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    Giz capacity creditstudiessa_final Giz capacity creditstudiessa_final Document Transcript

    • Capacity Credit of Wind Generation in South AfricaFinal ReportFebruary 2011Capacity Credit of Wind Generation in South AfricaFinal ReportFebruary 2011Capacity Credit of Wind Generation in South Africa
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0Commissioned by:Consultants:0 3 4 . 0 0Deutsche Gesellschaft für InternationaleZusammenarbeit (GIZ) GmbHOffice PretoriaFocal Area Energy & Climate333 Grosvenor StreetPretoria 0028South AfricaContact:Daniel Werner (daniel.werner@giDepartment of EnergyContact:Ompi Aphane (ompi.aphane@energy.gov.zaAndré Otto (andre.otto@energy.gov.zaEskomContact:Kevin Leask (LeaskK@eskom.co.za)DIgSILENT GmbHHeinrich-Hertz-Str. 972810 GomaringenGermanyWeb:http://www.digsilent.deContact:Markus Pöller (mpoeller@digsilent.de)Windlab Developments South Africa Pty Ltd9b Bell Crescent CloseWestlake 7945South AfricaContact:Francis Jackson (francis.jackson2Deutsche Gesellschaft für InternationaleDaniel Werner (daniel.werner@giz.de)energy.gov.za)energy.gov.za)Kevin Leask (LeaskK@eskom.co.za)Markus Pöller (mpoeller@digsilent.de)Windlab Developments South Africa Pty Ltdfrancis.jackson@windlab.com)
    • T a b l e o f C o n t e n t sP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 3Table of Contents1 Introduction......................................................................................................................................... 52 Part 1 – Capacity Credit Studies.......................................................................................................... 72.1 Approach and Methodology.....................................................................................................................72.1.1 Generation Adequacy.........................................................................................................................72.1.2 Consideration of Correlation Effects - Methodology of the Presented Studies...........................................82.1.3 Assignment of Capacity Credit to Individual Regions or Wind Farms.....................................................102.1.4 Modelling........................................................................................................................................122.1.4.1 Load Model ................................................................................................................................122.1.4.2 Modelling of Dispatchable Generation...........................................................................................142.1.4.3 Modelling of Wind Generation......................................................................................................142.1.4.3.1 Wind Speed Data...................................................................................................................142.1.4.3.2 Transformation into Power Time Series Data............................................................................152.1.5 Simulation Model.............................................................................................................................162.2 Scenario Definition ...............................................................................................................................182.2.1 Scenario 1 – Year 2015 with 2000MW of Installed Wind Capacity ........................................................182.2.2 Scenario 2 – Year 2020-Low Wind Scenario with 4800MW of Installed Wind Capacity............................182.2.3 Scenario 3– Year 2020-High Wind Scenario with 10 000MW of Installed Wind Capacity .........................192.3 Results of Simulation Studies.................................................................................................................202.3.1 Scenario 1 – Year 2015 with 2000MW of Installed Wind Capacity ........................................................202.3.2 Scenario 2 – Year 2020-Low Wind Scenario with 4800MW of Installed Wind Capacity............................222.3.3 Scenario 3– Year 2020-High Wind Scenario with 10 000MW of Installed Wind Capacity .........................232.3.4 Outlook for up to 25 000MW of Installed Wind Capacity......................................................................242.3.5 Wind Farms in the Western and Southern Region...............................................................................262.3.6 Comparison with Thermal or Hydro Power Plants ...............................................................................273 Part 2: Impact of Wind Generation in South Africa on System Operation....................................... 283.1 Approach and Methodology...................................................................................................................283.2 Results of Time Series Simulation Studies...............................................................................................293.2.1 Impact on Main System Performance Characteristics ..........................................................................293.2.2 Local versus Global Wind Generation.................................................................................................313.2.3 Energy Production and CO2 Emissions...............................................................................................324 Conclusions and Recommendations.................................................................................................. 335 References ......................................................................................................................................... 35A N N E X E S ...................................................................................................................... 36Annex 1: Results of Simulation Studies ............................................................................................... 37Annex 1.1: Scenario 1 – Year 2015 – 2000MW Installed Wind Generation.......................................................37
