Utility-scale

Workshop

October 7, 2009 – Cedar Rapids IA

NREL/BK -550-47514

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Contents

Introduction

Agenda: Utility-scale PV Variability Workshop October 7, 2009; 8am – 5pm, Cedar Rapids, Iowa

Article Reprint: Understanding Variability and Uncertainty of Photovoltaics for Integration with the Electric Power 

System, LBNL 

Presentations:

DOE Programs Addressing PV Integration into Utility Planning & Operations, Dan Ton, Program Manager Smart 

Grid Research & Development

U.S. Utilities & Solar And IEA-PVPS High Penetration PV Workplan, Christy Herig, Solar Electric Power 

Association

Utility-scale PV Variability Workshop Meeting motivation and overview, Ben Kroposki, National Renewable 

Energy Laboratory

Interconnection Standards for PV Systems Where are we? Where are we going? Abraham Ellis, Sandia National 

Laboratories

PV System Models For System Planning and Interconnection Studies, Abraham Ellis, Sandia National Laboratories

Utility operations and variable generation, Michael Milligan, National Renewable Energy Laboratory

Solar Power Forecasting, Perspective and Understanding on Solar Power Forecasting, Mark Ahlstrom and J. Adam 

Kankiewicz, WindLogics

Wind and Solar Integration Studies, Nicholas Miller, GE Energy Infrastructure

Development of Data Sets for PV Integration Studies, Ray George, National Renewable Energy Laboratory

On Using Mesoscale Models for Solar Resource Modeling, Michael Brower, AWS Truewind, LLC

Spatial and Temporal Scales of Solar Variability: Implications for Grid Integration of Utility-Scale Photovoltaic 

Plants, Andrew Mills and Ryan Wiser, Lawrence Berkeley National Laboratory

Comparison of PV and Wind Variability, Yih-huei Wan, National Renewable Energy Laboratory

Variability in a Large-Scale PV Installation, Carl Lenox, Sunpower

Quantifying PV Output Variability, Thomas E. Hoff and Richard Perez, Clean Power Research

Characterization of short-term PV variability for large PV systems, Joshua Stein, Sandia National Laboratories

The Need for PV Output Data, Travis Johnson, NV Energy

3
Introduction

Data and analysis are needed to understand the variability of photovoltaic (PV) plants to avoid 

unnecessary barriers to the interconnection of PV. To address this need, The National Renewable 

Energy Laboratory, Sandia National Laboratories, the Solar Electric Power Association, the 

Utility Wind Integration Group, and the Department of Energy hosted a day-long public 

workshop on the variability of photovoltaic (PV) plants. 

The workshop brought together utilities, PV system developers, power system operators, and 

several experts to discuss the potential impacts of PV variability and uncertainty on power 

system operations. The workshop was largely motivated by a need to understand and 

characterize PV variability from the perspective of system operators and planners to avoid 

unnecessary barriers to the rapid development and interconnection of PV to the electric power 

system. Understanding PV variability will allow system planners and operators to develop 

effective measures to manage variability at different levels of PV penetration. 

This document contains the papers and presentations developed for the workshop.

4
Utility-scale PV Variability Workshop 

October 7, 2009; 8am – 5pm 

Cedar Rapids Marriott, Cedar Rapids, Iowa 

Goal: To improve understanding of PV plant variability and its impact on utility 

planning and operations 

Participants: PV developers, utilities, DOE, labs, consultants 

Agenda: 

7:00 a.m. – 8:00 a.m. 

Registration & Breakfast

Location: Pre‐Con Area

8:00 a.m. – 5:00 p.m. 

Workshop

Location: Hickory

8:00 a.m. – 8:30 a.m. 

Welcome, Introductions, and Overview 

Welcome and introductions (Charlie Smith – UWIG, Dan Ton – DOE, 

Christy Herig – SEPA) 

•	 Industry relevance; connection to DOE Renewable Energy and Smart Grid 

Programs, IEA High Penetration workplan. 

Meeting motivation and overview (Benjamin Kroposki – NREL) 

•	 Overview of issues in PV variability, integration, interconnection; overview 

of agenda. 

8:30 a.m. – 9:30 a.m. 

PV Interconnection Update 

PV interconnection standards (Abraham Ellis – Sandia) 

•	 IEEE, NERC and FERC standards for distributed systems and utility-scale 

system 

Generic PV system models for interconnection and planning studies 

(Abraham Ellis – Sandia) 

•	 Positive-sequence system planning (PSS/E and PSLF) and distribution 

planning models 

5
  

9:30 p.m. – 10:30 p.m. 

Integration of PV in Utility Operations 

Utility operations and variable generation (Michael Milligan – NREL) 

•	 Overview of utility operations; possible impacts of PV variability and
 

uncertainty; mitigation alternatives
 

Solar resource forecasting (Mark Alhstrom – WindLogics) 

• State-of-the-art, challenges and opportunities for improvement; integration 

into operations 

10:30 – 10:45 

Break 

Location: Pre‐Con Area

10:45 p.m. – 12:00 p.m. 

PV Integration Studies 

Wind and Solar integration studies (Nick Miller – General Electric) 

•	 Solar integration study purpose, methodologies and data requirements; 

experience with wind integration studies 

Development of data sets for PV integration studies (Ray George – 

NREL) 

•	 Development of distributed generation and centralized system data sets 

for integration studies 

12:00 p.m. – 1:00 p.m. 

Lunch 

Location: Oak

1:00 p.m. – 2:30 p.m. 

Solar Resource Variability – What do we know? 

Modeling the solar resource at higher resolution (Michael Brower – AWS 

Truewind) 

•	 Mesoscale solar resource modeling methodologies, challenges and 

opportunities for higher time and space resolution 

Short-term variability of the solar resource over wide geographical area 

(Andrew Mills – LBNL) 

•	 Analysis of ARM data in the Southern Great Plains region; existing solar 

radiation database 

Comparison of PV, CSP, wind variability (Yih-Huei Wan – NREL) 

6
  

•	 Analysis of actual system output data to characterize PV variability and 

effect of geographic diversity, as compared to CSP and wind. 

2:30 p.m. – 2:40 p.m. 

Break 

Location: Pre‐Con Area

2:40 p.m. – 4:00 p.m. 

Modeling PV Plant Output Variability 

Short-term PV output variability in large PV systems (Carl Lenox – 

SunPower) 

•	 Observed short-term output variability within a single large PV plant 

Quantifying PV power output variability (Tom Hoff – Clean Power
 

Research) 

•	 Theory of solar resource variability and impact of geographical dispersion 

Characterization of short-term PV variability for large PV systems 

(Joshua Stein – Sandia) 

•	 Effect of plant size, tracking system and other factors on output 

characteristics of large and distributed PV systems; static, stochastic and 

dynamic models for short-term PV output behavior 

4:00 p.m. – 4:30 p.m. 

Data Collection Needs 

Discussion of data collection effort and analysis needs by PV Variability 

Ad Hoc Group (Travis Johnson – NV Energy) 

•	 Approach to collect high resolution, time-synchronized data; technical 

challenges; proposed data format and metadata; possible ways to 

overcome commercial issues 

4:30 p.m. – 5:00 p.m. 

Open discussion of next steps and priority needs 

7
LBNL-2855E 

Understanding Variability and Uncertainty 

of Photovoltaics for Integration with the 

Electric Power System

Andrew Mills1, Mark Ahlstrom2, Michael Brower3, Abraham Ellis4, Ray George5, 

Tom Hoff6, Benjamin Kroposki5, Carl Lenox7, Nicholas Miller

8

, Joshua Stein4, and 

Yih-huei Wan5 

1. Lawrence Berkeley National Laboratory

2. WindLogics Inc.

3. AWS Truwind, LLC 

4. Sandia National Laboratories

5. National Renewable Energy Laboratory

6. Clean Power Research, LLC

7. SunPower Corporation 

8. GE Energy

Environmental Energy 

Technologies Division

December 2009

Preprint of article submitted to The Electricity Journal.

Download from http://eetd.lbl.gov/EA/EMP

This work was funded by the Office of Energy Efficiency and Renewable Energy and by 

the Office of Electricity Delivery and Energy Reliability of the U.S. Department of 

Energy under Contract DE-AC02-05CH11231 with Lawrence Berkeley National 

Laboratory and Contract DE-AC36-08-GO28308 with the National Renewable Energy 

Laboratory. Sandia is a multiprogram laboratory operated by Sandia Corporation, a 

Lockheed Martin Company, for the United States Department of Energy's National 

Nuclear Security Administration under Contract DE-AC04-94AL85000. 

ERNEST ORLANDO LAWRENCE

BERKELEY NATIONAL LABORATORY

8
ii

Disclaimer

This document was prepared as an account of work sponsored by the United States 

Government. While this document is believed to contain correct information, neither 

the United States Government nor any agency thereof, nor The Regents of the 

University of California, nor any of their employees, makes any warranty, express or 

implied, or assumes any legal responsibility for the accuracy, completeness, or 

usefulness of any information, apparatus, product, or process disclosed, or represents 

that its use would not infringe privately owned rights. Reference herein to any specific 

commercial product, process, or service by its trade name, trademark, manufacturer, or 

otherwise, does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States Government or any agency thereof, or The Regents of 

the University of California. The views and opinions of authors expressed herein do 

not necessarily state or reflect those of the United States Government or any agency 

thereof, or The Regents of the University of California. 

Ernest Orlando Lawrence Berkeley National Laboratory is an equal opportunity 

employer.

9
iii

Acknowledgements

This work was funded by the Office of Energy Efficiency and Renewable Energy (Solar Energy 

Technologies Program) and by the Office of Electricity Delivery and Energy Reliability 

(Permitting, Siting, and Analysis Division) of the U.S. Department of Energy under Contract 

DE-AC02-05CH11231 with Lawrence Berkeley National Laboratory and Contract DE-AC36-

08-GO28308 with the National Renewable Energy Laboratory. Sandia is a multiprogram 

laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States 

Department of Energy's National Nuclear Security Administration under Contract DE-AC04-

94AL85000. 

10
iv

Abstract

Data and analysis are needed to understand the variability of photovoltaic (PV) plants to avoid 

unnecessary barriers to the interconnection of PV. Several datasets show clouds can cause rapid 

changes in solar insolation. Smoothing of rapid ramps, however, occurs within PV plants. The 

degree of smoothing depends on plant size. Smoothing occurs on even longer time-scales 

between separate plants. 

 

11
1

1. Introduction

The National Renewable Energy Laboratory, Sandia National Laboratories, the Solar Electric 

Power Association, the Utility Wind Integration Group, and the Department of Energy recently 

hosted a day-long public workshop on the variability of photovoltaic (PV) plants. The workshop 

brought together utilities, PV system developers, power system operators, and several experts to 

discuss the potential impacts of PV variability and uncertainty on power system operations. The 

workshop was largely motivated by a need to understand and characterize PV variability from 

the perspective of system operators and planners to avoid unnecessary barriers to the rapid 

development and interconnection of PV to the electric power system. Understanding PV 

variability will allow system planners and operators to develop effective measures to manage 

variability at different levels of PV penetration. The workshop generated considerable 

discussion on the topic and a number of lessons were learned by the end of the day. This paper 

explores the issue of variability and uncertainty in the operations of the U.S. power grid and 

presents a number of the findings from the workshop. 

2. Managing Variability and Uncertainty in Power Systems

Before focusing on the variability and uncertainty of PV plants, it is important to understand that 

variability and uncertainty are inherent characteristics of power systems. Loads, power lines, 

and generator availability and performance all have a degree of variability and uncertainty. 

Regulations, standards, and procedures have evolved over the past century to manage variability 

and uncertainty to maintain reliable operation while keeping costs down. There are many 

different ways to manage variability and uncertainty. Enforceable reliability standards, overseen 

by the North American Electric Reliability Corporation (NERC), generally focus on minimum 

performance standards for reliable operation. The standards, however, do not dictate how to 

meet many of the performance requirements. In general, system operators and planners use 

mechanisms including forecasting, scheduling, economic dispatch, and reserves to ensure 

performance that satisfies reliability standards in a least cost manner. 

The earlier that system operators and planners know what sort of variability and uncertainty they 

will have to deal with, the more options they will have to accommodate it and the cheaper it will 

be to mange the system. Planners look years into the future to project needs for generation and 

transmission capacity, estimate cost effective expansion of supply options, and assess flexibility 

needs. Flexibility of the generation fleet is characterized in terms of parameters such as 

minimum start-up and shut-down times, minimum stable generation, and ramp rates. Closer in, 

planners will schedule units for maintenance or to be available to meet expected loads. These 

units are committed to generate electricity for a system in the hours to days unit commitment 

time scale. In the 10-min to hours time scale system operators will change the output of 

committed units to follow the changes in load throughout the day. More capacity than is needed 

at any particular time is committed to ensure that errors in forecasts or unexpected events can be 

accommodated without compromising reliability. In the tens of minutes time scale, system 

operators schedule adequate regulation reserves to track minute-by-minute changes in the 

balance between generation and load, Figure 1. 

12
2

Time (hour of day)

0 4 8 12 16 20 24

System Load (MW)

seconds to minutes

Regulation

tens of minutes to hours

Load

Following

day

Scheduling

Time (hour of day)

0 4 8 12 16 20 24

System Load (MW)

seconds to minutes

Regulation

tens of minutes to hours

Load

Following

day

Scheduling

Source: Michael Milligan, NREL, presentation at PV Variability Workshop

Figure 1. Time scales relevant to operating power systems

Managing variability and uncertainty is easier and less expensive when transmission lines are 

used to aggregate several diverse sources of variability and uncertainty. The daily load shape 

that system operators use to plan for the real-time operation of the grid is dramatically smoother 

than the daily profile of an individual residential customer, due to the diversity of load usage 

among customers. Rather than being concerned with the timing and duration of each individual 

customer appliance, system operators know that the aggregate of all customers will follow a 

general trend that can be predicted and managed with relative ease. Similarly, experience with 

managing wind energy in several countries with high penetrations of wind indicates that 

aggregation of several diverse wind farms leads to much smoother wind profiles than would be 

expected from scaling the output of a single wind turbine (Holttinen et al., 2009). 

3. Studies are Required to Characterize Additional Variability and 

Uncertainty of Photovoltaic Plants 

The addition of variable generation to meet demand will increase the variability and uncertainty 

that must be managed by system operators and planners. Figure 2 shows data used in an 

integration study where flexible conventional generation is used during a morning demand ramp 

to meet the load or the net-load when integrating wind and solar. Integration studies characterize 

the additional expected variability and uncertainty in scenarios with high penetrations of variable 

generation. These studies also focus on strategies that can reduce the challenges and costs of 

integrating variable generation. A number of integration studies with large amounts of wind and 

some solar have evaluated the additional reserves required to accommodate the variable 

generation. The studies found, among other conclusions, that using forecasts of variable 

generation by system operators and decreasing the time between dispatch schedules for 

generation can greatly increase access to flexible generation (Kirby and Milligan, 2008). These 

13
3

measures reduce the costs of managing the net increase in variability and uncertainty from 

adding variable generation (Smith et al., 2007). 

Source: Piwko et al., 2007

Figure 2. Detailed analysis of the challenges system operators must be able to manage in the 

California Intermittency Analysis Project (Piwko et al., 2007). Across all of the time scales 

identified in Figure 1, system operators use dispatchable resources to manage the combination of 

the load and the aggregate of all wind and solar plants.

Integration studies separate variability into different time scales as each is associated with 

different impacts, management strategies, and costs. The following list highlights general issues 

that are important for different time scales when operating power systems with variable 

generation:

• Power quality (e.g. voltage flicker) – seconds 

• Regulation reserves – minutes

• Load following – minutes to hours 

• Unit-commitment and scheduling – hours to days 

Aside from the time dimension, it is also important to characterize variability along a spatial 

dimension. Problems with power quality are often managed within a single distribution feeder. 

The spatial scales of importance for power quality may be on the order of tens of square 

kilometers. On the other hand, balancing authorities must balance all generation and load within 

balancing areas that range from hundreds of square kilometers to tens of thousands of square 

kilometers. Arrangements that allow balancing authorities to exchange variability in ways that 

are beneficial to both balancing authorities, such as ACE Diversity Interchange (ADI), require 

understanding variability on the spatial scale of nearly an entire interconnection or hundreds of 

thousands of square kilometers. 

14
4

A fundamental challenge in integration studies is developing projections of the load and variable 

generation across all of these temporal and spatial scales for expected levels of variable 

generation that have yet to be experienced anywhere in the world. Integration studies for highpenetration scenarios of PV will require projections of variability from multiple GW of PV 

generation for both distributed PV and large utility-scale PV plants. Currently, wide-area solar 

data coverage is available with low time resolution or high time resolution data is available with 

limited spatial coverage. Solar data covering a large spatial extent is available from satellite 

images, but this data generally has an hourly temporal resolution. High-time resolution PV data 

and solar insolation measurements are available from individual points, but there are few 

networks with multiple time-synchronized PV or solar insolation sites. To develop projections 

of PV variability for integration studies analysts need to be able to model on the time scale of 

seconds to hours the output of: 

• Large PV plants (~1-10’s of sq. km) 

• Dispersed PV plants on distribution feeders (~10-100’s of sq. km) 

• The aggregate of all PV plants that must be managed by system operators (~1,000-

100,000’s of sq. km) 

4. Lessons Learned from Analysis of Limited Existing Datasets Managing 

Variability and Uncertainty in Power Systems

4.1 Clouds can cause significant ramps in solar insolation and PV plant output

The output of PV plants is necessarily variable simply because the sun changes position 

throughout the day and throughout the seasons. The rising and setting of the sun regularly leads 

to 10-13% changes in PV output over a period of 15 minutes for single-axis tracking PV plants. 

Clouds, however, are largely responsible for rapid changes in the output of PV plants that 

concern system operators and planners. Changes in solar insolation at a point due to a passing 

cloud can exceed 60% of the peak insolation in a matter of seconds. The time it takes for a 

passing cloud to shade an entire PV system, in contrast, depends on the PV system size, cloud 

speed, cloud height, and other factors. For PV systems with a rated capacity of 100 MW, the 

time it takes to shade the system will be on the order of minutes rather than seconds. 

4.2 Clouds are diverse

Unlike changes in the position of the sun which affects the output of all PV plants in a nearly 

uniform, highly correlated way, changes in PV output due to clouds are not driven by a similar 

uniform process. Clouds move across plants affecting one part of a plant before another or 

leaving some parts of plants unobstructed as the cloud passes. Clouds therefore cause diverse 

changes in PV output across plants and between separate plants. Just as electrical connections 

are used to aggregate diverse loads and conventional plants, electrical connections aggregate the 

diverse output of separate PV panels and blocks of PV panels within a plant or between separate 

PV plants. The degree of diversity between points or plants can be characterized by the 

correlation of simultaneous changes in the output. Similarly, diversity can be characterized by 

the relative reduction in the magnitude of ramps for the aggregate of multiple plants relative to a 

single point, Figure 3. 

15
5

 Source: Hoff and Perez, 2009

Figure 3. Aggregating the output of several different solar insolation meters illustrates the 

reduction in variability of multiple sites relative to a single site. The change in irradiance from one 

minute to the next (left) is dramatically reduced for multiple sites due to diversity. 

4.3 Smoothing occurs within PV plants 

Comparison of the variability of a solar insolation meter and a 30-kW PV plant in New Mexico 

shows that diversity, even within a small PV plant, can smooth rapid ramps relative to the 

expected ramps from just examining solar insolation. 1-second and 10-second ramps from the 

30-kW PV plant are less severe than the ramps in the insolation meter, Figure 4 (left figure). 1-

min ramps, however, are nearly identical between the two. 

Source: Joshua Stein, Sandia National Laboratories, adapted from presentation at the PV Variability Workshop

Figure 4. Cumulative distributions (95th to 100th percentiles) of irradiance and PV power changes 

over various time periods during a single day from a 30-kW PV system (left) and a multi-MW PV 

16
6

system (right) show a reduction in variability between single point measurements (irradiance) and 

PV plant output (power/ total plant)

Comparison between variability observed in insolation meters and the output of larger multi-MW 

plants exhibit more pronounced reductions in variability. For example, output from a multi-MW 

PV plant of undisclosed capacity ( >2 MW) shows the relative difference between ramps 

observed at a point (irradiance sensor) and power ramps from the entire plant decrease as the 

ramp duration increases, Figure 4 (right figure). Large 1-sec, 10-sec, and 1-min ramps in the 

multi-MW PV plant are approximately 60%, 40%, and >10%, respectively, less severe than 

observed at a point. The ramp distributions are nearly identical for 10-min ramps. 

Other large PV plants exhibit similar behavior. A 75% ramp in 10-seconds observed by an 

insolation meter was associated with only a 20% in 10-second ramp in a different 13.2-MW plant 

in Nevada. A severe event that changed the output of an insolation meter by 80% in 1-min 

therefore led to only a 50% in 1-min change in the output of this plant and a 10-min change 65% 

in 10-min was slightly less severe than the 75% in 10-min change observed in the nearby 

insolation meter, Figure 5. 1-min changes in output of inverters within this plant were nearly 

perfectly correlated for close inverters, but inverters far apart within the same plant show 

correlation coefficients between simultaneous 1-min changes in output that drop as low as 0.1, 

Figure 6. The magnitude of the reduction in the maximum 1-min change in output therefore 

depends on the size of the plant. Increasing the plant size increases the relative reduction in 1-

min changes in plant output, Figure 7. 

0.95

0.96

0.97

0.98

0.99

1.00

0.00 0.20 0.40 0.60 0.80

Delta: Fractional Change

CDF: Probability that Change < Delta

Power

Irradiance

10-sec

1-min

10-min

 Source: Carl Lenox, SunPower Corporation, adapted from presentation at PV Variability Workshop

Figure 5. Cumulative distributions (95th to 100th percentiles) of irradiance and PV power changes 

over various time periods during a highly variable day for a 13.2-MW system.

17
7

Source: Carl Lenox, SunPower Corporation, presentation at PV Variability Workshop

Figure 6. Correlation coefficient of 1-min step changes in power output between different inverters 

(relative to Inverter #2B) within a 13.2-MW PV plant in the Southwest on a highly variable day. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

0 2 4 6 8 10 12 14

PV System Size (AC Nameplate, MW)

Maximum 1-min Delta (% per min)

All Inverters

Irradiance Sensor

Source: Carl Lenox, SunPower Corporation, adapted from presentation at PV Variability Workshop

Figure 7. Maximum 1-min changes in the output of an irradiance sensor and aggregated blocks of 

a 13.2-MW PV plant on a highly variable day.

18
8

There are two key lessons from this analysis. First, diversity can occur even within plants and 

the amount of smoothing within a plant depends on the size of the plant. Comparisons of the 

variability of different technologies need to be done for plants of similar capacity to be 

meaningful. Second, for plants in the tens of MW scale, the output of an insolation meter will 

show distinctly more severe ramps in time scales up to about ten minutes than will be observed 

in the output of the PV plant. Changes in the output of an insolation meter for time scales longer 

than about 10-min however will be similar to the changes in the output of multi-MW PV plants. 

These observations are based on a limited sample of data, and should be verified with data from 

other locations. 

4.4 Diversity occurs between separate PV plants 

While diversity over longer time-scales may be limited within multi-MW PV plants, analysis of 

a network of several time-synchronized solar insolation measurements in the Great Plains region 

of the U.S., six PV plants in the city of Las Vegas, four PV plants in Arizona, and two PV plants 

in Colorado indicates that smoothing can occur on even longer time-scales between separate 

plants. Aggregating six plants within a ~200 square kilometer area in Las Vegas greatly reduced 

not only the 1-min ramps but also reduced the 10-min ramps relative to the individual plants, 

Figure 8. Sixty minute ramps were smoothed, but to a lesser degree, with aggregation. Analysis 

of the 10-min ramps for PV plants located 12.5 km to 50 km apart in Arizona show on the order 

of a 50% reduction in the 99.7th percentile of the most severe ramps by aggregating any pair of 

sites, Figure 9. This is the reduction that would be expected if the 10-min ramps at each site 

were uncorrelated. Aggregating the output of two PV plants in Colorado 8.8 km apart (but along 

the same mountain ridge) showed a smaller reduction in 10-min ramps indicating that the 

smoothing benefit of aggregation may vary by region. Data sets from multiple regions need to 

be analyzed and compared to determine the extent to which local features affect the smoothing 

benefits of geographic diversity. 

19
9

95%

96%

97%

98%

99%

100%

0% 10% 20% 30% 40% 50%

1-min Ramp (% of Capacity)

Annual Percentile

Ft Apache

Gd Canyon

Luce

Ronzone

Spg Mtn

LVSP

Total PV

95%

96%

97%

98%

99%

100%

0% 10% 20% 30% 40% 50%

10-min Ramp (% Capacity)

Annual Percentile

Ft Apache

Gd Canyon

Luce

Ronzone

Spg Mtn

LVSP

Total PV

Source: Yih-huei Wan, NREL, adapted from presentation at the PV Variability Workshop

Figure 8. Cumulative distributions (95th to 100th percentiles) of six individual PV plants within a

~200 square kilometer area in Las Vegas and the aggregate of the plants demonstrate that 

aggregation greatly reduces the magnitude of extreme 1-min (left) and 10-min (right) ramps in the 

aggregate (Total PV) relative to the individual plants. Note that LVSP is a fixed tilt array while the 

remaining five plants are single axis tracking plants. 

