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Abstract- This paper describes ongoing research in the area of

solar PV production forecasting intended to address a range of effects on the utility grid associated with high penetrations of PV. The ability to anticipate near-term –minutes ahead to hours ahead to day or multiple-day ahead-- production of the variable solar resource will be key to successfully integrating ever larger PV capacities with minimal costs. A number of forecast methodologies are surveyed and a mechanism for validating their performance is described.

Index Terms—photovoltaic system, demand forecasting, solar power generation, distributed power generation, power distribution lines, power distribution, power grids.

I. NOMENCLATURE

Standard Test Conditions (STC)

II. INTRODUCTION

HE Sacramento Municipal Utility District (SMUD), the country’s 6th largest utility has teamed with NEO

Virtus Engineering (NEO), a solar engineering, consulting and monitoring provider, to deploy a service-territory wide solar monitoring network for validating solar forecasting models. Thanks to a grant from the California Public Utilities Commission, under their California Solar Initiative RD&D program, SMUD has initiated a number of projects related to integrating high penetrations of solar PV.

NEO Virtus Engineering has developed a solar forecasting approach that makes use of the National Weather Service’s National Digital Forecast Database (NDFD) and a cloud cover radiation model (Kasten-Czeplak and Gul-Muneer). The cloud cover radiation model or CRM, in conjunction with array geometry calculations, will provide the necessary conversion from percentage-of-sky-covered-by-clouds to incident irradiance on the module surface. This irradiance

SMUD and NEO Virtus Engineering would like to acknowledge the grant

funding support from the California Public Utilities Commission California Solar Initiative RD&D program and the ratepayers of California who funded it. We would also like to acknowledge the administration of that program by Itron.

J. M. Bing is President of NEO Virtus Engineering, Inc., 410 Great Road, B-6, Littleton, MA 01460 (e-mail: jbing@neovirtus.com).

O. Bartholomy, is a Senior Project Manager in SMUD’s Energy R&D department, Sacramento Municipal Utility District, 6201 S Street, Sacramento, CA 95817 (e-mail: OBartho@smud.org).

P. Krishnani was a Former Engineering Technician with SMUD (current e-mail: krishnani.pramod@gmail.com).

forecast, by extension, becomes a forecast of photovoltaic energy production within the spatial-temporal context of the utility service territory. To validate this model, as well as potentially others, NEO has developed and SMUD has deployed a network of 71 solar monitoring devices covering most of SMUD’s 2330 square kilometer service territory. The project was started in June, 2010 and will continue for 2 years. Solar monitoring starting in May of 2011 and will last for at least 14 months.

III. INTEGRATION CONCERNS

The increase in intermittent decentralized power production, both in the form of small distributed generation and utility-scale PV power plants, presents a number of concerns to utilities such as voltage control, regulator equipment duty, relay desensitization increased regulation and reserve requirements, changes in load-following resource types, and increased O&M costs associated with cycling existing generation [1], [2]. As PV penetrations increase, utilities such as SMUD see an increasing need for tools to help plan for and schedule other resources around solar intermittency. Improved solar forecasting tools and validation of existing tool performance are both needed to enhance utilities’ ability to manage such intermittency from an operations and a planning perspective. At current solar installation rates, SMUD expects improved solar forecasting approaches will provide value within the next 2-3 years. Further, given planning horizons associated with adding significant new generation or storage resources, understanding overall forecast accuracy in the next few years will be critical to planning the right resources for managing significantly higher penetrations in the next decade. Recognizing this, SMUD and NEO decided to collaborate to provide a large scale validation of a specific forecasting approach and further, to assess appropriate performance metrics for judging forecasting success that were more relevant to time periods of interest.

