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Contract No. DE-AC36-08GO28308

Conference Paper

NREL/CP-5D00-70197

February 2019

Evaluating the Impact of Price-Responsive 

Load on Power Systems Using Integrated 

T&D Simulation

Preprint

Himanshu Jain, Bryan Palmintier, Dheepak Krishnamurthy,

Ibrahim Krad, and Elaine Hale

National Renewable Energy Laboratory

Presented at the 2019 IEEE Conference on Innovative Smart Grid Technologies 

(IEEE ISGT)

Washington, D.C.

February 17–20, 2019
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Operated by the Alliance for Sustainable Energy, LLC

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Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

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Conference Paper

NREL/CP-5D00-70197

February 2019

Evaluating the Impact of Price-Responsive 

Load on Power Systems Using Integrated 

T&D Simulation

Preprint

Himanshu Jain, Bryan Palmintier, Dheepak Krishnamurthy,

Ibrahim Krad, and Elaine Hale

National Renewable Energy Laboratory

Suggested Citation

Jain, Himanshu, Bryan Palmintier, Dheepak Krishnamurthy, Ibrahim Krad, and Elaine Hale. 

2019. Evaluating the Impact of Price-Responsive Load on Power Systems Using Integrated 

T&D Simulation: Preprint. Golden, CO: National Renewable Energy Laboratory. 

NREL/CP-5D00-70197. https://www.nrel.gov/docs/fy19osti/70197.pdf.
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Evaluating the Impact of Price-Responsive Load on Power Systems Using 

Integrated T&D Simulation

Himanshu Jain, Bryan Palmintier, Dheepak Krishnamurthy, Ibrahim Krad, Elaine Hale

National Renewable Energy Laboratory (NREL)

Golden, CO, USA

Abstract—This paper explores the differences between simulating 

price-responsive load (PRL) interactions with power systems 

using integrated transmission and distribution (T&D) models and 

transmission-only (T-only) models. This analysis uses the 

Integrated Grid Modeling System (IGMS) software to capture

“ISO-to-appliance” simulations using a synthetic T&D model 

built on the PJM 5-Bus transmission network with multiple fullscale taxonomy feeders that include physics-based models of

thousands of customers and PRLs. The results show important 

differences in the impacts of PRLs between integrated T&D and 

T-only models. Experiments with the synthetic integrated T&D 

dataset demonstrated that integrated T&D simulation revealed 

notably larger differences between the PRL and no-PRL cases for 

load, and prices compared to T-only simulation. Similarly, 

differences are observed between the price response of individual

buildings and distribution feeders and the corresponding 

transmission bus in the integrated T&D simulations, which are

difficult to capture in traditional T-only simulations.

Index Terms—flexible demand, co-simulation, distributed power 

generation, high performance computing, integrated 

transmission-distribution simulation

I. INTRODUCTION

In recent years, integrated Transmission (T) and 

Distribution (D) simulation has emerged as a new approach for 

power systems analysis [1]–[4]. These efforts are motivated by 

a blurring of the distinctions between T and D operations and

markets driven by increasing penetration levels of distributed 

energy resources (DERs). Moreover, the deployment of smart 

metering infrastructure provides a technological framework 

that can enable consumers to directly respond to electricity 

markets by controlling load such as heating ventilation and airconditioning (HVAC) in response to price. Using integrated 

T&D models, power systems stakeholders can simulate the 

interaction between price-responsive load (PRL) and modern 

power systems with more fidelity than is possible with T or D 

models alone.

Although several past studies have explored the impact of 

PRL on power systems, most used transmission only (T-only) 

models [5]–[7], and a few used distribution-only models [8], 

[9]. As described in [1], such separate models do not capture 

the closed loop interactions between T and D. Moreover, in 

these efforts the price-responsive end-use equipment was either 

not modeled in detail [5]–[8], or a simplified representation was

used where the impact of changes in the state of the distribution 

network on their power consumption was neglected [10].

In this paper we show that using an integrated T&D model 

to simulate the impact of PRL on power systems can help 

overcome these limitations. We also show that the integrated 

T&D model provides a more accurate and high-resolution view 

of the behavior of each PRL than is possible with a T-only 

model that cannot model each PRL individually and must make 

an assumption regarding the aggregate price-responsive 

behavior of PRLs at each transmission bus.

The remainder of this paper is organized into four sections

including this Introduction. Section II discusses the modeling 

methodology used to create integrated T&D and T-only

models, with particular focus on the modeling of PRLs. Section 

III presents the results of the simulations, focusing on the 

differences observed between integrated T&D models and Tonly models to study the impact of PRL on power systems. 