Source: Ray George, NREL, adapted from presentation at PV Variability Workshop

Figure 9. Cumulative distributions (98.6th to 100th percentiles) of ramps from individual PV 

plants in Arizona, pairs of variously spaced plants, and the aggregate of all plants (All4). 

Aggregating the output from pairs of PV sites 12.5 km to 50 km apart leads to a reduction in the 

magnitude of the 10-min ramps (as a percentage of the name plate capacity) relative to the 

individual site. Ramps are based on one year of 10-min data from one-axis tracking PV systems 

(courtesy Arizona Public Service Co.). 

20
10

In the Great Plains, irradiance ramps over time scales of 30-min were uncorrelated for sites that 

were on the order of 50 km apart. Ramps over time scales of 60-min were uncorrelated for sites 

on the order of 150 km apart. Ramps over time scales 15-min and shorter were uncorrelated for 

all distances between sites down to the minimum spatial resolution of 20 km between sites, 

Figure 10. When ramps over a particular time scale are uncorrelated between all N plants, the 

aggregate variability is expected to scale with 1/ N relative to the variability of a single point. 

This diversity between multiple PV sites on all sub-hourly time scales needs to be accounted for 

in projections of variability that must be managed by system operators. Comparison of the 

variability of multiple solar insolation meters and similarly sited wind anemometers (scaled to 

create a time series of wind power output) suggests that the variability of several PV plants may 

be similar to the variability of several similarly sited wind plants for time scales longer than 10-

15 minutes. 

Source: Andrew Mills and Ryan Wiser, LBNL, presentation at PV Variability Workshop

Figure 10. Correlation coefficient of step changes in the global clearness index (the ratio of the 

measured insolation to the clear sky insolation) for different distances between sites and different 

averaging intervals for the step changes (deltas).

4.5 Multiple methods are available for PV forecasting

Forecasts of PV output are required for days ahead down to hours and tens-of-minutes ahead. 

Forecasts should include information about the expected output and the degree of uncertainty in 

the expected output to indicate particularly volatile periods. Short-term PV forecasts are aided 

by the fact that clouds can be observed. Sky imagers near PV plants can be used to indicate 

approaching clouds and predict the impact the clouds will have on PV output. Successive 

satellite images have been shown to yield useful information about the direction and speed of 

21
11

approaching clouds. For longer time scales, numerical weather models can be used to predict 

solar insolation out to multiple days. Forecasts are an important method for managing both the 

variability and the uncertainty of PV and should be incorporated into system planning and 

operations. 

4.6 Grid events can impact the variability of PV 

Step changes in PV output can occur from simultaneous inverter trips within the plant. Although 

inverter trip events are far less common than cloud-induced ramps, the severity and magnitude of 

trips exceed the observed severity and magnitude of ramps due to clouds. Currently, these trips 

are normal operation as inverters are designed to shut off when abnormal events occur on the 

grid and cause voltage or frequency deviations outside of a tolerance envelope. Tripping is 

presently required by IEEE Standard 1547 for PV (and other distributed generation) that is 

embedded on distribution systems. This requirement stems from safety concerns surrounding 

inadvertent islanding. However, an unintended consequence of these rules is that wide spread 

tripping of PV will occur for large grid disturbances such as transmission faults that depress 

voltages below existing tolerances over a wide geographical area in systems with large amounts 

of IEEE 1547 compliant embedded PV. Preventing large simultaneous inverter trips due to low 

voltage on the grid will require some reconciliation of rules like IEEE 1547, that mandate low 

voltage tripping, and FERC Rule 661a, that prohibit low voltage tripping for large scale 

generation. From a technology perspective, application of low voltage ride through (LVRT) 

techniques (such as those developed for wind generation) will be needed for PV inverter design. 

Voltage ride-through standards for PV are already in place in interconnection standards in 

Germany (Troester, 2009). 

In addition to grid events, PV plants are subject to outages due to equipment malfunction or 

outages inside the plant similar to conventional generators. PV plant outages, like the outages of 

wind and conventional plants, should be planned for in the normal way that grid operators 

prepare for grid contingencies. 

5. Conclusions

The PV Variability Workshop was the beginning of a dialogue that will need to continue 

between utilities, PV system developers and owners, and regulators to characterize PV variability 

and develop effective measures to manage the variability and uncertainty. The initial lessons 

learned from the workshop include: 

• Rapid ramps are important to characterize and understand for PV, but in the end system 

operators need to maintain a balance between the aggregate of all generators and loads. 

Understanding the characteristics of aggregate PV output over large areas and correlation 

to load are critical to understanding potential impacts of large quantities of PV.

• PV variability can drive localized concerns, which typically manifest themselves as 

voltage or power quality problems. These issues are distinct from grid system level 

issues of balancing, and ought not to be confused. Management and remediation options 

for local power quality problems are generally different than options for maintaining a 

balance between load and supply at the system level.

22
12

• The variability observed by a point insolation measurement will not directly correspond 

to the variability of a PV plant. A point measurement ignores sub-minute time scale 

smoothing that can occur within multi-kW plants and sub-ten minute smoothing that can 

occur within multi-MW plants. Extrapolation suggests that further smoothing is expected 

for short time-scale variability within PV plants that are hundreds of MW, but this needs 

to be confirmed with field data from large systems. 

• Diversity over longer time scales (10-min to hours) can occur over broad areas 

encompassed by a power system balancing area. Data from the Great Plains region of the 

U.S. indicates that the spatial separation between plants required for changes in output to 

be uncorrelated over time scales of 30-min is on the order of 50 km. The spatial 

separation required for output to be uncorrelated over time scales of 60-min is on the 

order of 150 km. The assumption that variability on a 15-min or shorter time-scale is 

uncorrelated between plants separated by 20 km or more is supported by data from at 

least one region of the U.S. Additional data is required to examine this assumption in 

other regions with different weather patterns. 

• Multiple methods will be used for forecasting solar resources at differing time scales. 

Clouds are the primary influence in the solar forecast. Over short time scales, it is 

important to recognize that clouds (and their rate and direction of movement) are visible 

to satellites and ground-based sensors. Over longer time scales clouds can change shape 

and grow or dissipate, so numerical weather modeling methods may prove necessary. As 

with wind forecasting, solar forecasting will benefit from further development of weather 

models and datasets. 

• Photovoltaics fall under the broader category of variable generation. The experience with 

managing wind variability and uncertainty will benefit solar integration efforts. Where 

appropriate, unified approaches for managing variable generation will ease integration 

issues. 

The most important lesson from the workshop, however, is that the dialogue regarding PV 

variability requires, above all else, additional time-synchronized data from multiple PV plants 

and insolation meters over spatial scales ranging from sq. km to greater than 10,000 square 

kilometers. The data will need to cover at least a year and should be synchronized with 

comparable load data in order to understand the net impact on the variability that must be 

managed by the system operators. Certain questions, particularly questions concerning power 

quality and regulation reserves, will require data with as high of a time resolution as multiple 

seconds. Analysis of data from multiple time-synchronized PV plants will allow detailed 

evaluation of the degree to which rapid ramps observed in point measurements will be smoothed 

by large PV plants and the aggregation of multiple PV plants. Such studies will help remove 

unwarranted barriers to interconnection and provide the basis for setting appropriate 

interconnection standards that will allow solar energy from PV plants to reach significant 

penetration levels. 

Additional Reading and References

Presentations from the Utility-Scale PV Variability Workshop: 

http://www.uwig.org/pvworkshop-presentations.html

23
13

Hoff, T. and R. Perez. 2009. Quantifying PV Output Variability. Clean Power Research report 

to New York State Energy and Research Development Authority. May 2. 

http://www.cleanpower.com/research/capacityvaluation/QuantifyingPVPowerOutputVari

ability.pdf

Holttinen, H., P. Meibom, A. Orths, F. van Hulle, B. Lange, M. O’Malley, J. Pierik, et al. 2009. 

Design and operation of power systems with large amounts of wind power. Final Report, 

Phase one 2006-2008. IEA WIND Task 25. Espoo: VTT. 

http://www.vtt.fi/inf/pdf/tiedotteet/2009/T2493.pdf. 

IEEE Power & Society Special Issue on Large Scale Solar Integration: May/June 2009, 7(3). 

http://www.ieee.org/organizations/pes/public/2009/may/index.html

Kirby, B., and M.Milligan. 2008. Facilitating Wind Development: The Importance of Electric 

Industry Structure. The Electricity Journal 21(3), 40-54. 

Lew, D., M. Milligan, G. Jordan, L. Freeman, N. Miller, K. Clark, and R. Piwko. 2009. How do 

Wind and Solar Power Affect Grid Operations: The Western Wind and Solar Integration 

Study . Golden, CO: National Renewable Energy Laboratory, September. 

http://www.nrel.gov/docs/fy09osti/46517.pdf. 

North American Electric Reliability Corporation (NERC). 2009. Accommodating High Levels of 

Variable Generation. White Paper. April. 

http://www.nerc.com/files/IVGTF_Report_041609.pdf. 

Piwko, R., X. Bai, K. Clark , G. Jordan , and N. Miller. 2007. Intermittency Analysis Project: 

Appendix B: Impact of Intermittent Generation on Operation of California Power Grid. 

California Energy Commission, PIER Research Development & Demonstration Program, 

July.

Smith, J. et al., 2007. Utility Wind Integration and Operating Impact State of the Art. IEEE 

Transactions on Power Systems, 22(3), 900-908. 

 

Troester, E. 2009. New German Grid Codes for Concentrating PV Systems to the Medium 

Voltage Power Grid. 2nd International Workshop on Concentrating Photovoltaic Power 

Plants: Optical Design and Grid Connection. Darmstadt, Germany, March 10. 

http://www.concentrating-pv.org/pdf/papers/24-Troester-GermanGridCodes.pdf. 

Vitae

Andrew Mills is a Principal Research Associate in the Electricity Markets and Policy Group at 

Lawrence Berkeley National Laboratory, where he conducts research on issues in the electricity 

industry related to renewable energy markets and grid integration. 

Mark Ahlstrom is CEO of WindLogics, a leading company in the assessment, forecasting, 

operations and integration of renewable energy. WindLogics became a subsidiary of NextEra 

24
14

Energy Resources (formerly FPL Energy) in 2006, allowing it to expand its role as an applied 

R&D center and provider of services to utility, wind and solar energy clients. 

Michael Brower is a founding partner and Chief Technical Officer of AWS 

Truewind, LLC. AWS Truewind is an international renewable energy consulting firm providing 

wind and solar resource assessment and mapping, plant design and assessment, performance 

evaluation, due diligence, and grid integration services. 

Dr. Abraham Ellis is Technical Lead of Renewable Energy Grid Integration at Sandia National 

Laboratories. His work concentrates on impacts of high penetration PV and wind on power 

systems planning and operations. 

Ray George is a Senior Scientist at the National Renewable Energy Laboratory where he 

specializes in meteorology and GIS 

Dr. Thomas E. Hoff founded Clean Power Research in 1998. Clean Power Research provides 

consulting and software services to evaluate the economics of clean energy investments. Dr. 

Hoff holds a Ph.D. in Engineering Economic Systems from Stanford University and has 20 years 

of experience in the area of photovoltaic and other clean energy technologies. 

Dr. Benjamin Kroposki is a Principal Group Manager at the National Renewable Energy 

Laboratory. Dr. Kroposki leads a group of scientist and engineers in that focus in the area of 

integration of renewable and distributed energy into the electric power system.

Carl Lenox is a Principal Engineer at SunPower Corporation. He leads a cross-functional team 

that is addressing the challenges of integrating photovoltaic power plants into the utility system.

Nicholas Miller is Director, Energy Applications and Systems Engineering at GE Energy in 

Schenectady, NY. He has been a principal contributor to several major renewables integration 

studies and has provided consultation on renewables integration to governments and institutions 

in more than two dozen countries. 

Joshua Stein is a Principal Member of Technical Staff and member of the Photovoltaic Systems 

and Grid integration Department at Sandia National Laboratories. He conducts research aimed at 

better understanding the performance characteristics of fielded PV systems and develops models

that accurately predict the performance of systems in diverse climates of variable designs, and 

over variable time periods. 

Yih-huei Wan is a Senior Engineer at the National Wind Technology Center in the National 

Renewable Energy Laboratory.

25
 

 

Slide 2

DOE Programs Addressing PV Integration 

into Utility Planning & Operations 

Presentation at:
 

Utility Utility-Scale PV Variability Workshop Scale PV Variability Workshop
 

Dan Ton, Program Manager
 

Smart Grid Research & Development
 

October 7, 2009 Cedar Rapids, Iowa
 

DOE EERE/OE jointly developed and identified five strategic areas 

PV Integration ─ A Collaborative Area 

between EERE/OE 

 High-resolution renewable energy resource characterization 

for RSI to realize a significantly larger share of the nation’s energy 

consumption from renewable energy (wind, solar, geothermal, tidal wave) 

and renewable fuels (biomass, biofuels) 

 Advanced operational strategies with integrated renewables, 

energy storage, and load management 

 Advanced communications and controls for interconnection and 

interoperability 

 Comprehensive regional infrastructure planning and coordination 

 Education and workforce development 

2 

26

1
DOE

Solar Energy 

Technologies 

Program

- -

- -

 

 

The Program is implementing four key activities to reduce 

technology cost and achieve high market penetration 

Photovoltaics (PV) 

DOE 

Solar Energy

Technologies

Program 

Distributed Generation 

on-site or near point of use 

System Integration System Integration 

Market Transformation Market Transformation 

Concentrating Solar

Power (CSP) Centralized Generation 

large users or utilities 

3 

Challenges: System Integration 

High-penetration solar electricity into the grid 

affected by variability of solar resources 

& outdated electric delivery infrastructure 

 Resources characterization with 

inadequate temporal and spatial 

resolutions 

 System planning and operational 

tools inadequate to manage 

uncertainty from renewable uncertainty from renewable
 

energy generation
 

 Lack of system flexibility to 

accommodate additional variability 

introduced by renewables 

 Limited capacity for two-way power flow 4 

27

2
Slide 5

Slide 6

 

Slide 6

Addressing System Integration Challenges 

SEGIS focuses on developing intelligent hardware that 

interconnects PV to evolving “Smarter” electrical grid
 

Addresses integration 

application needs for: 

Communication 

portals 

micro-grids 

demand response 

zero -energy 

buildings 

PHEV integration 

PV system sizes, 

<1kW to >100kW 

Five industry awards downselected for prototyping, testing, and pilot production5 

Addressing System Integration Challenges 

High Penetration PV Development 

 Industry workshop held in February 2009 

with over 120 participants 

 Workshop report documenting high-priority 

needs, RD&D activities, and performance 

requirements 

 Up to $37.5M DOE investment over 5 years 

(FY10-14), selection for award announced 

in September 2009: 

–	 Modeling tools development Modeling tools development 

–	 Field verification of high-penetration 

levels 

–	 Modular power architecture 

–	 Demonstration of PV and energy 

storage for smart grids http://www1.eere.energy.gov/solar/ 

pdfs/pv_grid_penetration.pdf 

6 

28

3
Office of Electricity Delivery and 

Energy Reliability 

Office of the Assistant Secretary 

Corporate Business 

Operations 

 Clean Energy 

Transmission and 

Reliability 

 Modeling and Analysis 

 Electric Markets 

Technical Assistance 

Permitting, Siting, 

& Analysis 

(PSA) 

 Energy Infrastructure 

Protection 

 State/Local Gov’t 

Infrastructure Security 

& Emergency Response 

(ISER) 

Research & 

Development 

(R&D)* 

Operations 

 Smart Grid Research 

and Development 

 Energy Storage 

 Cyber Security for 

Energy Delivery 

Systems 

 Electricity Exports/ 

Presidential Permits 

 Power Marketing 

Administration Liaison 

Partnerships 

 Training and Exercises 

 Visualization 

 Critical/Vulnerability 

Assessment 

 Emergency response 

support 

* FY10 Budget Line Items 7 

Smart Grid Research and Development 

Smart Grid R&D focuses on developing next generation smart 

grid technologies for integration into the nation’s electric 

delivery network to enhance operational intelligence and 

connectivity throughout all application areas . 

Key SG Application Areas 8 

29

4
 

 

 









S t G 

– 

Defining Smart Grid Characteristics
 

Electricity delivery network modernized using 

latest digital/information technologies to meet 

key defining functions 

 Enabling Informed Participation by Customers Enabling Informed Participation by Customers 

 Accommodating All Generation and Storage Options 

 Enabling New Products, Services, and Markets 

Providing the Power Quality for the Range of Needs 

in the 21st Century 

Optimizing Asset Utilization and Operating Efficiently 

Addressing Disturbances – Automated Prevention, 

Containment and Restoration Containment, and Restoration 

Operating Resiliently Against Physical and Cyber 

Attacks and Natural Disasters 

7 smart grid characteristics reaffirmed through

the Smart Grid Implementation Workshop held June 2008
 

9 

Smart Grid R&D 

Planned Activities for FY10 

Smart Grid R&D M lti R&D Multi-Y P Pl (FY10 Year Program Plan (FY10-14) 

 Working groups being assembled to plan for development of each 

of the defined R&D areas to include the goal, objectives, 

challenges, tasks, and milestones 

 Two-stage development process 

– Meeting in October involving all WGs 

Industry Workshop in December Industry Workshop in December 

 MYPP to guide Smart Grid R&D investments, including a planned 

FY10 solicitation in February 2010 

10 

30

5
 

 

Recovery Act Smart Grid Funds: $4.5 Billion 

Office of Electricity Delivery and Energy Reliability $ Millions 

Smart Grid Investment Grant Program; ≤3 years $3,400 

Smaller projects, $300K-$20M; 40% of funding 

Larger projects, $20M-$200M; 60% of funding 

Smart Grid Demonstrations; 3-5 years $615 

Regional Demonstrations, up to $100M per project 

Grid-scale Energy Storage Demonstrations 

Interoperability Framework Development by NIST $10 

Resource Assessment and Interconnection Resource Assessment and Interconnection -Level $60

Transmission Analysis and Planning 

$60 

State Electricity Regulators Assistance $46 

Enhancing State Government Energy Assurance 

Capabilities and Planning for Smart Grid Resiliency 

$39.5 

Local Energy Assurance Planning (LEAP) Initiative $10.5 

11 

Contact Information 

Dan T. Ton 

Program Manager, S G martrid R&D 

Office of Electricity Delivery and Energy Reliability 

U.S. Department of Energy 

(202) 586-4618
 

Dan.ton@hq.doe.gov
 

For more Information: 

OE: www.oe.energy.gov 

Smart Grid: www.oe.energy.gov/smartgrid.htm 

Systems Integration: 

www1.eere.energy.gov/solar/systems_integration_program.html 

12 

31

6
 

U.S. Utilities & Solar 

And 

IEA-PVPS High Penetration 

PV Workplan 

Utility Scale PV Variability Workshop Utility Scale PV Variability Workshop 

Cedar Rapids, Iowa 

Oct. 7, 2009 

Christy Herig 

Solar Electric Power Association 

About SEPA 

• Mission is to facilitate utility use &

integration of solar electric power 

• Non-profit membership organization 

• Reliable source of unbiased information 

about solar technologies, policies, and 

programs 

• Bridge between utility & solar industries 

32

1 
 

SEPA Program Areas 

Research 

Education 

Direct Utility

Outreach 

Research Projects 

• Solar Incentive Program 

Ongoing Activities 

• One-on-One Utility Support 

About SEPA 

Survey 

• Solar Capacity 

Methodology Project 

• Utility Metering and 

Interconnection Survey 

• Decoupling White Paper 

• Utility Solar Case Studies 

• Solar Power International 

Conference and Expo w/Utility 

and Regulator Travel 

Scholarships 

• Utility Solar Conference 

• Online Resource Library 

• Monthly Phone Seminars Utility Solar Case Studies 

• Utility Solar Year in Review 

• Utility Business Models 

• Utility Integration Tracking 

• Bi-Weekly Electronic 

Newsletter and Email Alerts 

• Membership Directory 

• Fact finding missions to 

Germany, Spain, and...... 

33

2 
ll l i

Utility “Charter” 

Provide power that is 

• Safe 

• Reliable 

• Affordable 

And responsible for 

all planning 

Solar 

Industry 

Utility 

Industry 

34

3 
“High-Penetration of PV Systems in 

Electricity G ” rid s (working Title) 

Prepared by: 

Roland Bründlinger, IEA-PVPS Task 1, roland.bruendlinger@arsenal.ac.at
 

Christoph Mayr, IEA-PVPS Task 11, christoph.mayr@arsenal.ac.at
 

arsenal research, Electric Energy Systems, Vienna, Austria
 

Hubert Fechner, Technikum Vienna, PVPS ExCo Austria
 

fechner@technikum-wien.at 

In collaboration with: In collaboration with: 

Task 14 definition group participants 

Dan Ton, U.S. DOE; Ben Kroposky, NREL; Kazuhiko Ogimoto, University of Tokyo; 

Konrad Mauch, OA Task 11; Christy Herig, OA Task 10; 

PV generation 

Information 

gathering 

Analysis 

Outreach 

in correlation 

to energy 

demand 

HP PV in local 

distribution 

grids 

Smart inverter 

technology for 

high

penetration of

PV Outreach 

High

penetration

solutions for 

central PV gen. 

scenarios 

PV 

35

4 
„How to characterise and react on the fluctuating PV 

characteristics“ and make it more valuable for the 

Power System“ 

 Characterisation of the fluctuating nature of PV 

– Data for further applications

 Definition of Requirements for Forecast prediction 

 Definition for Energy Management Systems including 

storage solutions 

 Case studies 

 Target Group: Industry, Utilities, Researchers 

„Requirements for HP PV and 

Benefits of PV in the distribution system“ 

 „Ancillary Services, Impact on energy management on 

the grid, Benefits Benefits for the network network. Reactive Reactive and active 

Power Balancing, Change from distribution to supply 

grids, ... 

 Target Group: DNO, (TSO), Regulators, System 

operators 

36

5 
The electrical Power system with high Penetration PV 

 Power system wide PV generation forecasting (> Subtask 

1?) , smoothing smoothing effects effects (Subtask Subtask 1), 

 Power system operation and incentive generation to 

Distribution systems, Power system upgrade including PV 

 Target Group: Utilities, Energy Planning, Energy Policy 

Requirements for Inverters at HP PV 

 Smart inverter requirements for grid integration at HP 

Scenarios 

 Technical capabilities and Solutions 

 Remote Control and communication for Smart 

inverters 

 Target group: Inverter Industry, DSO, Large System 

operators 

37

6 
g g

Yesterday: 

Historical Utility 

Solar Engagement 

38

7 
Germany 

2008 ~1500 MW 

World 

2008 ~ 7000 MW 

Total ~ 12000 MW 

Total ~ 5300 MW 

Japan 

2008 ~ 220 MW 

Total ~ 7500 MW 

USA 

2008 ~ 330 MW 

Total ~ 800 MW 

Spain 

2008 ~ 2500 MW 

Total ~ 3100 MW 

39

8 
Thank you! 

Christy Herig 

cherig@solarelectricpower.org 

www.solarelectricpower.org 

40

9 
Utility-scale PV Variability Workshop 

Meeting motivation and overview 

Ben Kroposki, PhD, PE 

October 7, 2009 

Renewable Technology Integration 

Even places with great sun have cloudy days 

41

1 
Renewable Technology Integration 

Most beautiful places 

have clouds 

Renewable Technology Integration 

Some places have partly 

cloudy days --- Everyday! 

42

2 
 

Renewable Technology Integration 

Why is this important?
 

Renewable Technology Integration 

• Tucson Electric 

Power – Springerville 

Plant (4.5MWdc) 

• 44 Acres 44 Acres 

•	 10 sec data can show 10 sec. data can show 

tremendous variability 

•	 Ramp rates of PV at high 

penetration can effect electric 

power system operations 

•	 Effects of geographical diversity 

still TBD 

43

3 
Renewable Technology Integration 

Large –Scale PV is Coming! 

Renewable Technology Integration 

Solarpark Lieberose (Germany) - 53 MW (photo courtesy Juwi) 

44

4 
Renewable Technology Integration 

Parque Solar Olmedilla de Alarcon (Spain) - 60MW 

Renewable Technology Integration 

What is the industry doing to 

address this issue? 

•	 Ad-h PV V i bilit W ki G hoc PV Variability Working Group 

•	 Sharepoint site that has relevant papers, presentation 

and information on this topic. 

•	 email Ben (benjamin.kroposki@nrel.gov) for access 

•	 Starting in January 2009, we have had 5 conference 

calls on this topic. 

•	 Started to develop a work plan, data requirements, 

meta data definition, and data collection plan 

•	 This is the first face-to-face meeting to discuss the 

issues. Thanks to UWIG for allowing us to colocate! 