    • T a b l e o f C o n t e n t sP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 4Annex 1.2: Scenario 2 – Year 2020 – 4800MW Installed Wind Generation.......................................................40Annex 1.3: Scenario 3 – Year 2020 – 10000MW Installed Wind Generation.....................................................43Annex 2: Seasonal and Hourly Variations of the Wind Generation in South Africa............................ 46Annex 2-1: Scenario 1 – Year 2015/2000MW of Installed Wind Capacity.........................................................47Annex 2-2: Scenario 2 – Year 2020-Low/4800MW of Installed Wind Capacity..................................................49Annex 2-3: Scenario 3 – Year 2020-High/10000MW of Installed Wind Capacity ...............................................51Annex 3: Results of Time Series Assessment....................................................................................... 53Annex 3-1: Worst Case Situations and Duration Curves, Scenario 1 – 2015 .....................................................53Annex 3-2: Worst Case Situations and Duration Curves, Scenario 2 – 2020 .....................................................64Annex 3-3: Worst Case Situations and Duration Curves, Scenario 3 – 2020 .....................................................75
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 51 IntroductionRenewable energies, such as wind energy, allow electricity production without consuming fossil resources andwithout any direct carbon dioxide emissions. Just by producing electrical energy, the use of these sources isjustifiable and represents in many locations an economical alternative to the use of fossil resources such as coalor oil. Particularly the cost of wind generation has considerably decreased over the last decade and while fossilfuel prices are increasing it is anticipated that wind generation at high wind sites will represent the mosteconomical method for generating electrical energy in the near future.However, in contrast to more conventional power plants based on fossil resources, electricity production fromwind farms cannot be planned because of the variable nature of wind speeds. Therefore, and because therearen’t any suitable electricity storage technologies available (yet), conventional, dispatchable power plants usingfossil fuels are always required for times during which electricity demand is high and wind generation is low.This leads to the question about how much conventional generation capacity is still required in a system with highamount of wind generation. Is it the same amount as without wind generation or is it less? If it was the sameamount, there would still be the benefit of consuming less fossil resources and reducing carbon dioxide emissionscompared to a scenario without wind energy use, but there wouldn’t be any positive influence on the installedconventional generation capacity. However, in the case that less conventional generation capacity would have tobe installed, this capacity effect could be seen as an additional benefit from wind generation and would make thistechnology even more economical. In this case, a “Capacity Credit” could be assigned to wind generationmeaning that a certain percentage of the installed wind generation capacity could be considered as a contributionto the firm capacity of a system that is required for ensuring a safe and reliable electricity supply.Even in systems not using renewable energy generation, it is necessary that the installed generation capacityexceeds the peak demand because also conventional power plants are not permanently available because ofplanned outages (for maintenance) and unplanned outages (faults). The excess of installed capacity over thepeak demand is the Reserve Margin and represents an important constraint in the capacity planning process.According to [6], a reserve margin of 15% has been considered to be appropriate for ensuring a sufficientlyreliable electricity supply of South Africa.With the addition of wind generation, the classical, empirical approach based on the consideration of a reservemargin on the basis of typical availability indices of conventional power plants does not work anymore forassessing the required installed capacity:• When applying the same reserve margin to the total generation capacity, including wind generation, therequired installed capacity will be highly underestimated because the availability of wind generation ismuch below conventional generation based on fossil fuels.• When applying the same reserve margin to conventional generation capacity only, hence consideringthat wind generation has a capacity credit of zero, the required installed capacity will be overestimatedbecause the installed wind generation will tend to improve the reliability of supply, an effect whichwould be ignored by such a simplified approach.Hence, the question arises how the capacity credit of wind generation can actually be quantified and used in thecapacity planning process for determining the required electricity generation capacity.This report presents the results of simulation studies for assessing the capacity credit of planned wind farms inSouth Africa. Therefore, three scenarios have been defined and agreed between ESKOM, GTZ and DIgSILENT[1]:
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 6- Scenario 1: Year 2015: 2 000 MW of installed wind generation capacity- Scenario 2: Year 2020 – low wind generation scenario: 4 000 MW of installed wind generation capacity- Scenario 3: Year 2020 – high wind generation scenario: 10 000 MW of installed wind generation capacityBased on Scenario 3, additional simulations have been carried out looking at up to 25 000 MW of installed windgeneration capacity while keeping the specific distribution of wind generation constant.The three scenarios are based on realistic assumptions with regard to wind farm sites, the potential installed windgeneration capacity at these sites and the relevant characteristics of the South African power system, such asload and generation characteristics and planned expansions of thermal and hydro power plants, which have beenprovided by ESKOM [3].A second part of the presented studies is analyzing simulated time series of load and wind generation for thethree above defined scenarios.This part of the studies is mainly looking at the following aspects:• Worst case situations with regard to wind generation, load.• Worst case situations with regard to wind and load variations (ramp-up/ramp-down speeds).• Impact of wind generation on the cumulative probability curve of the residual load, which has to becovered by thermal and hydro power plants.• Impact on residual load variations.• Energy production and avoided carbon dioxide emissions.The results of these studies will help determining the impact of wind generation on the required dynamicperformance of thermal and hydro power plants.
