The Impact Of Improved Solar Forecasts On Bulk Power .
The Impact of Improved SolarForecasts on Bulk PowerSystem Operations in ISO-NEPreprintC. Brancucci Martínez-Anido, A. Florita, andB.-M. HodgeNational Renewable Energy LaboratoryTo be presented at the 4th International Workshop on Integration ofSolar Power into Power SystemsBerlin, GermanyNovember 10–11, 2014NREL is a national laboratory of the U.S. Department of EnergyOffice of Energy Efficiency & Renewable EnergyOperated by the Alliance for Sustainable Energy, LLCThis report is available at no cost from the National Renewable EnergyLaboratory (NREL) at www.nrel.gov/publications.Conference PaperNREL/CP-5D00-62817September 2014Contract No. DE-AC36-08GO28308
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The Impact of Improved Solar Forecasts on BulkPower System Operations in ISO-NECarlo Brancucci Martínez-Anido, Anthony Florita, and Bri-Mathias HodgeNational Renewable Energy LaboratoryGolden, Coloradopower output is mostly reshaping load from residential andcommercial installations. Because load is stochastic, for allintents and purposes, there would be uncertainty in netpower output even with perfect distribution solar powerforecasting. Of concern to grid operations is: what is theeconomic value of enhanced forecasting of thesecentralized and distribution perturbations?Abstract—The diurnal nature of solar power is madeuncertain by variable cloud cover and the influence ofatmospheric conditions on irradiance scattering processes. Itsforecasting has become increasingly important to the unitcommitment and dispatch process for efficient scheduling ofgenerators in power system operations. This study examinesthe value of improved solar power forecasting for theIndependent System Operator–New England system. A stateof-the-art production-cost modeling environment was usedwith both utility-scale and distributed solar photovoltaicpower. Current state-of-the-art numerical weather predictionforecasting accuracies in the day-ahead and four-hour-aheadhorizons were considered, in addition to uniform forecastingimprovements of 25%, 50%, and 75%, as well as a base caseof no solar power forecasting and a reference case of perfectforecasting. The results show how 25% solar powerpenetration reduces net electricity generation costs by 22.9%.If solar power forecasts were not considered, the power systemwould experience overcommitment of generation as well as amuch higher solar curtailment, which would lead to areduction in net generation costs of 12.3%. If solar powerforecasts are uniformly improved by 25%, the net generationcosts are further reduced by 1.56% ( 46.5 M). However, ifsolar power forecasts are further improved, the results do notshow significant differences in terms of net generation cost.The high solar power penetration case reduces electricityprices while increasing hourly variability. Solar powerforecasting plays an influential role in the impact of solarpower on electricity prices.Keywords-solarpowermodeling; electricity pricesI.forecasting;Power system operators’ commitment to the integrationof new forecasting approaches, models, and strategies mustbe predicated with knowledge of the associated economicbenefits. The benefits to the unit commitment and economicdispatch process from superior renewable energyforecasting are largely unquantified in the powercommunity. This is partly because such technologies arenew and unproven, but it is more likely because currentrenewable energy penetration levels in the United States [4]are too low to appreciably quantify the value of improvingrenewable energy forecasting. In addition, the powersystem is very complex, and it is often difficult to assigncosts or benefits to a single generator [5]. Nevertheless,solar power installations are rapidly growing, andpenetration levels will soon require that increased attentionis placed on the value of forecasting. A way forward is toexamine power systems with likely future solar powerscenarios and quantify how much value solar forecastingcan provide. Modeling and simulation is one avenue toexploring electrical grid scenarios that can informstakeholders of possible benefits and implications ofevolving systems.production-costThe Independent System Operator–New England (ISONE) system’s electrical grid encompasses six U.S. states andserves an estimate 6.5 million customers with a peak load in2006 of 28,130 MW [6]. The National Renewable