•	 Presentations will also be posted on UWIG website 

45

5 
Renewable Technology Integration 

Renewable Technology Integration 

46

6 
Renewable Technology Integration 

Renewable Technology Integration 

47

7 
–

 

 

Interconnection Standards 

forPV Systems
 

Where are we? Where are we going?
 

Abraham Ellis 

Sandia National Laboratories 

aellis@sandia.gov 

Cedar Rapids, IA – October 2009 

Generator Interconnection 

Standards 

•	 Establish uniform technical and procedural 

requirements for interconnection of generation on requirements for interconnection of generation on 

the electric grid 

•	 Interconnection standards are driven by 

–	 Safety (people and property) 

– Grid reliability, performance
 

Cost considerations Cost considerations 

– Fairness (“...just and reasonable, and not unduly 

discriminatory or preferential”)
 

48

1 
–

 

 

What Interconnection 

NEC 

Standards and Procedures Apply? 

Bulk System Guidelines 

NERC, FERC NERC, FERC 

IEEE, ANSI, IEC 

NESC 

Plenty of technical and 

jurisdictional overlap, 

confusion, contradiction... 

Distribution System Guidelines 

IEEE 1547, PUC/PRC 

IEEE, ANSI, IEC 

Federal-Jurisdictional 

Interconnection Standards (FERC Order) 

•	 Apply to generators, participating in wholesale market, 

regardless of size and interconnection location regardless of size and interconnection location 
 

–	 TSO/DSO follow pro-forma procedures via OATT 

•	 Procedures and requirements generally based on size 

–	 Large Generating Facilities (>20 MW): LGIP/LGIA 

–	 Small Generating Facilities (SGF) (<20 MW): SGIP/SGIA 

•	 SGIP has streamlined process for smaller DER
 

Fast Track Process Fast Track Process for certain generators no larger than 2 MW for certain generators no larger than 2 MW 
 

–	 10 kW Inverter Process for certified inverter-based generators no larger 

than 10 kW (SGIP Attachment 5) 

49

2 
 

 

 

State-Jurisdictional 

Interconnection Standards (PUC/PRC Rule) 

• Apply to generators up to a certain size, connected to 

the grid but the grid, but not participating in wholesale market participating in wholesale market 

– Net Metering, PURPA or similar arrangement for “sale” of 

electricity to the host utility only 

–	 Typically cover RE and other DG, all customer classes 

•	 Procedures and standards vary by state 

–	 Generally conform with FERC SGIP but some have Generally conform with FERC SGIP, but some have 

significant differences
 

•	 Technical standards focused on the distribution 

system and DER
 

State-Jurisdictional 

Interconnection Standards 

www.dsireusa.org / July 2009 

WA: 20,000 

MT: 50* 

OR: 25/2,000* 

CA: no limit 

MT: 50 

NV: 20,000 

UT: 25/2,000* 

NM: 80,000 

WY: 25* 

CO: 10,000 

MN: 10,000 

AR: 25/300* 

MI: no limit 

WI: 15,000 

MO: 100* 

IN: no limit 

IL: 10,000 

KY: 30* 

OH: 20,000 

NC: no limit 

VT: no limit 

NH: 100* 

MA: no limit 

CT: 20,000 

PA: no limit NJ: 2,000* 

DC: 10,000 

MD: 10,000 

NY: 2,000 

VA: 20,000 

SC: 20/100* 

NE: 25* 

KS: 25/200* 

SD: 10,000 

State policy 

* Standard only applies to net-metered systems 

HI: no limit 

LA: 25/300* 

FL: 2,000* 

37 states + 

DC & PR 

have adopted an 

interconnection 

policy 

Notes: Numbers indicate system capacity limit in kW. Some state limits vary by customer type (e.g., residential/non-residential).“No limit” means that there 

is no stated maximum size for individual systems. Other limits may apply. Generally, state interconnection standards apply only to investor-owned utilities. 

GA: 10/100* 

PR: no limit 

TX: 10,000 

50

3 
 

 

Interconnection Requirements 

for SGF (FERC Order 2006) 

•	 Fast Track Process for SGF that pass certain “screens” 

–	 SGF capacity <2 MW SGF capacity <2 MW 

–	 SGF meets codes, standards and certification 

–	 Total SGF capacity <15% of peak load in the circuit 

–	 If connecting to Spot Network, SGF must be inverter-based, not 

exceeding 5% of maximum load or 50 kW 

–	 Total SGF fault current <10% of total fault current 

–	 Addition of SGF does not cause distribution equipment and protective 

devices to exceed 87.5% of rating 

–	 Transformer connection compatible with utility circuit 

–	 <20kW for single phase, <20%imbalance among phases 

Typical DER Interconnection Process 

• Example: 

CPUC Rule 21 

Interconnection 

Process 

Source: California Interconnection Guidebook 

http://www.energy.ca.gov/distgen/interconnection 

/guide_book.html 

51

4 
 l

Typical DER Interconnection Process 

• Example: 

CPUC R l 21 I iti CPUC Rule 21 Initia
 l 

Technical Review 

Methodology 

Source: 

http://www.energy.ca.gov/distgen/interconnection 

/application.html 

IEEE 1547 Standard Family 

(Applies to DER no larger than 10 MVA) 

No. Title Status 

1547 Standard for Interconnecting Distributed Resources with Electric Power 2003 Systems 2003 

1547.1 Standard for Conformance Tests Procedures for Equipment 

Interconnecting Distributed Resources with Electric Power Systems 2005 

1547.2 Application Guide for IEEE 1547 Standard for Interconnecting Distributed 

Resources with Electric Power Systems 2008 

1547.3 Guide For Monitoring, Information Exchange, and Control of Distributed 

Resources Interconnected with Electric Power Systems 2007 

1547 4 Draft Guide for Design, Operation, and Integration of Distributed 1547.4 P di g , p , g 

Resource Island Systems with Electric Power Systems Pending 

1547.5 Draft Technical Guidelines for Interconnection of Electric Power Sources 

Greater than 10MVA to the Power Transmission Grid Pending 

1547.6 Draft Recommended Practice For Interconnecting Distributed Resources 

With Electric Power Systems Distribution Secondary Networks Pending 

1547.7 Draft Guide to Conducting Distribution Impact Studies for Distributed 

Resource Interconnection Pending 

52

5 
 

 

 

Interconnection Standards 

for Distributed Energy Resources 

•	 IEEE 1547 Voltage and Frequency Tolerance 

Voltage Range 

(% Nominal) 

Max. Clearing 

Time (sec) * 

V < 50% 0.16 

50% ≤ V < 88% 2.0 

110% < V < 120% 1.0 

V ≥ 120% 0.16 

Frequency Range 

(Hz) 

Max. Clearing 

Time (sec) 

f > 60.5 0.16 

f < 57.0 * 0.16 

59.8 < f < 57.0 ** Adjustable (0.16 

and 300) 

(*) 59.3 Hz if DER ≤ 30 kW ( ) * 	 Maximum clearing times for DER Maximum clearing times for DER ≤
 30 kW;

Default clearing times for DER > 30 kW (**) For DER > 30 KW
 

•	 Additional disconnection requirements 

–	 Cease to energize for faults on the Area EPS circuit 

–	 Cease to energize prior to circuit reclosure 

–	 Detect island condition and cease to energize within 2 seconds 

of the formation of an island (“anti-islanding”) 

50% < V > 88% 110% < V > 120% 

Interconnection Standards 

for Distributed Energy Resources 

V < 50% V > 120% 

53

6 
 wer factor 0.85 l or h t

 

 

Interconnection Standards 

for Distributed Energy Resources 

• Other applicable codes and standards (not exhaustive) 

Requirement 

Voltage Regulation Maintain service voltage within ANSI C84 Range A (+/-5%) 

Voltage control Not permitted (IEEE 1547) 

Flicker Maximum Borderline of Irritation Curve (IEEE 1453) 

Harmonics <5% THD; <4% below 11th; <2% for 11th – 15th, <1.5% for 17th 

– 21st; 0.6% for 23rd – 33rd; <0.3% for 33rd and up (IEEE 519) 

P F Output power factor 0.85 lead/lag or higher (equipment Power Factor Output po ead/lag igher (equipmen 

typically designed for unity power factor) 

Direct Current Injection <0.5% current of full rated RMS output current (IEEE 1547) 

Synchronization and 

Protection 

Dedicated protection & synchronization equipment required, 

except smaller systems with utility-interactive inverters 

Safety NFPA NEC, IEEE NESC 

Interconnection Standards 

for Transmission-Connected Systems 

• Some key differences compared to DER 

– Need to consider some of these for PV as system size & penetration increase Need to consider some of these for PV as system size & penetration increase 

Requirement 

Voltage Tolerance Ride through 3-phase fault POI for up to 150 ms 

Frequency Tolerance Based on interconnection requirements 

Power Factor Capability +/- 0.95 pf (or higher depending on study results) 

Voltage Control Power factor, reactive power or voltage control at the 

discretion of transmission operator 

Synchronization, Protection Dedicated switching and protection equipment 

required for transmission-connected systems 

SCADA/EMS integration Required in all cases 

Power Control Emerging for high penetration wind. May need handle 

with market instruments in some cases 

Other NERC FAC/TPL/MOD/PRC/VAR standards 

54

7 
 

 

Frequency and Voltage 

Tolerance Standards (Bulk System)
 

• Voltage Tolerance (LVRT) 

– Tolerate bolted fault (0 volts) at POI for Tolerate bolted fault (0 volts) at POI for 

up to 9 cycles (150 ms) 

– FERC Order 661-A: applies to wind 

generators >20 MVA 

– WECC LVRT criterion: applies to all 

generators >10 MVA 

• Frequency Tolerance 

– For example, WECC ONF standard, 

which applies to all generators 

• Rules still evolving in the US and 

elsewhere 

WECC Off-Nominal Frequency Tolerance 

Frequency Range 

(Hz) 

Max. Clearing 

Time (sec) * 

59.4 – 60.5 N/A 

59.4 – 58.5 or 

60.6 – 61.5 3 min 

58.4 – 57.9 or 

61.6 – 61.7 30 sec 

57.8 – 57.4 7.5 sec 

57.3 – 56.9 45 cycles 

56.8 – 56.5 7.2 cycles 

< 56.4 or 

>61.7 instantaneous 

Frequency and Voltage 

Tolerance Standards (Bulk System) 

• Proposed NERC PRC-024 – Would apply to all generators 20 MVA Would apply to all generators 20 MVA 

or larger, and stations with multiple units 

with total capacity of 75 MVA or more 

55

8 
 

Emerging Power Control 

for High Penetration Wind 

Pkan

P

Pkan

bør

Pbør

Pkan

P

Absolute power limit Delta power limit 

bør

Power ramp limit 

Source: Energinet.dk 

Medium Voltage Standard 

in Germany (10 kV to 110 kV) 

• Fault Tolerance 

– Applies to PV as of 2011 Applies to PV as of 2011 

– Inverters must comply with boundary line 2 

– Must provide reactive support during fault (voltage control) 

Source: E. Troester, New German Grid 

Codes for Connecting PV to the Medium 

Voltage Power Grid, 2nd International 

Conference on Concentrating 

Photovoltaic Power plant 

56

9 
 

 

 

 

Medium Voltage Standard 

in Germany (10 kV to 110 kV) 

• Static Voltage Support 

– Provide capability of +/ Provide capability of +/- 0 95 . pf at full output (impacts equipment rating) at full output (impacts equipment rating) 

– Dispatch could be constant pf, constant Var, Var support based on power 

output (see example below) or Var support based on voltage 

– Applies to PV as of 2010 

Source: E. Troester, New German Grid 

Codes for Connecting PV to the Medium 

Voltage Power Grid, 2nd International 

Conference on Concentrating 

Photovoltaic Power plant 

Medium Voltage Standard 

in Germany (10 kV to 110 kV) 

• Active Power Control 

– Reduce power output when frequency is above 50 2 Hz Reduce power output when frequency is above 50.2 Hz 

– Applies to PV as of 2010 

Source: E. Troester, New German Grid 

Codes for Connecting PV to the Medium 

Voltage Power Grid, 2nd International 

Conference on Concentrating 

Photovoltaic Power plant 

57

10 
 

A Few Observations
 

•	 Wind and PV interconnection standards on different tracks, but 

converging converging 

•	 North American approach to bulk system standards tends to be 

“technology neutral” 

–	 It makes technical sense to apply different requirements to different 

technologies (e.g., European approach) 

–	 Difficult to reach consensus, long process 

•	 IEEE 1547 provides great foundation for DER IEEE 1547 provides gr t ea f unda o tion for DER

•	 Some future capabilities should be mandated by standards; 

some should be incentivized by markets 

–	 E.g., primary frequency support 

Where are We Going?
 

•	 Reconcile distribution/transmission standards 

–	 Voltage/frequency tolerance 

– Reactive power capabili l / l bility, volt/var control 

–	 SCADA integration 

–	 Power/Frequency control 

• Special cases (e.g., islands) or future very high-penetration? 

•	 Harmonization is important for efficiency and cost 

•	 Several active efforts underway 

–	 IEEE 1547.4, 1547.5, 1547.6, P2030 (Smart Grid) 

(http://grouper.ieee.org/groups/scc21/1547/1547_index.html) 

–	 US: NERC IVGTF (http://www.nerc.com/filez/ivgtf.html) 

–	 Europe: Medium and low voltage grid codes (GR, SP, FR, IT) 

58

11 
Questions and Discussion Questions and Discussion 

Codes & Standards Specific to PV 

Source Documents 

IEEE SCC21 – Standards Coordinating 

Committee on Fuel Cells, Photovoltaics, 

Dispersed Generation, and Energy Storage 

• IEEE 1547 series (DER up to 10 MVA) 

• Stand alone PV systems, batteries (several) 

• P2030 (Smart Grid – New initiative) 

Underwriters Laboratories Inc. (UL) PV 

Standards Technical Panels 

• UL 1703 (PV modules) 

• US 1741 (Inverters, charge controllers) 

NFPA NEC, Article 690 (solar Photovoltaic Systems) 

ASTM E44.09 – Technical Committee on 

Photovoltaic Electric Power Conversion 

Several addressing PV module and array 

testing 

IEC TC82 – Solar photovoltaic energy 

systems 

Several addressing measurement, safety, test 

procedures 

59

12 
 

PV System Models For System
 

Planning PlanningPlanning Planning and Interconnection onnection nnectionnnection Studies
 

Abraham Ellis 

Sandia National Laboratories 

aellis@sandia gov aellis@sandia.gov 

Cedar Rapids, IA – October 2009 

Growth in Grid-Tied PV Systems 

• Grid-tied PV as of as of 2008 

– 791 MW total (most in CA) 

– ~1,500 MW “under development” 

– Tendency toward larger systems 

60

1 
P d PV C it (MW) B d LGIP Q

 

Utility Industry Modeling Needs 

•	 The not-so-distant future 

Proposed PV Capacity (MW) Based on LGIP Queue 

Utility 2010 2011 2012 2013 

SCE 1350 2822 1540 2180 

NV Energy South 469 776 484 980 

•	 Reasonable concerns emerging 

– Wi hi f i ithin a few years, inverter-b d i asePV generation will

displace a non-trivial amount of conventional generation 

–	 Some areas likely to see higher penetration, larger projects 

–	 NERC reliability criteria are not likely to ease 

–	 How do we plan for it? 

Utility Industry Modeling Needs 

•	 Utility planners need models of these facilities to 

assess planning and operating impacts assess planning and operating impacts 

–	 Steady-state power flow (thermal, voltage) 

–	 Dynamic (transient stability) 

–	 Short circuit (interrupting capacity, system protection) 

•	 With very few exceptions, utility simulation tools don’t 

have standard-librarymodels for PV systems 

– At best, project developers provide insufficiently validated, 

manufacturer-specific, proprietary, user-created models 

– Worse yet, modeling short cuts (e.g., load netting) are used 

without adequate technical basis 

61

2 
 

 

 

 

Utility Industry Modeling Needs 

•	 NERC Intermittent and Variable Generation Task Force has 

identified the lack of industry-standard validated models 

as maj b orarrier to renewabl e energy development 

“Validated, generic, non-confidential, and public standard power flow and stability 

(positive-sequence) models for variable generation technologies are needed. 

Such models should be readily validated and publicly available to power 

utilities and all other industry stakeholders. Model parameters should be 

provided by variable generation manufacturers and a common model 

validation standard across all technologies should be adopted. The NERC 

Planning Committee should undertake a review of the appropriate Modeling, 

Data and Analysis (MOD) Standards to ensure high levels of variable generation 

can be simulated.” 

Source: NERC Special Report, Accommodating High Levels of Variable Generation, 

http://www.nerc.com/files/IVGTF_Report_041609.pdf 

Type of Planning Models 

Type Main Application 

Example of 

Main Application Commercial 

Platforms 

Power flow, 

unbalanced 

Power flow (static) simulation of 

distribution networks. Software also does 

motor start, protection coord., etc. 

FeederALL, SynerGEE, 

EasyPower 

Power flow, positive 

sequence 

Large-scale power flow simulations of bulk 

transmission systems 

PSS/E, PSLF, ETAP, 

Power World 

Dynamic, positive Large-scale dynamic simulations of bulk PSS/E PSLF ETAP sequence transmission systems PSS/E, PSLF, ETAP 

Transient, three 

phase 

Detailed analysis of power system 

electromagnetic/mechanical and control 

interaction and performance 

PSCAD, Matlab, 

EMTP-RV 

Short Circuit Fault analysis protection coordination Aspen, SynerGEE, 

62

3 
.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

PV System Power Flow Models 

•	 PV inverters may be modeled as conventional generators in 

the steady state 

•	 Over and under excitation limits must be set with cognizance 

of inverter reactive capabilities and modes: 

–	 Constant power factor 

–	 Constant reactive power 

–	 Voltage regulation 

•	 For large-scale simulations, power flow modeling data 

requirements must be reduced through “equivalencing”, 

similar to wind plants 

–	 Refer to existing WECC Wind Power Plant Power Flow Modeling Guide 

(www.wecc.biz) 

PV System Power Flow Models 

• For utility-scale PV 

PV Array = ... ... ... ... ~ 

= 

~ 

= 

~ 

= 

~ 

= 

~ 

= 

~ ~ 

= = 

~ 

= 

~ Equivalencing Inverter 

Transmission
 

system
 

PV Inverter 

Medium Voltage Transformer
 

PV Feeder
 Other PV plant 

Station Feeders
 

transformer
 

Equivalent PV Transmission Equivalent Inverter system PV Feeder Transformer 

~ 

Station Equivalent 

transformer generator 

63

4 
 

• For distributed PV 

PV System Power Flow Models 

Utility-scale PV 

Transmission 

system 

Distribution 

system 

Unit Station 

Transformer 

Transmission 

system 

Unit Station 

Transformer (LTC) 

Residential 

Commercial 

Pad/Pole 

Transformer 

~

DG Load

Model feeder impedance for dynamics 

(e.g., WECC Composite Load Model) 

PV System Dynamic Models
 

•	 Vendor-specific, user-written models have a role, but 

don’t meet critical needs 

•	 Generic models have multiple advantages 

–	 Provide viable alternative to meet utility requirements 

– Allow interconnection studies to proceed before inverter 

and PV module manufacturers are selected 

– Reduce manufacturer confidentiality concerns with respect 

to proprietary aspects of user-created models 

–	 Provide improvements in: 

•	 Quality (thoroughly tested for compatibility with core software) 

•	 Portability (across different simulation platforms) 

•	 Usability (similar to familiar look and feel of conventional 

generator/turbine/exciter standard library models) 

64

5 
–

 

 

 

PV System Dynamic Models 

•	 A user-friendly generic PV system dynamic model would: 

–	 Approximate the aggregate dynamic response of a large number of 

inverters at a common point of interconnection inverters at a common point of interconnection 

–	 Be suitable for simulating plant response of the plant to grid 

disturbances, typically 3-phase faults up to 200 ms in duration and 

ground faults up to one second in duration 

–	 Allow for simulation of the post transient response of the plant for 

periods of 20 to 30 seconds 

–	 Allow for simulation of the response of the plant to cloud-induced 

i di t t irradiance transients 

–	 Allow for simulation time steps as high as 1⁄2 cycle (8.3 ms) 

–	 Provide for user-settable gains, time constants, and feature on/off 

switches (e.g., for voltage control, droop response, etc.) that would be 

specified by the manufacturers for their specific hardware 

–	 Validated against field data, higher order (EMTP-type) models, or both 

PV System Dynamic Models
 

•	 Generic models pose challenges that can be overcome 

–	 Evolving technology and performance standards Evolving technology and performance standards 

•	 Models need to include existing as well as future capabilities 

•	 Grid codes activities can guide model development 

– Access to detailed technical information from manufacturers 

for model development and validation 

•	 Timing is right 

–	 Numerous user-written models have been developed 

–	 Industry interest is very high 

– Have opportunity to build on technical work and industry 

connections laid out by WECC/IEEE generic wind generator 

modeling effort 

65

6 
 

A Potential Implementation 

• Model connectivity 

Model 

Solar 

Irradiance 

DC 

Voltage 

DC 

Current 

D- and Q-Axis 

Current 

D- and Q-Axis 

Voltage 

Desired 

Q-Axis 

Current 

Network 

Model 

(implemented 

in PSS/E or 

PSLF) 

PV Array 

Model 

Inverter 

Reactive Power or 

Voltage Regulator 

Model 

A Potential Implementation 

• Inverter model 

66

7 
A Potential Implementation 

• PV array model 

0.6 

0.8 

1 

1.2 

1.4 

Array Current (pu)Array Power (pu)

Increasing 

Irradiance 

Increasing 

Irradiance 

Increasing 

Irradiance 

0 

0.2 

0.4 

AA 

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 

Array Voltage (pu) 

A Potential Implementation 

• Basic Assumptions 

–	 PV array is voltage- and irradiance-dependent current source 

•	 Temperature impacts can be neglected in simulation frames of 

interest for transient stability 

•	 Current response to voltage or irradiance transient is instantaneous 

(algebraic) 

– Inverters are high-frequency, pulse-width-modulated, ac 

current-regulated, voltage-source type 

•	 Wi h 1⁄2 l i With 1⁄2 cycle time step, current regul d d l d i lator and modulator dynamics 

may be neglected... perhaps 

•	 Primary dynamics are due to inverter dc capacitor bank, dc voltage 

regulator and ac phase lock algorithms 

•	 Additional dynamics may be introduced to influence real and 

reactive power ramp rates 

67

8 
–

 

Activities Already Underway 

•	 PV modeling initiative already started under the auspices of the WECC 

Modeling and Validation Work Group (MVWG) 

–	 Development, implementation & di Development, implementation & dissemination ssemination to be conducted openly to be conducted openly 

– Similar in character to the generic wind modeling, also championed by WECC 

•	 WECC group has broad industry representation 

–	 National Laboratories (Sandia coordinating effort) 

–	 Utilities 

–	 Equipment manufacturers 

–	 Project developers and systems integrators 

–	 Software developers Software developers 

–	 Consultants 

•	 Initial focus is on positive-sequence power flow and dynamic models 

•	 Models to be introduced to broader audience via technical coordination and 

outreach (WECC, NERC, IEEE) 

Conclusions
 

•	 PV generation proposed and under development is becoming 

too large to ignore in the planning process 

•	 The lack of access to user-friendly, validated models is a 

barrier to rapid deployment of PV 

–	 User written models have a role, but don’t meet critical utility 

requirements 

–	 Need to make progress on standardized, validated PV system models 

for use in traditional transmission planning 

•	 Generic models pose technical and institutional challenges 

that can be overcome 

•	 Industry efforts already underway... Stay Tuned! 

68

9 
Questions and Discussion Questions and Discussion 

69

10 
1

Utility operations and variable 

generation

Utility-scale PV 

Variability 

Workshop

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

Michael Milligan

Oct 7, 2009

Outline

• Overview of utility operations

– Interconnections

– Balancing

– Time frames for operations

• Possible impacts of PV variability and 

uncertainty

• Miti ti lt ti Mitigation alternatives

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70
2

OVERVIEW OF UTILITY 

OPERATIONS: VG 

INTEGRATION

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1 2

National Renewable Energy Laboratory Innovation for Our Energy Future

3

71
3

General Operating Requirements

• The interconnection must be balanced 

∑ loads = ∑ generation *

• Implication for individual balancing 

areas (BAs)

∑ loads = ∑ generation + Imports – Exports

* DC ties can span two 

interconnections, but 

these are limited

Balancing Areas in the West

National Renewable Energy Laboratory Innovation for Our Energy Future

72
4

Markets cover part of the U.S.

NPPD has 

joined SPP

How does the utility schedule resources?