Section IV concludes and discusses ways to further improve the 

simulation of PRL using integrated T&D models.

II. DEVELOPING INTEGRATED T&D AND T-ONLY MODELS

A. Integrated T&D Model

The synthetic integrated T&D model used in this study is

referred to as the 5-Bus/11-Feeder System. The transmission 

topology in the system was modeled using the PJM 5-bus 

transmission system [11], where the lumped loads were

replaced with taxonomy feeders developed by the Pacific 

Northwest National Laboratory [12]. To restrict the size of the 

resulting T&D system, 11 taxonomy feeders were used to 

replace the lumped load and a scaling factor of 15.70 was 

applied to the power flowing through the feeder head before it 

was sent to the transmission network. The integrated T&D 

model consisted of 27,000 distribution nodes, over 25,000 

distribution lines and transformers, and more than 9,000 

residential and commercial buildings. Detailed description of 

the 5-Bus/11-Feeder System can be found in [13].

The integrated T&D model is simulated in the Integrated 

Grid Modeling System (IGMS) software, which co-simulates 

GridLAB-D [14], MATPOWER [15] and the Flexible Energy 

Scheduling Tool for Integrating Variable Generation (FESTIV) 

[16] to perform quasi steady-state simulations [1].

B. Load Modeling in the Integrated T&D Model

The integrated T&D model uses a bottom-up approach, in 

which the load at the transmission buses is obtained from 

detailed models of distribution feeder primary and secondary 

circuits complete with individual PRLs such as HVACs, 

dishwashers, and water-heaters, and additional ZIP loads. To 

overcome the challenge of modeling thousands of individual 

loads we use glmgen [17] to automate the distribution feeder 

creation process [1]. The bottom-up load modeling approach is 

This work was supported by the U.S. Department of Energy (DOE) Solar 

Energy Technologies Office award DE-EE0001748 and the Grid 

Modernization Laboratory Consortium. It was conducted by the National 

Renewable Energy Laboratory, operated for the U.S. Department of Energy

(DOE) by the Alliance for Sustainable Energy, LLC under Contract No. DOEAC36-08GO28308. The U.S. Government retains and the publisher, by 

accepting the article for publication, acknowledges that the U.S. Government 

retains a nonexclusive, paid up, irrevocable, worldwide license to publish or 

reproduce the published form of this work, or allow others to do so, for U.S. 

Government purposes. 
2

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particularly useful for studying the impact of PRLs on power 

systems as we can directly capture the physical state 

dependence of their response to prices, and the resulting impact 

on transmission and distribution networks. In this paper, the 

price-responsive behavior is modeled only for HVACs.

C. Load Modeling in the T-Only Model

The T-only model used the identical transmission network 

model as was used in the PJM 5-Bus/11-Feeder system. The 

load in the T-only model was aggregated at the three 

transmission buses (B2, B3, and B4) using the constant power 

load model. The time-varying load profiles at each of the three 

transmission buses (B2, B3, and B4) were obtained by first 

calculating the real and reactive power flows at the head of each 

of the feeders using separate off-line GridLAB-D simulations

and then summing to compute the resulting total power flow at

each transmission bus. These separate GridLAB-D simulations 

assume the transmission bus voltages of one per unit on the 

high-voltage side of the feeder-head transformer. The T-only 

model was simulated in FESTIV.

D. Price-responsive Controller for HVAC Loads in 

Integrated T&D Model

The Passive Controller of GridLAB-D was used to model 

the price-responsive behavior of HVAC loads in the integrated 

T&D model [18], [19]. The price passed on to the individual 

passive controllers was the LMP of the transmission bus to 

which the HVACs were connected via the distribution feeders. 

The detailed description of the passive controller can be 

found in [18], and the operation of the controller can be 

understood from (1), which describes the RAMP control mode 

of the passive controller [18] to calculate the new temperature 

setpoint for the HVAC system. In (1), either the {RRhiiiih, ΔTTHH}

pair, or the {RRllllll, ΔTTLL} pair is selected. {RRhiiiih, ΔTTHH} is selected 

if Δpp is positive; {RRllllll, ΔTTLL} is selected if if Δpp is negative.