EnergyLaboratory (NREL) has modeled and simulated the ISO-NEsystem using a state-of-the-art production-cost modelingenvironment [7]. An envisioned future scenario of 25% solarpower penetration was examined. Day-ahead (DA) and fourhour-ahead (4HA) forecasting time horizons wereconsidered, with forecasting accuracies derived from a stateof-the-art NWP routine. A base case scenario of no solarpower forecasting and a reference case of perfect solarpower forecasting were also considered to provide lowerand upper bound estimates on the value of solar provements of 25%, 50%, and 75% were considered toassess the value of forecasting for the assumed futurescenario.INTRODUCTIONSolar power forecasting is complex, because deviationsin clear-sky power estimates are a localized phenomenon;whereas predicting solar power most likely occurs from amore global perspective. For example, sparse cumulusclouds over a photovoltaic (PV) plant sporadically attenuatepower output, but the magnitude and timing of thesechanges are uncertain. For centralized solar powerconversion, the resolution of models—whether based onnumerical weather prediction (NWP) [1, 2] or satelliteimagery [3]—is never perfect, and the aggregation of manyplants can lead to additive or destructive variabilitydepending on a plant’s geographic proximity. There is alimit to the benefits that can be had from the law of largenumbers and geographic smoothing effects. For distributedsolar power conversion, problems occur because solarThis work was supported by the U.S. Department of Energy underContract No. DE-AC36-08-GO28308 with the National RenewableEnergy Laboratory.1This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
II.ahead, and week ahead. Statistics considered include MAE,MBE, and RMSE.LITERATURE REVIEWWind power forecasting is more common than solarpower forecasting in the United States because of windpower’s greater level of penetration, but literature about thetrue value of forecasting improvements is still sparse.Because of the overlapping concerns about wind and rsion—both were considered in a literature review tounderstand the issues surrounding renewable powerforecasting.III. METHODOLOGYThe PLEXOS production-cost modeling software [7] isused to simulate the operation of the ISO-NE power systemto assess the value and impact of improved solar forecastson bulk power system operations. The ISO-NE modelsimulates the DA, 4HA, and real-time (RT) markets. ISONE does not have a 4HA market in place; however, wemodel it in PLEXOS to represent the rolling securityconstrained unit commitment process at the last timescalewhen it is possible to commit gas combined-cycle (CC)power plants as well as gas and oil steam turbines. Thethree markets are modeled with 1-hour time steps.Pinson et al. [8] considered probabilistic forecasts for awind farm within the Dutch electricity market with theassumption that wind generation had negligible influenceon imbalance prices, and they showed that regulation costsare reduced when wind power is overestimated rather thanunderestimated. McGarrigle and Leahy [9] considered theIrish electricity market and found an average savings of 4.1million pounds for every percentage point decrease in thenormalized mean absolute error (NMAE) as measuredbetween forecast and actual wind for the 4% to 8% meanabsolute error (MAE) range for the year 2020. The caveat isthat the NMAE metric can be misleading, because forecaststhat have different error distributions can have the sameNMAE but differ significantly in cost, especially duringtime steps characterized by extreme forecast errors.Meibom et al. [10] appraised the value of a perfect forecastfor the Irish electrical grid to be 1 million to 65 million,finding that that value generally increased with the level ofwind penetration. Tuohy, Meibom, et al. [11] used a modeladapted from earlier work [10] that compared deterministic,stochastic, and perfect forecasting; interestingly, in somecases demand was not met. Ummels et al. [12] foundminimal cost savings for thermal generation units in theDanish interconnection. This may be because the majorityof thermal units were combined heat and power, whichhave additional operating constraints.The ISO-NE model assumes a mixed-integerprogramming (MIP) optimality gap of 0.1%. The model isrun nodally for voltage levels equal to or above 69 kV using2006 load [18] and solar time series [19]. The electricitygeneration mix includes the conventional