• Non-market areas (regulated by state 

PUCs)

– Balancing Authority is responsible for 

scheduling generation to meet expected 

loads

– Individual utilities will often provide 

schedules to BA, based on economics

• Market areas Market areas

– Similar, except the schedules are induced 

by the energy market, not single entity

73
5

Economic Dispatch

Load Duration Curve

16x103

14

12

10

8

6

MW

4

2

0

0 2000 4000 6000 8000

Hours

Economic Dispatch

16x103

14

Peaking gens provide 

capacity, not much energy

12

10

8

6

MW

Base load generation 

does not change its 

Intermediate gens change 

output in response to load

4

2

0

0 2000 4000 6000 8000

Hours

output

74
6

(MW)

A Chronological View

Time (hour of day)

048 12 16 20 24

System Load (

tens of minutes to hours day seconds to minutes

Regulation

tens of minutes to hours

Load

Following

day

Scheduling

Hours to Day ahead: use load forecast to commit units

10 minutes to few hours: manually adjust generator set points

Seconds to minutes: AGC automatically adjusts generator output

Load is inherently variable

14x103

12

6,600 Hours of 5-minute Electric Load

10

8

6

4

2

Load (MW)

1000

500 changes

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0

5-Minute Periods for Approximately 9 Months

0

-500

-1000

5-minute c

75
7

Impact of 25% Wind Energy 

Penetration: 5-minute data

14x103

12

10

8 oad (MW)

6,600 Hours of 5-minute Electric Load 14 x1 03

12

10

8 oad (MW)

6,6 00 H o urs o f 5-m inut e Elec tric Loa d and W in d

• Wind: Ramp requirements increase with 25% wind 

energy penetration The upper panel also shows the

6

4

2

Lo

0

5-Minute Periods for Approximately 9 Months

1000

500

0

-500

-1000

5-minute changes

6

4

2

Lo

0

5 -M inu te Pe riods fo r A pprox im ate ly 9 Mon ths

-1000

-5 00

0

500

1000

5-minute changes

National Renewable Energy Laboratory Innovation for Our Energy Future

energy penetration. The upper panel also shows the

importance of being able to achieve lower minimum 

loads by the conventional generation fleet. 

• Solar: Increase in ramping requirements; min-gen not 

an issue

Variable generation increases the variability 

that must be managed on the grid

• Wind

– Some impact on regulation

More significant impact on System Load (MW)– More significant impact on

load-following and unit 

commitment

• PV

– Potentially large impact on 

regulation

– Some impact on load

Time (hour of day)

048 12 16 20 24

S

seconds to minutes

Regulation

tens of minutes to hours

Load

Following

day

Scheduling

Some impact on load

following, likely less 

significant than on regulation 

and wind

National Renewable Energy Laboratory Innovation for Our Energy Future

76
8

Unit commitment and uncertainty

• Is there sufficient committed capacity to cover 

uncertainty of load forecast error, VG forecast 

error? (spin)

• How much non-spin is available? 

• Can I transact with the neighbors on fast time 

scales?

15x103

 Load

 Net Load

 Wind

One week in Early Aug

15x103

One week in Early Aug Load

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10

5

0

Load and Wind (MW)

5040 5060 5080 5100 5120 5140 5160 5180 5200

Hour of Year

10

5

0

Load and Wind (MW)

5040 5060 5080 5100 5120 5140 5160 5180 5200

Hour of Year

Advanced unit commitment

• Rolling commitment as new information 

becomes available ~6-hour time steps

• Stochastic unit commitment can help 

hedge your bets

• Does unit commitment handle the 

“sharp edges” or box you in?

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77
9

Data Requirements for Integration 

Analysis: VG and Load

• Weather is common driver

• Hourly VG and load data must be from same year for 

consistent analysis and plausible results

• Use of meso Use of meso-scale weather models or actual VG production is scale weather models or actual VG production is

state of the art

• Utility Wind Integration and Operating Impact State of the Art, 

IEEE Transactions, Aug 2007. 

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CAPACITY VALUE

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78
10

System Adequacy

• Often measured based on installed capacity, peak load, 

and a planning reserve

• A fixed planning reserve margin (15%) does not in itself 

provide a measure of adequacy provide a measure of adequacy

• No system can be perfectly adequate

• How adequate is adequate enough?

• Quantify the number of times system will be inadequate –

often measured as hours/year; days/year (1d/10y ≈ 99.97%)

National Renewable Energy Laboratory Innovation for Our Energy Future

Effective Load Carrying Capability (ELCC)

0.12

s

0 11

/year)

 Original Reliability Curve

 Reliability Curve after Adding New Generation

 Target Reliability Level

Each generator added to the system 0.11

0.10

0.09

0.08

oad Expectation (days

g y

helps increase the load that can be 

supplied at all reliability levels

Gi Gi+1 Gi+2

Added Generators

National Renewable Energy Laboratory Innovation for Our Energy Future

0.07

0.06

Loss of L

o

8.0 8.5 9.0 9.5 10.0 10.5

Load (GW)

<-400 MW->

79
11

Pitfalls (things to avoid)

• Using load and VG data from different 

years

• Applying load forecasts to load shape 

that changes underlying “weather” 

assumption

• Sampling VG/resource data from 

different years (i e long term database)

National Renewable Energy Laboratory Innovation for Our Energy Future

different years (i.e. long term database)

• Assume that one year is enough

Simplified approaches are sometimes applied 

(full ELCC computation can be expensive)

ISO New England

October-May

Peak Period Methods

NY ISO

Idaho Power

PNM

ISO New England

June-September

July

July

December -February

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12:00 PM 1:00 PM 2:00 PM 3:00 PM 4:00 PM 5:00 PM 6:00 PM 7:00 PM 8:00 PM

CPUC

PJM

NY ISO

June - August

June - August

May -September

80
12

Issues for high VG penetration: what is 

needed for successful integration?

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Key Challenges for VG Integration at 

High Penetration

• Can the increased variability be 

accommodated?

• Can the increased uncertainty be 

accommodated?

• Is there sufficient turn-down capacity?

• Is there sufficient transmission to 

National Renewable Energy Laboratory Innovation for Our Energy Future

ensure deliverability?

81
13

Can the non-wind fleet ramp quickly 

enough?

3200

3000

2800

Peaking -

$90/MW h

Energy Price $10/MW h E nergy Price $10/MW h E nergy Price Increases

to $90/MW h because

base unit can't ramp

fast enough

National Renewable Energy Laboratory Innovation for Our Energy Future

2600

2400

4:00 AM 6:00 AM 8:00 AM 10:00 AM 12:00 P M 2:00 PM 4:00 PM 6:00 PM

Base Load - $10 /MW h

Integrating “large” VG penetrations: 

what does it take?

• Better use of existing flexibility

• Acq y uire additional flexibility across BAs

• Acquire additional physical flexibility

National Renewable Energy Laboratory Innovation for Our Energy Future

82
14

Better use of existing flexibility

• Tap into maneuverable 

generation that may be 

“behind the wall”1 4000%

5000%

ity vs Load Need

CA ISO PJM WAPA

42,400 55,600 3,087 MW Peak Load

 Generation

21,900 47,000 2,911 Fossil (measured)

 13,100 2,500 700 Hydro

 4,600 13,500 0 Nuclear

3 700 600 11 Other • Provide a mechanism 

(market, contract, other) 

that benefits system 

operator and generator

• Fast energy markets help 

provide needed flexibility2 

and can often supply load

0%

1000%

2000%

3000%

0 2000 4000 6000 8000

Hours/Year

Measured Fossil Ramping Capac

PJM

CA ISO

 3,700 600 11 Other

 2002 Hourly Data and Generation Capacity

WAPA

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and can often supply load

following flexibility at no 

cost3

1Kirby & Milligan, 2005 Methodology for Examining Control Area Ramping Capabilities with Implications for Wind 

http://www.nrel.gov/docs/fy05osti/38153.pdf

2Kirby & Milligan, 2008 Facilitating Wind Development: The Importance of Electric Industry Structure. 

http://www.nrel.gov/docs/fy08osti/43251.pdf

3Milligan & Kirby 2007, Impact of Balancing Areas Size, Obligation Sharing, and Ramping Capability on Wind Integration . 

http://www.nrel.gov/docs/fy07osti/41809.pdf

Acquire additional flexibility across BAs

• Reduce the need for ramping by combined BAs (real or virtual)

– Inter-BA scheduling rules/practices

– Ramping capability adds linearly

– Ramping need adds less than linearly

-1000

-500

0

500

1000

Ramp (MW/hr) m

400

ping (MW/hr)

Operating separate balancing areas causes 

extra ramping compared to combined operations.

Blue: up-ramp

Green: down-ramp

Yellow: combined ramp

Some areas are ramping up nearly 1000 MW/hr 

while other areas are ramping down nearly 500 MW/hr

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5280 5286 5292 5298 5304

Hour of Year (one day)

-400

-200

0

200

400

Excess Ram

Ramping that could be eliminated by combining operations

Milligan & Kirby 2007, Impact of Balancing Areas Size, Obligation Sharing, and Ramping Capability on 

Wind Integration . http://www.nrel.gov/docs/fy07osti/41809.pdf

83
15

Large, infrequent 

5-Minute Ramps 

can be 

significantly 

reduced

-400

-200

0

200

400

600

mp Comparison: Separate vs. Combined Operations

 Load and Wind

Load Only

This graph shows the difference in excess ramping

requirements between wind and no-wind cases.

Operations

-600

Excess Ram

0 2 4 6 8 10

5-Minute Periods

Load Only

400

-200

0

200

400

600

m p C om parison: Separate vs. Com bined 

Load and W ind

-600

-400

Excess Ram

0 200 400 600 800 1000

5-Minute Periods

Load and W ind

 Load Only

Milligan & Kirby 2008, An Analysis of Sub-Hourly Ramping Impacts of 

Wind Energy and Balancing Area Size: 

http://www.nrel.gov/docs/fy08osti/43434.pdf

Recognized Importance 

of Fast Energy Markets

• Fast markets

– Will improve overall 

syst f em performance 

and economics

– Will correctly 

separate load 

following from 

regulation, increasing

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g g

flexibility and 

reducing costs

Milligan & Kirby 2007, Impact of Balancing Areas Size, Obligation Sharing, and Ramping Capability on 

Wind Integration . http://www.nrel.gov/docs/fy07osti/41809.pdf found that fast energy markets can often 

supply the required load following capacity as a by-product of the energy market.

84
16

Balancing Area Consolidation: What Other 

Analyses/Experiments are Underway?

• Virtual consolidation

– NTTG’s ADI

– P ibl i t l d Possible expansion to loadfollowing time scale

– Joint Initiative DSS, ITAP

• NREL’s Large-scale studies

– Western Wind and Solar 

Integration Study (WWSIS)

National Renewable Energy Laboratory Innovation for Our Energy Future

– Eastern Wind and 

Transmission Study (EWITS) 

with JCSP

– Nebraska Power Association

Balancing Area Consolidation: What Other 

Analyses/Experiments are Underway?

• Activity in the NW includes BPA’s ‘feedforward’ AGC concept

• Joint NREL-PNNL work

– Interest in WECC-wide analysis 

collaboratively with WECC and Variable 

Generation Subcommittee

– Northwest analysis Northwest analysis

National Renewable Energy Laboratory Innovation for Our Energy Future

85
17

Other Flexibility Options

• Fast-ramping generation with 

good heat rates, low turndown, low start-up cost

• Bi-lateral pooling agreements lateral pooling agreements

(similar to ADI but longer time 

frames)

• Innovation in hydro 

scheduling

• Economic VG curtailment, 

ramp limitations during critical 

periods

National Renewable Energy Laboratory Innovation for Our Energy Future

– Morning load pickup or evening 

load drop off

– Other

• Storage has value, but may 

not be currently cost-effective

Summary

• Aggregation damps variability (solar, wind, 

load, and solar+wind+load together)

• Variability can be measured statistically and Variability can be measured statistically and

mapped to current operational practice

– Load following

– Regulation

• We care about tail events, and these often 

have important VG and load influences 

time-synchronized data

National Renewable Energy Laboratory Innovation for Our Energy Future

– Potential to reduce tail events thru aggregation

• Forecasts help, but must be brought into 

standard operating practice/tools

86
18

National Renewable Energy Laboratory Innovation for Our Energy Future

87
 

 

 

 

 

Solar Power Forecasting 

Perspective and Understanding

on Solar Power Forecasting 

Utility-Scale PV Variability Workshop Scale PV Variability Workshop
 

October 7, 2009

Cedar Rapids, Iowa
 

Mark Ahlstrom and J. Adam Kankiewicz
 mark@windlogics.com
 

WindLogics, NextEra Energy Resources & FPL 

 WindLogics founded in 1989 

by supercomputer architects 

 Assessment, forecasting,

operations and integration 

of renewable energy 

 Staff of 80, including a 

Ph.D.-level research center 

 Became FPL Energy subsidiary 

(now NextEra) in 2006 (now NextEra) in 2006 

 NextEra is largest wind and

solar developer/owner in 

North America 

Copyright 2008, WindLogics Inc. All Rights Reserved	 2 

88

1 
de s o e so a o ecas s o a a o

 

 

 

The General Situation 

Clouds are the number one influence on a solar forecast
 

 You can see coherent patterns of motion for You can see coherent patterns of motion for “stable” clouds 

 Convective events (“unstable” clouds) will always be a challenge to 

predict, though like for wind, we can roughly predict the risk 

 Weather models have challenges with cloud forecasting 

There are excellent satellite-based cloud resources available to 

gu ide short term solar forecasts (no analogy d)for wind)

Aerosols and haze also have a significant impact on energy

production (but less so on ramps)
 

Copyright 2008, WindLogics Inc. All Rights Reserved 3 

So what’s the problem here... 

Very short term ramp & variability forecasting Very short term ramp & variability forecasting 

 Next thirty minutes 

Load following forecasting 

 Next five hours 

Day-ahead unit commitment forecasting 

 Next six hours to two days 

Copyright 2008, WindLogics Inc. All Rights Reserved 4 

89

2 
Visual Solar Forecasting (next minutes)
 

Yankee 

Environmental 

Systems
 

Total Sky Imager 

( TSI-880)

Copyright 2009, WindLogics Inc. All Rights Reserved 

http://www.nrel.gov/midc/srrl_bms 

5 

Sub-Hourly Solar Forecasting (next minutes) 

NORTH 

E 

S 

T 

W E • Digitized cloud mask 

A • Short term forecasting

S • Cloud tracking capable T 

• Operational assessment 

SOUTH Courtesy of 

Copyright 2009, WindLogics Inc. All Rights Reserved 6 

90

3 
Satellite-based Solar Forecasting (next hours)
 

July 15, 2009 

Satellite imagery, 

retrieved clouds and 

surface radiation from 

GOES West from NOAA’s 

GOES Surface and 

Insolation Products 

(GSIP) Algorithm 

Satellite visible Cloud properties 

Copyright 2009, WindLogics Inc. All Rights Reserved 7

(GSIP) Algorithm 

Courtesy of 

Dr. Manajit Sengupta 

Dr. Istvan Laszlo 

NOAA/NESDIS/STAR 

Dr. Andrew Heidinger 

UW/CIMSS/NOAA 

Satellite infrared Surface radiation (GHI) 

Satellite-based Solar Forecasting (next hours)
 

July 15, 2009 

Satellite visible Cloud properties Satellite imagery, 

retrieved clouds and 

surface radiation from 

GOES West from NOAA’s 

GOES Surface and 

Insolation Products 

(GSIP) Algorithm 

Copyright 2009, WindLogics Inc. All Rights Reserved 8

Satellite infrared Surface radiation (GHI) 

(GSIP) Algorithm 

Courtesy of 

Dr. Manajit Sengupta 

Dr. Istvan Laszlo 

NOAA/NESDIS/STAR 

Dr. Andrew Heidinger 

UW/CIMSS/NOAA 

91

4 
Satellite-based Solar Forecasting (next hours) 

Copyright 2009, WindLogics Inc. All Rights Reserved 9

© Richard Perez et al., ASRC 

A Satellite-based Cloud Forecasting Example 

Motion Vector Analysis of Cloud Fields 

Copyright 2009, WindLogics Inc. All Rights Reserved 10

Assumes Steady-State Cloud Field 

92

5 
Comparison of satellite-based forecasts and forecasts 

based on Numerical Weather Prediction (NWP) models 

Satellite-based Solar Forecasting 

Copyright 2009, WindLogics Inc. All Rights Reserved 11

RUC2 Model (Analysis) RUC2 Model (6-Hour Forecast) 

NWP Solar Forecasting 

Copyright 2009, WindLogics Inc. All Rights Reserved 12

Integrated Liquid and Frozen Hydrometeors (all levels) Courtesy of 

93

6 
Visible Satellite (Analysis) Visible Satellite (6-Hour Forecast) 

NWP Solar Forecasting 

Copyright 2009, WindLogics Inc. All Rights Reserved 13

Courtesy of 

Infrared Satellite (Analysis) Infrared Satellite (6-Hour Forecast) 

NWP Solar Forecasting 

Copyright 2009, WindLogics Inc. All Rights Reserved 14

Courtesy of 

94

7 
t

NWP Solar Forecasting – Convective Challenges
 

NCEP Model (GFS) Forecast Examples (a convective example on left) 

Copyright 2009, WindLogics Inc. All Rights Reserved 

Predicted 

Satellite 

Irradiance 

(11μm) 

Observed 

S llit atellite 

Irradiance 

(11μm) 

Courtesy of 

15 

Power (MW) 

Motivation for Forecast Training 

Correcting for Systematic Biases with Model Training

200
 

160
 

120
 

80
 

40
 

Raw 

Trained 

Observed 

23 00 01 02 03 04 05 06 07 08 09 

Copyright 2009, WindLogics Inc. All Rights Reserved 

Forecast Horizon (Hours) 

0

16 

95

8 
 

 

Conclusions 

Solar forecasting & integration build on the industry’s wind 

integration experience
 

While accurate forecasting is challenging, solar has some

characteristics that make it easier than wind forecasting
 

Although still in development, we can expect to see a variety of 

good solar forecasting models and services 

In the control room, we should look work toward a unified view 

of both wind and solar integration of both wind and solar integration 

Because NERC and others have already started work on 

variable generation issues, and because solar is already being

included in this work, solar is on a fast path 

Copyright 2008, WindLogics Inc. All Rights Reserved 17 

Near Future
 

Mark Ahlstrom
 

CEO
 

651.556.4262
 

mark@windlogics.com
 

Adam Kankiewicz
 Solar Technical Product Manager
 

651.556.4228
 

kankie@windlogics.com
 

Florida Power & Light DeSoto Project (25 MW PV) 

in Central Florida - Commissioning Fall 2009 

Copyright 2008, WindLogics Inc. All Rights Reserved 18 

96

9 
2

Wind and Solar 

Integration 

Studies 

October 7, 2009 

Utility Scale PV Variability Workshop 

“oh yeah, and we do Wind, too.” 

• Data (what we want in the future) 

GE Energy Infrastructure GE Energy Infrastructure 

Cedar Rapids 

Nicholas W. Miller 

Large Scale Solar Integration Studies
 

Overview 

• Experience 

• Objectives 

• Data (as available) 

• Methods and results 

97
Large scale renewable studies by GE
 

These studies were commissioned by the 

Energy Commissions and ISOs of each 

region... . . 

• Examining the Feasibility of 100+ GW of 

new wind and other renewable resource 

additions 

• Considering Operability, Costs, Emissions, 

Transmission Constraints, Forecasting 

2006 California: 

2004 New York: 

3 GW Wind 

10% of Peak Load 

4% of Energy 

2005 Ontario: 

15 GW Wind 

50% Peak Load 

~30% Energy 

13 GW Wind 

3 GW Solar 

5 GW Bio & Geo 

26% Peak Load 

15% Energy (33% Total) 

2007 Texas: 

15 GW Wind 

25 % Peak Load 

17% Energy 

2008-9 Western Wind & Solar:
 Oahu 

Wind (all of Western US)
 Solar PV 

72 GW Wind 

15 GW Solar 

50% Peak Load Lanai 

Wind	 27% Energy 

Variable Generation 

Wind 

•	 Variations cover many timescales 

•	 Season, day, hour, minute 

Solar 

•	 Variations are dominated by day/night cycle 

and sky conditions (clouds) 

Biomass and geothermal generation are not 

intermittent 

3 

4 

98
Objectives (typical large scale study) 

Evaluate grid operation with increasing levels of 

variable generation 

•	 Target levels of wind and solar penetration 

Identify and quantify system performance and 


operation problems
 

• Load following, regulation, minimum load, etc. 

Identify and evaluate possible mitigation methods 

5 

A mix of examples from the California and Western Wind and 

Solar Integration studies follows... 

California Study: 4 Scenarios Analyzed
 

2006 Base Case – Existing transmission system with 

existing mix of generation resources 

• Includes 2,100 MW wind and 330 MW solar 

2010T Tehachapi Case – 20% Renewable Energy 

•	 7,500 MW wind and 1,900 MW solar in California 

•	 Includes 4,200 MW of wind in Tehachapi region and 

new 500 kV transmission to support it 

2010X Accelerated Case – 33% Renewable Energy 

• 12,500 MW wind and 2,600 MW solar in California 

2020 Case – 33% Renewable Energy 

• 12,700 MW wind and 6,000 MW solar in California 

6 

99
7

Wind and Solar Generation in California
 

2006 2010T 2010X 2020 

Concentrating Solar (CS) 

Number of Sites 7 12 42 43 

Total CS MW 330 1200 2100 3100 

Photovoltaic (PV) 

Number of Sites 0 * 136 128 228 

Total PV MW 0 * 630 530 2900 

Wind Plants 

Total Sites in CA 57 98 142 147 

Sites in Tehachapi 16 40 54 54 

Total Wind MW 2100 7500 12500 12700 

* Existing PV aggregated with load 

Types of Analysis 

Statistical Analysis 

• Multiple time periods (Hourly, 5-minute, 1-minute) 

Production Cost Simulation with MAPS 

•	 Hour-by-hour simulation of grid operations for an 

entire year (3 years of different wind, solar and load 

profiles) 

Quasi-Steady-State Simulation with PSLF 

•	 Minute-by-minute time-sequenced power flows for 

entire WECC grid for several hours 

8 

100
Data 

Power flow data from Davis Power Consultants 

• Scenarios 2006, 2010T, 2010X, 2020
 

Load data from CAISO, 2002-2004
 

•	 Hourly load MW, forecast and actual 

•	 4-sec load MW for about 400 days 

• Load data scaled up to peak for 2006, 2010, 2020 

Wind data from AWS Truewind, 2002-2004 

•	 Hourly wind MW, forecast and actual 

•	 1-minute wind MW for 51 selected periods 

•	 Separate wind profile data for each wind farm 

Production simulation data for California and WECC from 

Rumla, Inc. 

Data 

Solar data from multiple sources 

•	 Hourly and 1-min MW for Sungen and Luz for 2002­

2004 (CAISO and UC-Davis) 

•	 Hourly Stirling solar MW for Mojave and Imperial for 

2002-2004 (NREL and SES) 

•	 Hourly and 15-min Photovoltaic MW for one year, 

aggregated by zip code (CPUC - SGIP) 

•	 1-min or 3-min solar insolation data at two sites, for 

January and July 2002 (NREL, ARSC SUNY Albany) 

Based on this data, GE compiled solar profiles 

for multiple sites across California 

9 

10 

101
Compiling and Extrapolating
 

Tricks we used to extend the data 

•	 Extraction of 1-min or 3-min variability – 

essentially high pass filter 

•	 Retain temporal and spatial diversity for slower 

variations (1 hour and 15 minute samples) 

•	 Overlay ‘fast variability’ 

•	 Random draw of variability from a limited sample 

•	 Assumes NO correlation for high frequency 

variability 

11 

Best available...but is it good enough? 

16-Jan 

17-Jan 

18-Jan 

19-Jan 

20-Jan 

21-Jan 

22-Jan 

23-Jan 

24-Jan 

25-Jan 

26-Jan 

27-Jan 

28-Jan 

29-Jan 

30-Jan 

31-Jan 

1 minute resolution; single site insolation (w/m^2) in CO. 

Jan 2002 

800 

700 

600 

500 

400 

300 

200 

100 

0 

-100 

0:00 4:00 8:00 12:00 16:00 20:00 

12 

102
13

PV fast variability overlay
 

1.00 

0.80 

0.60 Comparison of 


Original 15-minute 


Data and Final 


Power Output (pu) 0.40 

Profile with 1- 0.20 

15-minute w/ Interpolation 

15-minute CA + 1-minute CO 

minute Variability. 0.00 

0 240 480 720 960 

1-min Intervals 1400 

1200 Irradiation Data 

used as Source of 


1-minute 


Variability in 


Example.
 

Insolation (W/m ^2) 1000 

800 

600 

400 

200 

0 

0 240 480 720 960 

1-min Intervals 

Example PV Solar Zip Code Profile for a 

July Morning 

150 

100 

50 

5 10 15 20 

Zip Code 920 

Zip Code 91a 

Zip Code 95a 

Zip Code 91b 

Zip Code 905 

Zip Code 900 

Zip Code 921 

Zip Code 956 

Zip Code 94a 

Zip Code 945 

0 

14 

103
Example Concentrating Solar Project 

Profile for a May Day 

500
 

400
 

300
 

200
 

100
 

0 

Sungen & Luz 

5 10 15 20
 

Example Stirling Solar Project Profile 

for a May Day 

16 

0 

100 

200 

300 

400 

500 

5 10 15 20 

Stirling 500MW 

Stirling 300MW 

15 

104
GE Energy Infrastructure Some of the things we observed and learned
 

Temporal Pattern: July 2003 Average Day 

50000
 5000
 

40000
 4000
 

Average Load 

Average L-W-S 

Average Wind 

Average Solar 

Wind & Solar tend to 

be complementary. 

