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Predicting Solar Power Production:
Irradiance Forecasting Models, Applications and Future Prospects
Steven Letendre, PhD
Green Mountain College
Vermont
Miriam Makhyoun
Solar Electric Power Association
Mike Taylor
Washington, DC
March, 2014
A c k n ow le d g m e nts
The authors would like to thank the following
individuals for taking the time to share their expertise
and insights on solar forecasting: Jon Black, ISO New
England; John Tedesco, Green Mountain Power; Steve
Steffel, Pepco Holdings; Joshua Stein, Sandia National
Laboratory; Richard Perez, SUNY Albany Atmospheric
Sciences Research Center; Drake Bartlett, Xcel Energy;
Jim Blatchford, CAISO; Dallas Cornier, SDG&E; Jack
Peterson, Southern California Edison; Jan Kleissl,
University of CA San Diego; Sue Ellen Haupt, NCAR;
Manajit Sengupta, NREL; Tom Hoff, Clean Power
Research; David Edelson, New York ISO; Marc Keyser,
MISO; William Holmgren, University of Arizona;
Mark Ahlstrom, WindLogics Inc.; and John Zack, AWS
Truepower. While we appreciate the many insights
provided to us, any errors or omissions in the final
report are the sole responsibility of the authors.
Front cover photo of control room has been provided
courtesy of ISO New England.
G l os s a r y o f Te r m s
Area Solar Forecast: A forecast of two or more solar
energy plants.
DG Solar: Solar energy systems deployed within utility
distribution networks.
Direct normal irradiance (DNI): The amount of solar
radiation received per unit area by a surface that is
always held perpendicular (or normal) to the rays that
come in a straight line from the direction of the sun at
its current position in the sky.
2
Diffuse Horizontal Radiation (DHI): The amount
of radiation received per unit area by a surface (not
subject to any shade or shadow) that does not arrive on
a direct path from the sun, but has been scattered by
molecules and particles in the atmosphere and comes
equally from all directions.
Global Horizontal Irradiance (GHI): The sum of DNI
and DHI.
Global NWP: Predictions of global weather patterns.
Insolation: Short for incident or incoming solar
radiation, which is a measure of solar radiation energy
received on a given surface area and recorded during a
given time.
Mesoscale NWP: Predictions of regional weather
patterns.
Nowcast: A detailed description of the current weather
along with forecasts up to 30 minutes.
Numerical Weather Prediction (NWP): The use of
mathematical models of the atmosphere and oceans
to predict the weather based on current weather
conditions.
Persistence Forecast: Forecasts based on extrapolating
current conditions into the future.
Point Solar Forecast: A solar forecast for a single solar
energy plant.
Variable Generation: Sources of power that vary based
on weather conditions, including wind and solar.
so l a r el ectri c p o w er a sso ciation
C on t e n t s
Contents
Acknowledgments
Contents
List of Figures.......................................................................................................................................................ii
List of Tables.........................................................................................................................................................ii
Executive Summary...........................................................................................................................................iii
1.Introduction.................................................................................................................................................... 1
2. The Solar Resource......................................................................................................................................... 3
2.1 Fundamentals of solar irradiance....................................................................................................... 3
2.2 The variable nature of solar energy.................................................................................................... 4
3. Forecasting Solar Irradiance......................................................................................................................... 7
3.1 Physical methods to forecast solar irradiance................................................................................... 8
3.1.1 Numerical weather prediction (NWP)..................................................................................... 8
3.1.2 Cloud imagery............................................................................................................................. 9
3.2 Statistical/hybrid methods to forecast solar irradiance.................................................................10
3.3 Understanding solar forecast errors and variability.......................................................................11
3.3.1Point versus area solar energy plant predictions and PV power output variability........14
4. Using Solar Power Production Predictions for Utility Planning and Operations...............................17
4.1 Transmission-level applications of solar energy predictions........................................................17
4.1.1 CAISO’s approach to using solar power predictions...........................................................21
i
4.2 Distribution-level applications of solar energy predictions..........................................................24
4.2.1 Case Study: Hawaiian Electric Company..............................................................................25
4.2.2 Case Study: Tucson Electric Power........................................................................................27
5. Solar Forecast Provider Survey Results.....................................................................................................29
6. Future Developments and Recommendations.........................................................................................33
6.1 Current solar forecasting research projects.....................................................................................33
6.2 Conclusions and Recommendations................................................................................................35
Works Cited........................................................................................................................................................38
Pr ed i ct i ng S o l a r P o w e r P r o d u c tion
i
L i s t o f F i g ur e s
Figure 1: Modern Solar Forecasting System Schematic............................................................................. iv
Figure 2: National Solar Radiation Data Base (NSRDB) stations by class................................................ 4