Current approaches to solar forecasting have been

benchmarked and show that solar forecasting approaches available today have a typical RMSE on the order of 100 – 150 W/m2 for day ahead. However, this value includes many clear days, and therefore likely obscures a much poorer performance for days of high variability. Understanding performance metrics for specific time-periods of interest to the

Validation of Solar PV Power Forecasting Methods for High Penetration Grid Integration

James Bing, Senior Member, IEEE, and Obadiah Bartholomy and Pramod Krishnani

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utility will assist in understanding integration costs, forecast value, and integration resource needs.

IV. PV PRODUCTION FORECASTING METHODS

In this early stage of development of PV power forecasting technology there are a number of competing technologies and methodologies . Some of the key differences amongst the competitors are time horizon, geographical area covered, accuracy (both absolute and over time), and cost.

TABLE I TYPES OF IRRADIANCE FORECASTING TECHNOLOGIES

Technology Time Horizo

n

Coverage Comments

Satellite 12hr to 7 days

global Errors associated with Satellite-based weather are greatest over short time periods[3]

Mesoscale 12hrs to months

global All GFS-based models, including NDFD, have similar accuracies as quantified by RSME and MAE, but European or Canadian global weather simulations tend to deliver better RSME results.[5]

AggriGround 1hrs to 3hrs

regional Nationwide coverage of higher accuracy ground sensors make better short term prediction based on network sensor impact on local sensor or local prediction.

Sky Imager 30min to 3hrs

2 to 10km radius

This method reduces a 50-60% error compared to persistence forecasting.[8]

Array Scale 1 to 30 minutes

Array size This method shows the impact of cloud speed on the complete array and also shows the impact of larger and smaller clouds on the PV production depending on the length of time averaging. [11]

A. Satellite Imagery

Satellite imagery is an established means of estimating ground level irradiance for photovoltaic system performance assessment [3]. More recently satellite cloud motion data are beginning to be used as a means of generating short-term forecasts (hours ahead to days ahead) of ground level irradiance [4]. Predictions of ground level irradiance are then extended to forecasts of PV production.

B. Mesoscale Weather Model Forecasts

Numerical weather prediction (NWP) models are being used by a number of practitioners in the field of irradiance and PV power forecasting. The models simulate cloud fraction which is then extended to ground level irradiance and, further, to PV power production forecasts. These models typically have effective forecast windows between one half a day to a week [5].

C. Aggregated Ground Based Solar Measurements

By one account the US has over 3500 publically accessible ground based measurement sites which report hourly and daily observations [6]. An approach being developed aggregates these public data, in combination with national weather service forecasts, to produce regional one hour and three hour forecasts. A pilot study has been done in the greater Los Angeles region [7].

D. Sky Imager Technology

In recent years researchers at the University of California San Diego and at commercial utility scale installations have developed a method of intra-hour, sub-kilometer forecasting using a device known as a "Sky Imager." Reflected images of cloud motion are translated into estimates of ground level irradiance and, by extension, PV production [8], [9].

Fig. 1. Sanyo – UC San Diego fisheye lens technology sky imager in La Jolla, California.

E. Array Scale Irradiance Sensor Networks

Instrumentation such as silicon pyranometers and thermopile pyrheliometers are routinely installed in utility scale PV arrays providing high granularity global horizontal irradiance (GH) and plane of array irradiance (POA). The National Renewable Energy Laboratory (NREL) is presently engaged in this form of high spatial/temporal monitoring in a test bed at a measurement site in Hawaii [10]. This instrumentation resource will eventually be integrated into the very near term energy production forecasts for these plants [11].

V. REGIONAL SOLAR FORECAST VALIDATION

SMUD was awarded a grant from the CPUC in 2010 for a 2 year project to deploy hardware and software tools to model and mitigate impacts of high penetrations of PV on the distribution network. SMUD’s grant partners and subcontractors on the project include Hawaiian Electric Company (HECO), BEW Engineering, Sunpower Corporation, and NEO Virtus Engineering. The full scope includes modeling and measuring high PV penetration circuits, developing utility interfaces to enhance the understanding of the performance of intermittent resources, developing methods to utilize the smart meters to communicate with PV inverters, and finally this project, deploying a network of irradiance sensors to monitor and validate solar forecasting approaches. Overall, these efforts will benefit the utilities involved as well as all California ratepayers by identifying solutions to integrating increasing amounts of PV onto the distribution grid.

