IEEE PES 2012 - Validation of PV Forecast


Published on

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.

Published in: Technology
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

IEEE PES 2012 - Validation of PV Forecast

  1. 1. 1 Validation of Solar PV Power Forecasting Methods for High Penetration Grid Integration James Bing, Senior Member, IEEE, and Obadiah Bartholomy and Pramod Krishnani forecast, by extension, becomes a forecast of photovoltaic Abstract- This paper describes ongoing research in the area of energy production within the spatial-temporal context of thesolar PV production forecasting intended to address a range of utility service territory. To validate this model, as well aseffects on the utility grid associated with high penetrations of PV. potentially others, NEO has developed and SMUD hasThe ability to anticipate near-term –minutes ahead to hours deployed a network of 71 solar monitoring devices coveringahead to day or multiple-day ahead-- production of the variablesolar resource will be key to successfully integrating ever larger most of SMUD’s 2330 square kilometer service territory. ThePV capacities with minimal costs. A number of forecast project was started in June, 2010 and will continue for 2 years.methodologies are surveyed and a mechanism for validating their Solar monitoring starting in May of 2011 and will last for atperformance is described. least 14 months. Index Terms—photovoltaic system, demand forecasting, solarpower generation, distributed power generation, power III. INTEGRATION CONCERNSdistribution lines, power distribution, power grids. The increase in intermittent decentralized power I. NOMENCLATURE production, both in the form of small distributed generation Standard Test Conditions (STC) and utility-scale PV power plants, presents a number of concerns to utilities such as voltage control, regulator II. INTRODUCTION equipment duty, relay desensitization increased regulation and reserve requirements, changes in load-following resourceT HE Sacramento Municipal Utility District (SMUD), the country’s 6th largest utility has teamed with NEOVirtus Engineering (NEO), a solar engineering, consulting and 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 tomonitoring provider, to deploy a service-territory wide solar help plan for and schedule other resources around solarmonitoring network for validating solar forecasting models. intermittency. Improved solar forecasting tools and validationThanks to a grant from the California Public Utilities of existing tool performance are both needed to enhanceCommission, under their California Solar Initiative RD&D utilities’ ability to manage such intermittency from anprogram, SMUD has initiated a number of projects related to operations and a planning perspective. At current solarintegrating high penetrations of solar PV. installation rates, SMUD expects improved solar forecasting approaches will provide value within the next 2-3 years.NEO Virtus Engineering has developed a solar forecasting Further, given planning horizons associated with addingapproach that makes use of the National Weather Service’s significant new generation or storage resources, understandingNational Digital Forecast Database (NDFD) and a cloud cover overall forecast accuracy in the next few years will be criticalradiation model (Kasten-Czeplak and Gul-Muneer). The to planning the right resources for managing significantlycloud cover radiation model or CRM, in conjunction with higher penetrations in the next decade. Recognizing this,array geometry calculations, will provide the necessary SMUD and NEO decided to collaborate to provide a largeconversion from percentage-of-sky-covered-by-clouds to scale validation of a specific forecasting approach and further,incident irradiance on the module surface. This irradiance to assess appropriate performance metrics for judging forecasting success that were more relevant to time periods of SMUD and NEO Virtus Engineering would like to acknowledge the grant interest.funding support from the California Public Utilities Commission CaliforniaSolar Initiative RD&D program and the ratepayers of California who fundedit. We would also like to acknowledge the administration of that program by Current approaches to solar forecasting have beenItron. benchmarked and show that solar forecasting approaches J. M. Bing is President of NEO Virtus Engineering, Inc., 410 Great Road,B-6, Littleton, MA 01460 (e-mail: available today have a typical RMSE on the order of 100 – O. Bartholomy, is a Senior Project Manager in SMUD’s Energy R&D 150 W/m2 for day ahead. However, this value includes manydepartment, Sacramento Municipal Utility District, 6201 S Street, Sacramento, clear days, and therefore likely obscures a much poorerCA 95817 (e-mail: P. Krishnani was a Former Engineering Technician with SMUD (current performance for days of high variability. Understandinge-mail: performance metrics for specific time-periods of interest to the
  2. 2. 2utility will assist in understanding integration costs, forecast C. Aggregated Ground Based Solar Measurementsvalue, and integration resource needs. By one account the US has over 3500 publically accessible ground based measurement sites which report hourly and daily IV. PV PRODUCTION FORECASTING METHODS observations [6]. An approach being developed aggregatesIn this early stage of development of PV power forecasting these public data, in combination with national weathertechnology there are a number of competing technologies and service forecasts, to produce regional one hour and three hourmethodologies . Some of the key differences amongst the forecasts. A pilot study has been done in the greater Loscompetitors are time horizon, geographical area covered, Angeles region [7].accuracy (both absolute and over time), and cost. D. Sky Imager Technology In recent years researchers at the University of California San TABLE I TYPES OF IRRADIANCE FORECASTING TECHNOLOGIES Diego and at commercial utility scale installations haveTechnology Time Coverage Comments developed a method of intra-hour, sub-kilometer forecasting Horizo using a device known as a "Sky Imager." Reflected images of cloud motion are translated into estimates of ground level n Errors associated with irradiance and, by extension, PV production [8], [9]. Satellite 12hr to global Satellite-based weather are 7 days greatest over short time periods[3] Mesoscale 12hrs to global All GFS-based models, including NDFD, have months similar accuracies as quantified by RSME and MAE, but European or Canadian global weather simulations tend to deliver better RSME results.