Figure 3: U.S. solar resource map .................................................................................................................. 4
Figure 4: Time scale relevant to operating power systems ........................................................................ 5
Figure 5: Illustration of solar plant power prediction system.................................................................... 7
Figure 6: Total sky imager image ................................................................................................................10
Figure 7: Satellite cloud images ...................................................................................................................10
Figure 8: Annual Root Mean Squared Error (RMSE) as a function of forecast time horizon ............12
Figure 9: Surface Radiation Network (SURFRAD) Stations Locations .................................................12
Figure 10: Quantifying Solar Variability ......................................................................................................15
Figure 11: Forecast Solar vs. Actual Output, Northern California............................................................22
Figure 12: Forecast Solar vs. Actual Output, Southern California............................................................22
Figure 13: Forecast Solar vs. Actual Output, Northern California............................................................22
Figure 14: Forecast Solar vs. Actual Output, Southern California............................................................22
Figure 15: California Independent System Operator Day-Ahead Market Timeline................................23
Figure 16: California Independent System Operator Hour-Ahead Market Timeline..............................23
Figure 17: California Independent System Operator Net Load 2012 - 2020 ............................................23
Figure 18: Distribution Impacts of Distributed Generation Solar Hawaiian Electric Company ...........26
Figure 19: Impact on System Load for Hawaiian Island .............................................................................26
Figure 20: Prototype of Variable Generation Display for System Operators ............................................26
ii
Figure 21: Prototype Web Interface for Variable Generation Tucson Electric Power Service Territory ......27
Figure 22: Spatial Resolution Capability of Solar Forecast Services...........................................................30
L i s t o f Tab le s
Table 1: SEPA Recommendations to Advance Solar Forecast Market Development...........................iii
Table 2: Annual Error Metrics Day-Ahead and Hour-Ahead Forecasts for
Surface Radiation Network (SURFRAD) Locations.................................................................13
ii
Table 3:
Event Example—Up-Ramp or Down-Ramp of 20% Capacity in 1 Hour..............................13
Table 4: Solar Forecast End-Users and Potential Applications..............................................................18
Table 5: Solar Forecasting Service Providers Invited to Complete Online Survey..............................29
Table 6: Number of Forecast Providers Active in Each Area.................................................................30
Table 7: Solar Forecasting Providers Information Needs........................................................................31
so l a r el ectri c p o w er a sso ciation
Executive Summary
Executive Summary
Solar energy represents a vast, renewable resource
that can be tapped to meet society’s growing demand
for electrical energy. Deployment of systems that
convert solar irradiance into useful electrical energy
has accelerated in the past decade in the U.S. and
globally, particularly for photovoltaic (PV) systems—
the direct conversion of irradiance to electricity. Utility
companies play an integral part in managing this
growth and integrating solar generating plants into the
existing grid infrastructure and utility operations.
Grid integration studies of variable generation resources,
including wind and solar, conclude that it is technically
feasible for these resources to provide a significant
portion of the nation’s energy needs at manageable
costs. These studies recommend operational strategies
and market structure changes needed to address
increased levels of uncertainty that high-penetration of
renewable resources presents to utility companies and
grid operators due to their intermittent nature. The use
of advanced forecasting of variable generation is one of
these essential strategies. Forecasts of future solar energy
system output allows grid operators and utilities to
proactively manage variable output, and thus integrating
solar resources into the existing grid at lower costs to
society.
This report provides a review of solar forecasting
approaches and how they are being used by grid
operators, utility companies, and other market
Table 1: SEPA Recommendations to Advance Solar Forecast Market Development
I. Development
of Forecasting
Standards/
Guidelines
The solar forecasting industry lacks a set of standards or industry guidelines, which acts
as a barrier to greater use and understanding of the value of solar forecasting to utility
planning and operations. Guidelines or standards with regard to defining forecast time
horizons relevant to utility and market operations, measuring and reporting forecast
error metrics, and data and communication requirements for modern solar forecasting
systems are needed.
II. Economic
Assessment of
Solar Forecasting
Value Relative to
Costs
There is a lack of information within the literature on the economic value that solar
forecasts could provide to grid operators and utilities. While the value of solar
forecasting qualitatively seems quite large relative to the costs associated with
producing solar forecasts, there are currently no quantitative analyses to support this
notion. Solar forecasts provide value to numerous stakeholders; an attempt should
be made to quantify these values to provide the economic basis for expanded use.
Furthermore, these analyses could help to identify the incremental value associated
with improved forecast accuracy.
III. Distributed
Solar Working
Group and
Workshops
While there are various groups working on different aspects of solar deployment and
variable generation forecasting, there is a need for a focused effort on DG solar. A
working group comprised of relevant stakeholders should be convened to identify the
critical issues and begin a collaborative process to address the unique challenges of DG
solar. Periodic workshops should be held to share research and current practices used
to understand and manage the impacts of DG solar and the role of solar forecasting.