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As part of this research and to validate forecast accuracy, irradiance measurements will be made using a combination of six Rotating Shadowband Radiometers (primary stations) and sixty six global horizontal measurements systems (secondary stations). This combination of primary and secondary monitoring stations will be deployed on the same five kilometer square grid as used by the NDFD for their skycover (cloud cover) forecasts.

Fig. 2. Rotating Shadowband Radiometer (RSR) primary station (left) and secondary station (right).

The monitored area will span 1800 square kilometers within SMUD’s service territory.

Fig. 3. Map of SMUD Service Territory, NDFD Grid cells and Centroids with 1km buffer.

The sixty six secondary stations are measuring global horizontal irradiance and ambient temperature. The primary stations measure global horizontal, diffuse horizontal and direct normal irradiance, and ambient temperature (This pairing of primary and secondary stations mimics the format of the National Solar Resource Database [NSRDB]). All monitoring stations are recording one minute averages. The monitoring stations will be located in the "nominal centroid" of each 25 square kilometer cell in the NDFD grid. The funded research will first establish the irradiance forecasts over the monitored region and then will quantify the error between measured and forecast irradiance over the term of the experiment.

A primary deliverable of this research will be the database of irradiance and temperature measurements. The monitoring regime calls for one-minute records of global horizontal and ambient temperature measured in locations roughly evenly distributed across the utility's service region. As noted previously, approximately six additional sites within the same grid will also contain diffuse horizontal and direct normal irradiance. The monitoring network will be deployed for approximately 14 months so that the database will cover a full 12 months with all 71 stations deployed. Once completed this database will be made available to researchers in the field of solar energy forecasting. The installation of the network was completed in the March-June 2011 timeframe, with the development of the forecasting models following closely behind. The validation of the PV production forecasts will be done by comparing the forecasted PV output to the actual PV output for 100 MW of systems currently being installed under SMUD’s Feed in Tariff program. Forecast validation will occur for 9 individual sites as well as the aggregation of sites to evaluate how well the forecast does for a utility service area. The validation of the irradiance forecasts will compare measured irradiance in each grid cell to the forecast irradiance values for that grid cell, as well as aggregations of grid-cells representing larger areas to determine the spatial effects on accuracy and the benefits and drawbacks of different forecast granularities. Figure 4 demonstrates the extreme variability in a one-day timeframe over the monitored region. As the red line demonstrates, averaging over this spatial region can significantly dampen minute to minute variability, but does not completely address intra-hour and multi-hour swings in output. Understanding how spatial averaging and overlays of projected PV installations will smooth forecast and variability models will be a key component of this research.

Fig. 4 Measured 1-minute data for 62 global irradiance monitors across SMUD service territory, including average and hourly simulated dispatch based on average

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VI. PV POWER FORECASTING WITH NDFD DATA

The National Digital Forecast Database or NDFD is a National Weather Service product developed in the past decade which provides digital forecasts of a range of meteorological parameters in both numeric and graphical form. Grids for the continental United States are currently available from NDFD at 5 kilometer spatial resolution. The temporal resolution for skycover (cloud cover) and temperature is every three hours out to 72 hours and every six hours out to 168 hours. The forecast data may be viewed with a web browser or retrieved via file transfer protocol in binary form. Figure 4 is an example of display of forecast skycover for an area of northern California which includes Sacramento County. NEO uses the NDFD forecast elements of skycover, temperature, and relative humidity to model direct normal and diffuse horizontal irradiance parameters which are then used to simulate PV power production.

