• The snapshot and QSTS PV Hosting Capacity studies were 

compared on a feeder model developed using real feeder data in the 

United States. 

• The detailed feeder model consists of the primary network and added 

secondaries as well.

• Out of the 525,600 time points analyzed about 0.73% had ANSI 

Range A over voltage violations but only 0.0057% (~30) time points 

had the more serious Range B over-voltage violations. 

• These can be attributed to sudden load drops which caused the 

quickly regulated voltage spikes. 

Innovative Smart Grid Technologies (ISGT 2019)

Washington, D.C

February 17-20, 2019

NREL/PO-5D00-73304

Snapshot PV Hosting Capacity

Quasi-Static Time-Series Photovoltaic Hosting 

Capacity Methodology and Metrics

Akshay Kumar Jain, Kelsey Horowitz, Fei Ding, Nicolas Gensollen, Barry Mather, Bryan Palmintier

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, 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.

QSTS PV Hosting Capacity Challenges

QSTS PV Hosting Capacity Metrics

Snapshot vs QSTS PV Hosting Capacity

References

Test Feeder

• To get the most accurate PV hosting capacity results, the QSTS 

simulations should be conducted at a time resolution that can 

adequately capture the time delays of the voltage control devices.

• Computational and data challenges have limited the efforts to use 

long (e.g. hourly) timesteps. 

• Longer time steps may not accurately capture cloud-induced 

generation variability or corresponding regulator and capacitor 

control responses.

• A new set of metrics will have to be defined that conform more 

closely with the established ANSI standards and can allow 

infrequent parameter violations.

Potential small rooftop PV locations

Utility scale PV units

Substation

Figure: Test Feeder topology and PV unit 

locations

1.5

2

2.5

3

3.5

4

4.5

0.99

1.02

1.05

1.08

1 9 17 25 33 41 49 57 65 73 81 89

Load MVA

Voltage (p.u.)

Time points (Minutes of the day)

Max. Inst. voltage Min. Inst. voltage

Figure. Voltage peak because of sudden load drop, 

quickly regulated 

0 500 1000 1500 2000 2500 3000

PV Penetration (kW)

1.035

1.04

1.045

1.05

1.055

1.06

1.065

p.u.

Maximum Voltage Magnitude v.s. PV Penetration

 

 

65% Pen. Level

Figure. Maximum instantaneous voltage observed in 

each deployment and PV penetration scenario of the 

snapshot PV hosting capacity study.

50

70

90

0 20 40 60 80 100

% of rated Ampere rating

PV Penetration Levels (% of customers deployed)

 Maximum line loading (%) Maximum tfr. loading (%)

200

220

240

260

280

300

320

0 20 40 60 80 100

Total cap. state changes

PV Penetration Levels (% of customers deployed)

2820 3431

22.10

34.80

0

20

40

60

80

100

0

750

1500

2250

3000

3750

4500

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

% of peak feeder loading

Number or minutes

PV Penetration Levels (% of customers deployed)

Number Range A overvoltages Time outside 5min volt avg metric (minutes) PV penetration as % of peak loading

Snapshot:

QSTS:

QSTS first violation:

Figure. QSTS Results: (Top (a)) Number of overvoltage violations and total time for which the 5-minute

average voltage was outside ANSI Range A; (Bottom left (b)) Sum of state changes of all capacitor banks;

(Bottom right (c)) Maximum line and transformer loading observed at each PV penetration

The objective of this paper was to explore potential metrics for 

determining QSTS PV hosting capacity, however the threshold for 

these metrics will be determined by the comfort level of utilities in 

allowing voltage deviations or thermal violations on their system for 

small periods of time

Loading Metrics:

• Moving n-hour average loading: A n-hour moving average of each 

transformer’s and line’s loading as a percentage of its rated 

capacity is calculated. 

• Instantaneous over-loading: The instantaneous loading of the 

transformers and lines should not exceed pre-determined threshold 

values.

• “Snapshot” or “static” hosting capacity study is the method for 

determining the amount of PV the feeder can host without adverse 

grid impacts that might require changes or upgrades to the 

distribution system.

• The analysis is historically conducted at worst-case time points.

• The first parameter violated determines the hosting capacity and is 

often the upper voltage limit as per ANSI standard C84.1-2016, 

Range A .

• However, this ANSI standard also allows infrequent violations of the 

voltage limit as long as they are corrected.