IV. Expanded
Engagement of
Policymakers and
Regulators
Policymakers and regulators need to gain a greater understanding of the value that
solar forecasting can bring to meeting policy objectives, including renewable portfolio
standards. As grid-connected solar deployment expands and forecasting needs
increase, utilities, regulators and forecast service providers need to investigate the best
mechanisms in terms of reporting system characteristics providing the needed data to
produce accurate solar forecasts, standards in terms of allocating the costs incurred
by grid operators and utilities associated with solar forecasting, and promoting market
structures and scheduling timelines that leverage modern solar forecasting services.
Pr ed i ct i ng S o l a r P o w e r P r o d u c tion
iii
iii
iv
iv
participants for planning and operations. This report
concludes with a section on future developments and
is based on a survey of the literature and interviews
recommendations.
with experts from three broad stakeholder groups
Although the solar forecasting industry is in the early
(ISO/RTO managers, utility managers, and research
stages of market development and acceptance, several
scientists). Seventeen companies globally were
companies currently offer solar forecasting services
identified that potentially provide solar forecasting
to various end-users as part of other services they
services and asked to complete an online questionnaire.
provide to the renewable energy
Thirteen companies completed
industry and other weather-sensitive
the questionnaire, all of whom
“Although the solar forecasting
industries. Modern solar forecasting
work with utilities or related
industry is in the early
services use sophisticated
organizations. These companies
stages of market development
physical numerical weather
use state of the art solar forecasting
and acceptance, several
prediction models (NWP), cloud
systems to provide customized
companies currently offer
imagery analyses, and statistical
forecasts to meet their clients’
solar forecasting services”
manipulations to provide state of
particular needs.
the art solar forecasting services
The research and interviews conducted for this report
covering a range of forecast time horizons (see Figure
suggest several initiatives that should be undertaken to
1). A point solar forecast is prepared for a particular
accelerate the adoption of solar forecasting in response
solar power facility, while an area forecast represents
to the anticipated growth in solar energy system
the predicted aggregate output of two or more solar
deployment in the coming decade. These initiatives can
power plants within a defined geographic region.
be grouped into the following four broad categories
One study finds that solar irradiance forecast error
presented in Table 1.
measured in root mean square error, a measure of the
The report begins with a review of the variable nature
standard deviation of the differences between predicted
of solar and is then followed by an examination of
values and observed values, can range from 100 watts
the various ground-level solar irradiance forecasting
– 200 watts per square meter depending on the forecast
approaches and models used for each solar production
method and time horizon. To place this in context, the
forecast time horizon. Next the various ways in which
solar resource is approximately 1,000 watts per square
utilities and grid operators can use forecasts of variable
meter for a surface perpendicular to the sun’s rays
generation are explored, including specific uses of
at sea level on a clear day. Irradiance forecast errors
solar forecasts at the transmission and distribution
vary depending on the overall weather conditions;
levels. Given that California is leading the nation in
forecasts on clear sky days are more accurate than
the adoption of solar energy, extended coverage of the
Figure 1: Modern Solar Forecasting System
California Independent System Operator’s (CAISO’s)
Schematic
uses of a central solar forecast is provided. Extended
coverage of the Hawaiian Electric Company (HECO)
DATA
and Tucson Electric Power (TEP) is also provided to
(NWP, CLOUD MOTION, WEATHER, SOLAR PLANT)
highlight efforts at the distribution level to integrate
solar forecasting into energy management systems.
HECO has the highest PV penetration levels of
SOLAR PLANT PREDICTION ENGINE
(PHYSICAL & STATISICAL MODELS)
any utility company and is on the leading edge of
exploring how to use solar forecasts to manage high
penetration of distributed PV. TEP is a power company
SOLAR PLANT SIMULATION
and balancing authority in the Southwest that has
experienced significant growth in utility-scale and
SOLAR PLANT POWER PREDICTION
distributed PV deployment. Next the results of the solar
forecast provider survey are presented and the report
so l a r el ectri c p o w er a sso ciation
Executive Summary
forecasts on partly cloudy days. Furthermore, forecast
errors are lower for area solar power forecasts relative
to point forecasts due to the fact that aggregation of
geographically disperse systems reduces the random
impact clouds have on solar generation system
output. There is no standardized approach, however,
to calculating and reporting forecast error for solar
system predictions thus making comparisons between
alternative forecasts difficult.
While much can be learned from the experiences
gained using wind forecasts in the past decade for
utility planning and operations, the distributed nature
of solar deployment and the impact that clouds have
on solar resource variability create unique and different
challenges. By and large distributed (DG), behind the
meter solar is invisible to balancing authorities and
utility companies meaning they do not have real-time
production data to provide situational awareness. The
demand-side nature of DG solar shows up through
shifting load patterns. While utilities have decades of
experience with load forecasting, DG solar results in a
new element of variability and uncertainty to system
load not captured by existing load forecasting methods.
