7 9 11 13 15 17 19 21 23

−0.4

−0.2

Winter Month

4C, 5C, 6B (NWPP)

1 3 5 7 9 11 13 15 17 19 21 23

Winter Month

5A−1 (NYUP)

1 3 5 7 9 11 13 15 17 19 21 23

Winter Month

5A−2 (RFCW)

1 3 5 7 9 11 13 15 17 19 21 23

Winter Month

5B (RMPA)

1 3 5 7 9 11 13 15 17 19 21 23

Winter Month

6A−1, 7 (MROW)

1 3 5 7 9 11 13 15 17 19 21 23

Winter Month

6A−2 (NEWE)

1 3 5 7 9 11 13 15 17 19 21 23

Winter Month

Net load shapes projected for 2050 for summer and winter

inform flexible measure operation

Data: EIA EMM, projection year 2050

• Flexibility measures are designed to remove load during net peak periods and build load during low net demand periods (if possible), flattening the net load shape.

• The year 2050 is used because it includes the highest renewable penetration levels.

2A (FRCC)

1 3 5 7 9 11 13 15 17 19 21 23

Summer Month

2B, 4B (AZNM)

1 3 5 7 9 11 13 15 17 19 21 23

Summer Month

3A−1 (SRVC)

1 3 5 7 9 11 13 15 17 19 21 23

Summer Month

3A−2 (SPSO)

1 3 5 7 9 11 13 15 17 19 21 23

−0.2

Summer Month

3B (ERCOT)

1 3 5 7 9 11 13 15 17 19 21 23

Summer Month

3C (CAMX)

1 3 5 7 9 11 13 15 17 19 21 23

Summer Month

4A−1 (NYCW)

1 3 5 7 9 11 13 15 17 19 21 23

Summer Month

4A−2 (SPNO)

1 3 5 7 9 11 13 15 17 19 21 23

−0.4

−0.2

Summer Month

4C, 5C, 6B (NWPP)

1 3 5 7 9 11 13 15 17 19 21 23

Summer Month

5A−1 (NYUP)

1 3 5 7 9 11 13 15 17 19 21 23

Summer Month

5A−2 (RFCW)

1 3 5 7 9 11 13 15 17 19 21 23

Summer Month

5B (RMPA)

1 3 5 7 9 11 13 15 17 19 21 23

Summer Month

6A−1, 7 (MROW)

1 3 5 7 9 11 13 15 17 19 21 23

Summer Month

6A−2 (NEWE)

1 3 5 7 9 11 13 15 17 19 21 23

Summer Month

SRVC, Climate Zone 3A — Summer

Hour Ending (Local Standard Time)Hour Ending (Local Standard Time)Fr

SRVC, Climate Zone 3A — Winter

Current limitations

• Primary focus is on technical potential results

• Results do not generally consider market conditions, consumer preferences,

payback period, or price elasticity

• Measures are based on the highest performance technologies currently

available

• Does not include prospective technologies currently in development

• Measure operation is not based on real-time signals

• Flexible operation is defined based on preset net peak (high demand) and

low demand periods set by EMM region

EE measures reduce load throughout the day, DF measures

decrease load at peak times

0 4 8 12 16 20

Time of Day0 4 8 12 16 20

Combining EE and DF measures together has the effect of

combining the savings from EE with the shift/shed of DF

0 4 8 12 16 20

Time of Day0 4 8 12 16 20

Efficiency and flexibility are complementary for peak demand

reduction

Data: Scout Acronyms: Energy Efficiency (EE), Demand Flexibility (DF)

Increased demand

Decreased demandDecreased electricity use

Increased demand

Decreased demand

*DRAFT*

EE+DF DF EE−100

Scenario

Annual Impacts (2020)

Technical Potential (TP)

EE+DF DF EE−30

Scenario

Net Peak Period Impacts (2020)

TP−SummerTP−Winter

−15−11

−9 −8

EE+DF DF EE−30

Scenario

Low Net Demand Period Impacts (2020)

−9 −8

−11 −10

EE+DF DF EE−100

Scenario

EE+DF DF EE−30

Scenario

−15−11

−9 −8

Residential (New)Residential (Existing)Commercial (New)Commercial (Existing)

EE+DF DF EE−30

Scenario

−9 −8

−11 −10

Residential buildings drive changes in load across metrics

*DRAFT*

EE+DF DF EE−100

Scenario

ricity

EE+DF DF EE−30

Scenario

−15−11

−9−4

−9 −8

HeatingCoolingVentilationLighting

Water HeatingRefrigerationPlug LoadsOther

EE+DF DF EE−30

Scenario

−9 −8

−11 −10

Cooling and heating drive peak reductions, water heating adds

*DRAFT*

Residential measures show a diversity of summer peak and

total impacts among scenarios

●● ●● ● ●●0

−0.5 0.0 0.5 1.0 1.5 2.0Avg. Decrease in Summer Net Peak Demand (GW)