1 5 9 13 17 21
 

Load (MW)

Wind & Solar (MW)

30000
 3000
 

20000
 2000
 

10000
 1000
 

0 

Hour 18
 

0 

105
1500 

Temporal Pattern: All Days of July 2003
 

2000
 

Average Solar 

0 4 8 12 16 20 24
 

1000
 

500
 

0 

Hour 

Solar (MW) 

Temporal Pattern: January 2002 Average Day
 

50000
 5000
 

30000
 

Average Load 

Average L-W-S 

Average Wind 

Average Solar 

3000
 

20000
 2000
 

10000
 1000
 

0 0 

40000
 4000
 

Wind & Solar (MW)

Load (MW)

1 5 9 13 17 21
 

Hour 

19 

20 

106
Bin

MW

1500 

Temporal Pattern: All Days of January 2003
 

2000
 

1000
 

500
 

0 

Solar (MW) 

Average Solar 

0 4 8 12 16 20 24
 

Hour 

2006 Hourly Wind & Solar Penetration
 

0.0% 

0.5% 

1.0% 

1.5% 

2.0% 

2.5% 

3.0% 

3.5% 

4.0% 

1 2 3 4 5 6 7 8 9 10 

Wind Penetration 

Solar Penetration 

Decile 

% 

Wind Penetration = Average Wind MW / Average Load MW 

Solar Penetration = Average Solar MW / Average Load MW 

Solar penetration 

has positive 

correlation with load. 

Wind penetration 

has inverse 

correlation with load. 

21 

22 

107
MW

Solar Generation and Penetration Duration Curves – 

2010X 

2500 10% 

Total Actual Solar (MW) 

Total Actual Solar Penetration (%) 9% 

8% 

7% 

6% 

5% 

4% 

3% 

2% 

1% 

0% 

Solar MW


2000 

1500 

1000 

500 

0 

Wind & Solar (MW) Penetration 

0 2630 5260 7890 10520 13150 15780 18410 21040 23670 26300 

Hours 

Morning Load Rise Detail: July 21, 2003

45000 2000 

40000 1500 

6:00 7:00 8:00 9:00 

0 

Load 

Load-Wind-Solar 

Wind 

Solar 

Load (MW)


35000 1000 

30000 500 

25000 

Time 

23 

24 

108
Load Following Requirement: July 21, 2003 

(5 Minute Deltas on 15 Minute Rolling Average) 500 250 

Mean ( 

Load 265 54 

L-W-S 275 52 

Wind -16 18 

Solar 7 13 

300 150 

200 100 

100 50 

0 0 

-100 -50 

6:00 7:00 8:00 9:00 

Load 

Load-Wind-Solar 

Wind 

Solar 

400 200 

Wind & Solar (Delta MW per 5 Minutes)


Load (Delta MW per 5 Minutes)
 

Time 

300 

Regulation Requirement : July 21, 2003 

(1 Minute Deltas from 15 Min Rolling Average) 200 

Mean ( 

Load 6.5 56 

L-W-S 6.8 61 

Wind -0.3 9 

Solar 0.0 22 Wind & Solar (Delta MW per 1 Minute)


0 

Load 

Load-Wind-Solar 

Wind 

Solar 

100 

-300 0

Load (Delta MW per 1 Minute)
 

-600 -100 

6:00 7:00 8:00 9:00 Time 

25 

26 

109
Hour of Week

50000 

2010 Forecast vs. Actual, July Week 

1 25 49 73 97 121 145 

-5000 

0 

5000 

10000 

15000 

20000 

Actual Load Forecast Load Actual Wind Forecast Wind Actual Solar Forecast Solar 

Wind & Solar (MW) 

On large scale, 

departure from 

averages not too 

bad 

40000 

30000 

20000 

10000 

Load (MW) 

0 

daily average by month forecast seems OK for low 

penetrations, but won’t work forever... 

2010 Forecast Errors, July Week
 

28 

Hour of Week 

-5000 

-4000 

-3000 

-2000 

-1000 

0 

1000 

2000 

3000 

4000 

5000 

1 25 49 73 97 121 145 

Load Forecast Error (F-A) 

Wind Forecast Error (A-F) 

Solar Forecast Error (A-F) 

L-W-S Forecast Error 

Load Forecast > Actual Load 

Actual Load > Load Forecast 

Forecast Error (MW) 

27 

110
2 3 4 5 6 7 8 9 10

36776.3 33502.6 31858.7 30654.6 29429.7 27776.0 26093.2 24615.4 23073.6

33504.8 31859.9 30655.7 29429.8 27776.7 26093.2 24616.4 23073.8 19443.1

1253.5 1555.3 1470.9 1363.1 1555.2 1816.4 1663.3 1435.9 1075.3 668.9

4854.2 4775.9 4374.8 6123.0 6070.9 3823.8 3245.1 2862.1 1706.5

4445.8 4382.4 -4371.6 5122.3 4017.0 -3535.5 3199.9 2868.1 2567.1

317.3 209.3 129.9 116.6 19.4 178.1 -261.5 309.3 228.6

34994.6 32598.0 31240.2 30062.7 28656.8 26910.6 25352.7 23839.9 22058.5

313.4 140.8 178.4 147.4 93.5 35.6 1.9 -24.1 59.2

1023.1 745.7 704.3 639.7 672.0 622.7 557.9 589.4 520.2

6533.2 4761.5 4813.4 3790.9 3585.2 4172.8 2531.2 3840.6 2208.4

3896.0 3063.2 -3400.1 3422.8 3588.7 -2759.4 2777.6 1940.3 1674.5

2 3 4 5 6 7 8 9 10

35985.0 32800.0 31197.2 29993.8 28722.4 27068.3 25376.5 23849.1 22406.8

32800.7 31197.3 29994.3 28722.4 27068.7 25376.6 23850.7 22407.2 18567.2

1546.6 1456.4 1398.8 1587.0 1828.5 1699.9 1446.4 1048.9 698.9

4728.7 4857.2 4580.7 6090.8 5981.0 3946.6 3240.9 2914.2 2447.8

4533.4 4450.4 -4592.1 5155.4 4173.7 -3956.1 3451.9 2814.0 2613.0

320.0 183.1 149.5 128.5 52.7 146.0 -265.4 307.1 250.3

34243.5 31937.5 30590.7 29386.4 27927.0 26188.3 24619.5 23135.7 21373.8

571.3 520.8 549.3 611.3 692.6 662.6 704.5 698.6 745.7

156.4 104.8 88.2 77.1 61.1 44.2 24.5 5.6 13.3

0.017 0.016 0.018 0.021 0.025 0.025 0.029 0.030 0.035

0.005 0.003 0.003 0.003 0.002 0.002 0.001 0.000 0.001

447.6 247.0 300.8 275.9 261.4 201.6 184.5 141.6 269.0

1059.7 803.2 749.1 713.8 738.8 684.4 660.6 635.9 570.5

6554.7 4462.6 4518.7 3958.8 3396.6 4686.7 4973.7 3978.8 2321.2

3954.0 3035.8 -3205.0 3343.1 3555.5 -2667.8 1773.2 1742.9 1700.6

127.9 111.5 -125.8 133.9 169.9 159.1 -176.8 171.1 202.7

227.3 229.6 240.0 246.1 254.3 260.0 261.4 273.4 284.7

629.8 645.6 706.9 617.9 697.9 719.9 701.2 611.9 712.5

1134.2 992.4 -1083.1 1058.8 1078.5 -1072.7 1079.3 1135.5 1136.6

4.1 1.1 2.6 5.5 4.6 4.1 0.8 0.4 3.0

60.5 52.9 55.0 53.9 51.4 48.6 39.2 25.0 37.5

285.6 205.1 237.1 278.2 225.2 210.1 222.9 174.9 130.9

245.8 240.7 -658.7 717.0 724.4 656.1 -670.8 342.7 547.9

Define penetration; 

modify operation

2006 Hourly Statistics: Example from One Decile
 

Each Bin (Load) 1 
 

P_Load (Max) 48113.5
 

36778.9
 

4529.3
 

Delta L (Min) -4334.4 ­

Delta L (Avg) 225.2
 

Load (Avg.) 

Load F-A (Avg) 

Load F-A (Sigma) 

40162.9 

P_Load (Min) 

Sigma (Delta L) 

Delta L (Max) 

- - - - - -

- - - -

Standard Deviation, ( : 

68.3% of Values within 1( of Mean 

99.7% of Values within 3( of Mean 

156.9	 ­

1317.8
 

Load F-A (Max) 5824.7
 

Load F-A (Min) -6281.2 ­ - Forecast Error is F-A = Forecast minus Actual - - - - -

Each Bin (L-W-S) 1 
 

P_L-W-S (Max) 47736.2
 

P_L-W-S (Min) 35988.1
 

Sigma (Delta L-W-S) 1294.4 

Delta L-W-S (Max) 4924.3 

Delta L-W-S (Min) -4294.7 - - - - - - ­

Delta L-W-S (Avg) 241.6 - - - ­

Load_L-W-S (Avg.) 39397.6
 

Wind (Avg.) 492.0
 

252.9 

0.006
 

L-W-S F-A (Avg) 231.5
 

L-W-S F-A (Sigma) 1339.4
 

L-W-S F-A (Max) 6402.1
 

L-W-S F-A (Min) -6442.8 ­

Wind F-A (Avg) -78.7 ­

Wind F-A (Sigma) 196.5
 

Wind F-A (Max) 536.5
 

Wind F-A (Min) -1104.4 - - - - - - ­

Solar F-A (Avg) -14.1 - - ­

Solar (Avg.) 

Wind Penetration 0.012 

Solar Penetration 

- - - - - -

- - - - - -

Wind Penetration = Average Wind/ Average Load 

(The average is over all hours in a decile) 

Solar F-A (Sigma)	 60.0 

Solar F-A (Max) 305.8 

Solar F-A (Min) -174.8 - - - - - - ­

2006 Hourly Statistics
 

Each Bin (Load) 1 2 3 4 5 6 7 8 9 10 All year 

P_Load (Max) 48113.5 36776.3 33502.6 31858.7 30654.6 29429.7 27776.0 26093.2 24615.4 23073.6 48113.5 

P_Load (Min) 36778.9 33504.8 31859.9 30655.7 29429.8 27776.7 26093.2 24616.4 23073.8 19443.1 19443.1 

Sigma (Delta L) 1253.5 1555.3 1470.9 1363.1 1555.2 1816.4 1663.3 1435.9 1075.3 668.9 1436.3 

Delta L (Max) 4529.3 4854.2 4775.9 4374.8 6123.0 6070.9 3823.8 3245.1 2862.1 1706.5 6123.0 

Delta L (Min) -4334.4 -4445.8 -4382.4 -4371.6 -5122.3 -4017.0 -3535.5 -3199.9 -2868.1 -2567.1 -5122.3 

Delta L (Avg) 225.2 317.3 209.3 129.9 116.6 -19.4 -178.1 -261.5 -309.3 -228.6 0.2 

Load (Avg.) 40162.9 34994.6 32598.0 31240.2 30062.7 28656.8 26910.6 25352.7 23839.9 22058.5 29587.2 

Load F-A (Avg) 156.9 313.4 140.8 178.4 147.4 93.5 35.6 -1.9 -24.1 59.2 109.9 

Load F-A (Sigma) 1317.8 1023.1 745.7 704.3 639.7 672.0 622.7 557.9 589.4 520.2 781.1 

Load F-A (Max) 5824.7 6533.2 4761.5 4813.4 3790.9 3585.2 4172.8 2531.2 3840.6 2208.4 6533.2 

Load F-A (Min) -6281.2 -3896.0 -3063.2 -3400.1 -3422.8 -3588.7 -2759.4 -2777.6 -1940.3 -1674.5 -6281.2 

Each Bin (L-W-S) 1 2 3 4 5 6 7 8 9 10 All year 

P_L-W-S (Max) 47736.2 35985.0 32800.0 31197.2 29993.8 28722.4 27068.3 25376.5 23849.1 22406.8 47736.2 

P_L-W-S (Min) 35988.1 32800.7 31197.3 29994.3 28722.4 27068.7 25376.6 23850.7 22407.2 18567.2 18567.2 

Sigma (Delta L-W-S) 1294.4 1546.6 1456.4 1398.8 1587.0 1828.5 1699.9 1446.4 1048.9 698.9 1451.0 

Delta L-W-S (Max) 4924.3 4728.7 4857.2 4580.7 6090.8 5981.0 3946.6 3240.9 2914.2 2447.8 6090.8 

Delta L-W-S (Min) -4294.7 -4533.4 -4450.4 -4592.1 -5155.4 -4173.7 -3956.1 -3451.9 -2814.0 -2613.0 -5155.4 

Delta L-W-S (Avg) 241.6 320.0 183.1 149.5 128.5 -52.7 -146.0 -265.4 -307.1 -250.3 0.1 

Load_L-W-S (Avg.) 39397.6 34243.5 31937.5 30590.7 29386.4 27927.0 26188.3 24619.5 23135.7 21373.8 28879.5 

Wind (Avg.) 492.0 571.3 520.8 549.3 611.3 692.6 662.6 704.5 698.6 745.7 624.9 

Solar (Avg.) 252.9 156.4 104.8 88.2 77.1 61.1 44.2 24.5 5.6 13.3 82.8 

Wind Penetration 0.012 0.017 0.016 0.018 0.021 0.025 0.025 0.029 0.030 0.035 0.022 

Solar Penetration 0.006 0.005 0.003 0.003 0.003 0.002 0.002 0.001 0.000 0.001 0.003 

L-W-S F-A (Avg) 231.5 447.6 247.0 300.8 275.9 261.4 201.6 184.5 141.6 269.0 256.1 

L-W-S F-A (Sigma) 1339.4 1059.7 803.2 749.1 713.8 738.8 684.4 660.6 635.9 570.5 829.0 

L-W-S F-A (Max) 6402.1 6554.7 4462.6 4518.7 3958.8 3396.6 4686.7 4973.7 3978.8 2321.2 6554.7 

L-W-S F-A (Min) -6442.8 -3954.0 -3035.8 -3205.0 -3343.1 -3555.5 -2667.8 -1773.2 -1742.9 -1700.6 -6442.8 

Wind F-A (Avg) -78.7 -127.9 -111.5 -125.8 -133.9 -169.9 -159.1 -176.8 -171.1 -202.7 -145.7 

Wind F-A (Sigma) 196.5 227.3 229.6 240.0 246.1 254.3 260.0 261.4 273.4 284.7 250.9 

Wind F-A (Max) 536.5 629.8 645.6 706.9 617.9 697.9 719.9 701.2 611.9 712.5 719.9 

Wind F-A (Min) -1104.4 -1134.2 -992.4 -1083.1 -1058.8 -1078.5 -1072.7 -1079.3 -1135.5 -1136.6 -1136.6 

Solar F-A (Avg) -14.1 -4.1 -1.1 2.6 5.5 4.6 4.1 0.8 0.4 -3.0 -0.4 

Solar F-A (Sigma) 60.0 60.5 52.9 55.0 53.9 51.4 48.6 39.2 25.0 37.5 49.8 

Solar F-A (Max) 305.8 285.6 205.1 237.1 278.2 225.2 210.1 222.9 174.9 130.9 305.8 

Solar F-A (Min) -174.8 -245.8 -240.7 -658.7 -717.0 -724.4 -656.1 -670.8 -342.7 -547.9 -724.4 

29 

30 

111
06

Footprint % Monthly Energy from Wind and Solar for 2004 – 2006
 % of Load Energy 

y y gy 

 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

PV 

CSPws 

Wind 

‘04 

‘05 

‘06 

30% is not 

always 

30% 

(30% Wind Energy - In Area Scenario*) 

55% of energy from 

wind and solar 60%

50% 

40% 

30% 

20% 

10% 

0% 

31 

1 

2006 % monthly energy ranges from 

18% (Jul) to 55% (Apr) in study footprint 

Source: NREL Western Wind 

& Solar Integration Study 

Average Hourly Energy from Wind and Solar Over Entire Year 

(30% In Area Scenario) 

Study Area Average Daily Wind and Solar Energy 

14.00 

PV 

CSPws 

Wind 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 

12.00 

10.00 

8.00 

6.00 

4.00 

Energy (GWh) % of Total Energy 2.00 Solar tends to 0.00 complement 

wind variation 

Study Area Percent Average Daily Wind and Solar Energy over the day
 PV 

CSPws 

Wind 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 

Hour of Day 

40% 

35% 

30% 

25% 

20% 

15% 

10% Source: NREL Western Wind 

& Solar Integration Study 

5%
 

0%
 

32 

112
33

34

9000 

18000 

45000 

1000 

1500 

2000 

2500 

3000 

18000 

27000 

36000 

45000 

54000 

Study Area CSPws and PV Average Daily Profiles By Seasonal 

Month (30% In Area Scenario) 

3000 54000
 

2500 45000
 

Load (MW) Load (MW) 

Solar (MW) Solar (MW) 

Load 

PV 

CSPws 

Jan Fairly narrow PV and 

CSP contribution; none 

at peak or morning rise 

0 4 8 12 16 20 24 
 

Load 

PV 

CSPws 

Apr Substantial CSP output 

in late evenings 

0 4 8 12 16 20 24
 

2000
 36000
 Solar (MW) Solar (MW)
 Load (MW) Load (MW)
 

1500
 27000
 

1000
 18000
 

9000
 500
 9000
 500
 

0 0 0 0 

54000
 3000 54000
 3000
 Load 

PV 

CSPws 

Oct Generally narrow PV 

and CSP output 

0 4 8 12 16 20 24
 

Hour of Day 

Load 

PV 

CSPws 

Jul 

Extended PV and 

CSP output; peak 

and mornings 

0 4 8 12 16 20 24
 

Hour of Day 

2500
 45000


36000
 2000
 36000
 2000


27000
 1500
 27000
 1500
 

1000
 18000
 1000
 

0 

500 9000
 

0 0 0 

Source: NREL Western Wind 

& Solar Integration Study 

Operations... 

500 

2500 

113
Study Footprint Total Load, Wind and Solar Variation Over Month of April 

(30% Wind Energy in Footprint) 

Ld(base)= Load Demand 35000 

30000 

25000 

20000 

15000 

10000 

Ld(Base) 5000 

Wd(30%) 

PV(30%) 0 

CSP(30%) 

L-W-S = Load – Wind – Solar = “Net Load” L-W-S(30%) 

-5000 

1-Apr 8-Apr 15-Apr 22-Apr 29-Apr 

Source: NREL Western Wind 

& Solar Integration Study 35 

1 

Study Footprint Total Load, Wind and Solar Variation Over Month of July 

(30% Wind Energy in Footprint) 

0 

10000 

20000 

30000 

40000 

50000 

60000 

MW 

Ld(Base) 

Wd(30%) 

PV(30%) 

CSP(30%) 

L-W-S(30%) 

1-Jul 8-Jul 15-Jul 22-Jul 29-Jul 

Source: NREL Western Wind 

& Solar Integration Study 36 

1 

114
• 

Study Area Dispatch - Week of April 10th – No Renewables	 Study Area Dispatch - Week of April 10th - 10% Renewables 

0 

10,000 

20,000 

30,000 

40,000 

50,000 

MON APR 10 TUE APR 11 WED APR 12 THU APR 13 FRI APR 14 SAT APR 15 SUN APR 16 

MW 

0 

10,000 

20,000 

30,000 

40,000 

50,000 

MW 

Nuclear Steam Coal Wind 

Solar CSP w/ Storage Solar PV Combined Cycle 

Gas Turbine Pumped Storage Hydro Hydro 

MON APR 10 TUE APR 11 WED APR 12 THU APR 13 FRI APR 14 SAT APR 15 SUN APR 16 

Study Area Dispatch - Week of April 10th - 20% Renewables Study Area Dispatch - Week of April 10th - 30% Renewables 

0 

10,000 

20,000 

30,000 

40,000 

50,000 

MW 

MON APR 10 TUE APR 11 WED APR 12 THU APR 13 FRI APR 14 SAT APR 15 SUN APR 16 

0 

10,000 

20,000 

30,000 

40,000 

50,000 

MON APR 10 TUE APR 11 WED APR 12 THU APR 13 FRI APR 14 SAT APR 15 SUN APR 16 

April 10, 2006 Source: NREL Western Wind 

& Solar Integration Study 

37 

1 

Data Requirements (what we might need in future) 

One year (or more, depending on study) of 10-minute time synchronized wind and 

solar power data for each of the sites. 10-minute resolution: 

•	 has been used effectively in recent studies 

•	 (roughly) the lower limit for resolution using meso-scale analytical 


techniques
 

One year (or more) of hourly, day-ahead solar power forecast data 

Higher resolution time synchronized wind and solar power data for selected windows 

(e.g. 1-3 hours) of interest 

•	 Selected windows typically screened from longer-term data and system 

considerations 

•	 Resolution of 1-2 second sampling needed for small or granular systems 

[experience jury is out on what “small or granular means” – needs thought 

and depends on the focus of the study] 

• Resolution of 1-2 minute needed for larger system analysis 

Solar plant size, type, substation location for each site 

Each data must be time synchronized for the future study year(s) 

Maintain solar/wind/load time and space relationship. 

MW 

38 

115
116
-

 

 

Development of Data Sets for PV Integration Studies 

Utility scale PV 

Variability Workshop 

Ray George 

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 

7 October 2009 

Problem Identification 

• Analytical studies need consistent data for any 

location, with greater resolution (temporal and 

spatial) than the current data sets provide. 

• S l PV M d (R l) d t Solar or PV Measured (Real) data – S ti ll Spatially sparse 

but temporally dense – applies only to a single point 

or a small area 

• Satellite (Modeled) data – Spatially denser but 

temporally sparse. Fundamental limitations preclude 

their direct use for dispatch and grid stability. 

• Existing modeled and measured data must be 

combined, using a detailed spatial and temporal 

analysis, to match the desired analysis. 

• Unrealistic outcomes must be avoided! 

National Renewable Energy Laboratory Innovation for Our Energy Future 

117

1 
Solar Radiation and PV 

118

2 
 

Example of 60 second “averaging” and 

“sampling” – POA Irradiance 

Partly Cloudy Day Clear Day

5 

WWSIS PV Modeling Approach 

Western Wind and Solar Integration Study 

Goal: Assess the grid environment under high 

penetrations of wind, CSP, PV. 

Required: PV output data for any location in SW US SW, 10 

minute time step. 

Analysis focused on 10 minute ramp rates of load and 

renewables. 

Input Data – Hourly satellite estimated solar resource, 

and surface measured weather data. 

Output – Estimated 10 minute power production from a 

mixture of PV systems totaling 100 MW 

119

3 
WWSIS PV Modeling at 29 Sites 

PV Output Data for Model Validation 

120

4 
 

 

Providing PV data for Analytical Studies 

1. Produce a time series of solar measured data, or PV output, 

which replicates the ramp distribution of a small system or solar 

radiation measurement, for each desired location. 

2. If available, use surface measured data. Select the best data 

for each location in your scenario, using GIS techniques 

3. If not enough measured data is available, create a model to 

synthesize the time series you need. 

4. Use a lowpass filter to modify the ramp rates of each time 

series to match the PV deployment scenario. 

5. Assess the spatial correlation between the sites you have 

chosen in your scenario. Assure that there is not too much or 

too little correlation between and among the sites. 

WWSIS Solar Data Requirements 

- Outputs should be consistent, even from locations far 

from any measured data. 

- Initial requirement is for 10 minute average outputs, Initial requirement is for 10 minute average outputs, 

for three years (2004 - 2006). 

- We assume random subhourly fluctuations are 

UNCORRELATED across all sites. 

- Later requirement is output from the same systems, 

except at one minute resolution, for selected days 

in the two year period in the two year period. 

- Ramp rates of PV output are the most important test 

of realism. 

121

5 
 

WWSIS 10-Minute PV Model 

0.6 

0.8 

1 

Habitat House 

-0.4 

-0.2 

0 

0.2 

0.4 

5 7 9 11 13 15 17 19 

Hab. House Ramp Rate 

SUNY Hourly PV Output 

Clear Sky Hourly PV Output 

Synthetic 10 Minute PV Output 

Synthetic Ramp Rate 

Clear Sky Deviation 

Model Inputs: AC power from PVWatts, for... 

1.) Hourly satellite modeled, 2.) 10-minute clear sky. 

Model Outputs: Synthetic AC power on 10 minutes, interpolated to 

match satellite, random fluctuations to match annual ramp 

distribution 

WWSIS Model Approach 

1. Normalize all data to percent of DC power rating. 

2. Find hourly (absolute) deviation between clear sky 

and modeled. 

3. Use transformation function to match slope 

difference of deviations to Standard Deviation of 

random fluctuations 

If Slope Difference < 0.1, StDev = 0 (clear sky or close to it.) 

If Slope Difference => 0.1, StDev = ( log(Slope Difference)+1)*0.27 + 0.11 

06 

0.7 

0.8 

0 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0 0.2 0.4 0.6 0.8 1 

Slope Difference 

Standard Deviation

Lognormal 

Linear (*0.7) 

122

6 
 

WWSIS PV Data Ramp Rate Validation 

- PV output ramp rates are primary validation data. 