[5] Fig. 1. Sanyo – UC San Diego fisheye lens technology sky imager in La Jolla, Nationwide coverage of California.AggriGround 1hrs to regional higher accuracy ground 3hrs sensors make better short term prediction based on E. Array Scale Irradiance Sensor Networks network sensor impact on local sensor or local Instrumentation such as silicon pyranometers and prediction. thermopile pyrheliometers are routinely installed in utility Sky Imager 30min 2 to 10km This method reduces a 50- scale PV arrays providing high granularity global horizontal 60% error compared to to 3hrs radius persistence forecasting.[8] irradiance (GH) and plane of array irradiance (POA). TheArray Scale 1 to 30 Array size This method shows the National Renewable Energy Laboratory (NREL) is presently minutes impact of cloud speed on engaged in this form of high spatial/temporal monitoring in a the complete array and also test bed at a measurement site in Hawaii [10]. This shows the impact of larger and smaller clouds on the instrumentation resource will eventually be integrated into the PV production depending very near term energy production forecasts for these plants on the length of time [11]. averaging. [11] V. REGIONAL SOLAR FORECAST VALIDATIONA. Satellite Imagery SMUD was awarded a grant from the CPUC in 2010 for a 2 year project to deploy hardware and software tools to modelSatellite imagery is an established means of estimating ground and mitigate impacts of high penetrations of PV on thelevel irradiance for photovoltaic system performance distribution network. SMUD’s grant partners andassessment [3]. More recently satellite cloud motion data are subcontractors on the project include Hawaiian Electricbeginning to be used as a means of generating short-term Company (HECO), BEW Engineering, Sunpowerforecasts (hours ahead to days ahead) of ground level Corporation, and NEO Virtus Engineering. The full scopeirradiance [4]. Predictions of ground level irradiance are then includes modeling and measuring high PV penetrationextended to forecasts of PV production. circuits, developing utility interfaces to enhance theB. Mesoscale Weather Model Forecasts understanding of the performance of intermittent resources,Numerical weather prediction (NWP) models are being used developing methods to utilize the smart meters toby a number of practitioners in the field of irradiance and PV communicate with PV inverters, and finally this project,power forecasting. The models simulate cloud fraction which deploying a network of irradiance sensors to monitor andis then extended to ground level irradiance and, further, to PV validate solar forecasting approaches. Overall, these effortspower production forecasts. These models typically have will benefit the utilities involved as well as all Californiaeffective forecast windows between one half a day to a week ratepayers by identifying solutions to integrating increasing[5]. amounts of PV onto the distribution grid.
  3. 3. 3As part of this research and to validate forecast accuracy, A primary deliverable of this research will be the database ofirradiance measurements will be made using a combination of irradiance and temperature measurements. The monitoringsix Rotating Shadowband Radiometers (primary stations) and regime calls for one-minute records of global horizontal andsixty six global horizontal measurements systems (secondary ambient temperature measured in locations roughly evenlystations). This combination of primary and secondary distributed across the utilitys service region. As notedmonitoring stations will be deployed on the same five previously, approximately six additional sites within the samekilometer square grid as used by the NDFD for their skycover grid will also contain diffuse horizontal and direct normal(cloud cover) forecasts. 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 byFig. 2. Rotating Shadowband Radiometer (RSR) primary station (left) and comparing the forecasted PV output to the actual PV outputsecondary station (right). for 100 MW of systems currently being installed under SMUD’s Feed in Tariff program. Forecast validation willThe monitored area will span 1800 square kilometers within occur for 9 individual sites as well as the aggregation of sitesSMUD’s service territory. 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. 3. Map of SMUD Service Territory, NDFD Grid cells and Centroids with1km buffer.The sixty six secondary stations are measuring globalhorizontal irradiance and ambient temperature. The primarystations measure global horizontal, diffuse horizontal anddirect normal irradiance, and ambient temperature (Thispairing of primary and secondary stations mimics the formatof the National Solar Resource Database [NSRDB]). Allmonitoring stations are recording one minute averages. Themonitoring stations will be located in the "nominal centroid"of each 25 square kilometer cell in the NDFD grid. Thefunded research will first establish the irradiance forecastsover the monitored region and then will quantify the error Fig. 4 Measured 1-minute data for 62 global irradiance monitors acrossbetween measured and forecast irradiance over the term of the SMUD service territory, including average and hourly simulated dispatchexperiment. based on average