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 72 Part 1 – Capacity Credit Studies2.1 Approach and Methodology2.1.1 Generation AdequacyFigure 1: Generation Availability CurveFor assessing the capacity credit of wind generation, probabilistic methods for analyzing generation adequacyhave to be applied. The basic concepts can best be explained using the generic example according to Figure 1.The blue curve in Figure 1 represents the cumulative probability curve for the available generation in a powersystem. The x-axis depicts probabilities in %, the y-axis shows the available capacity in MW. The blue curveshows the minimum available capacity at a specified probability level.The available generation depends on the following parameters:• Pinst: The installed capacity of the system• Pplanned: Planned capacity during the observation period. The planned capacity is equal to the installedcapacity minus planned outages (maintenance).• Finally, the cumulative probability of the available capacity can be calculated on basis of the unplannedoutages of all power plants in the system.Using this approach, the demand that can be supplied with sufficient reliability (Psecure) is defined by thecapacity that is at least available at a given confidence level (e.g. 99%).0 20 40 60 80 100PinstPsecurePplannedConfidenceReserve MarginProbability in %CapacityinMW
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 8Figure 2: Generator Availability Curve - With WindFor analyzing the influence of wind generation on the firm capacity the cumulative probability curve of the totalavailable capacity, including conventional power plants and wind power plants, as depicted in Figure 2 needs tobe calculated. The two curves have the following meaning:• Blue curve: Cumulative probability curve of conventional generation.• Green curve: Cumulative probability curve of conventional and wind generation together.When comparing the green curve and the blue curve, it can be observed that the load level that can be suppliedat the given confidence level has increased. This increase of securely supplied load is also named “EquivalentLoad Carrying Capability” (ELCC) of the system and is a useful index for defining the capacity credit of windgeneration.2.1.2 Consideration of Correlation Effects - Methodology of the Presented StudiesIn countries, in which a very distinct peak load situation can be identified, an approach purely based on the peakload situation might be sufficiently accurate because it can be assumed that only the peak load level contributesconsiderably to the average Loss of Load Probability (LOLP). In this case, it is sufficient to calculate thegeneration availability during the peak load time (season and hour of day) and to work out the capacity credit ofwind generation purely on the basis of this simplified analysis.However, in systems, in which the yearly load profile is relatively flat, not only the peak load situation might leadto loss of load situations but also other load situation may considerably contribute to the average LOLP. A simpleanalysis of generation availability at peak load is then inappropriate.0 20 40 60 80 100PinstPsecurePwinstELCCConfidenceCapacityinMWProbability in %
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 9For this reason, the approach on which these studies are based on considers generation availability and theseasonal variation of daily peak loads together and calculates a cumulative probability curve of the availableReserve, whereas Reserve is defined by the excess of generation capacity over the daily peak load.The relevant reliability index, on which the capacity credit definition of the presented studies is based on, is theaverage loss of load probability (LOLP) for daily peak loads. The average LOLP during peak load hours can betranslated into a figure indicating the expected number of days per year during which the peak load cannot becovered by the available generation capacity.The advantages of this approach are the following:• It provides a realistic measure for system adequacy improvements due to wind generation.• Considers correlation between load and planned outages.• Considers correlation between wind speeds and seasonal load variations.• Considers correlation between wind speeds and daily load variations.The LOLP at daily peak loads is expressed in % but can also be expressed in terms of an average number of daysper year during which a loss of load situation has to be expected. For example:• An average LOLP at daily peak load of 1% would be equivalent to an average of 0,01x365=3,65 daysper year during which the peak load cannot be covered by the available generation.• An average LOLP at daily peak load of 0,1% is equivalent to an average of 0,365 days per year, hencearound 1 day in 2,7 years at which the daily peak load cannot be covered.The use of the average LOLP at daily peak load requires an assessment that considers the yearly load variationand the available capacity simultaneously. Instead of a cumulative probability curve of the available generationcapacity, as presented in the previous section, the cumulative probability of the Reserve is calculated. A negativeReserve indicates a loss of load situation. Consequently, the probability with which the Reserve is lower than zerorepresents the average probability with which the daily peak load cannot be covered by the available generationcapacity.In this study, Capacity Credit is defined in terms of Equivalent Firm Capacity, which represents a firm capacity(capacity with 100% availability) that has the same influence on the reliability of supply of the system as theactually installed wind generation. Because the addition of a perfectly firm capacity would increase the Reserveby exactly the amount of the added firm capacity, the increase of Reserve at a given confidence level is a suitablemeasure for assigning an Equivalent Firm Capacity (EFC) to wind generation.
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 1 0Figure 3: Cumulative Probability of the Available ReserveFigure 3 shows the cumulative probability of the “Reserve” for a generic system.According to Figure 3, the average LOLP without wind generation (blue curve) is equal to around 2%, which isequivalent to around 7,3 loss of load events per year. The LOLP of the system with wind generation (green curve)is equal to 99,9%, which corresponds to 0,365 loss of load events per year or 1 loss of load event within 2,7years on average.In a real system, there is always some operational reserve required for being able to compensate frequencydeviations etc. Therefore, the equivalent firm capacity has not been defined at the zero crossing of the probabilitycurve of the Reserve in the presented studies, but instead, the increase of available Reserve at a givenconfidence level of 99,9% has been taken.Besides this, only the winter season has been considered, which represents the South African peak load season.Because the observed time frame corresponds only to half a year, a confidence level of 99,9%, as used in thesestudies, corresponds to less than 1 loss of load event within 5 years.2.1.3 Assignment of Capacity Credit to Individual Regions or Wind FarmsThe capacity credit of wind generation is a non-linear function that depends on• Average generation of wind farms or capacity factor.• Wind penetration level (ration between installed wind capacity and installed conventional generationcapacity).• Planned and unplanned outage rates of conventional power plants.• Load variations.0 20 40 60 80 100EFC100%-LOLP0Probability in %ReserveinMW
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 1 1Because of the nonlinear relationship, it is not possible to calculate the capacity credit of individual wind farms orwind farms in different regions directly. Capacity credit calculations must always consider the whole system.However, with the following assumptions it is possible to assign portions of the total equivalent firm capacity toindividual wind farms or regions:It can be shown that the Equivalent Firm Capacity (EFC) of wind generation can very well be approximated by aformula that decomposes nonlinear and linear parts:100)(×=⋅=rflPEFCCCPavcpCREFCwith:• EFC: Equivalent Firm Capacity (in MW)• CR: Capacity Reduction factor• cp: Wind penetration level (ratio of installed wind capacity and installed conventional generationcapacity)• Pavfl: Average production during full load period (season and hour per day)• CC: Capacity Credit (in %)• Pr: Rated power of totally installed wind generation (in MW)In this approximate formula, all non-linear parts are lumped into the “Capacity Reduction Factor” CR, which is asystem-wide parameter that mainly depends on the wind penetration level and which is highly independent fromthe wind conditions at individual sites or regions.The dependence of EFC on site-specific parameters, such as the average production during full load hours, is alinear relationship in this formula and therefore superposition can be applied:∑∑ =⋅=iiiifl EFCPavcpCREFC )(The index i represents the individual wind farms or regions of a system. The equivalent firm capacity of eachwind farm or region i can be calculated using:ifli PavcpCREFC ⋅= )(A verification of this approach is depicted in Figure 15 on page 27 showing the factor CR in function of the windpenetration level for the two different study years 2015 and 2020. Based the calculated EFC for each study yearand the average production during full load hours (Pavfl), which can directly be obtained by analyzing theavailable wind speed time series data, CR can be calculated using CR(cp)=EFC/ Pavfl.As it can be seen in Figure 15, the two curves representing the two study years are lying almost on top of eachother even if wind conditions, number and size of the modelled wind farms are highly different. This confirms theassumption that CR is mainly independent from site specific data and only depends on system characteristics.Besides regional allocation of capacity credit of wind generation, the simplified formula allows an easy predictionof capacity credit and equivalent firm capacities for variations with regard to the wind scenarios (see section 2.2).