TTssssss( ) =

⎩

⎨

⎧

TTdddddd( ) + kkΔpp ΔTTHH ≤ kkΔpp ≤ ΔTTLL; σσ( ) ≠ 0

TTdddddd( ) + ΔTTHH kkΔpp > ΔTTHH; σσ( ) ≠ 0

TTdddddd( ) + ΔTTLL kkΔpp < ΔTTLL; σσ( ) ≠ 0

TTdddddd( ) σσ( ) = 0 ⎭

⎬

⎫

(1)

Where,

Δpp = �pp( ) − ppaaaa ( )�; kk = |ΔTTHH oo ΔTTLL |

σσ(tt)∗�RRhiiiih oo RRllooww�

TT ( )=calculated HVAC setpoint temperature; 

TTdddddd( )=desired HVAC setpoint temperature if the passive 

controller were absent; pp( )= price that is passed onto all the 

passive controllers connected to feeders supplied by the 

transmission bus; ppaaaa ( ) =moving average of the price for the 

last XX hours, X here is selected as 12 hours; σσ( )=standard 

deviation of the price calculated using the last XX hours of prices 

and ppaaaa ( ); ΔTTHH= maximum positive offset from TTdddddd( ),

ΔTTHH ≥ 0; ΔTTLL= maximum negative offset from TTdddddd( ), ΔTTLL ≤

0; RRhiiiih and RRllllll=unitless constants, positive for cooling 

mode, and negative for heating mode.

Although (1) governs the setpoint that is applied by the 

passive controller to the HVAC thermostat, the time-interval at 

which the setpoint is applied can be varied for each customer. 

In the paper, the setpoint is applied either at 15, 30, 45 or 60-

minute intervals to avoid too many, or too few HVAC setpoint 

changes, and reflect the diversity in setpoint update interval 

settings that consumers might have in a large power system.

These intervals are assigned randomly to the passive controllers 

governing the HVACs at the start of the simulation, and they 

are not changed during the course of the simulation. The ΔTTHH

values for the controllers used in the paper are normally 

distributed with mean of 2.26 oF and standard deviation of 0.99 

oF. The ΔTTLL values are fixed at -0.005 oF for all the controllers, 

which assumes HVAC setpoints will not be lowered when the 

actual LMP is lower than the trend. The RRhiiiih values are also 

normally distributed with mean of 2.55 and standard deviation 

of 0.20. The RRllllll values are equal to RRhiiiih . To keep the peak 

load close to the maximum system load specified in the PJM 5-

Bus system (1,000 MW), approximately 40% of the HVAC

loads are disabled in the PJM 5-Bus/11-Feeder system.

E. Price-Responsive Behavior of Load in T-Only Model

Because the distribution network is not captured in detail 

within the T-only model, PRL is instead simulated by modeling 

the aggregate load response at the transmission bus level. This 

is done by applying own price elasticities (or simply price 

elasticities) to loads, which modify the loads as the price is 

changed [20]–[23].

The own price elasticity of demand is calculated as the ratio 

of the relative change in demand of a quantity, to the relative 

change in its price, provided that all other factors affecting the 

demand are held constant [24]. Mathematically, the own price 

elasticity can be written as (2):

oo = �

∆DD

DD � / �

∆pp

pp � = ∆DD∆pp .

pp

DD (2)

where, oo is the own price elasticity, DD is the previous 

demand (load), pp is the previous price, ∆DD is the change in 

demand from DD, and ∆pp is the change in price from pp.

The literature suggests that the price elasticities for 

residential and commercial customers vary considerably, 

ranging from slightly less than zero to -0.4 in most cases [21]–

[24]. Given this, it is important to model a similar level of 

responsiveness in the T&D and T-only simulations. This can be 

done by running a number of T-only simulations at different 

levels of demand elasticity that cover the range of interest [23], 

or by using the load and price data obtained from the integrated 

T&D simulation to calculate price elasticities. We opted for the 

latter approach to provide more comparable, model-specific

values for price elasticity. Moreover, in the absence of data 

regarding the distribution network, the number of PRLs, and the 

controllers used in the PRLs, a transmission operator or a 

planner is restricted to using aggregate demand and real-time 

LMP data to estimate the demand elasticity. Price elasticity 

estimated using this approach, however, is an approximation 

because the conditions under which (2) is valid are difficult to 

fulfill. These conditions are (i) price should be the only

independent variable, and (ii) price should change at the instant 

when the price elasticity is calculated.