  4. 4. 4 VI. PV POWER FORECASTING WITH NDFD DATAThe National Digital Forecast Database or NDFD is aNational Weather Service product developed in the pastdecade which provides digital forecasts of a range ofmeteorological parameters in both numeric and graphicalform. Grids for the continental United States are currentlyavailable from NDFD at 5 kilometer spatial resolution. Thetemporal resolution for skycover (cloud cover) andtemperature is every three hours out to 72 hours and every sixhours out to 168 hours.The forecast data may be viewed with a web browser orretrieved via file transfer protocol in binary form. Figure 4 is Fig. 6. Satellite map of solar monitoring network sites in Sacramento County,an example of display of forecast skycover for an area of Californianorthern California which includes Sacramento County.NEO uses the NDFD forecast elements of skycover,temperature, and relative humidity to model direct normal and VIII. FUTURE WORKdiffuse horizontal irradiance parameters which are then usedto simulate PV power production. 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 potentialFig. 5. Skycover variable depiction from National Weather Service NDFD PV power production contribution to an entire utility service territory. VII. PRELIMINARY PV POWER FORECAST RESULTS The sequence of three dimensional graphs shown in figures 7As a result of both the timing of SMUD’s Feed in Tariff PV through 12 below is taken from the 71 station monitoringsystem installations and the development timeline for the solar network. The devices measure global horizontal data for Mayforecasting model, it is unlikely that sufficient generation data 14, 2011. The day is one of intermittent clouds with a peakwill be available, nor a forecast model ready, for PV power irradiance occurring both before and after the typical highforecasting validation until February, 2012. Depending on the irradiance time of the day, solar noon, 12:06pm. The abilityavailability of both of these elements, it is unclear to what to forecast and thus anticipate the equivalent PV generationextent the PV power validation piece will be available for the carrying capacity in the geographic context of SMUD’sfinal submittal of this paper. service territory could greatly facilitate the management of both load following and regulation requirements.
  5. 5. 5Fig. 7. Measured global horizontal irradiance (GH) levels viewed across Fig. 10. Measured global horizontal irradiance (GH) 11:29am.SMUD service territory on May 14, 2011. Fig. 11. Measured global horizontal irradiance (GH) 12:58pm.Fig. 8. Measured global horizontal irradiance (GH) 8:26am.Fig. 9. Measured global horizontal irradiance (GH) 9:28am. 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
  6. 6. 6recommendations expressed in this material are those of the evaluation for meeting SMUD’s Renewable Portfolio Standards. He also leads SMUD’s Climate Change and Energy Efficiency R&D programs. He earned aauthor(s) and do not necessarily reflect the views of the BS in mechanical engineering from Cal Poly, San Luis Obispo and an MS inCPUC, Itron, Inc. or the CSI RD&D Program. Transportation Technology & Policy from UC Davis, and is a registered Professional Engineer in the state of California. XI. REFERENCES Pramod N. Krishnani is the Performance Monitoring Engineer of Belectric, Inc. He is a Mechanical Engineer[1] J. Smith "Feeder Characterization for PV Integration Assessment," working in data acquisition, monitoring, prediction and presented at DOE Hi-Pen PV Workshop, SMUD, Sacramento, power systems design in the photovoltaic industry. Prior California, June 13, 2011. to the work at Belectric, Mr. Krishnani has worked for[2] M. Thomson and D. G. Infield, "Network Power-Flow Analysis for a Sacramento Municipal Utility District (SMUD) in the High Penetration of Distributed Generation," in Proc. 2009 IEEE Power Research and Development Department from 2008 to Engineering Society General Meeting. 2011; where he has evaluated PV performance, meteorological solar data and[3] J. Stein, R. Perez and A. Parkins, "Validation of PV Performance developed custom PV modeling and cloud transient modeling tools. At Models Using Satellite-Based Irradiance Measurements: A Case Study " SMUD, Mr. Krishnani has completed work related to other renewable energy presented at American Solar Energy Society annual SOLAR 2010 like Biogas, wind energy, storage technology and Geothermal. All the work conference proceedings. presented by Pramod Krishnani in this paper was completed during the period[4] R. Perez, S. Kivalov, J. Schlemmer, K. Hemker Jr., D. Renné, T. Hoff, of employment at SMUD and has no affiliation with Belectric Inc. He has "Validation Of Short and Medium Term Operational Solar Radiation simulated several study projects related to utility load regulation, impact of Forecasts In The Us," presented at American Solar Energy Society solar and wind on utility load and simulated Utility scale PV from 500kW to annual SOLAR 2009 conference proceedings. 300 MW. In addition to his Mechanical Engineering Credentials, Mr.[5] R. Perez, S. Kivalov, S. Pelland, M. Beauhamois, E. Lorenz, J. Krishnani has three years of experience in project management, research and Schlemmer, kHemker, Jr., G. Van Knowe,"Evaluation Of Numerical estimating. Weather Prediction Solar Irradiance Forecasts In The Us," presented at Mr. Krishnani holds a MS in Mechanical Engineering from California State American Solar Energy Society annual SOLAR 2011 conference University – Sacramento and BS and Diploma in Mechanical Engineering proceedings. from University of Mumbai.[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 andtechnology. In addition to his engineering credentials Mr. Bing possessesover twenty years of experience in the areas of electrical system design,project management, estimating, and personnel supervision in the electricalconstruction industry. Mr. Bing holds a BS in Electrical Engineering from theUniversity of Massachusetts at Lowell, as well as an AB in Anthropologyfrom 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