- Same model used for all collector types. 

- Validation using Arizona 1-axis tracking PV, and 

Colorado fixed system with 22 5 degree tilt Colorado fixed system with 22.5 degree tilt. 

Phoenix Latitude Tilt 

Phoenix Area, Latitude Tilt Fixed PV - 10 Minute Ramp Rate 

Distribution 

70% 

Desert + Challenger 

30% 

40% 

50% 

60% 

cent of DC Rated Power 

Desert 

Challenger 

Model Luke AFB 

Model Deer Valley 

Model Luke + Deer Valley 

0% 

10% 

20% 

0 5 10 15 20 25 30 35 40 45 

Hours per Year 

Perc 

123

7 
SRRL Measured PV

 

 

10 Min PV From Habitat House (4 MW Fixed PV), and 

from Measured Solar at NREL SRRL 

Distance Between Sites = 8.8 km. 

SRRL and Habitat House, 2006 (306 days), Ramp Rate Distribution 

0.7 

0.8 

SRRL Measured PV 

0.3 

0.4 

0.5 

0.6 

p Rate, Fraction of DC Rating

Habitat House Measured PV 

SRRL and Habitat House Measured PV, Combined 

SRRL and Habitat House, Modeled, Different Random Numbers 

SRRL and Habitat House, Modeled, Same Random Numbers 

0 

0.1 

0.2 

0 50 100 150 200 250 300 

Hours/Year 

Ramp 

WWSIS Model - Problems 

1. All model data from 1 location. 

2. No use of measured data. 

3. Results not very deterministic. Correlations with 

wind could be missed. 

4. PVWatts does not match observed PV output 

0.4 

0.6 

0.8 

1 

Habitat House 

Hab. House Ramp Rate 

Clear Sky Hourly PV Output 

-0.4 

-0.2 

0 

0.2 

53 55 57 59 61 63 65 67 

Synthetic 10 Minute PV Output 

Synthetic Ramp Rate 

SUNY Hourly PV Output 

124

8 
 

 

 

Summary of WWSIS 10 Minute Model 

1. Model applied for each of 12 different PV collectors using the 

same parameters over a wide range of conditions, locations, 

times of year. For each location, the model runs are applied 

using the SAME random numbers using the SAME random numbers. 

2. Model outputs are weighted according to a distributed PV 

scenario, fixed orientations are preferred, only 15% tracking PV 

3. Phoenix – Low correlation between sites as close as 12.5 km. 

apart. 

4. Golden, CO – Fairly high spatial correlation between sites 8.8 

km. apart 

5. In general data from a single point when averaged over 10 In general, data from a single point, when averaged over 10 

minutes, give the correct ramps for any compact plant. 

6. NO lowpass filter applied to model time series. 

The Problem... 

Multi-MW PV plants plus DG PV planned in 

the Big Wind timeframe 

Very little is known about multi-MW PV 

variability on a fast (subhourly) timescale 

– High wind, partly cloudy days can lead to 

extremely fast ramps (faster fluctuations than wind 

plants) 

PV Variability Working Group (labs, utilities, 

developers) established to work on this issue developers) established to work on this issue 

There is little sub-minute data for large-scale 

PV systems and not much sub-hourly data 

State-of-the-art solar resource modeling is 

hourly, 10 km resolution 

125

9 
 

 

OWITS PV Deployment Scenario 

15 MW Residential PV (Distributed Generation) 

5 MW PV Plant Kaneohe area 5 MW PV Plant, Kaneohe area. 

20 MW Utility PV plant, Campbell Industrial Park area. 

60 MW Utility PV plant, centrally located North of Pearl 

Harbor 

Residential PV spread over Honolulu urban corridor 

Model a 

60MW PV 

f ilit acy 

Model a 

20MW PV 

facility 

>5MW utility scale PV 

<5MW industrial/ 

residential PV 

50MW each wind 50MW each wind 

Model large FIT 

4MW PV facility 

Model 1-2MW distributed 

PV clusters totaling 15MW 

126

10 
 

 

Data Available for Oahu PV Modeling 

Measured GHI and PV output from schools – 15 

minutes – for 2 years or more. 

Measured Solar radiation – from 4 sites – 1 second – 

starting in June 2009 starting in June 2009 

-----------------

Solar radiation – clear sky – 10 minute averages –

model and input data are matched to satellite model. 

Modeled PV output – uses PVWatts for any collector 

orientation 

------------------

Weather service – observed ceiling, clouds, winds. 

Also numerical modeled/interpolated winds, etc. 

RAWS – hourly global radiation 

OWITS Solar Data Requirements 

- PV system output in MW for 4 different “systems” 

- Initial requirement is for 10 minute average outputs, 

for two years (2007 and 2008) for two years (2007 and 2008). 

- Later requirement is output from the same systems, 

except at two second resolution, for selected days in 

the two year period. 

- Data should be as realistic as possible, given the 

state of our knowledge 

- Ramp rates of PV output are the most important test 

of realism. 

127

11 
 

NREL/HECO 1 Second Solar Data Stations 

- Data from 4 locations, starting June 4, 2009 

- One second data rate for Global Horizontal only One second data rate, for Global Horizontal only. 

- Two sites are 1060 meters apart, providing data on 

correlations within a large PV plant. 

- Analysis of these data used to guide the synthesis of 

10 minute (and later 2 second) data. 

Map of NREL/HECO 1-second Solar Stations 

128

12 
One Second Data Shows High Variability 

Size of PV Array = Distance Constant M 

200 

400 

600 

800 

1000 

1200 

1400 

1600 

ength of Side (Square), M 

Distance Constant (Meters), for PV system density of 5 acres/MW or 

49.4 watts/m2 

1600 

Distance Constant 

(Meters) 

0 10 20 30 40 50 60 70 80 90 100 

PV System DC rating, MW 

L

0

e 

129

13 
 

Spatial Smoothing for 1 KM (60 MW) 

CIP and KAL 15 minute Ramps 

Distance = 1.06 Km. 

700 

200 

300 

400 

500 

600 

700 

amp (W/M2)/(15 inutes) 

Kaleaeloa 

Campbell IP 

CIP and KAL averaged 

0 

100 

0 100 200 300 400 500 600 

Hours/Year 

Ra 

Filter Single Point GHI to Match Area Distribution 

700 

800 

900 

1000 

CIP_ramp 

KAL_ramp 

WAI ramp 

100 

200 

300 

400 

500 

600 

700 

Occurences in 2 years 

WAI_ramp 

WAR_ramp 

CIP_KAL 

WAI_WAR 

WaiFilt 

0 

300 350 400 450 500 550 600 650 700 750 800 

10 minute delta 

130

14 
 

 

10 Minute Ramp Rates in MW 

ALL HECO - Modeled 10 Minute PV Ramp Rates 

Year 2007 

40 

45 

AC 60MW 1axis 

15 

20 

25 

30 

35 

Ramp Rate MW/10 min. 

AC_60MW_1axis 

AC_60MW_Flat 

AC_15MWflat 

AC_5MW_1axis 

AC_5MW_flat 

AC_20MW_1axis 

AC_20MW_flat 

Sum(15MW flat, 60,20,5MW 1-axis) 

0 

5 

10 

0 100 200 300 400 500 600 700 800 

Hours/Year 

R 

OWITS PV Technology Assumptions 

(All assumptions open to modification) 

- Data processing and validation for Global Horizontal (GHI) 

radiation 

- PV system output will be modeled for crystalline silicon PV PV system output will be modeled for crystalline silicon PV 

technology. 

- PV system size is the rated DC output of the PV panels for 

GHI = 1000 watts/m2 (industry standard) 

- Grid connected PV systems will have maximum inverter output 

equal to PV system size (DC rated capacity) 

- PV output data streams will be available for fixed horizontal PV 

collectors and 1-axis tracking collectors. ( y Other collector typ es

could be added if needed.) 

- Residential PV will be assumed all fixed horizontal 

- Utility PV plants will be 1-axis tracking. 

131

15 
 

Two GHI Stations, 1060 Meters Apart 

KAL = 1 station – 

2 min averaging – more 

and larg p er ramps

than 15 minutes. 

CIP_KAL = 

2 station 

average – 

2 minute and 15 minute 2 minute and 15 minute 

ramps almost the 

same. 

Ramps for KAL and CIP_KAL sites 

800 

900 KAL_300s 

CIP_KAL_300s 

KAL_120s 

CIP_KAL_120s 

KAL 60s 

400 

500 

600 

700

Ramp Size Watts/M2 

KAL_60s 

CIP_KAL_60s 

KAL_30s 

CIP_KAL_30s 

KAL_10s 

CIP_KAL10s 

200 

300 

1 301 601 901 1201 1501 1801 2101 2401 

# Ramps in 3 Months 

132

16 
Ramp Rate 

P

ercent/Minute

4.6 MW Plant – Distance Const ~450 m. 

Springerville Plant - 1 Minute Ramp Rates -

Pct Output 1 Year (2006)

70% 65% 

60%
 

50%
 

40%
 

Ramp Rate

Percent/Minute60% 

55% 

50% 

45% 

40% 

35% 

30% Plant Pct Output 

30% 1 61 121 181 

Minutes/Year 

241 

20% 

10% 

0% 

1 601 1201 1801 2401 3001 3601 

Minutes/Year 

Large Ramps at All Daylight Hours 

Springerville 2006 1-Minute Plant Pct 

Output - Ramp Events by Hour 

70% 

Ramp Rate 

Percent/Minute60% Plant Pct Output 

50% 

40% 

30% 

20% 

10% 

0% 

6 8 10 12 14 16 18 

Hour of Day - LST 

133

17 
PV Ramp Rate vs. Wind Speed

 Springerville - 2006 - Ramp Rate per 

Minute vs. Wind Speed 

70% 

60% Plant Pct Output 

PV Ramp Rate 50%
 

40%
 

30%
 

20%
 

10%
 

0% 

0 10 20 30 40 

Wind Speed - MPH 

Example of 600 second “averaging” and 

“sampling” – POA Irradiance Ramps 

Partly Cloudy Day Clear Day 

36 

134

18 
Example of 60 second “averaging” and 

“sampling” – POA Irradiance Ramps 

Partly Cloudy Day Clear Day

37 

Power Output Ramp (as fraction of median ramp) 

Distribution for 1 minute data using single and 

multiple sensors (840 hours) 

38 

135

19 
Power Output Ramp (as fraction of median ramp) 

Distribution for 10 minute data using single and 

multiple sensors (840 hours) 

39 

OWITS PV Data Ramp Rate Validation 

Jarrett and McKinley 15 Minute Ramp Analysis 

Distance Between Schools = 5.4 Km. 

900 

300 

400 

500 

600 

700 

800 

900 

Minute Delta Radiation

Jarrett 

McKinley 

Jar+Mck 

0 

100 

200 

0 200 400 600 800 1000 1200 1400 1600 1800 2000 

Annual Ramp Count 

15 M 

136

20 
Springerville 10 Minute Ramps Match 

Springerville Tucson Electric 4.6 MW PV Plant 10-minute Ramp Rates 

07 

0.8 

0.9 

Output

PV Plant 2005 

PV Modeled 2005 

0 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

Ramp Rate as Fraction of DC O

PV Plant 2006 

PV Modeled 2006 

0 50 100 150 200 250 300 

Hours/Year 

137

21 
CONFIDENTIAL

CONFIDENTIAL

On Using Mesoscale Models

for Solar Resource Modeling 

Michael Brower, CTO 

AWS Truewind, LLC 

October, 2009 

mbrower@awstruewind.com 

Outline 

• Background and motivation 

• Mesoscale modeling status and challenges 

• Mean solar patterns 

• High-frequency fluctuations 

• Next steps 

138

1 
CONFIDENTIAL

CONFIDENTIAL

Background 

• NSRDB is currently the main nation-wide data set for solar 

resource assessment resource assessment 

• Based on a combination of 

– direct radiation measurements (1% of data) 

– cloud observations/METSTAT model 

– GOES satellite imagery/SUNY model 

• Gridded data have 10 km resolution 

• Ti i i h l i t l f 1991 Time series in hourly intervals from 1991-1997 (METSTAT) 1997 (METSTAT) 

and 1998-present (SUNY) 

• Results compare well with direct observations 

W m-2 

Desert Rock NV 

W m-2 

4 

139

2 
CONFIDENTIAL

 

 

CONFIDENTIAL

W m-2 

Harvard Forest, MA 

W m-2 

Why Consider 

Mesoscale Modeling? 

• May be difficult to improve on SUNY model accuracy, but 

• Would provide additional flexibility with respect to Would provide additional flexibility with respect to 

– Period of record (pre-1991) 

– Spatial resolution (down to hundreds of meters) 

– Temporal resolution (minutes) 

– Geographic regions (any area of globe may be modeled) 

• Easily integrated with mesoscale-model-based wind resource 

mapping mapping 

• Essential for real-time solar forecasting 

– May employ satellite data to improve initial conditions 

140

3 
CONFIDENTIAL

CONFIDENTIAL

Status and Challenges 

• Mesoscale modeling not commonly used for 

solar resource assessment 

– But there is a precedent for wind (e.g., EWITS, 

WWSIS) 

• Accuracy of technique for solar not yet proven 

– Critical challenge: clouds 

• High temporal resolution requires high spatial 

resolution = large CPU time 

Mesoscale Modeling Process 

topography 

roughness 

vegetation greenness 

sea temperatures 

Cloud 

Data 

Geophysical 

Data Mesoscale 

Simulations 

(MASS) 

Solar Maps 

Met 

Data Time Series 

MOS 

full equations 

p 

q 

of motion 

dynamic 

multi-day simulations 

variable resolution 

global reanalysis 

rawinsonde 

surface met data 

comparisons 

with solar obs 

adjustments 

error estimates 

141

4 
CONFIDENTIAL

CONFIDENTIAL

Nested Grids 

1 km 

4 km 

12 km 

Simulaton Snapshot 

Wind Vectors and Temperatures 

142

5 
CONFIDENTIAL

CONFIDENTIAL

Preliminary Tests Using 

AWST windTrends Dataset 

• Mesoscale simulations covering North 

A i mer ca 

• “Controlled reanalysis” 

– Assimilates only rawinsonde data at fixed heights 

• 1997-present 

• 20-k ti l 1 km spatial, 1-h t l l ti hour temporal resolution 

• Performed MOS to solar observations 

(validation data not included in training data) 

Bondville, Illinois 

143

6 
CONFIDENTIAL

CONFIDENTIAL

Desert Rock, Nevada 

Harvard Forest, Massachusetts 

144

7 
CONFIDENTIAL

CONFIDENTIAL

Error Statistics 

Six Stations 

Monthly Means Diurnal Means 

NWP SUNY NWP SUNY 

Mean Bias (W/m2) 1.1 0.3 0.1 0.3

Standard Error (W/m2) 12.8 14.3 12.0 9.1

Daily Fluctuations 

145

8 
CONFIDENTIAL

 

 

CONFIDENTIAL

 

 

Preliminary Solar Map 

W/m2

<125

125-150

150-175

175-200

200-225

>225

High-Frequency Fluctuations 

• Cloud passage time depends on cloud height, wind speed, size 

of PV array. Typical ramp times:* 

– For a point measurement: 1 For a point measurement: 1 -5 s 

– For a 50 MW PV project: 60-120 s 

• Wind projects cover a larger area, and wind speeds are lower 

at hub height, therefore wind variability tends to be less. 

– For a 50 MW wind project: 180-320 s 

•	 Direct mesoscale modeling for wind is practical down to 10 

minute time resolution minute time resolution 

*Assumes clouds at 1000-5000 m height, 7-15 m/s speed, 10% 

efficient array, 1000 W/m2 maximum irradiance 

146

9 
CONFIDENTIAL

CONFIDENTIAL

Wind Ramp Spectrum 

10-Minute & 60-Minute Ramps 

Required Solar Model Resolution 

To Simulate Cloud Passage Directly 

147

10 
 

CONFIDENTIAL

CONFIDENTIAL

Alternative High-Frequency 

Variability Techniques 

• Direct cloud observations ( , , ) i.e., ceilometer, satellite)

– Ground measurements have a limited range of view 

– Satellites have limited spatial, temporal resolution 

• Statistical sampling of irradiance or PV data 

– Successfully applied to wind energy 

– Requires representative data from actual plants or measurements 

– Scale-up issues

– Generally assume no correlation between projects – may need 

refinement to capture time lags 

Wind Sampling Technique 

Mesoscale 

domain 

Sampling 

domain 

Observed Synthesized 

148

11 
CONFIDENTIAL

 

Conclusions 

• Mesoscale modeling can produce mean solar resource 

estimates of similar accuracy to SUNY model 

• A l l l As a complementary tool, mesoscale mod eli ld ling could 

provide additional spatial, temporal, and geographic 

flexibility 

• High-frequency fluctuations for <100 MW PV arrays 

could potentially be modeled, but this would be costly 

in CPU; >100 MW needs to be demonstrated in CPU; >100 MW needs to be demonstrated 

• Methods combining mesoscale modeling with 

statistical sampling and/or direct or inferred cloud 

observations are likely necessary 

149

12 
• 

1

Spatial and Temporal Scales of Solar 

Variabilit y: Implications f id I ti or Grid Integration

of Utility-Scale Photovoltaic Plants 

Andrew Mills and Ryan Wiser 

Lawrence Berkeley National Laboratory

Electricity Markets and Policy 

Utility-scale PV Variability Workshop 

October 7, 2009 

Energy Analysis Department 

Presentation Overview
 

• Motivation 

Data and Approach Data and Approach 

• Characterizing Variability at a Single Site 

• Characterizing Temporal and Spatial Scales of Diversity 

• Variability at the System Level 

• Estimating the Costs of Managing Short-term Variability 

• Impact of Geographic Diversity on the Costs of Managing 

Short-time scale PV Variability 

Energy Analysis Department 2

150
 

 

3

Concern that Rapid Fluctuations in PV tuations in PV Output 

Are a Potential Roadblock to PV Integration a Potential Roadblock to PV Integration 

•	 NERC Integrating Variable Generation Task Force: “...PV 

installations can change output by +/- 70% in a time frame of 

two to ten minutes, many times per day. Therefore, these 

plants should consider incorporating the ability to manage 

ramp rates and/or curtail power output.” 

•	 Numerous academic studies between 1980 – 1996 suggested

potential limits to increasing PV penetration due to inability or

high cost of operating conventional generation to respond to 

rapid fluctuations in PV 

•	 Many of these concerns/studies lack detailed consideration of 

the effect of geographic diversity in smoothing aggregate the effect of geographic diversity in smoothing aggregate 

output of several PV plants 

- Cloud models, anecdotal evidence, and increasingly available

actual plant output suggests geographic diversity will play an

important role in mitigating rapid fluctuations at the system

level, as is already well known for wind energy 

Energy Analysis Department 

Data and Approach
 

•	 Use time-synchronized data from multiple sensors to develop 

relationships between: 

- Time-scale of variability scale of variability 

- Variability at one site; variability of aggregate of multiple sites 

- Number of sites and geographic orientation of sites 

•	 Apply similar approach to solar and wind data in the same region 

•	 Estimate the potential implications of geographic diversity on the cost

of managing variability 

- Use ‘back-of-the-envelope’ estimation applied similarly to wind and 

solar 

•	 Data source: Southern Great Plains in ARM – 1-min data from 2004 

- 23 time-synchronized solar insolation sites (20-450 km spacing) 

- 14 time-synchronized 10-m wind anemometers (40-450 km) 

Energy Analysis Department 4

151
Clouds Can Produce Rapid Ramps Clouds Can Produce Rapid Ramps 

in Solar Insolation at a Single Point in Solar Insolation at a Single Point 

Energy Analysis Department 5

Deltas: Step change from one averaging interval to the next 

Clearness Index: Ratio of clear-sky solar insolation to measured insolation 

Clouds Can Produce Rapid Ramps Clouds Can Produce Rapid Ramps 

in Solar Insolation at a Single Point in Solar Insolation at a Single Point 

Energy Analysis Department 6

152
 

7

scales as: 

(1) The standard 

deviation of the deltas 

(2) The 99.7th 

percentile of the 

deltas 

Extreme changes are 

Variability Metric is Standard Deviation and Variability Metric is Standard Deviation and 

99.7th Percentile of Deltas Percentile of Deltas 

Characterize variability 

over different time over different timeg 

observed from one 

hour to the next (60-

min deltas) and even 

one minute to the 

next (1-min deltas) 

Energy Analysis Department 

Short Time Short Time-Scale Changes in Insolation are in Insolation are 

Uncorrelated Between Sites Uncorrelated Between Sites 

Energy Analysis Department 8

153
f

 

9

Temporal and Spatial Temporal and Spatial Scales ofScales of Diversity Can Diversity Can 

be Used to Predict Variability at System Used to Predict Variability at System Level 

• (Δσt

k/N): Average variability for a time-scale t at system

level for N sites 

• Δσt

1: Variability at a single site 

•	 ρt

: Correlation coefficient of step-changes in clearness 

index between two sites 

• If all sites are uncorrelated (ρt = 0), average variability

is 1/sqrt(N) times the variability at a single site 

• If all sites are perfectly correlated (ρt = 1), average

variability is equal to the variability at a single site 

Energy Analysis Department 

Diversity Within a Control Area Will Diversity Within a Control Area Will 

Significantly Smooth Rapid Ramps Significantly Smooth Rapid Ramps 

0.15 

0.20

 Deltas

Ramps at a single site can 

be severe, but diversity 

between sites can mitigate 

0.00 

0.05 

0.10 

Standard Deviation of 

Solar: 1 Site 

Solar: 5 Sites 

Solar: 25 Site Grid 

W ind: 25 Site Grid 

5 close sites 

~ 7,000 sq. km 

g 

ramps. 

Similarly sited wind and 

solar appear to have similar 

variability 

Energy Analysis Department 10

0 50 100 150 200 

Averaging Interval for Deltas (min) 

Caveat: Each site is based on a single point measurement, 

additional smoothing will occur for both wind and solar over 

short-time scales within individual sites. These results overstate 

variability of plants below ~10-min time scale 

25 site grid 

5 X 5 Site array with 40 km 

spacing between sites 

~ 40,000 sq. km 

154
 

11

Costs to Costs to Manage Short- Manage Short-term Variability: term Variability:

Assumptions Assumptions 

•	 Short-term variability is managed by increasing reserves at the power 

system level (as opposed to plant level) 

•	 Reserves are increased to manage variabilityover three time scales: 

- Regulation (1-min deltas) 

- Load Following (5-min deltas) 

- Operating Reserve Margin (60-min deltas) 

•	 Grubb (1991): Cost of reserves is due to: 

- Part-load efficiencyp penaltyfor spinning plants (assumed to be 15%)

- Use-of high cost energy from quick-start standing plant when 

standing reserves are deployed (applicable to 60-min deltas only) 

•	 Used characteristics of MISO load and assumptions made in 2006 

MISO Wind Integration Study to estimate increase in reserve costs due 

to increased variability from solar and wind 

Energy Analysis Department 

Integration Costs Integration Costs of Solar Dramatically of Solar Dramatically 

Impacted By Geographic Impacted By Geographic Diversity, and May Diversity, and May 

Be Less than for Comparably Sited Wind
 

Integration costs include unit-commitment costs which are not considered here 

Energy Analysis Department 

Increased Reserve Costs ($/MWh) 

Reserves Constant Throughout Reserves Change 

Time Scale Time Scale 

1 Site
 

1-min Deltas $14.7 (Regulation)
 

5-min Deltas $7.0 (Load Following) 

60-min Deltas 

(Reserve Margin $5.2 for Hour-ahead 

Forecast Error)
 

Total Cost $26.9 

Year 

Solar 

5 Sites 

$5.0 

$2.1 

$2.2 

$9.3 

$1.6 

$0.7 

$1.3 

$3.5 

with Position of Sun 

Wind Solar 

25 Site Grid 

$1.1 $0.2 

$0.2 $0.1 

$0.8 $0.2 

$2.1 $0.5 

Example costs 

based on 10% 

penetration of 

solar or wind on 

capacity basis 

Why are solar 

costs lower?
 

Reserves can be 

held in proportion 

to clear-sky 

insolation for solar 

Reserves are held 

at the same level 

all year for wind 

12

155
 

13

Preliminary Conclusions Preliminary Conclusions 

• Variability in solar insolation at a single site can be 

severe. Scaling a single point measurement of insolation 

leads to projections of high costs to manage PV 

variability 

• 1-min to 10-min step changes in solar insolation for sites 

as close as 20 km apart, however, are uncorrelated 

• Aggregation of multiple sites at the system level leads to 

significant smoothing of ramps particularly over short significant smoothing of ramps, particularly over short 

time-scales 

• Costs to manage short-term PV variability from multiple 

sites aggregated to the system level may be similar to the 

modest costs to mange short-term variability of wind 

Energy Analysis Department 

Acknowledgements
 

• Project funded by the Office of Electricity and the Solar 

Technologies Program at DOE 

• Special thanks to: 

Michael Milligan, Yih-huei Wan, Debbie Lew, Ben 

Kroposki, Dave Renne, Mark O’Malley, Abe Ellis and 

Brian Parsons 

Energy Analysis Department 14

156
 - 

Energy Analysis Department

15

Backup Slides
 

Energy Analysis Department 

Next Steps Next Steps 

•	 Characterize temporal and 

spatial scales of geographic 

diversity in high diversity in high -solar regions solar regions 12 25 sq km = 12.25 sq. km = 

3025 acres • Estimate the degree to which ~ 350 MW PV smoothing occurs at the plant plant level; compare within-plant 

smoothing to wind
 

•	 Perform more detailed studies 

of reserve costs using 

prod ti uc on cost models and 

time-synchronized PV and load 

data Source: 

Kawasaki 

et al. 2006. 