To help isolate the effect of price in calculating the price

elasticity, the change in load is calculated as the difference 

between the average load in the 5-minutes just preceding and 
3 

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succeeding the instant of the change in the LMP. The short time

duration limits the impact of weather on the change in power 

demand, while allowing the impact of instantaneous change in 

HVAC power demand to be included as these are switched ON 

or OFF. Using the average load of the 5-minutes interval 

immediately after the change in real-time LMP also removes 

any short-term impact of PRLs on the LMP, because the new 

real-time LMP will not have been computed/applied until after 

the end of the 5-minute interval. However, the impact of timevarying non-price responsive load cannot be eliminated in this 

approach, as separate data regarding the distribution of load is 

lost when the load is aggregated at the transmission buses. 

The real-time LMPs and aggregated loads at each of the 

three transmission buses obtained from the integrated T&D 

simulations are used for calculating the price elasticities. It is 

assumed the operator has no knowledge about the PRLs or their 

temperature update intervals. 

The average price elasticities (averaged over the instants 

when real-time LMPs changed) calculated using the above 

approach for the three buses for the 24-hour simulation period 

were found to be -0.46 for bus B2; -0.25 for bus B3; and -0.11 

for bus B4. These values are close to the typical range of price 

elasticities of loads reported in the literature. 

III. RESULTS AND DISCUSSION

For the T-only simulations, price elasticities obtained using 

the methodology described above were used to estimate 

demand for every 15-minute interval, corresponding to the 

smallest of the four intervals at which the HVAC thermostats 

were updated in the integrated T&D simulations. Specifically: 

ቆ௡ ௪௢݁ + 1൭) ∗ ݐ)௘௟௜௙௢௥௣ܦ = (ݐ)௪௡௘ܦ

(ݐ∆ − ݐ)݌ − (ݐ)݌

 (3) ቇ൱) ݐ∆ − ݐ)݌

where, ܦ௘௡௪(ݐ (is the load for a transmission bus that coincides 

with a 15-minite interval; ܦ)௘௟௜௙௢௥௣ݐ (is the load of the 

transmission bus in the absence of PRLs; ݁௢௪௡ is the price 

elasticity for the transmission bus; ݌)ݐ (is the real-time LMP; 

∆ݐ is the real-time economic dispatch time step (5 min). 

1) Differences between Integrated T&D Simulations with 

and without PRL 

Figures 1 and 2 compare the real power demand and the 

real-time LMPs at the transmission buses, obtained from 

integrated T&D simulations with and without PRL. Figure 3 

shows the histogram of change in average power consumption 

of all the buildings modeled in the distribution feeders. 

Tables I and II summarize the differences in operational 

impacts obtained from integrated T&D simulations performed 

in the presence and absence of PRLs. The parameters are 

calculated for all eleven feeders, and also at the three 

transmission buses. The “% Delta” column in tables I and II is 

 ;ܮܴܲ_/ܱܰ(ܮܴܲ_ܱܰ − ܮܴܲ) ∙ 100 = ܽݐ݈݁ܦ % as calculated

“NO_PRL” here and in all the figures and tables in the paper 

refers to the simulations performed in the absence of PRL. 

TABLE I. INTEGRATED T&D SIMULATIONS : DIFFERENCES IN ENERGY 

CONSUMED AND AVERAGE POWER OVER A DAY 

Feeder 

/Bus 

Energy Consumed by Loads 

(MWh) 

Average Power of the Loads 

(MW) 

PRL NO_PRL % Delta PRL NO_PRL % Delta 

1 1149.66 1157.51 -0.68% 47.90 48.23 -0.68% 

2 933.11 939.47 -0.68% 38.88 39.14 -0.68% 

3 1018.63 1025.18 -0.64% 42.44 42.72 -0.64% 

4 1094.01 1101.66 -0.69% 45.58 45.90 -0.69% 

B2 4195.88 4223.49 -0.65% 174.83 176.03 -0.68% 

5 1462.22 1472.14 -0.67% 60.93 61.34 -0.67% 

6 1470.74 1481.07 -0.70% 61.28 61.71 -0.70% 

7 1438.04 1447.27 -0.64% 59.92 60.30 -0.64% 

8 1464.81 1474.53 -0.66% 61.03 61.44 -0.66% 

B3 5836.15 5875.44 -0.67% 243.17 244.83 -0.68% 

9 1222.82 1230.35 -0.61% 50.95 51.26 -0.61% 

10 2935.41 2953.66 -0.62% 122.31 123.07 -0.62% 

11 2726.20 2743.55 -0.63% 113.59 114.31 -0.63% 

B4 6888.09 6928.29 -0.58% 287.00 288.75 -0.60% 

TABLE II. INTEGRATED T&D SIMULATIONS : DIFFERENCES IN 

AVERAGE REAL-TIME LMPS AT LOAD BUSES AND COST OF ELECTRICITY 

OVER A DAY 

Feeder

/Bus 

Average LMP ($) Cost of Electricity (thousand $) 