This is a new paradigm for utilities and much work is
needed to integrate solar forecasting into existing load
forecasting practices. The consistent patterns of load
seen over time are changing due to DG solar and thus
load forecasting techniques will need to be modified in
response, including greater emphasis on intra-day and
intra-hour load forecasting.
Utilities and grid operators need to understand total
system load, including the load being served by DG
solar, to provide situational awareness. This is critical
to proactively prepare for the net load impacts of
DG solar variability resulting from changing weather
patterns and emerging cloud formations. Systems and
models are being developed today to make DG solar
“visible” to grid operators and utilities. Clean Power
Research is currently providing the California System
Operator (CAISO) with forecasts of the 130,000+ DG
solar energy systems in the State on an experimental
basis. DG solar forecasts require data on DG solar
systems, most of which is being collected through the
net metering approval and interconnection processes,
but might not yet be available for forecasting
organizations to access.
Pr ed i ct i ng S o l a r P o w e r P r o d u c tion
Solar Forecasting Service ProviderS
ƒƒClean Power Research (U.S.)
ƒƒ3 Tier (U.S.)
ƒƒAWS TruePower (U.S.)
ƒƒJHtech--Solar Data Warehouse (U.S.)
ƒƒWindlogic, Inc. (U.S.)
ƒƒGlobal Weather Corporation (U.S.)
ƒƒGreen Power Labs (Canada)
ƒƒIberdrola Renewables, LLC (Spain)
ƒƒirSOLaV (Spain)
ƒƒMeteologica SA (Spain)
ƒƒNNERGIX Energy Management, S.L. (Spain)
ƒƒMeteocontrol Energy and Weather Services
(Germany)
ƒƒWEPROG (Germany)
ƒƒReuniwatt (France)
ƒƒENFOR A/S (Denmark)
ƒƒDatameteo (Italy)
ƒƒDNV-GL (Garrad Hassan, Inc.) (Norway)
In California the CAISO, utility companies and other
market participants are using solar forecasting services
for a variety of applications. Similar organizations in
other regions including New Jersey, Hawaii, Arizona,
and Colorado are beginning to experiment with using
forecasts of solar plant production for operations
and planning purposes. Grid operators and utilities
in other regions are actively researching forecasting
needs and potential solutions. Regional grid operators
and balancing authorities can use forecasts of variable
generation to determine the need for operating reserves
and for scheduling utility-scale solar plant generation.
An emerging use of variable generation forecasting is
to predict significant ramping events caused by sudden
changes in output from wind and solar generators.
Utilities and energy traders can use forecasts of
variable generation to develop bidding strategies for
hour-ahead and day-ahead markets. Solar energy
forecasts of distributed, behind the meter solar systems
can be used to create more accurate load forecasts,
as grid operators must prepare to meet system load
v
v
net of DG solar. Distribution utilities can also use
area forecasts of distributed solar to gain situational
awareness to address potential reliability concerns
within the distribution networks they manage. In
addition, forecasts of variable generation, both central
utility scale and distributed systems, can be used for
planning purposes. In practice today, solar forecasting
is used primarily at the system level to schedule
energy production from large utility-scale solar plants.
Developing tools that use forecasts of DG solar is still in
the research and development stage in the U.S.
Based on questionnaire responses, some forecast
providers report challenges in obtaining timely and
reliable data from their utility clients to prepare
a solar forecast and in some cases a lack of the
necessary information technologies to efficiently
integrate with their forecasting systems. In addition,
communications between forecast providers and end
users can be challenging in terms of clearly articulating
forecasting needs and the value that solar forecasting
can bring to utility planning and operations. All
solar forecasting service providers responding to the
online questionnaire indicated ongoing research and
development efforts to enhance their solar forecasting
services.
Predicting the output of solar energy plants for various
forecast time horizons can be a valuable tool allowing
grid operators and utilities to reduce the costs of
integrating solar sources of generation into the existing
grid. This report represents a snapshot in time, as we
anticipate rapid developments in forecasting methods
and uses in the coming years.
Types of solar forecast
Type
vi
Day-ahead, hour-ahead, & intra-hour
Forecasts based on time horizon.
Central utility-scale vs. DG solar
Large-scale vs. Small scale solar installations.
Probabilistic vs. Deterministic
A range of possible solar system output based on probability or a
specific output value.
Point vs. Area forecasts
Forecasts for one solar plant versus a forecast for an aggregate of
geographically dispersed solar energy systems.
Output vs. Rate of change
Forecast of power output versus expected change in output over
the forecast time step.
vi
so l a r el ectri c p o w er a sso ciation