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0In the case that the actual wind farm development will considerably deviate from the assumptions on which thescenarios of the presented studies were based, it will still be possible to calculate equivalent firm capacity andcapacity credit of wind generation by using the approximate formula and the capacity credit reduction factor CRin function of the wind penetration level according toproduction of the considered wind farms.2.1.4 Modelling2.1.4.1 Load ModelThe load data used for the presented studies correspond to the “Moderate Load Growth Scenario” of the IRP2010[6]. Further, the load model considers the Net Demand forecast that has been made for 2015 and 2020considering the regular load forecast and the effect of Demand Side Managemenimplemented until 2015 and 2020.Figure 4: Forecast of Daily Peak Load0 3 4 . 0 0In the case that the actual wind farm development will considerably deviate from the assumptions on which thenarios of the presented studies were based, it will still be possible to calculate equivalent firm capacity andcapacity credit of wind generation by using the approximate formula and the capacity credit reduction factor CRion level according to Figure 15 and to multiply the CR with the averageproduction of the considered wind farms.ented studies correspond to the “Moderate Load Growth Scenario” of the IRP2010[6]. Further, the load model considers the Net Demand forecast that has been made for 2015 and 2020considering the regular load forecast and the effect of Demand Side Management measures that will beimplemented until 2015 and 2020.: Forecast of Daily Peak Load (Net Demand) for 2015 and 20201 2In the case that the actual wind farm development will considerably deviate from the assumptions on which thenarios of the presented studies were based, it will still be possible to calculate equivalent firm capacity andcapacity credit of wind generation by using the approximate formula and the capacity credit reduction factor CRand to multiply the CR with the averageented studies correspond to the “Moderate Load Growth Scenario” of the IRP2010[6]. Further, the load model considers the Net Demand forecast that has been made for 2015 and 2020t measures that will be
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 1 3Figure 5: Typical hourly load variations in winterFigure 6: Typical hourly load variations in summerMore precisely, the load model is based on the following load data according to [3]:• Forecast of daily peak load for 2015 and 2020 (see Figure 4)• Typical relative hourly load variations for a typical week in winter (see Figure 5) and a typical week insummer (see Figure 6).Based on the typical hourly load variations (see Figure 5 and Figure 6), the following information has beenextracted and used by the studies:• In winter, full load hours are between 18:00h and 21:00h.• In summer, the daily load is relatively flat and full load hours can occur between 08:00h and 21:00h.00,20,40,60,811,21 10 19 28 37 46 55 64 73 82 91 100 109 118 127 136 145 154 163Hour per weekrelativeload00,20,40,60,811,21 10 19 28 37 46 55 64 73 82 91 100109 118 127 136 145 154163Hour per weekrelativeload
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 1 42.1.4.2 Modelling of Dispatchable GenerationFor modelling the availability of dispatchable power plants, ESKOM provided planned and unplanned outage ratesof all South African power stations [3]:Unplanned outages are modelled by two-state Markov-models considering their unplanned outage rates asprovided by ESKOM.For considering planned outages (maintenance) the monthly system-wide planned outage rate has been providedby ESKOM [3]. Based on this information, a number of generators have been “put on maintenance” in eachmonth. The total number of generators on outage in each month considers the maximum limits that wereprovided by ESKOM.2.1.4.3 Modelling of Wind Generation2.1.4.3.1 Wind Speed DataWindlab Systems has carried out studies [4] for producing time series data of hourly average wind speeds at 80mheight at all potential wind farm sites, which have been defined by ESKOM [2],The Windlab methodology is based on an atmospheric modelling approach whose input are synoptic weather datagathered by the world meteorological organisation (WMO).Based on these data a regional scale model is initially applied allowing modelling wind speeds with a 6 hours/1degree resolution.This regional model accounts for forces associated with stratification and inertial forces, friction with the earth’ssurface, accelerations and steering over and around large scale topographical features (such as mountains) andthermal circulations such as sea breezes.The fine-scale model then uses the regional-scale model as a boundary condition to create a yet finer grid,typically of 100 meter resolution. This model takes into account friction from various types of land cover (forests,crops etc) and acceleration over and around smaller-scale topography.The fine-scale model is then validated using Automatic Weather Stations (AWS) and Meteorological mast data,where available. This validation is applied to the model to reflect the long-term average.Time-series of wind speed with a time resolution of 1 hour and direction at the points of interest are extractedfrom the model. These allow the generation of statistical tools such as wind roses, probability distributions andmonthly averages. These time-series are point statistics and unless great attention has been paid to the selectionof these locations they cannot be used to represent the absolute wind resource of a region such as a wind farmor collection of wind farms, for instance for assessment of individual project feasibility.These time series data of wind speeds have been calculated for each potential wind farm site as defined byESKOM for the different scenarios and were used as input into the capacity credit studies.