Fig. 5. Skycover variable depiction from National Weather Service NDFD

VII. PRELIMINARY PV POWER FORECAST RESULTS

As a result of both the timing of SMUD’s Feed in Tariff PV system installations and the development timeline for the solar forecasting model, it is unlikely that sufficient generation data will be available, nor a forecast model ready, for PV power forecasting validation until February, 2012. Depending on the availability of both of these elements, it is unclear to what extent the PV power validation piece will be available for the final submittal of this paper.

Fig. 6. Satellite map of solar monitoring network sites in Sacramento County, California

VIII. FUTURE WORK

Given the importance to SMUD of improving the utility and industry understanding of solar forecasting accuracies, we anticipate future work in this area expanding the number and type of forecasts that the monitoring network is used to validate. We also anticipate evaluating variability of the solar resource across our service territory, modeling variability impacts of high penetration PV scenarios using different spatial distributions of solar arrays, evaluating the performance of satellite-based vs. ground-based solar monitoring regimes, and evaluation of optimal spacing and temporal resolution of ground-based solar monitoring regimes for meeting utility needs. These follow-on items will assist SMUD and the industry in better understanding how solar forecasts perform over a utility service territory, and how important geographic diversity is to mitigating variability. Ultimately this work will lead to a much clearer picture of the costs and types of grid assets and services that will be needed to accommodate high penetrations of solar PV.

IX. APPENDIX

A secondary benefit of the validation monitoring network which spans SMUD’s service territory is that it provides an unprecedented high spatial-temporal resolution measurement of the solar resource, and by extension view of the potential PV power production contribution to an entire utility service territory. The sequence of three dimensional graphs shown in figures 7 through 12 below is taken from the 71 station monitoring network. The devices measure global horizontal data for May 14, 2011. The day is one of intermittent clouds with a peak irradiance occurring both before and after the typical high irradiance time of the day, solar noon, 12:06pm. The ability to forecast and thus anticipate the equivalent PV generation carrying capacity in the geographic context of SMUD’s service territory could greatly facilitate the management of both load following and regulation requirements.

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Fig. 7. Measured global horizontal irradiance (GH) levels viewed across SMUD service territory on May 14, 2011.

Fig. 8. Measured global horizontal irradiance (GH) 8:26am.

Fig. 9. Measured global horizontal irradiance (GH) 9:28am.

Fig. 10. Measured global horizontal irradiance (GH) 11:29am.

Fig. 11. Measured global horizontal irradiance (GH) 12:58pm.

Fig. 12. Measured global horizontal irradiance (GH) 2:28pm.

X. ACKNOWLEDGMENT

SMUD and NEO Virtus Engineering would like to acknowledge the grant funding support from the California Public Utilities Commission California Solar Initiative RD&D program and the ratepayers of California who funded it. We would also like to acknowledge the administration of that program by Itron.

Any opinions, findings, and conclusions or

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recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the CPUC, Itron, Inc. or the CSI RD&D Program.

XI. REFERENCES

[1] J. Smith "Feeder Characterization for PV Integration Assessment,"

presented at DOE Hi-Pen PV Workshop, SMUD, Sacramento, California, June 13, 2011.

[2] M. Thomson and D. G. Infield, "Network Power-Flow Analysis for a High Penetration of Distributed Generation," in Proc. 2009 IEEE Power Engineering Society General Meeting.

[3] J. Stein, R. Perez and A. Parkins, "Validation of PV Performance Models Using Satellite-Based Irradiance Measurements: A Case Study " presented at American Solar Energy Society annual SOLAR 2010 conference proceedings.

[4] R. Perez, S. Kivalov, J. Schlemmer, K. Hemker Jr., D. Renné, T. Hoff, "Validation Of Short and Medium Term Operational Solar Radiation Forecasts In The Us," presented at American Solar Energy Society annual SOLAR 2009 conference proceedings.