PRL NO_PRL % Delta PRL NO_PRL % Delta 

1 27.82 28.42 -2.09% 

2 22.44 22.88 -1.95% 

3 24.60 25.10 -1.98% 

4 26.46 27.01 -2.03% 

B2 22.38 22.61 -1.02% 101.31 103.39 -2.01% 

5 35.17 35.76 -1.65% 

6 35.36 35.97 -1.68% 

7 34.49 35.06 -1.62% 

8 35.17 35.77 -1.66% 

B3 23.07 23.35 -1.18% 140.21 142.57 -1.65% 

9 34.16 35.10 -2.68% 

10 79.53 81.54 -2.46% 

11 74.01 75.88 -2.46% 

B4 24.97 25.37 -1.57% 187.70 192.52 -2.50% 

Figure 1. Real Power Demand at the Transmission Buses in Integrated 

T&D Simulations 

Figure 2. LMPs at the Buses B2, B3 and B4 in Integrated T&D Simulations 
4 

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Figure 3. Histogram of Buildings where Average Power calculated over a 

day changed due to PRL in Integrated T&D Simulations 

The key takeaways from the above tables and figures are: 

• Figure 3 shows that the change in power consumption of 

individual buildings varied over a wide range (-5% to 

0.7%). Interestingly, over 3,000 buildings witnessed a 

small increase in power consumption (between 0.1 to 0.2%) 

in the presence of PRLs. 

• The addition of PRL reduced the demand during the 

morning load up-ramp (figure 1). As a result, the LMPs 

(figure 2) were also reduced. The increase in demand 

during the evening load down-ramp was minimal due to the 

very small Δܶ௅ (-0.005 oF) assigned to all the passive 

controllers. This prevented HVAC thermostat setpoints 

from being substantially reduced from their desired 

setpoints resulting in minimal increase in demand. 

• A few spikes are seen in the load profile with PRLs. This 

can be attributed to the switching of states of a cluster of 

HVACs as they respond to the thermostat setpoints set by 

the passive controller, and the weather. 

• Table I shows that at the transmission bus level, the average 

power and the energy consumed at all the three transmission 

buses reduce with the addition of PRL. Table II shows that 

the average LMPs, and the cost of electricity consumption 

(sum of product of LMP and energy) at the transmission 

buses are also reduced when price-responsive behavior of 

HVAC loads in enabled. There is a small variation in the 

magnitudes of changes in power, energy, and cost for the 

feeders when compared with the changes observed at the 

transmission buses. 

2) Differences between T-only and Integrated T&D 

Simulations 

Figures 4 and 5 show the real power demand and the realtime LMPs at the transmission buses obtained from T-only 

simulation with and without PRL. 

In the T-only simulations, price-spikes were observed at a 

couple of instances during the evening load down-ramps when 

PRLs were present (figure 5). These occurred as price elasticity 

caused loads to increase in response to reduced real-time LMPs,

and small amount of load was shed (4 MW at 5:30 p.m.) and 

reserves were deployed (at 8:00 p.m.) to meet the increased 

demand. Since the time of the price spikes did not coincide with 

the 15-minute PRL update interval, the load did not change as 

a consequence of the price-spikes. This can be seen in figure 4, 

where load spikes are not observed in the presence of PRLs. 

Tables III and IV summarize the results obtained from T-only 

simulations after neglecting the price-spikes. Since the T-only

model does not include distribution feeders, only transmission 

bus level values are provided in tables III and IV. 

The following key differences can be observed between the 

integrated T&D simulations and the T-only simulations: 

• Figure 3 revealed wide variation in the change in average 

power at individual buildings between PRL and NO_PRL 

simulations using the integrated T&D model. A large 

number of buildings even witnessed a slight increase in the 

average power when PRLs were enabled in the integrated 

T&D model. Such observations cannot be made from the Tonly model. 

• Comparing tables I and II with tables III and IV reveals 

significant differences between PRL and NO_PRL 

simulations using integrated T&D and T-only models. E.g., 

while the maximum change in the cost of electricity 

between PRL and NO_PRL simulations using the integrated 

T&D model was 2.50% (table II), it was only 0.26% in the 

T-only simulations (table IV). 