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 1 52.1.4.3.2 Transformation into Power Time Series DataFigure 7: Generic power curve used for Capacity Credit StudiesFor transforming wind speed data into power data, a generic power curve was considered (see Figure 7).Losses or technical unavailability have not been considered explicitly because these effects are covered by thegeneral uncertainty margin that has to be expected by the study results.For accurately considering correlation between wind generation and load, time series of wind speeds have beenused and converted into a power time series (see e.g. Figure 8) at each site using the power curve according toFigure 7. The daily and seasonal correlation between wind speeds and load is therefore automatically consideredby the corresponding time series data.The diurnal correlation between wind speed and load is considered by only using wind speed data during full loadhours (see also Figure 8). As described in section 2.1.3, these full load hours are different for summer and winter.For this reason, two capacity credit calculations have been carried out for each scenario, one for the summerseason and one for the winter season.00,20,40,60,811,20 5 10 15 20 25 30wind speed in m/spoweroutputinp.u.
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 1 6Figure 8: Example of Wind Generation Time Series as used for studies (red line)The following wind speed data were considered for each season:• Winter season: Wind speed time series of the months April to September considering only wind speedsduring 18:00h and 21:00h every day.• Summer season: Wind speed time series from October to March considering only wind speeds during08:00h and 21:00h every day.Because the winter load is considerably higher than the summer season it can be assumed that events in summerhave only a very low contribution to the LOLP. Therefore, only the results obtained for the winter season havefinally been used for assessing capacity credit of wind generation.2.1.5 Simulation ModelCapacity credit studies for the South African case have been carried out by the use of 3 main scenarios,representing different study years and different assumptions with regard to the installed wind generationcapacity.The DIgSILENT PowerFactory model integrates all conventional generators, all wind farms as defined for eachscenario and the system load represented by a daily peak load characteristic.4440,04426,04412,04398,04384,04370,01700,001400,001100,00800,00500,00200,0044000,0040000,0036000,0032000,0028000,0024000,00x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Wind Generation in MWTimeSeries: Total Load in MWDIgSILENT
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 1 7For calculating the capacity credit of wind generation, a Monte Carlo analysis approach has been used,considering:• Daily peak load characteristic.• Planned and unplanned outages of conventional generators.• Correlation of wind speeds at different sites.• Daily, weekly and monthly correlation between wind speeds and the daily peak load.• Correlation between wind speeds and daily full load hours.The Monte Carlo Simulation model calculates the following key quantities:• Cumulative probability curve of the available conventional generation capacity• Cumulative probability curve of daily peak-loads (peak-load duration curve)• Cumulative probability curve of the residual load (load minus wind generation)• Cumulative probability curve of the “Reserve” (generation – load)The average loss of load probability at daily peak load can be obtained from the cumulative probability of theReserve: Every case, in which the Reserve is less than zero indicates that the available generation capacity is notable to cover the load at this point. An evaluation of the cumulative probability curve of the Reserve allowsquantifying the probability of such a loss of load situation.
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 1 82.2 Scenario DefinitionFor analyzing the contribution of wind generation in South Africa to the firm capacity of the system, three mainscenarios for different study years and different assumptions with regard to the prospected installed windgeneration capacity have been defined:• Scenario 1: Year 2015 with 2000MW of wind generation• Scenario 2: Year 2020 with 4800MW of wind generation (low wind scenario)• Scenario 3: Year 2020 with 10000MW of wind generation (high wind scenario)2.2.1 Scenario 1 – Year 2015 with 2000MW of Installed Wind CapacityScenario 1 applies to the year 2015. The assumed installed wind generation capacity is equal to 2000MW.The main parameters of Scenario 1 are the following:• Total installed conventional (thermal, hydro, pump storage and nuclear) capacity: 52 537MW• Installed wind generation capacity – total: 2 000MW• Installed wind generation capacity in Western Region: 1 550MW• Installed wind generation capacity in Southern Region: 450MW• Peak load (Peak Net Demand): 40 582MW• Wind penetration level based on installed conventional capacity: 3,8%• Wind penetration level based on peak load 4,9%2.2.2 Scenario 2 – Year 2020-Low Wind Scenario with 4800MW of Installed WindCapacityScenario 2 applies to the year 2020 and makes rather pessimistic assumptions for the amount of wind generationthat will be installed in South Africa until then. The main parameters of Scenario 2 are:• Total installed conventional (thermal, hydro, pump storage and nuclear) capacity: 59 753MW• Installed wind generation capacity – total: 4 800MW• Installed wind generation capacity in Western Region: 3 200MW• Installed wind generation capacity in Southern Region: 1 600MW• Peak load (Peak Net Demand): 48 316MW• Wind penetration level based on installed conventional capacity: 8,0%• Wind penetration level based on peak load 9,9%
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 1 92.2.3 Scenario 3– Year 2020-High Wind Scenario with 10 000MW of Installed WindCapacityScenario 3 is identical to Scenario 2 with regard to the conventional generation and the load data but makingmore optimistic assumptions with regard to the wind generation capacity that will be installed until 2020. The keyparameters of scenario 3 are:• Total installed conventional (thermal, hydro, pump storage and nuclear) capacity: 59 753MW• Installed wind generation capacity – total: 10 000MW• Installed wind generation capacity in Western Region: 7 800MW• Installed wind generation capacity in Southern Region: 2 200MW• Peak load (Peak Net Demand): 48 316MW• Wind penetration level based on installed conventional capacity: 16,7%• Wind penetration level based on peak load 20,7%The wind penetration level of the three scenarios varies between around 5% and 20% (based on peak load),which can be considered to be moderate, even in Scenario 3.