[5] R. Perez, S. Kivalov, S. Pelland, M. Beauhamois, E. Lorenz, J. Schlemmer, kHemker, Jr., G. Van Knowe,"Evaluation Of Numerical Weather Prediction Solar Irradiance Forecasts In The Us," presented at American Solar Energy Society annual SOLAR 2011 conference proceedings.

[6] James Hall and Jeffery Hall,"Quality Analysis Of Global Horizontal Irradiance Data From 3500 U.S. Ground-Based Weather Stations," presented at American Solar Energy Society annual SOLAR 2011 conference proceedings.

[7] James Hall and Jeffery Hall,"Forecasting Solar Radiation For The Los Angeles Basin – Phase II Report," presented at American Solar Energy Society annual SOLAR 2011 conference proceedings.

[8] B. Urquhart, C. W. Chow, M. Lave, J. Kleissl, "Intra-Hour Forecasting With a Total Sky Imager at The UC San Diego Solar Energy Testbed," presented at American Solar Energy Society annual SOLAR 2011 conference proceedings.

[9] M. Ahlstrom and A. Kankiewicz, "Solar Power Forecasting," presented at Solar Power International conference Los Angeles, California, 2010

[10] M. Sengupta,"Measurement and Modeling of Solar And PV Output Variability," presented at American Solar Energy Society annual SOLAR 2011 conference proceedings.

[11] A. Kankiewicz, M. Sengupta, D. Moon,"Observed Impacts of Transient Clouds on Utility-Scale PV Fields" presented at American Solar Energy Society annual SOLAR 2010 conference proceedings.

XII. BIOGRAPHIES

James M. Bing (M’ 1987, SM' 2006) James Bing is the founder and president of NEO Virtus Engineering, Inc. (formerly New Energy Options, Inc.) He is a professional electrical engineer working principally in data acquisition and power system design in the photovoltaic industry. Prior to working in the photovoltaic industry Mr. Bing worked a for the R&D firm of Bolt Beranek and Newman in the area of sensor system and

technology. In addition to his engineering credentials Mr. Bing possesses over twenty years of experience in the areas of electrical system design, project management, estimating, and personnel supervision in the electrical construction industry. Mr. Bing holds a BS in Electrical Engineering from the University of Massachusetts at Lowell, as well as an AB in Anthropology from the University of California at Berkeley.

Obadiah Bartholomy is a Project Manager in the Energy R&D group at SMUD. He currently works on solar R&D, including intermittency impacts and forecasting, evaluation of high penetrations of PV on the distribution system, utility scale solar siting, commercial and residential solar mapping tools including the SMUD Solarmap, and utility scale solar

evaluation for meeting SMUD’s Renewable Portfolio Standards. He also leads SMUD’s Climate Change and Energy Efficiency R&D programs. He earned a BS in mechanical engineering from Cal Poly, San Luis Obispo and an MS in Transportation Technology & Policy from UC Davis, and is a registered Professional Engineer in the state of California.

Pramod N. Krishnani is the Performance Monitoring Engineer of Belectric, Inc. He is a Mechanical Engineer working in data acquisition, monitoring, prediction and power systems design in the photovoltaic industry. Prior to the work at Belectric, Mr. Krishnani has worked for Sacramento Municipal Utility District (SMUD) in the Research and Development Department from 2008 to

2011; where he has evaluated PV performance, meteorological solar data and developed custom PV modeling and cloud transient modeling tools. At SMUD, Mr. Krishnani has completed work related to other renewable energy like Biogas, wind energy, storage technology and Geothermal. All the work presented by Pramod Krishnani in this paper was completed during the period of employment at SMUD and has no affiliation with Belectric Inc. He has simulated several study projects related to utility load regulation, impact of solar and wind on utility load and simulated Utility scale PV from 500kW to 300 MW. In addition to his Mechanical Engineering Credentials, Mr. Krishnani has three years of experience in project management, research and estimating. Mr. Krishnani holds a MS in Mechanical Engineering from California State University – Sacramento and BS and Diploma in Mechanical Engineering from University of Mumbai.