ricity

h) ● DF

End Use●

Appliances

Envelope

Lighting

Plug Loads

Pool Pumps

Refrigeration

Water Heating

●● ●● ● ●●0

ricity

h) ● DF

End Use●

Appliances

Envelope

Lighting

Plug Loads

Pool Pumps

Refrigeration

Water Heating

Scenario

Efficiency measures that yield larger decreases in overall

demand tend to also show larger decreases in peak demand

ricity

End UseAppliances

Envelope

Lighting

Plug Loads

Pool Pumps

Refrigeration

Water Heating

●● ●● ● ●●0

ricity

h) ● DF

End Use●

Appliances

Envelope

Lighting

Plug Loads

Pool Pumps

Refrigeration

Water Heating

Scenario

Demand flexibility measures do not substantially influence

total annual electricity use

●● ●● ● ●●0

ricity

End Use●

Appliances

Plug Loads

Pool Pumps

Water Heating

●● ●● ● ●●0

ricity

h) ● DF

End Use●

Appliances

Envelope

Lighting

Plug Loads

Pool Pumps

Refrigeration

Water Heating

Scenario

Combining efficiency and flexibility splits the difference in

peak demand reductions and annual electricity use reductions

ricity

End UseAppliances

Plug Loads

Pool Pumps

Water Heating

●● ●● ● ●●0

ricity

h) ● DF

End Use●

Appliances

Envelope

Lighting

Plug Loads

Pool Pumps

Refrigeration

Water Heating

Scenario

Residential measures show a diversity of summer peak and

total impacts among scenario

●● ●● ● ●●0

ricity

h) ● DF

End Use●

Appliances

Envelope

Lighting

Plug Loads

Pool Pumps

Refrigeration

Water Heating

●● ●● ● ●●0

ricity

h) ● DF

End Use●

Appliances

Envelope

Lighting

Plug Loads

Pool Pumps

Refrigeration

Water Heating

Scenario

InsulationWindowsAir Sealing

Heat Pump Water Heater

Air Source Heat Pump

Preconditioning

Central AC

Clothes Dryer

Commercial measures also show significant diversity,

particularly among various HVAC strategies

●●

ricity

h) Portfolio● DF

End Use●

Envelope

Lighting

Plug Loads

Refrigeration

Water Heating

DF, EE+DF Plug Loads

Ice StorageHVAC + Ice Storage

HVAC + GTAHVAC + GTA + Precooling

HVAC + Precooling

All Envelope

WindowsOpaque Envelope

Scenario

Conclusion

Results from Scout can help quantify the potential for individual technologies, portfolios to impact peak, total electricity use

A quantitative framework was established for time-sensitive evaluation of

building efficiency and flexibility measures, with results shown for a single

region (and available for other regions of the U.S.)

• Adapts the Scout impact analysis software to enable assessment of hourly building

electricity use under baseline conditions and with efficiency/flexibility measure adoption

• Leverages ResStock (residential) and DOE Prototype Models (commercial) to develop

hourly baseline and measure electric load shapes across 14 climate zones

Initial results show a large potential peak reduction resource from buildings,

interactions between efficiency and flexibility

Residential and commercial cooling, residential heating, and commercial plug

loads show large potential for impacts on peak electricity demand and overall

electricity use

Discussion1. Would you benefit from seeing similar results, specific

to your region?

2. Is the granularity of the results sufficient for your use?

3. What other data or information would be particularly

useful? How can we provide more helpful information

that can be applied to your needs?

4. How would/will you use data on GEB potential? (Inform

goal-setting? Inform program design?)

Additional Tool Development

Additional Tool Development and Next Steps

NREL is preparing to conduct additional analysis in 2020/2021 expanding the Scout modeling to provide:

• Estimate upper and lower bound of customer participation for various DR technologies.

• Speed of adoption of DR technologies and diffusion into customer base.

• Analysis of elasticity of customer participation when influenced through changes in compensation (i.e., rates, tariffs)

• Using this analysis, we are hoping to develop a tool or simple web-app allowing states and utilities to simulate novel compensation mechanisms and estimate their impact on participation.

Additional Tool Development and Next Steps

In order to conduct this analysis and begin developing a tool, NREL is looking for data and input including:

• Any anonymized data on residential or commercial utility demand response programs (i.e., participation rates, compensation structures, availability)

• Data detailing customer participation in any ‘EV’ or ‘smart’ rates.

Additionally, and fitting with the larger technical assistance efforts of GEB, NREL is looking for an initial cohort of users that may be interested in piloting and contributing to the specification of the tool.

This work was authored by Alliance for Sustainable Energy, LLC, the manager and operator of the National Renewable Energy Laboratory for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308 and by The Regents of the University of California, the manager and operator of the Lawrence Berkeley National Laboratory for the DOE under Contract No. DE-AC02-05CH11231. Funding was provided by the DOE Office of Energy Efficiency and Renewable Energy Building Technologies Office. The views expressed in this presentation and by the presenter do not necessarily represent the views of the DOE or the U.S. Government.

Thank youChioke Harris chioke.harris@nrel.gov

Joyce McLaren joyce.mclaren@nrel.gov

Sam Koebrich sam.koebrich@nrel.gov

Scout: scout.energy.gov

ResStock: www.nrel.gov/buildings/resstock.html

Commercial Prototypes:https://www.energycodes.gov/development/commercial/prototype_models

Technical Assistance for States: Grid-Interactive Efficient Buildings · 2020-07-17 · *...

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