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 2 02.3 Results of Simulation StudiesFor analyzing the capacity credit of planned wind farms in South Africa, Monte-Carlo simulations using the powersystem analysis software DIgSILENT PowerFactory have been carried out applying the methods described insection 2.1.The increase of the available Reserve at a confidence level of 99,9% has been defined to be the Equivalent FirmCapacity of wind generation in South Africa (see section 2.1). The results of the capacity credit assessment forthe three scenarios are presented in the following sections.2.3.1 Scenario 1 – Year 2015 with 2000MW of Installed Wind CapacityFigure 9: Available Reserve with and without wind generation for Scenario 1 (range > 99%)Figure 9 shows the cumulative probability curve of the available Reserve in the South African System with windgeneration (red) and without (blue) wind generation for the winter (high load) season.At the given confidence level of 99,9%, the difference between the two curves is equal to 536MW, whichcorresponds to a capacity credit of 26,8%.Additional results of capacity credit simulations for the winter 2015 case are depicted in Annex 1.1.The key results for Scenario 1 (2015) are the following:100,0099,8099,6099,4099,2099,00 [%]8000,006000,004000,002000,000,00-2000,00Summary Grid: Total Reserve Generation (Unconstrained) in MWSummary Grid: Reserve Dispatchable Generation (Unconstrained) in MWX = 99,900 %2251.571 MW2786.828 MW0.000 MWY=0,000MW99.998 %DIgSILENT
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 2 1• Available Reserve at 99,9% confidence without wind: R=2 252 MW• Available Reserve at 99,9% confidence with wind: R=2 787 MW• Equivalent Firm Capacity: EFC= 535 MW• Capacity Credit: CC= 26,8%• Average Wind Generation (year): Pav= 543 MW(Av. Capacity Factor: 27,17%)• Average Wind Generation during full load hours: Pav_full= 605 MW (30,25% of Pwinst)Because the wind penetration level is still relatively low in Scenario 1, the capacity credit of wind generation isvery close to the average capacity factor of wind generation in Scenario 1.Because of the low wind penetration level (around 5% based on peak load), the Capacity Reduction Factor CR isvery close to 0,9 (see also Figure 15). On the other hand, the average wind generation during full load hours isconsiderably higher than the yearly average wind generation. These two aspects – Capacity Reduction Factor andpositive correlation between wind generation and load – almost compensate each other leading to a capacitycredit, which is almost equal to the capacity factor.
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 2 22.3.2 Scenario 2 – Year 2020-Low Wind Scenario with 4800MW of Installed WindCapacityFigure 10: Available Reserve with and without wind generation for Scenario 2 (range > 99%)The cumulative probability curve of the available Reserve with and without wind generation of Scenario 2 isdepicted in Figure 10. At the given confidence level of 99,9% the difference between the Reserve with windgeneration and without wind generation is here equal to 1218 MW, which is equal to 25,4% of the installed windgeneration capacity of this scenario.Additional results of capacity credit simulations for the winter 2020/low wind generation case are depicted inAnnex 1.2.The key results for Scenario 2 (2020-low wind generation) are the following:• Available Reserve at 99,9% confidence without wind: R=1 459 MW• Available Reserve at 99,9% confidence with wind: R=2 678 MW• Equivalent Firm Capacity: EFC=1 218 MW• Capacity Credit: CC= 25,4%100,0099,8099,6099,4099,2099,00 [%]1.20E+49.00E+36.00E+33.00E+30.00E+0-3.00E+3Summary Grid: Total Reserve Generation (Unconstrained) in MWSummary Grid: Reserve Dispatchable Generation (Unconstrained) in MWX = 99,900 %1459.123 MW2677.937 MW0.000 MWY = 0,000 MW99.991 %99.999 %DIgSILENT
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 2 3• Average Wind Generation (year): Pav=1 468 MW(Av. Capacity Factor: 30,6%)• Average Wind Generation during full load hours: Pav_full=1 652 MW (34,4% of Pwinst)Comparing the capacity credit figures of Scenario 2 with Scenario 1 it can be observed that capacity credit forScenario 2 is only slightly below the Scenario 1 capacity credit, even if the wind penetration level increasesconsiderably (CP around 10% based on peak load), which would normally lead to a reduction of the capacitycredit (see Figure 15).The reason for this effect is that the average wind speeds of the wind farm sites considered for Scenario 2 arehigher than in Scenario 1 (Av. Capacity Factor of 30,6% compared to 27,2%/Scenario 1). This means that thesites with the best wind conditions will not necessarily be developed first, but other aspects, such as thesurrounding infrastructure (e.g. the next grid access point) might play an important role in the wind farmdevelopment process too. Additionally, wind farm developers will first develop smaller projects requiring lowerinvestments and less grid expansion so that some of the very large wind farm projects proposed in high windareas in South Africa will be expected to be developed at later stages.2.3.3 Scenario 3– Year 2020-High Wind Scenario with 10 000MW of Installed WindCapacityFigure 11: Available Reserve with and without wind generation for Scenario 3 (range > 99%)100,0099,8099,6099,4099,2099,00 [%]5976,04550,63125,21699,9274,54-1150,8Summary Grid: Total Reserve Generation (Unconstrained) in MWSummary Grid: Reserve Dispatchable Generation (Unconstrained) in MWX = 99,900 %1465.463 MW3768.771 MW-0.000 MWY=-0,000MW99.993 %DIgSILENT