16

157
t

 

 

 

 

17

Variations are Smoother for Aggregate of Variations are Smoother for Aggregate of 

MultipleMultiple Sites Compared to a Single Sites Compared to a Single Site 

Note change in scale 

• Deltas are relatively smaller with more sites; aggregation is particularly effective for 

shorter time-scales 

• Shape of distribution of deltas becomes less “fat-tailed” 

Energy Analysis Department 

Costs to Costs to Manage Short- Manage Short-term Variability: term Variability:

Simple Example Simple Example for Windfor Wind 

Time-Scale 

1-min Deltas (Regulation) 

5-mi Dneltas $10 

$8 

$6 Reserve Margin (60-min Deltas) 

Load Following (5-min Deltas) 

Regulation (1-min Deltas) 

(Load Following) 0.24% 1.3% 

60-min Deltas 

(Reserve Margin for Hourahead Forecast Error) 0.36% 4.7% 

0% 10% 20% 30% 40% 50% 

Penetration (% Peak Load) 

Additional Reserve Costs ($/MWh)

$4 

$2 

$0 

Energy Analysis Department 

Load 

(% Peak)
 

0.13% 

Wind
 

(% Nameplate)
 

1.7% Analysis Relies on 

Many Simplifying Many Simplifying 

Assumptions: 

Reserves are constant 

throughout year for 

both load and net-load 

One type of plant 

provides spinning 

reserve throughout reserve throughout 

year (marginal cost at 

full load = $55/MWh) 

One type of plant 

provides standing 

reserve (marginal cost 

= $85/MWh, no startup cost) 18

158
L d F ll i )

-

-

 

-

19

Broad Sketch of Broad Sketch of Research Required to Research Required to 

Assess the Operational Integration Impacts the Operational Integration Impacts 

of Utility of Utility-Scale Photovoltaic Plants Scale Photovoltaic Plants

NREL Focus 

for WWSIS
 

Energy Analysis Department 

Performance of 

existing PV plants, 

insolation data, 

satellite data 

Time-synchronized PV and 

insolation data across 

different spatial and 

temporal scales 

High-time-resolution load 

data, hourly reserve 

requirements, hourly 

reserve prices 

Impact of PV on 

Reserves 

(Regulation and 

Model of 

Utility Scale 

PV Plant 

Geographic 

Smoothing: 

Time and 

Space Scales 

Variability of 

Dispersed 

Utility Scale 

PV Plants 

satellite data temporal scales reserve prices 

Current 

Potential for 

NREL/ Sandia/ LBNL 

Collaboration 

Load Following) 

and Costs Focus Variability of 

Concentrated 

Utility Scale 

PV Plants Comparison to 

Wind and 

Solar Thermal 

Comparison 

to Wind 

LBNL 

Geographic Diversity Smoothes Rapid Geographic Diversity Smoothes Rapid 

Variations in Variations in Output
 

Information about the benefits of geographic 

diversity need to be incorporated into the 

analysis of utility-scale PV grid integration 

impacts 

Energy Analysis Department 20

Source: Hoff et al. 2008 Source: Weimken et al. 2001 

159
 

Correlation of changes in wind power output is a function of distance and time-scale. 5-

min variations in wind plants over 20 km apart are statistically uncorrelated. 

Temporal and Spatial Scales for Correlation of Temporal and Spatial Scales for Correlation of 

Changes in Wind Power are Well Understood Changes in Wind Power are Well Understood 

Energy Analysis Department 21

Source: Ernst, Wan, and Kirby 1999 

AnalysisAnalysis of Temporal and Spatial Scales of of Temporal and Spatial Scales of 

Geographic Diversity: ARM Program
 

•	 Use 1-min data from 2004 to estimate 

correlation coefficient for changes in: 

- global insolation, 

- direct insolation, and 

- clearness index* 

between pairs of stations in the 

Southern Great Plains (SGP) site of 

the Atmospheric Radiation 

Measurement (ARM) Program. 

•	 Calculate histograms of changes in 

solar insolation over different timesteps for a single site (C1) and all 23 

available sites (C1 & E1-27 excl. E-14 

& E-26) 

*Clearness Index: Ratio of measured insolation to 

“clear sky” insolation (i.e. insolation in absence of 

www.arm.gov clouds). SGP data includes clear sky insolation 

Energy Analysis Department 22

160
 

 

Diversity Decreases Probability of Large Diversity Decreases Probability of Large 

Swings in Solar Insolation for Multiple Sites Swings in Solar Insolation for Multiple Sites 

1200 

Jul.02, 2004 Event: 1-min Change in Avg. Insolation from 23 Sites >150 W/m2 

Average of 23 Sites 

One Site (C1) 

400 

600 

800 

1000 

Global Insolation (W/m2) 

Energy Analysis Department 23

07/03 6:00 07/03 12:00 07/03 18:00 0 

200 

CST Chart shows a day in 2004 with one of the most extreme 1-min 

changes in global solar insolation simultaneously measured at 

all 23 sites 

Diversity Decreases Probability of Large Diversity Decreases Probability of Large 

Swings in Solar Insolation for Multiple Sites Swings in Solar Insolation for Multiple Sites 

1200 

Sep.04, 2004 Event: 1-min Change in Avg. Insolation from 23 Sites > 150 W/m2 

Average of 23 Sites 

One Site (C1) 

400 

600 

800 

1000 

Global Insolation (W/m2 ) 

Energy Analysis Department 24

09/04 6:00 09/04 12:00 09/04 18:00 0 

200 

CST Chart shows a day in 2004 with one of the most extreme 1-min 

changes in global solar insolation simultaneously measured at 

all 23 sites 

161
t t

-

 

Comparison of PV and Wind Variability 

Utility Scale PV 

Variability Shop 

Cedar Rapids, Iowa 

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 

Yih-huei Wan 

October 7, 2009 

Presentation Outline 

• Introduction 

• Data Sources 

• Average Profiles of PV and Wind Power 

• Ramping Statistics on Different Time Scales 

• Comparison of PV and Wind Ramp 

Distribution 

C l i Pl 

National Renewable Energy Laboratory Innovation for Our Energy Future 

• Correlation among Plants 

• Summary 

2 

162

1 
 

 

 

 

Introduction 

• Both PV and wind power are variable in nature 

• The outline of PV production is well-known because 

the position of sun is fixed at any given time 

• Outputs from PV facilities within the same time zone 

could be highly correlated 

• Although wind can change quickly, its short-time 

frame (1-second and 1-minute) changes are limited. 

Coupled with turbine inertial, the short-time frame 

wind power changes are small and not totally 

National Renewable Energy Laboratory Innovation for Our Energy Future 

wind power changes are small and not totally 

random. 

• There is no inertial in PV panel and the associated 

inverter. 

3 

Data Sources 

• PV Data – 1-minute PV output data from 6 

LVWD facilities (2007 through April 2009) 

1. Ronzone (675 kW single Ronzone (675 kW, single-axis tracking) axis tracking) 

2. Ft Apache (325 kW, single-axis tracking) 

3. Gd Canyon (325 kW, single-axis tracking) 

4. Spg Mtn (450 kW, single-axis tracking) 

5. Luce (450 kW, single-axis tracking) 

6. LVSP (450 kW, fixed-tilt) 

National Renewable Energy Laboratory Innovation for Our Energy Future 

• Wind Data – 1-minute plant output data from 

plants in Buffalo Ridge area (Minnesota) during 

the same period; mostly 750-kW turbines 

4 

163

2 
 

 

Locations of PV Facilities 

National Renewable Energy Laboratory Innovation for Our Energy Future 

6 miles 

5 

National Renewable Energy Laboratory Innovation for Our Energy Future 

Locations of turbines at the wind plant 

6 

164

3 
 

 

 

PV Profiles (clear sky) 

2500 

3000 

Typical Clear Sky PV Output 

Ronzone 

Ft Apache 

1000 

1500 

2000 

Power (kW) 

Luce 

LVSP 

Gd Canyon 

Spg Mtn 

Total 

National Renewable Energy Laboratory Innovation for Our Energy Future 

0 

500 

3:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:00 

7 

PV Profiles (cloudy day example) 

2000 

2500 

Fluctuations of PV Outputs 

Ronzone 

Ft Apache 

1000 

1500 

2000 

Power (kW) 

Luce 

LVSP 

Gd Canyon 

Spg Mtn 

Total 

National Renewable Energy Laboratory Innovation for Our Energy Future 

0 

500 

3:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:00 

8 

165

4 
Fixed-tilt PV Monthly Average Profiles
 

250 

Jan 

Feb 

200 Mar 

Apr 

May 

Jun 

150 Jul 

Aug 

Sep 

Oct 

100 Nov 

Dec 

50 

0 

National Renewable Energy Laboratory 9 Innovation for Our Energy Future 

Single-axis Tracking PV Monthly Average Profiles 

700 

Jan 

600 Feb 

Mar 

Apr 500 May 

Jun 

Jul 400 

300 

200 

100 

0 

(kW) 

3:004:00

3:004:00

(kW) 

5:006:00

5:00

7:00

6:007:00

8:00 8:009:00 9:00

10:00 10:0011:00 11:0012:00 12:0013:00 13:0014:0015:0016:00

14:0015:0016:00

17:00 17:0018:00 18:0019:00 19:00

20:00 20:00 

Aug 

Sep 

Oct 

Nov 

Dec 

National Renewable Energy Laboratory 10 Innovation for Our Energy Future 

166

5 
PV Monthly Average Profiles
 

2008 LVWD Average Daily Profile 2500 

Jan 

Feb 

Mar 2000 Apr 

May 

Jun 

1500 Jul 

Aug 

Sep 

Oct 

1000 Nov 

Dec 

500 

0 

National Renewable Energy Laboratory 11 Innovation for Our Energy Future 

Wind Power Monthly Average Profiles 

40000 

35000 

30000 

25000 

Jan 

20000 Mar 

Jul 

Aug 

15000 Annual 

10000 

5000 

0 

(kW) 

3:00

0:00

4:00

1:002:003:004:005:00

5:006:007:00

6:00

8:00

7:008:009:00

9:00

10:00

10:0011:0012:00

11:0012:00

13:0014:0015:00

13:00

(kW) 

16:0017:0018:00

14:0015:00

19:0020:0021:00

16:0017:00

22:0023:00 

18:0019:0020:00 

National Renewable Energy Laboratory 12 Innovation for Our Energy Future 

167

6 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Wind Ramp Statistics 

Delta (22.5 MW) Golf (41.3 MW) LB (63.8 MW) BR (240 MW) 

1-minute 

Stdev Max (+) Max (−) Stdev Max (+) Max (−) Stdev Max (+) Max (−) Stdev Max (+) Max (−) 

2007 1.6% 71% 94% 1.2% 75% 92% 1.0% 50% 53% 0.5% 30% 57% 

2008 1.5% 73% 86% 1.2% 68% 93% 0.9% 58% 53% 0.5% 20% 79% 

2009 1.6% 71% 82% 1.2% 67% 60% 1.0% 54% 81% 0.5% 12% 28% 

10-minute 

2007 5.0% 78% 85% 4.3% 88% 90% 3.7% 79% 86% 2.5% 44% 92% 

2008 4.5% 87% 90% 4.2% 66% 86% 3.5% 56% 57% 2.4% 39% 82% 

2009 4.5% 68% 77% 4.1% 73% 73% 3.4% 63% 63% 2.6% 23% 36% 

1-hour 

2007 10.7% 74% 84% 10.1% 85% 68% 9.6% 76% 65% 8.1% 57% 48% 

2008 9.8% 76% 65% 9.9% 75% 79% 9.4% 74% 55% 8.0% 51% 56% 

2009 9.7% 83% 75% 9.6% 77% 65% 9.3% 75% 69% 8.6% 49% 38% 

• Outputs from bigger the wind plants (or more turbines) are 

l i bl th ll l t 

National Renewable Energy Laboratory Innovation for Our Energy Future 

less variable than smaller plants 

• Detailed analysis shows that the severe down ramps in 

shorter time frame are invariably caused by outages or 

curtailment; severe up ramps are the results of starting 

under high wind 

13 

PV Ramp Statistics

 LVSP (450 kW) Spring Mtn (450 kW) Ronzone (675 kW) Total (2675 kW) 

1-minute

 Stdev Max (+) Max (−) Stdev Max (+) Max (−) Stdev Max (+) Max (−) Stdev Max (+) Max (−) 

2007 1.8% 42% 38% 4.3% 76% 83% 4.0% 72% 87% 1.7% 27% 24% 

2008 2.0% 46% 45% 4.4% 75% 77% 4.2% 73% 89% 1.7% 32% 35% 

2009 2.1% 47% 40% 4.7% 79% 72% 4.5% 77% 78% 1.9% 32% 28% 

10-minute 

2007 3.2% 29% 33% 8.7% 79% 84% 8.3% 77% 70% 4.9% 31% 38% 

2008 3.6% 35% 36% 8.2% 85% 77% 8.3% 81% 78% 5.0% 46% 42% 

2009 3.5% 35% 39% 8.3% 76% 73% 8.5% 71% 69% 5.4% 41% 37% 

1-hour 

2007 8.4% 34% 30% 20.0% 75% 76% 21.2% 76% 79% 16.8% 46% 54% 

2008 8.7% 35% 35% 20.6% 89% 70% 20.8% 71% 74% 16.9% 50% 62% 

2009 8.9% 34% 38% 21.4% 64% 82% 22.3% 62% 74% 17.9% 54% 51% 

• Outputs from single-axis PV appears to be more variable 

National Renewable Energy Laboratory Innovation for Our Energy Future 

than fixed-tilt PV. It’s not clear this is true in other areas. 

• No clear conclusion about PV and wind variability can be 

drawn because the size differences between PV and 

wind (more PV data needed) 

14 

168

7 
1-minute Ramp Distribution of PV and Wind
 

Ramp (% of Capacity) 

National Renewable Energy Laboratory 15 Innovation for Our Energy Future 

10-minute Ramp Distribution of PV and Wind 

Distribution of 10-minute Ramps 

1 

Delta 

Golf 

LB2 

0.1 BR 

Ft Apache 

Gd Canyon 

0.01 Luce 

Ronzone 

Spg Mtn 

LVSP 

0.001 Total PV 

0.0001 

0.00001 

-100% -100%

-90% -90%-80% -80%-70% -70%-60% -60%-50% -50%-40% -40%-30% -30%-20% -20%-10% -10%

0% 0%

10% 10%20%30%40%

20%30%40%

50% 50%60% 60%70% 70%80% 80%90% 90%

100% 100% 

1 

Distribution of 1-minute Ramps 

Delta 

Golf 

0.1 LB2 

BR 

0.01 

Ft Apache 

Gd Canyon 

Luce 

Ronzone 

0.001 Spg Mtn 

LVSP 

Total PV 

0.0001 

0.00001 

0.000001 

Ramp (% of Capacity) 

National Renewable Energy Laboratory 16 Innovation for Our Energy Future 

169

8 
se es

 

 

1-hour Ramp Distribution of PV and Wind 

1 

Distribution of 1-hour Ramps 

Delta 

Golf 

LB2 

BR 

0.01 

0.1 

Ft Apache 

Gd Canyon 

Luce 

Ronzone 

Spg Mtn 

LVSP 

Total PV 

National Renewable Energy Laboratory Innovation for Our Energy Future 

0.0001 

0.001 

-100%

-90%-80%-70%-60%-50%-40%-30%-20%-10%

0%

10%20%30%40%50%60%70%80%90%

100% 

Ramp (% of Capacity) 

17 

Correlation among Plants 

• Correlation between adjacent PV facilities is 

hig her than further away PV facilities ( gav . 0.93

vs 0.78) 

• PV facilities have slightly higher correlation 

coefficients than adjacent wind plants (avg. 0.84) 

• Further analysis will look into 1-second PV time 

series 

National Renewable Energy Laboratory 18 Innovation for Our Energy Future 

170

9 
 

 

Summary 

• PV have relatively large up ramp in the morning 

and down ramps in the afternoon, but their 

magnitudes is bounded by clear sky values magnitudes is bounded by clear sky values. 

• Even for relatively short distance and small 

installations, PV facilities still benefit from spatial 

diversity. 

• More high resolution data from larger PV 

i t ll ti d d t h b tt 

National Renewable Energy Laboratory Innovation for Our Energy Future 

installations are needed to have a better 

understanding of the variability issue. 

19 

171

10 
bl l p p y

Variability In A Large-Scale PV Installation 

October 7, 2009 

Carl Lenox 

Principal Engineer, Technology Development 

Established and Proven Technology Innovator 

Diversified portfolio: residential, 

commercial, power plant & utility 

Incorporated in 1985 

4,000 employees, 100 % solar 

Over 400 MW installed worldwide 

25 years of R&D, 85 patents 

Publicly traded company: NASDAQ 

500 systems over 4 continents 

World record cell efficiency: 23.4% 

2 

Manufacturing U.S., Philippines, Malaysia 

172

1 
Over 200 MW of power plants installed in Europe 

Muehlhausen, 

Germany, 6 MW 

Olivenza, Spain 

18 MW 

Serpa, Portugal 

11 MW 

Isla Mayor, Spain 

8 MW 

Jumilla, Spain 

23 MW 

3 

Tolentino, Italy 

7 MW 

FPL Desoto 25 MW SunPower T0 Tracker 

4 

173

2 
SPWR’s California Valley Solar Ranch – 210 MW 

≈ 5 km 

Preliminary Plant Layout * 

* 2010 Projected Construction Start 

5 

Today’s Presentation 

• Based on 1-second resolution data from ~13 MW (AC) site in Nevada 

• Data is at the inverter level (typically 250 kW), time synchronized 

• Dates from 5/11 – 9/7/2009 – 120 days 

• We consider these results to be preliminary – more to be done! 

6 

174

3 
 

 

Questions For Today 

• What are useful interval(s) for data collection? 

• How frequently do large changes in output occur? 

• How does variability change with scale, and over different intervals? 

7 

An Important Note 

• This site uses typical UL Listed commercial inverters, IEEE 1547 compliant 

• Multiple, simultaneous inverter trips cause the largest short-duration changes in output (1-sec and 10-sec). 

• The signature of these trips indicate they are most likely due to anti-islanding – operating as designed. 

• Consistent LVRT requirements Consistent LVRT requirements and implementation should significantly mitigate this issue implementation should significantly mitigate this issue. 

• This cause of variability is excluded from most of the analysis – noted where included. 

8 

175

4 
 

Averaging Interval – Single Day 

9 

Day with highest 10-sec maximum variability 

Red = 1 sec, Green = 10 sec, Blue = 1 minute, Orange = 15 min 

Except for 1 sec data, all are centered moving averages. 

Averaging Interval – Single Day 

10 

1 Hour 

Noise in 1-second data is a data acquisition artifact (pulse meter) 

176

5 
Averaging Interval – Single Day 

11 

4 Minutes 

• 1 minute data captures 80% of deviation from the long term trend even in this very extreme event. 

• 10 second data captures 97% of deviation. 

• 1 second data deviation from 10-second trend is at least partially pulse count noise. 

Averaging Interval – Highest Variability Day 

Distributions show the 

difference between the 

shorter interval data, and 

the longer interval trend. 

12 

Interval to Trend 95% within All 

1 sec to 10 sec +/- 2% +/- 5% 

10 sec to 1 min +/- 4% +11% / -14% 

1 min to 15 min +/- 20% + 50% / -34% 

177

6 
 

Data Intervals - Conclusion 

• One second resolution data may not be required to characterize variability of 10 MW+ plants. 

• One minute data is very useful for some purposes since it is much easier to handle and 

analyze, but it seems to underestimate variability in extreme events. 

• Use of ten Use of ten-second data is recommended where it is important that short second data is recommended where it is important that short-term transients are term transients are
 

captured completely.
 

• Ten second data does not adequately characterize rapid changes in irradiance measured at a 

single point. This also likely holds for smaller PV arrays which can be approximated as a point.
 

• 15 minute data misses large, short interval transients. 

Future Work: 

13 

• Verify relationship between 1 sec and 10 sec data at large PV site with better data acquisition. 

• Extend this type of analysis to more days – not just one, extreme day. 

• Data interval selection should comprehend what is relevant to system operations. In other 

words, do transients that last 1 second matter? 10 seconds? 1 minute? 

Distribution of Maximum Variability 

14 

This shows the distribution of the largest absolute change between periods as % of AC nameplate, on a given 

day, for all days analyzed. 

For 10-second data, the data point for a day is the single largest change between the 4320 intervals in that day. 

178

7 
t t

 

Distribution of Maximum Variability – All Days 

Averaging Interval 90% of days (108 days) 10% of days (12 days) 

Note floor on 15 minute 

data.10-13% in 15 minutes 

is max change on a clear 

day – driven by sunrise or 

sunset. 

15 

Averaging Interval 90% of days (108 days) 10% of days (12 days) 

10 second max <12% (1.2% / sec, sustained) 12% - 19% 

1 minute max < 39% (0.65% / sec, sustained) 39% - 50% 

15 minute max < 44% (2.9% / min, sustained) 44% - 62% 

Distribution Within A Highly Variable Day 

P b f h b 1 ii l 

Day with largest 1-minute interval change 

16 

Prob. of change between 1-min intervals 

80% of all intervals between -4% & +3% 

95% of all intervals between -16% & +20% 

99% of all intervals between -35% & +38% 

179

8 
 

Maximum Variability Snapshot 

A Few Points: 

• A “spiky looking” graph can be misleading. 

50% / minute 

30 minutes 

17 

Even large, rapid changes occur over a minute or two, 

not in “a second” as has been stated. 

Not readily apparent from visual inspection of a plot spanning 12 hours! 

• No operational issues: the utility has investigated, and has not found, any 

operational issues related to this site. 

• This system is “high penetration” relative to the distribution system 

capacity (as defined by DOE, “high penetration” PV is >30% peak on peak). 

30 minutes 

Peak 1-minute change (12:01 – 12:02) of all days 

(1 of 100,800 intervals) 

61% - 3 minutes 

Variability Occurrence - Conclusions 

• Approximately 1/3 of days are clear and cloudless during these 4 months. 

• Remaining 2/3 have a wide spread of variability – long, flat tails in distribution. 

• These observations may not be generalizable - obviously, results will vary for 

different seasons and locations. 

• Reporting the maximum change between intervals can be misleading, because 

this is an by definition a statistical outlier – especially an issue as intervals 

become shorter. 

• It is also misleading to report variability over one interval in the same units as 

another interval. 

18 

For instance, while it may be tempting to report a measured change of 10% in 10 

seconds as 60% / minute, this is incorrect, because the former value is a nearly 

instantaneous maximum and not sustained. 

180

9 
 

 

 

How Does Variability Scale? 

19 

Ordered Correlation of changes in 1-minute output across inverters 

Reference is median peak variability inverter for this day 

How Does Variability Scale? 

Maximum 1-minute change, on the highest 1-minute variability day 

Single-Point 

Irradiance 

Lines: 

Several alternate sum 

orders, where order is 

determined by ranked 

correlation starting with 

the highest, median, and 

lowest peak variability 

single inverter 

respectively. 

Irradiance 

Sensors 

20 

Large Data Points: 

Contiguous plant sectors 

or single inverters of note. 

Sector 1 is single-axis 

tracking. All others are 

tilted single axis. 

kW 

181

10 
How Does Variability Scale? 

Maximum 10 second change, on the highest 1-minute variability day 

21 

kW 

How Does Variability Scale? 

Maximum 15 minute change, on the highest 1-minute variability day 

22 

182

11 
How Does Variability Scale? 

23 

How Does Variability Scale? 

24 

183

12 
–

–

 

 

 

 

 

Variability Comparisons – Scale Matters! 

Is CSP really smoother? 

A few factors: 

• Thermal mass - commonly cited

• Scale - CSP plant shown is 6X size of PV plant 

• Plant Operations - CSP operators often 

actively control plant output to avoid transients 

which can damage the equipment 

What is the relative contribution of these factors? 

Si il ti t i t i ith 

25 

Figures from NERC Special Report: Accommodating High Levels of Variable Generation 

Similar questions pertain to comparisons with 

wind generation. 

Variability Scale – Conclusions* 

• Mitigation of variability with geographical diversity depends strongly on the time 

interval considered and is significant. At this scale (teens of MW), maximum daily
 

variability (%):
 

• Is constant with size on a 15 min basis 

• Modestly decreases with size o Modestly decreases with size on a 1 min basis a 1 min basis ~35% reduction versus point measurement ~35% reduction versus point measurement. 