generators presentin ISO-NE in 2010 in addition to 25% solar powerpenetration in terms of electricity demand. The PV solarpower generators are grouped into utility-scale solar powerand distributed solar power. Table I shows the installedcapacity for each electricity generation source.TABLE I.Pedro and Coimbra [13] compared variousmathematical approaches to solar power forecasting andassessed their performance using MAE, mean bias error(MBE), and coefficient of correlation. Findings showed thatartificial neural networks outperformed other techniques,and enhancements from genetic algorithm optimizations oftheir parameters were vital. Mathiesen and Kleissl [14]validated common NWP forecasting methods usingSURFRAD data. The European Centre for Medium-RangeWeather Forecasts (ECMWF) model was found to be mostaccurate for cloudy conditions; whereas the Global ForecastSystem had the best clear-sky accuracy. Bacher et al. [15]considered online forecasting of PV power production bypredicting hourly values during a 36-hour forecastinghorizon using 15-min data. Autoregressive (AR) and ARwith exogenous (ARX) methods (fed with NWP inputs)showed that short-term ( 2h) performance was dominatedby recent data; whereas longer term ( 2h) performance wasdriven by the NWP system; and ARX outperformed AR by35% as measured by the root mean square error (RMSE).Lorenz et al. [16] examined regional PV power forecastingup to a 3-day horizon using the ECMWF. Forecastingaccuracy was found to be a function of the size of theregion, in which a single location showed an RMSE of 36%and the entire area of Germany showed an RMSE of 13%.Kostylev and Pavlovski [17] proposed a commonmethodology to evaluate forecasting performance. Theproposed standards include intra-hour, hour ahead, dayINSTALLED CAPACITY FOR DIFFERENT ELECTRICITYGENERATION SOURCESElectricity Generation SourceInstalled Capacity 75Pumped hydro1,692Biomass844Solar—utility scale11,344Solar—distributed16,304NREL’s solar power data for integration studies [19] isthe source for the solar sites included in this study. Thedatabase includes 68 utility-scale solar power plants and 76distributed solar power plants in ISO-NE. The totalinstalled capacity of all the sites in the database present inISO-NE amounts to 4,874 MW. To simulate a futurescenario with 25% solar power penetration, we multiply thedistributed solar power plants by eight and the utility-scalesolar power plants by four. Both the utility-scale and thedistributed solar power plants are already well distributedthroughout the ISO-NE power system; thus, the solaroutput smoothing for high solar penetrations is the same inthe case with additional solar sites as it is in our case, inwhich we multiply the units included in the database. Anexamination of the spatial smoothing experiences forvarious subsets of the data revealed that there was verylittle additional smoothing when a large number of plants( 20) were considered. Table II shows the normalizedhour-to-hour solar variability for scenarios with solar powerpenetrations constructed from the solar database introducedabove.2This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
TABLE II.NORMALIZED SOLAR VARIABILITY FOR DIFFERENTSOLAR POWER PENETRATION LEVELSto be always available from units outside of the generationstack. Upward and downward regulation reserves are a 5minute dynamic product equal to 1% of load withoutconsidering the variability provided by solar power.Penetration Level(%)Solar Capacity(MW)Normalized Hour-toHour SolarVariability (%)0.545846.851.081,1806.782.082,2466.77 No solar power2.52,4246.81 4.54,8746.73No solar power forecasting12.513,8246.73 Solar power forecasting2527,6486.73 Solarpowerimprovementforecasting—25%uniform Solarpowerimprovementforecasting—50%uniform Solarpowerimprovementforecasting—75%uniform Perfect solar power forecasting (100% uniformimprovement)The following seven scenarios have been modeled foran entire year to study the value and impact of improvedsolar power forecasting in ISO-NE:The time series for the actual RT solar power as well asthe 4HA forecasts are provided in the solar database [19].DA solar power forecasts are derived from a state-of-the-artNWP model, namely the Weather Research and ForecastingModel (WRF) [20]. DA and 4HA solar power and loadforecasts are included in the ISO-NE model to moreaccurately represent the corresponding DA and 4HAmarkets.IV.The ISO-NE model simulates the