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0Figure 11 shows the cumulative probability curve of the Available Reserve with and without wind generation forScenario 3. The Equivalent Firm Capacity of this scenario is equal to 2 256MW, which is eqinstalled wind generation capacity.Additional results of capacity credit simulations for the winter 2020/high wind generation case are depicted inAnnex 1.3.The key results for Scenario 3 (2020• Available Reserve at 99,9% confidence without wind:• Available Reserve at 99,9% confidence with wind:• Equivalent Firm Capacity:• Capacity Credit:• Average Wind Generation (year• Average Wind Generation during full load hours:The average capacity factor of wind farms in this scenario further increase compared to Scenario 2, while tcapacity credit decreases considerably. This is because of the considerably increased wind penetration level(CP=20,7% based on peak load)Capacity Reduction Factor CR is down to 0compared to Scenario 1 and Scenario 22.3.4 Outlook for up to 25 000MW ofFigure 12: EFC in function of the installed wind capacity0 3 4 . 0 0shows the cumulative probability curve of the Available Reserve with and without wind generation forThe Equivalent Firm Capacity of this scenario is equal to 2 256MW, which is eqinstalled wind generation capacity.results of capacity credit simulations for the winter 2020/high wind generation case are depicted inThe key results for Scenario 3 (2020-high wind generation) can be summarized as followsAvailable Reserve at 99,9% confidence without wind: R=1 456 MWAvailable Reserve at 99,9% confidence with wind: R=3 716 MWEquivalent Firm Capacity: EFC=2 256 MWCC= 22,6%Average Wind Generation (year): Pav=3 200 MW(Av. Capacity Factor: 32%)Average Wind Generation during full load hours: Pav_full=3 577 MW (35,8% of Pwinst)The average capacity factor of wind farms in this scenario further increase compared to Scenario 2, while tcapacity credit decreases considerably. This is because of the considerably increased wind penetration level(CP=20,7% based on peak load), which leads to a degradation of capacity credit of wind generationCapacity Reduction Factor CR is down to 0,63 in this scenario, which explains the degradation of Capacity Creditcompared to Scenario 1 and Scenario 2 (see also Figure 15).Outlook for up to 25 000MW of Installed Wind Capacity: EFC in function of the installed wind capacity2 4shows the cumulative probability curve of the Available Reserve with and without wind generation forThe Equivalent Firm Capacity of this scenario is equal to 2 256MW, which is equal to 22,6% of theresults of capacity credit simulations for the winter 2020/high wind generation case are depicted inas follows:(Av. Capacity Factor: 32%)Pav_full=3 577 MW (35,8% of Pwinst)The average capacity factor of wind farms in this scenario further increase compared to Scenario 2, while thecapacity credit decreases considerably. This is because of the considerably increased wind penetration level, which leads to a degradation of capacity credit of wind generation. The,63 in this scenario, which explains the degradation of Capacity Credit
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0Figure 13: Capacity Credit in function of the installed wind capacityFor estimating the dependence of capacity credit onhave been carried out looking at increased wind generation capacitieswind generation constant.In practical terms this means thatfarm is scaled up while keeping all other parameters unchanged.The results of the sensitivity studies are depicted inincreasing wind penetration levels, the capacity credit of wind generation is reducing.But even at a level of 25 000MW of installed wind generation capacitylevel of around CP=50% (based on the 2020 peak load)equivalent firm capacity of the South African system is still considerableBecause the Capacity Reduction Factor CR mainly depends on the wind penetration level, it is possible that theactual Capacity Reduction Factor CR will actually be higher when the installed wind generation capacity will reacha level as high as 25 000MW because tcredit for a totally installed wind generation capacity of 25 000MW can be considered to be a conservativeestimate.0 3 4 . 0 0: Capacity Credit in function of the installed wind capacityFor estimating the dependence of capacity credit on further increasing wind penetration levelat increased wind generation capacities while keeping the specific distribution ofpractical terms this means that for the purpose of these sensitivity studies the rating of each individual windfarm is scaled up while keeping all other parameters unchanged.studies are depicted in Figure 12 and Figure 13. The results show that withincreasing wind penetration levels, the capacity credit of wind generation is reducing.000MW of installed wind generation capacity, which corresponds to a wind penetrationlevel of around CP=50% (based on the 2020 peak load) , the contribution of additional wind generation to theequivalent firm capacity of the South African system is still considerable and amounts to around 17,6%.the Capacity Reduction Factor CR mainly depends on the wind penetration level, it is possible that theactual Capacity Reduction Factor CR will actually be higher when the installed wind generation capacity will reacha level as high as 25 000MW because the load will grow beyond the 2020 level. Hence, the estimated capacitycredit for a totally installed wind generation capacity of 25 000MW can be considered to be a conservative2 5wind penetration levels, additional studieswhile keeping the specific distribution ofthe rating of each individual wind. The results show that withh corresponds to a wind penetration, the contribution of additional wind generation to theand amounts to around 17,6%.the Capacity Reduction Factor CR mainly depends on the wind penetration level, it is possible that theactual Capacity Reduction Factor CR will actually be higher when the installed wind generation capacity will reachHence, the estimated capacitycredit for a totally installed wind generation capacity of 25 000MW can be considered to be a conservative
