NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Value of Demand Response: 

Quantities from Production

Cost Modeling

Marissa Hummon

PLMA Spring 2014

April 15-16, 2014

Denver, CO

NREL/PR-6A20-61815
2

Background

DOE-led, multiple national laboratory 

research project investigating the 

potential of DR to provide services to 

the grid.

Resolution: Balancing areas (in WECC), 

hourly data, zonal constrained 

transmission between BAs

More information (publications and 

presentations):

https://www1.eere.energy.gov/analysis/response

_storage_study.html

Assess potential of load to 

provide: energy, regulation, 

contingency, and load-following

Model the performance of DR in 

a production cost model

Market & policy barriers to DR 

entry into wholesale markets
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DR Grid Service Availability

DR availability for energy is constrained by a hourly profile that is a fraction of the total end use 

load. Example: Commercial Space Cooling has a peak “sheddability” that is acceptable and 

controllable of 12.5% of the total commercial cooling load. [see Olsen, et al. 2013]
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DR Grid Service Availability

Load shedding, in some types of DR, results in a shift of load. We expect commercial buildings to 

primarily use a pre-cooling strategy between the hours of 6 am and 6 pm. The system operator 

optimizes the load shedding and shifting to minimize the overall system cost.
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DR Grid Service Availability

End use loads can provide ancillary services, depending on the control technologies. We use these 

four profiles to define the maximum availability of DR to provide each grid service: energy, 

regulation, contingency, and load-following.
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DR Grid Service Availability

The sum of the DR allocations across energy and ancillary services is constrained by the maximum 

availability of end use load. All services were bid at $0/MW-h, and thus were preferentially selected 

to be “first” in the dispatch/service stack.
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DR Modeling

• Commercial

o Space cooling, space 

heating, lighting, ventilation

• Residential

o Space cooling, space 

heating, water heating

• Municipal

o Freshwater pumping, 

highway lighting, wastewater 

pumping

• Industrial

o Agricultural irrigation 

pumping, data centers, 

refrigerated warehouses

Demand response, from a modeling 

perspective, is very similar to storage.

Implemented in PLEXOS – production 

cost model – DR is modeled as a 

virtual, co-optimized storage device 

(dis-charge, charge) with hourly 

profiles defined for energy and each 

ancillary service.

Hour 1 Hour 2 Hour 3

Storage Capacity

from Loads
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Modeling Results: Residential Water Heating

The allocation of residential water heating demand response for all grid services (top) always uses the 

full capacity of the DR resource. The marginal cost of each service (bottom) explains the hour(s) of 

energy provision from water heaters and the majority of the remaining hours is allocated to 

regulation reserves.
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Modeling Results: Residential Water Heating

Average daily and hourly allocation is optimized by the model against the net load of the system (load 

minus solar and wind generation). As the renewable penetration increases, or the ratio of solar to 

wind generation changes, the daily and seasonal use of DR will change.
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Value of Demand Response

Value to Generation 

System Value to Load

Production cost savings

• Avoided Fuel Off take

• Avoided Generator Startups and 

Shutdowns

• Avoided Generator Ramping

Revenue: 

• $/kW (peak capacity) of end use 

offered to system

• $/end use enabled

• $/MW-h of grid service provided

Production cost models optimize the 

total cost (fuel, starts, VO&M, and wear 

& tear bids) of producing energy under 

transmission, generator operation, and 

other defined constraints.

Revenue is based on the marginal cost 

of the grid service (during each hour) 

multiplied by the provision of that 

service. Marginal costs represent the 

production cost of providing the next 

unit of energy/reserves – and 

therefore are generally an 

overestimate of the total production 

cost.
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Value to the System Operator

Production Cost [M$] Base Case Base Case 

with DR

Decrease in 

Cost with DR

Fuel Cost 1215.0 1208.0 -7 / -0.6%

Variable O&M Cost 151.8 152.2 0.4 / 0.3%

Start & Shutdown Cost 58.4 58.7 0.4 / 0.6%

Regulation Reserve Bid Price 4.5 2.9 -1.7 / -36.8%

Total Generation Cost 1429.7 1421.8 -7.9 / -0.6%
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Value to the System Operator

Production Cost [M$] Base Case Base Case 

with DR

Decrease in Cost 

with DR

Fuel Cost 1215.0 1208.0 -7 / -0.6%

Variable O&M Cost 151.8 152.2 0.4 / 0.3%

Start & Shutdown Cost 58.4 58.7 0.4 / 0.6%

Regulation Reserve Bid Price 4.5 2.9 -1.7 / -36.8%

Total Generation Cost 1429.7 1421.8 -7.9 / -0.6%

Dividing $7.9M in production cost savings by the peak DR capacity

enabled, 293 MW, yields a value of $26.91/kW-yr of DR capacity.

Dividing $7.9M in production cost savings by the total DR provided 

to the system, 682 GW-h, yields a value of $0.01/kWh.

Dividing $7.9M in production cost savings by the total energy DR 

provided to the system, 116 GWh, yields a value of $0.07/kWh.
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Value to Load

• Revenue is based on 

the marginal cost of 

the grid service (during 

each hour) multiplied 

by the provision of that 

service. Marginal costs 

represent the 

production cost of 

providing the next unit 

of energy/reserves –

and therefore are 

generally an 

overestimate of the 

total production cost.

• The annual revenue 

per unit of grid service 

provided by DR is fairly 

constant because the 

marginal cost for each 

grid service is fairly 

constant.
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Value to Load

• Revenue can be 

attributed to a particular 

grid service. Energy 

service revenue include 

pre- or re-charge costs.

• Revenue per peak kW of 

capacity is closely 

related to the 

availability factor –

equivalent to the 

capacity factor of a 

generator. 

• Some DR resources are 

more flexible or better 

correlated with system 

requirements.

• Revenue per annual 

availability 

demonstrates the 

“premium” of such 

resources.
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Value to Load

Value Metric Residential Cooling Residential Water Heating

Revenue per peak capacity $5/kW-year $45/kW-year

Revenue per annual 

availability

$15/MW-h $31/MW-h

Revenue per enabled 

capacity

$3.1/kW-year $0.7/kW-year

Revenue per unit $7.4/unit-year $3.3/unit-year

• Cost – benefit 

analysis requires 

understanding the 

cost of enabling 

the service to the 

grid and the 

benefit accrued by 

providing the 

service.

• Example: 

residential space 

cooling has a 

higher value per 

unit enabled, while 

water heating has a 

higher value per 

annual availability.
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Conclusions

• DR benefits:

o The system benefits by reducing production cost – mainly avoided fuel costs

o Loads providing DR have potential for multiple 

revenue streams

• Modeling DR with increased fidelity enables 

more detailed observations, such as:

o revenue per kilowatt of enabled DR capacity 

varies significantly across the resources from 

less than $1/kW-year to more than $65/kW-year

o across all DR resources, only 20% of the revenue 

came from the energy market, while more than 

50% of revenue came from the regulation 

reserve market and the remainder from the 

contingency reserve market

• Modeling DR with increased fidelity paves the 

way for sensitivity analysis across renewable 

penetration, grid operation, and evolution of 

load

Thank you!

Questions/comments:

Marissa Hummon

marissa.hummon@nrel.gov

NREL team:

Paul Denholm, Marissa 

Hummon, Jennie Jorgenson, 

and David Palchak

LBNL team:

Sila Kiliccote, Daniel Olsen, 

Nance Matson, Cody Rose, 

Michael Sohn

DOE: Ookie Ma