• Operation of voltage control devices which can regulate voltages 

cannot be captured faithfully using this study methodology.

• Similarly, behavior of some advanced control algorithms which 

could be used to expand the hosting capacity can not be captured.

• Monte Carlo simulation was 

used to generate PV 

deployment scenarios 

covering 5% to 100% of the 

customers on the feeder, in 

a 5% step size. 

• The study was repeated 10 

times for each of these 

percentage penetration 

levels. 

• 200 scenarios were 

analyzed at the peak and 

minimum loading time 

points. 

Time Vector Violation

0 0

15 (tf2) 2 (tf2 - tc2)

14 0

13 (tc2) 0

0 0

11 (tf1) 1 (tf1 - tc1)

10 (tc1) 0

Time : 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Store: All Bus V All Bus V All Bus V All Bus V All Bus V All Bus V All Bus V All Bus V All Bus V All Bus V All Bus V All Bus V All Bus V All Bus V All Bus V All Bus V Total_violation (mins) 3

10 min average >1.05 or 10 min average < 0.95 - store final time (16) else store (0)

if: 10 min average >1.05 or 10 min average < 0.95 - store final time (15); else: store (0)

if: 10 min average >1.05 or 10 min average < 0.95 - store final time (10); else: store (0)

if: 10 min average >1.05 or 10 min average < 0.95 - store final time (11); else: store (0)

if: 10 min average >1.05 or 10 min average < 0.95 - store final time (12); else: store (0)

if: 10 min average >1.05 or 10 min average < 0.95 - store final time (13); else: store (0)

if: 10 min average >1.05 or 10 min average < 0.95 - store final time (14); else: store (0)

Figure. Moving n-minute average voltage metric

0

200

400

600

800

1000

1200

1400

1600

1.0375

1.0425

1.0475

1.0525

1.0575

1.0625

Time (minutes)

Voltages

Max. avg. voltage Max. inst. voltage Range A Range B Time vector

0

200

400

600

800

1000

20

40

60

80

100

120

140

160

1 201 401 601 801 1001 1201 1401

Percent Loading

Time (minutes)

Analyzed time points (Minutes of the day)

Max. avg. line loading Max. int. line loading Avg. limit Inst. Limit Time Vector

tfn tcn

Figure a (top) Maximum instantaneous and average voltages, and violations captured in a time vector. Figure b 

(bottom) Maximum instantaneous and average percentage line loading, and violations captured in a time vector

• IEEE P1547.7 D110, “Draft Guide to Conducting Distribution Impact 

Studies for Distributed Resource Interconnection,” 2013.

• M.J. Reno, J. Deboever, B. Mather, “Motivation and requirements for 

quasi-static time series (QSTS) for distribution system analysis,” IEEE 

PES-GM, 2017.

• EPRI Distributed PV Monitoring and Feeder Analysis. Available at: 

http://dpv.epri.com/hosting_capacity_method.html, accessed July 2018.

• D. Zhu, A.K. Jain, R. Broadwater, F. Bruna, “Feeder Voltage Profile 

Design for Energy Conservation and PV Hosting Capacity 

Enhancement,” Electric Power Systems Research, vol. 164, 2018.

Control Equipment Operations:

High PV penetration can lead to increased state changes of the 

voltage control equipment. These increased mechanical movements 

can reduce their life span. In this paper, we only tracked the number 

of control device operations and did not use this as a limiting factor.

Voltage Metrics:

• Moving n-minute average voltage: Moving average voltage of each 

bus for a n-minute duration is determined and any violations outside 

ANSI Range A are stored to determine the total time outside limits.

• Instantaneous maximum voltages: The instantaneous voltages at 

any time point should not be outside ANSI Range B or, if Range B is 

exceeded in the base case, then the number of these instantaneous 

violations with PV should never be more than those in the base no 

PV case. • The PV hosting capacity limit was reached for deployment 8 at 65 

percent PV penetration.

QSTS Results:

• All the penetration levels for deployment 8 were analyzed using a 

year-long QSTS PV hosting capacity study with 1-minute resolution 

load and PV profiles.

• Our QSTS results suggest that snapshot hosting capacity may be 

conservative in some scenarios compared to violations allowable 

under the ANSI standard. 

• In this case, our QSTS/dynamic hosting capacity was 35% higher 

than the snapshot analysis.