• As tables III and IV show, differences in the performance of 

each feeder in the presence and absence of PRLs can be 

obtained directly from integrated T&D simulations. 

However, the transmission bus is the finest level of 

resolution present in the T-only model . 

Figure 4. Real Power Demand at the Transmission Buses in T-only 

Simulations 

Figure 5. LMPs at the Transmission Buses B2, B3 and B4 in T-only 

Simulations 

TABLE III. T-ONLY SIMULATIONS : DIFFERENCES IN ENERGY 

CONSUMED AND AVERAGE POWER OVER A DAY 

Feeder

/Bus 

Energy Consumed by Loads 

(MWh) 

Average Power of the Loads 

(MW) 

PRL NO_PRL % Delta PRL NO_PRL % Delta 

B2 4583.69 4586.21 -0.05% 191.65 191.76 -0.05% 

B3 5433.36 5435.51 -0.04% 227.18 227.27 -0.04% 

B4 6962.24 6964.24 -0.03% 291.10 291.19 -0.03% 
5

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TABLE IV. T-ONLY SIMULATIONS : DIFFERENCES IN AVERAGE REALTIME LMPS AT LOAD BUSES AND COST OF ELECTRICITY OVER A DAY

Feeder

/Bus

Average LMP ($) Cost of Electricity (thousand 

$)

PRL NO_PRL % Delta PRL NO_PRL % Delta

B2 20.728 20.747 -0.09% 110.27 110.52 -0.22%

B3 22.692 22.726 -0.15% 136.07 136.37 -0.22%

B4 23.446 23.486 -0.17% 193.20 193.7 -0.26%

IV. CONCLUSIONS AND FUTURE WORK

The results discussed in section III show that by using high 

resolution integrated T&D models that include detailed models 

of end use appliances and PRLs, the impact of PRLs on 

individual customers, distribution feeders, and the bulk power 

system can be calculated in one simulation. Such detailed 

analysis cannot be performed with T-only models that 

aggregate distribution network load to the transmission load 

buses. The detailed data generated from integrated T&D modelbased simulations can be used by utilities and policy makers to 

design or adjust the real-time pricing tariffs such that the 

benefits of the tariffs for utilities and customers alike can be 

maximized.

While it is possible that with improved estimates of price 

elasticities T-only simulations may provide similar differences 

in loads and real-time LMPs between PRL and no-PRL cases 

as are obtained from integrated T&D simulations, obtaining 

such estimates under high penetration levels of PRLs that 

respond to real-time LMPs is difficult due to the following 

reasons:

• A wide range of price elasticities for residential, commercial 

and industrial loads have been reported in various studies. 

Selecting an appropriate price elasticity from the reported 

values is therefore difficult. Even if a value each is selected 

for residential, industrial, and commercial loads, it may not 

reflect the price-response of loads modeled in the study 

system as was seen in figure 3 where a large number of loads 

witnessed a slight increase in their power consumption 

when price-responsive behavior of loads was modeled.

• If load and price data generated from a simulation is used to 

calculate the price elasticities, the simulation should model 

the T and D networks, the electricity market, and the 

behavior of PRLs with reasonable accuracy. Therefore, 

some form of integrated T&D model is needed. 

• Since price elasticity of load is a function of the 

“equilibrium point”, i.e., the price and load at which it is 

calculated and price should be the only independent variable 

when calculating the price elasticity, it is not easy to extract 

correct price elasticities from integrated T&D simulations. 

Even if the price elasticities are obtained, they need to be 

recalculated when system operating conditions, such as 

demand, generation characteristics, network topology, and 

price-responsive controllers change.

If improved price elasticity estimates are obtained by 

overcoming the above challenges, T-only simulations may 

provide differences in the aggregate power system response 

between the PRL and no-PRL cases that are similar to those 

observed in integrated T&D simulations. However, variation in 

the responses of individual buildings, as seen in figure 3 cannot 

be obtained with T-only simulations.

Preliminary simulations have been performed to evaluate 

the interactions between PRL and high DER penetration levels

using integrated T&D models. At present, the market 

simulations do not directly consider DER generation and PRLs 

while making the day-ahead and real-time unit commitment 

decisions. Work is in progress to include this capability in the 

modeling tools with a hope of identifying best practices for 

incorporating DERs and PRLs into the day-ahead and real-time 

unit commitment. Once included, this capability will allow 

better evaluation of the interactions between DERs and PRLs

and the ability of PRLs to increase load during low LMPs to 

prevent/reduce events where minimum generation limits of 

conventional generation are reached.

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