electricity exchangesamong ISO-NE and its neighboring regions of NewBrunswick, Hydro Québec, and the New York IndependentSystem Operator. The model holds both contingency andregulation operating reserves. Contingency reserves are a10-minute product defined by the largest systemcontingency. Only the spinning part of contingencyreserves is considered; non-spinning reserves are assumedRESULTSA comparison of net generation costs for the sevensimulations listed in the previous section is shown in Figure1. Net generation costs are defined as the annual electricitygeneration costs in ISO-NE (the sum of fuel costs, variableoperations and maintenance costs, and start-up andshutdown costs), plus the annual electricity import costs,minus the annual electricity export revenues.4500Net Generation Costs (M 929.425002000150010005000No Solar No Solar Solar Fcst Solar Fcst Solar Fcst Solar Fcst PerfectFcstImprvImprvImprv Solar Fcst25%50%75%Figure 1. Net generation costsFigure 1 shows how 25% solar power penetrationreduces net generation costs by 12.3% ( 473.5 M), evenwhen solar power forecasts are not considered in the DAand 4HA generation unit commitment decisions. Therelatively small reduction in net generation costs is drivenby the very large solar curtailment. As shown in Figure 2,the ISO-NE power system experiences 34.5% solar powercurtailment without solar power forecasts. In other words,solar power penetration is reduced from 25% to 16.4%because of the very large curtailment.On the other hand, if DA and 4HA solar power forecastsare used when committing conventional generators in theDA and 4HA simulated markets, 25% solar power3This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
penetration reduces net generation costs by 22.9% ( 883.7M). In this case, solar power curtailment equals 11%, andthe actual solar power penetration is reduced from 25% to22.3%. DA and 4HA solar power forecasts reduce solarcurtailment to less than one-third, because conventionalpower plants are committed more efficiently. If solar powerforecasts are not used, the ISO-NE power system must dealwith an overcommitment of electricity generation when thesun is shining and the PV power plants are producingelectricity.4034.49Solar Power Curtailment (%)353025201510.981011.0511.0711.1411.17500No Solar No Solar Solar Fcst Solar Fcst Solar Fcst Solar Fcst PerfectFcstImprvImprvImprv Solar Fcst25%50%75%Figure 2. Solar power curtailmentIf DA and 4HA solar power forecasts are uniformlyimproved by 25%, net generation costs are reduced by1.56% ( 46.5 M) when compared to the case with thecurrent solar power forecasts. This reduction in netgeneration costs is driven by a more efficient commitmentof conventional power plants and not by lower solar powercurtailment, which does not vary by a significant amount. Ifsolar power forecasts are further improved, the results donot show any significant differences in terms of netgeneration costs and solar power curtailment, as shown inFigure 1 and Figure 2, respectively. (The differences aresmaller than or very close to the MIP gap used in the ISONE PLEXOS model.) In other words, 50%, 75%, and 100%(perfect) uniform improvements of solar power forecasts donot provide any significant reductions in net generationcosts and solar power curtailment when modeling the ISONE power system with 25% solar power penetration. Solarpower curtailment is not reduced further by solar powerforecasting improvements.system is characterized by a very large share of gas-firedgenerators, as shown in Figure 3. Net generation costs canonly be further reduced with more efficient commitment ofconventional power plants. The ISO-NE power system doesnot have much “base load” generation, which means thatthe DA and 4HA solar power forecasts do not have a largeimpact on DA commitments, because all the nuclear, coal,and biomass power plants are almost always committed.The large available gas-fired electricity generation capacitypresent in ISO-NE reduces the value of improving solarpower forecasts above a certain threshold, because
forecasting. The results show how 25% solar power penetration reduces net electricity generation costs by 22.9%. If solar power forecasts were not considered, the power system would experience overcommitment of generation as well as a much higher solar curtailment, which would lead to a reduction in net generation costs of 12.3%. If solar power
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