• Strongly decreases with size on a 10 sec basis – ~70% reduction versus point measurement. 

• For a given day and location, the maximum variability of a “system” of a given 

nameplate rating can vary widely on a percentage basis, especially for small 

systems. Contributing factors include:
 

• Sum order – Whether it is physically contiguous; aspect ratio; orientation relative to cloud movement. 

• Specific variations in cloud pa Specific variations in cloud patterns relative to location relative to location non -uniform uniform , 2D effects of small clouds 2D effects of small clouds.

• Varying DC ratings of inverter blocks, all of which have the same nominal AC nameplate. 

• Relatively minor sources of variance may appear as large differences on a % per interval basis
 

which will make it difficult to draw meaningful conclusions about larger systems. The differences are 

small on a kW / interval basis for small systems. 

* For the analyzed data set 

26 

184

13 
 

Variability Scale - Conclusions 

• Variability of even a relatively small PV block (250 kW) is, in general, significantly 

lower than single-point irradiance measurements at 1 minute intervals and below. 

Use of a single point irradiance measurement to estimate the short-interval variability of commercial and 

utility scale PV systems will likely significantly over predict actual variability. 

27 

What’s Next? 

• Continue to develop understanding of how variability scales with system size, and 

geographical dispersion, for different locations and system types. 

• Determine what data intervals are most critical and work to standardize. 

• Develop modeling tools to study the impact of PV system dynamics on the utility system – 

both distribution and transmission. 

• Develop appropriate, consistent interconnection requirements to ensure that PV systems 

do not needlessly contribute instability in the event of a fault. 

• Develop forecasting methods
 

SunPower is committed to participating in collaborative activities to get this done!
 

• NERC IVGTF participant
 

• WECC PV Integration Task Force member
 

• DOE Solar Vision 2030 Study participant
 

• And of course, honored to be with you here today! 

28 

185

14 
186

15 
y

 

Quantifying PV Output 

Variability 

Thomas E. Hoff and Richard Perez 

Clean Power Research 

www cleanpower com www.cleanpower.com 

Utility-scale PV Variability Workshop 

October 7, 2009 

Copyright © 2009 Clean Power Research 

Why This Topic is Important to Utilities 

System Planning 

Maximizing the benefits while minimizing 

grid impacts of PV requires utilities to 

influence where PV systems are installed 

System Operation 

Optimize utility system operation once Optimize utility system operation once 

systems are installed requires the ability 

to forecast PV variability 

187

1 
Intermittency Questions 

1. What is the effect of short-term output 

variability?

2. What needs to be done to forecast shortterm output variability? 

Question #1 

What is the effect of short-term output 

variability? 

188

2 
 

Objective 

Quantify relative power output variability 

for a fleet of identical PV systems 

Key Findings 

Relative Output Variability is based on: 

1. Number of PV systems 

2. Dispersion Factor 

Relative Output Variability 

Output variability for fleet / Output variability at single location 

189

3 
 

 

1 location 

12:00 

What is Meant by Variability? 

Irradiance 

1250 

1 location 

1000 

750 

500 

250 

0 

Irradiance (Watts/m^2) 

6:00 12:00 18:00 

Time of Day 

Relative Output Variability 

Output variability for fleet / Output variability at single location 

What is Meant by Variability? 

Irradiance	 Change in Irradiance 

1250 1000 

18:00 

1 location 

Irradiance Change (Watts/m^2)

1000 

Irradiance (Watts/m^2) 

500 

750 

0 

500 6:00 

-500 

250 

0 -1000 

6:00	 12:00 18:00 Time of Day 

Time of Day 

Relative Output Variability 

Output variability for fleet / Output variability at single location 

190

4 
 

 

How Number of Systems Affects Variability 

Irradiance Change in Irradiance 

500 

750 

1000 

1250 

adiance (Watts/m^2) 

1 location 

16 locations 

500 

0 

500 

1000 

6:00 12:00 18:00 

nce Change (Watts/m^2) 

1 location 

16 locations 

Time of Day 

0 

250 

6:00 12:00 18:00 

Irra 

-1000 

-500 

Irradian 

Time of Day 

Relative Output Variability 

Output variability for fleet / Output variability at single location 

What is Dispersion Factor 

• Dispersion Factor is the number of Time 

I t l f l dt Intervals for a cloud to pass across th e

distance of the entire PV Fleet 

Relative Output Variability Dispersion Factor 

Output variability for fleet / Output variability at single location Number of Time Intervals for cloud to pass across the PV Fleet 

191

5 
 

 

What is Time Interval? 

Time 

Interval 

Data 

Recording 

Rate 

A l i 

12:00:0012:00:30

12:01:00

... 12:59:30

13:00:00 

1 Minute 

Analysis 

Period 

1 Hour 

Relative Output Variability Dispersion Factor 

Output variability for fleet / Output variability at single location Number of Time Intervals for cloud to pass across the PV Fleet 

Dispersion Factor For 

Moderate Cloud Transit Speed 

Moderate Cloud Transit Speed 

(Dispersion Factor = 4) 

12:0321 12:0043 12:0210 12:04
 

2 

3 

4 

5 

1 

Dispersion 

Factor 

Relative Output Variability Dispersion Factor 

Output variability for fleet / Output variability at single location Number of Time Intervals for cloud to pass across the PV Fleet 

192

6 
 

 

Dispersion Factor 

Fast Cloud Transit Speed 

(Dispersion Factor = 2) 

12:0210 12:0210
 

2 

3 

4 

5 

1 

Dispersion 

Factor 

Relative Output Variability Dispersion Factor 

Output variability for fleet / Output variability at single location Number of Time Intervals for cloud to pass across the PV Fleet 

Model Results Categorized in 4 Regions 

Crowded Number of Systems > Dispersion Factor 

Optimal 

(Point) 

Number of Systems = Dispersion Factor 

Limited Number of Systems < Dispersion Factor 

Spacious Number of Systems << Dispersion Factor 

Relative Output Variability Dispersion Factor 

Output variability for fleet / Output variability at single location Number of Time Intervals for cloud to pass across the PV Fleet 

193

7 
0

 

is less than 4 less than 4

0

 

lity 

Additional systems do not 

reduce variability if Dispersion 

Factor 

4x systems cuts 

variability in half when 

16 Systems 

50% 4 Systems 

25% 

elative Output Variabil 

Spacious 

Factor y 

systems are independent 

4 16

Re

Crowded 

Relative Output Variability: 4 Systems 

100% 

ility 

Variability is minimized 

when number of systems 

equals Dispersion Factor 

50% 4 Systems 

elative Output Variabi 

Optimal 

Limited Spacious 

Crowded equals Dispersion Factor 

Variability stabilizes when 

4 

0% 

60 

Re 

Dispersion Factor 

1 

systems are independent 

Dispersion Factor 

Number of Time Intervals for cloud to pass across the PV Fleet 

Relative Output Variability 

Output variability for fleet / Output variability at single location 

Relative Output Variability: 16 Systems 

100% 

Limited 

Optimal 0% 

1 

Dispersion Factor 

Relative Output Variability Dispersion Factor 

Output variability for fleet / Output variability at single location Number of Time Intervals for cloud to pass across the PV Fleet 

194

8 
20-second Time Interval (Dashed) 

 

20-second Time Interval (Dashed) 

 

Validation: Construct Model for 4 Systems 

Model Validation Results (Virtual 

Network 5 - May 7, 1999) 

100% 

Relative Output Variability 

1 4 64 

Dispersion Factor 

60-second Time Interval (Solid) 

75% 

4 Systems 50% 

25% 

0% 

Relative Output Variability Dispersion Factor 

Output variability for fleet / Output variability at single location Number of Time Intervals for cloud to pass across the PV Fleet 

Add Measured Data 

ty 

1 4 64 

Dispersion Factor 

60-second Time Interval (Solid) 

Model Validation Results (Virtual 

Network 5 - May 7, 1999) 

100% 

Relative Output Variabili

75% 

4 Systems 50% 

25% 

0% 

Relative Output Variability Dispersion Factor 

Output variability for fleet / Output variability at single location Number of Time Intervals for cloud to pass across the PV Fleet 

195

9 
20-second Time Interval (Dashed) 

 

20-second Time Interval (Dashed) 

 

Repeat for 9 Systems 

ty 

1 4 9 64 

Dispersion Factor 

60-second Time Interval (Solid) 

Model Validation Results (Virtual 

Network 5 - May 7, 1999) 

100% 

Relative Output Variabili

75% 

4 Systems 50% 

9 Systems 

25% 

0% 

Relative Output Variability Dispersion Factor 

Output variability for fleet / Output variability at single location Number of Time Intervals for cloud to pass across the PV Fleet 

Repeat 16 Systems 

Relative Output Variability 

1 4 9 16 64 

Dispersion Factor 

60-second Time Interval (Solid) 

Model Validation Results (Virtual 

Network 5 - May 7, 1999) 

100% 

75% 

4 Systems 50% 

9 Systems 

25% 16 Systems 

0% 

Relative Output Variability Dispersion Factor 

Output variability for fleet / Output variability at single location Number of Time Intervals for cloud to pass across the PV Fleet 

196

10 
20-second Time Interval (Dashed) 

 

 

 

Repeat for 25 Systems 

ty 

1 4 9 16 25 64 

Dispersion Factor 

60-second Time Interval (Solid) 

Model Validation Results (Virtual 

Network 5 - May 7, 1999) 

100% 

Relative Output Variabili

75% 

4 Systems 50% 

9 Systems 

25% 16 Systems

25 Systems 

0% 

Relative Output Variability Dispersion Factor 

Output variability for fleet / Output variability at single location Number of Time Intervals for cloud to pass across the PV Fleet 

Repeat w/ 20 Second Time Interval 

ty 

1 4 9 16 25 64 

Dispersion Factor 

Model Validation Results (Virtual 

Network 5 - May 7, 1999) 

100% 

Relative Output Variabili

75% 

4 Systems 50% 

9 Systems 

25% 16 Systems

25 Systems 

0% 

20-second Time Interval (Dashed) 60-second Time Interval (Solid) 

Relative Output Variability Dispersion Factor 

Output variability for fleet / Output variability at single location Number of Time Intervals for cloud to pass across the PV Fleet 

197

11 
MODELS

 

i 

 blue scale

Question #2 

What needs to be done to forecast shortterm output variability? 

DAYLIGHT

TILTED IRRADIANCE

Luminous efficacy

DAYLIGHT 

TILTED IRRADIANCE 

Luminous efficacy 

Modeled Irradiance 

1-hour to 7-Day 

Forecasts 

Vertical D ffuse

Illuminances

Sky luminance 

Angular Distribution

Vertical D ffuse 

Angular Distribution Illuminances 

© Richard Perez et al. © Richard Perez et al. 

198

12 
Patterned after Lorenz et al. 

Current-to-5 hours 

CLOUD MOTION 

© Richard Perez et al. © Richard Perez et al. 

How This Relates To Output Variability 

Relative Output Variability is based on: 

1. Number of PV systems 

2. Dispersion Factor 

Dispersion Factor is the number of Time Intervals 

for a cloud to pass across the distance of the 

entire PV Fleet 

199

13 
Conclusions: Relative Output Variability 

• Can be quantified based on the number of 

PV systems and the Dispersion Factor

• Equals inverse of the square root of the 

number of systems for dispersed PV 

systems systems 

• Can be minimized for optimally-spaced PV 

systems for a given Cloud Transit Speed 

Next Steps 

• Further model validation 

• Extend model to arbitrary fleet configuration 

• Integrate with SolarAnywhere® 

(www.solaranywhere.com) forecasting 

200

14 
 

 

 

© 2009 Clean Power Research, L.L.C. All rights reserved. Clean Power Research, Clean Power Estimator, QuickQuotes, PowerClerk, SolarAnywhere and other product 

names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. 

The information herein is for informational purposes only and represents the current view of Clean Power Research, L.L.C. as of the date of this presentation. 

Because Clean Power Research must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Clean Power Research, 

and Clean Power Research cannot guarantee the accuracy of any information provided after the date of this presentation. CLEAN POWER RESEARCH, L.L.C. MAKES 

NO WARRANTIES, E�PRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION. 

201

15 
 

Characterization of short-term PV 

variability for large PV systems 

October 7, 2009
 

Joshua Stein Ph.D.
 

Principal Member of Technical Staff
 

Sandia National Laboratories
 

Utility Wind Integration Group: Utility-Scale PV Integration Workshop
 

Cedar Rapids, IA
 

Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company,
 

for the United States Department of Energy’s National Nuclear Security Administration
 

under contract DE-AC04-94AL85000.
 

How Important is Variability? 

• Utilities are in the business of servicing a variable Utilities are in the business of servicing a variable 

load. 

• Ancillary services provide variability control 

– Voltage Control (seconds) [VAR support] 

– Regulation (~ 1 min) [online AGC*] 

– Spinning Reserve (seconds to <10 min) [online] 

– S l t l R (<10 i ) [ ffli b t Supplemental Reserve (<10 min) [offline but
 

staffed]
 

– Replacement Reserve (<30 min) [offline but staffed] 

– Planning and forecasting (hours – days) 

* Automatic generation control 

202
 

 

 

 

When is Variability Important? 

• Variability is only important if it significantly adds Variability is only important if it significantly adds 

to the net load variability. 

net load = load – non-dispatchable generation 

• Impact of variability depends on where the PV 

system is connected to the grid, penetration level, 

types of load serviced, and available generation 

options. 

• On clear days, solar (diurnal) variability can help 

utilities serve peak loads. 

Presentation Outline 

• Part 1: Discuss differences between irradiance : Discuss differences between irradiance 

and PV power. 

– Identify factors affecting power variability for clear 

and partly cloudy days 

• Part 2: Explore measures of variability 

• Part 3: Present variability analysis results for 

existing PV systems existing PV systems. 

– Small (30kW) 

– Large (many MWs) 

203
 

Part 1 

• The difference between irradiance and PV output The difference between irradiance and PV output 

power... 

Sandia 30 kW Array (1-Sec Data) 

POA LICOR Irradiance Sensor 

35 m 

Array 1 

Array 2 

Array 1 

30 kW latitude tilt array 

204
 

Difference Between Irradiance and PV Power 

• PV power output is not a simple linear function of 

irradiance especially on partly cloudy days irradiance, especially on partly cloudy days. 

• Spatial-temporal effects 

• Inverter effects 

• Incident angle effects 

• Temperature effects 

dropout 

Spatial-Temporal Effects 

• Short periods (5-20 seconds) of non-linear excursion are 

likely due to spatially- heterogeneous irradiance over 

distances as small as 30-50 m (slow moving, sharp 

shadows). 

• Sandia is developing a wireless 1-sec irradiance sensor 

network 

• Deployments in: Albuquerque, NM, Lanai, HI, and other 

sites in the near future 

205
 

 

 

Inverter Effects 

• Array + Inverters may not convert 100% of available irradiance. 

– MPPT issues, IEEE 1547 dropouts, inverter “clipping”, partial 

shading, ... etc. 

• Single 200 W module with micro-inverter 

Morning shading 

• Scatter indicates that inverter causes some of the variation 

between irradiance and A/C power. 

Module located 5 m from irradiance sensor (2-sec data frequency). 

Incident Angle Effects (1) 

• Global irradiance is measured on 

a horizontal plane a horizontal plane 

• PV arrays are either fixed or 

tracked. 

• PV output is proportional to 

irradiance on the plane of array 

• Tracked systems can harvest 

more energy than fixed tilt more energy than fixed tilt 

systems and therefore have 

greater potential for larger power 

changes from passing clouds. 

206
 

Incident Angle Effects (2) 

• Power changes will be greater with tracked PV systems than for 

fixed tilt systems at the beginning and end of the day. 

Temperature Effects 

• PV efficiency decreases as temperature increases. 

• May 15 2009 air temp increased all day from 16 to May 15, 2009, air temp increased all day from 16 to 30
 

deg C
 

Morning 

Afternoon 

207
 

 

Part 2 

• Measures of variability... Measures of variability... 

How to Characterize PV Output 

Variability? 

• Examine the distribution of irradiance and power Examine the distribution of irradiance and power 

changes (‘ramps’) over a fixed time interval (e.g., 

1-sec, 1-min, 10-min, etc.) (e.g., Wan and 

Bucaneg, 2002) 

• Step Changes: Pt – Pt+k, where t is time (1 to nt) 

and k is fixed time interval 

•	 Ramping Rates Ramping Rates: 

1) rate of change of moving average 

2) least squares linear regression slope of Pt  t+k 

208
 

 

Steps for Characterizing Variability 

• Normalize irradiance and power Normalize irradiance and power 

• Calculate ramp rates for fixed time intervals (e.g., 

1-sec, 10-sec, 1-min, 10-min, etc.). (absolute 

value). 

• Compare distributions of ramp rates for different 

unit sizes (irradiance sensor, single inverter, 

multiple inverters etc ) multiple inverters, etc.) 

Part 3 

• Analysis of PV output variability for two existing Analysis of PV output variability for two existing 

PV systems. 

– Small system (30kW) 

– Very large system (many MWs) 

209
Example of PV Output Variability 

Reduction for 30 kW Latitude-Tilt System 

How Much Reduction? 

1-Sec 10-Sec 

0.95 0.95 

• For small systems ramp rate reduction is measurable for 

intervals between 1-10 sec but essentially disappears when 1-

min ramps are analyzed. 

210
 

 

 

Variability Analysis of Large PV Plant Output 

• PV plant is multi PV plant is multi -megawatt in capacity. megawatt in capacity. 

• 1-sec irradiance and power output has been 

normalized.
 

• Explore variability reduction with increasing plant 

size. 

– Irradiance (cm2) 

– Si l i t t t (h d d f kW ) Single inverter output (hundreds of kWs) 

– Half of plant’s inverters (multi MW) 

– Total plant output (multi MW x 2) 

Irradiance Measurements 

• June 15, 2009: Partially cloudy day selected for analysis 

• Two irradiance measurements (opposite ends of plant) Two irradiance measurements (opposite ends of plant) 

• Irradiance is normalized. 

211
 

Analysis Method 

• Compare distributions of power changes for Compare distributions of power changes for
 

different combinations of unit sizes and time 

intervals
 

Irradiance  Single Inverter  Half Plant  Whole 

Plant. 

1 sec  10 sec  1 min  10 min 

1-Sec Changes 

0 5. 1%

• 1-Sec power variability relative to irradiance 

decreases as a function of unit size. 

• Single Inverter = ~30% reduction of large ramps 

• Total Plant = >60% reduction of large ramps 

212
10-Sec Changes 

0 5. 10%

• 10-Sec power variability relative to irradiance 

decreases as a function of unit size. 

• Single Inverter = ~20% reduction of large ramps 

• Total Plant = >40% reduction of large ramps 

1-Min Changes 

0 5. 30%

• At 1-Min, variability difference between unit sizes 

is not as significant as for shorter time intervals. 

• Single Inverter = ~5% reduction of large ramps 

• Total Plant = >10% reduction of large ramps 

213
 

10-Min Changes 

05 100%

• 10-min power variability is not influenced by unit 

size and is essentially equivalent to 10-min 

irradiance variability. 

0.5 

Summary 

• Variabilityof PV power output is not a simple linear 

function of variability in plane-of-array point irradiance, 

especially on partly cloudy days. 

• Preliminary results suggest that >10 min variability of multiMW PV plants can be approximated by the variability of 

point irradiance averaged over a similar time window. 

• Short term (<10 min) variability is influenced by the size of 

the plant, with variability decreasingwith increasing size. 

214
October 2009 October 2009 

The Need for PV Output 

Data 

Travis Johnson, PE Travis Johnson, PE 

Manager, Substation Construction & Maintenance 

NV Energy NV Energy 

What is the Need? What is the Need? 

 There is still much to be learned about large scale penetration of 

PV into the utility grid. 

 There is fear among utility leadership that variability will cause 

problems including: problems including: 

System Stability, Flicker, Voltage Regulation, CPS2 (ACE)

Violations, Possible Increase in Required Reserves 

 These fears must be addressed to remove arbitrary caps on PV 

deployment. 

 How can we address the fear? 

 We need DATA! 

FEAR! 

215

1 
 

What Type of Data is Needed? What Type of Data is Needed? 

 Frequency (for high penetration systems) 

 Irradiance (to understand ramp rates & variability issue) 

 Watts 

 Vars 

 Voltage 

0 

1000 

2000 

3000 

4000 

-291 

-191 

-91 

Active and Reactive Power 

P3-(kW) (W) var) (VAr) -591 

-491 

-391 

0 20000 40000 60000 80000 

Electrotek Concepts® TOP, The Output Processor® Q3-(kv 

Time (s) 

What Resolution is Needed? What Resolution is Needed? 

 1 second data is best 

 5 second data is good 5 second data is good 

 Dead-band settings can limit file size and still provide 

adequate resolution 

 Interval can be a function of array size (large arrays respond 

more slowly than small arrays) 

216

2 
What are we Willing to Share? What are we Willing to Share? 

 PV Variability Ad Hoc Group Developed a Metadata Standard 

 Standard addressed “metadata” of PV sites more than the 

actual “data” 

 Standard was divided into 3 groups of metadata: 

 1) Public Data 

 DC Plant Rating (STC DC Rating) 

 Type (if various technology, specify splits - mono-crystalline, thin film, 

etc.) 

 Rating of panels 

 Number of inverters 

 Location (latitude, longitude, and elevation) of site 

 Sampling rate & recording rate 

 Irradiance sensor type, orientation, and number 

 Power system voltage at point of delivery (or interconnection) 

What are we Willing to Share? What are we Willing to Share? 

 PV Variability Ad Hoc Group Developed a Metadata Standard 

 Standard addressed “metadata” of PV sites more than the 

actual “data” 

 Standard was divided into 3 groups of metadata: 

 2) Optional Public Data 

 Array tilt angle and azimuth 

 Spacing of module rows 

 Tracking characteristics of array 

217

3 
What are we Willing to Share? What are we Willing to Share? 

 PV Variability Ad Hoc Group Developed a Metadata Standard 

 Standard addressed “metadata” of PV sites more than the 

actual “data” 

 Standard was divided into 3 groups of metadata: 

 3) Private Data (never shared) 

 Inverter logic/control/programming 

 Design specifics (wire size, structure type, civil design) 

 Levelized cost of energy (LCOE) 

 Name of site owner 

 Street address of project 

 Cost of system components 

 Manufacturer 

 Date of installation 

 Price of installation 

What are we Willing to Share? What are we Willing to Share? 

 What Was Missing? Data!! 

 Watts 

 Vars 

 Irradiance 

 Frequency 

 Voltage 

 The standard simply addressed that this data is considered confidential and 

may be addressed on a case by case basis. 

 It also states “sharing monitored data with third parties, such as 

national laboratories, research organizations, and industry partners, 

may result in benefits that accrue to the industry as a whole.” 

218

4 
 Utilities only? 

 Industry? 

 National Labs? National Labs? 

 Industry Partner? 

Who Will Use the Data? Who Will Use the Data? 

Where Should the Data Reside? Where Should the Data Reside? 

 Secure site? 

 Data warehouse? 

 NDA required? NDA required? 

 Other conditions? 

219

5 
 

Data Set Data Set 

 Flat ASCII file (comma delimiter, etc.) 

 Excel not really a good option 

 Files should be daily Files should be daily – one day per file one day per file. 

 Time zone issues – need proper time stamp (GMT offset

required) 

Less desirable flat file formats. 

6 

Data Set Data Set 

220
 

 

 

 

 

 

Data Set Data Set 

 Column headers (first row only), each string 12 characters or less. 

 Timestamp format: YYYY, MM, DD, HH, MM, SS 

 Frequency 

 Real and reactive power (kW and KVar, 3 phase) 

 Total for the entire PV plant, and 

 At each inverter (desirable for selected large systems) 

 For distribution-connected systems, RMS voltage (line-line or line-neutral, each phase) 

 At PCC or other PV feeder bus location (best), or 

 At terminals of two PV inverters connected to different transformers 

 For transmission-connected systems, RMS voltage (line-line, positive sequence) 

 At POI or high side of station transformer, and 

 At terminals of electrically closest and farthest inverter in the PV plant (desirable) 

 Irradiance - Plane of Array (POA) and Global Horizontal (GH) irradiance captured by each reference 

cell and pyranometer in the PV system. Column header (and possibly metadata as well) should 

indicate approximate location of sensors with respect to the PV array 

What Mechanism Will Make it 

Happen? Happen? 

 How do we get there? 

 Need to finalize a “data” standard (quickly) 

 Should be assigned, not ad hoc group (too slow) 

 Needs to be compatible with PI Historian and other data 

historian software (flat file should be) 

221

7 
What Mechanism Will Make it 

Happen? Happen? 

 Discussion: 

 What model of data sharing is acceptable to

manufacturers? 

 Wh t i th f d th d f th l b ? What is the preferred method for the labs? 

 What do utilities prefer? 

 Action Items: 

 Close meeting with volunteer to set final format of data 

 Seek agreement between PV manufacturers to supply

data to _________. 

222

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Proceedings from the Utility Scale Photovoltaic Workshop held in Cedar Rapids Iowa on October 7, 2009.

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