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 02.3.5 Wind Farms in theFigure 14: Capacity Credit of Wind Farms in South Africa (Western and Southern Region)For assigning a capacity credit to the wind farms of the individual regions, it can be assumed that capacity creditis approximately proportional to the average winday). The factor of proportionality isload probability and the wind penetration levelThe results of capacity credit calculations for the different regionsdescribed in section 2.1.3, are depicted incapacity credit of wind farms in the Southern Region is considerably higher than the average capacity credit ofwind farms in the Western Region, whereas in 2020 (Scenario 2 and 3), the average capacity credit of wind farmsin both regions is almost equal.This means that the wind farm sites in the Southern Region that have been selected for Scenario 1 have betterwind conditions than the wind farm sites in the Western Region. In the longer term (Scenario 2020) the windconditions of wind farms in both regions are very simila0 3 4 . 0 0Wind Farms in the Western and Southern RegionCapacity Credit of Wind Farms in South Africa (Western and Southern Region)For assigning a capacity credit to the wind farms of the individual regions, it can be assumed that capacity creditis approximately proportional to the average wind generation during the peak load period (season and time of theday). The factor of proportionality is predominantly a system-wide parameter and depends mainly on thewind penetration level (see also section 2.1.3).The results of capacity credit calculations for the different regions that have been calculated using the formulasare depicted in Figure 14. This figure shows that in Scenario 1/2015, the averagewind farms in the Southern Region is considerably higher than the average capacity credit ofwind farms in the Western Region, whereas in 2020 (Scenario 2 and 3), the average capacity credit of wind farmse wind farm sites in the Southern Region that have been selected for Scenario 1 have betterwind conditions than the wind farm sites in the Western Region. In the longer term (Scenario 2020) the windconditions of wind farms in both regions are very similar.2 6Capacity Credit of Wind Farms in South Africa (Western and Southern Region)For assigning a capacity credit to the wind farms of the individual regions, it can be assumed that capacity creditd generation during the peak load period (season and time of thewide parameter and depends mainly on the loss ofthat have been calculated using the formulasin Scenario 1/2015, the averagewind farms in the Southern Region is considerably higher than the average capacity credit ofwind farms in the Western Region, whereas in 2020 (Scenario 2 and 3), the average capacity credit of wind farmse wind farm sites in the Southern Region that have been selected for Scenario 1 have betterwind conditions than the wind farm sites in the Western Region. In the longer term (Scenario 2020) the wind
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0Figure 15: Capacity Reduction Factor in function of installed wind generationThe “Capacity Reduction Factor”load hours is depicted in Figure 15depends mainly on the following parameters:• System characteristics (generation/load), especi• Wind penetration level.and is (almost) independent from speccurves for 2015 and 2020 are lying almost on top of each other.2.3.6 Comparison with Thermal or Hydro Power PlantsThe results of the presented capacity credit studies are expressedmeans that the contribution of wind generation to the reliability of the South African power system is comparedwith a perfect power plant with an availability of 100%However, actual coal fired power stationsplanned and unplanned outages). This means that when comparing wind generation in South Africa with actualcoal fired power stations, the Equivalent Capacity (EC) is between 11% and 25%Equivalent Firm Capacity. Considering this aspect the following capacity credit figures can be assigned togeneration in South Africa:• Scenario 1: EFC= 536MW• Scenario 2: EFC=1218MW• Scenario 3: EFC=2256MWThese figures mean that the installed capacity of a wind farm must be around 3 to 4 times higher than theinstalled capacity of a coal fired power pla0 3 4 . 0 0: Capacity Reduction Factor in function of installed wind generationCR, which is the ratio of capacity credit and average wind generation during full15 for the winter seasons 2015 and 2020. The “Capacity Reduction Factor” CRdepends mainly on the following parameters:System characteristics (generation/load), especially the LOLP without wind generation.is (almost) independent from specific wind site characteristics. This is confirmed bycurves for 2015 and 2020 are lying almost on top of each other.Comparison with Thermal or Hydro Power PlantsThe results of the presented capacity credit studies are expressed in terms of “Equivalent Firm Capacity”. Thismeans that the contribution of wind generation to the reliability of the South African power system is comparedwith a perfect power plant with an availability of 100%.However, actual coal fired power stations in South Africa have an availability between 80% and 90% (consideringplanned and unplanned outages). This means that when comparing wind generation in South Africa with actualcoal fired power stations, the Equivalent Capacity (EC) is between 11% and 25% higher than the reportedEquivalent Firm Capacity. Considering this aspect the following capacity credit figures can be assigned toMW 596MW < EC < 670MW 29,8% < CC <MW 1253MW< EC < 1523MW 26,1% < CC < 3MW 2507MW < EC < 2820MW 25,1% < CC <These figures mean that the installed capacity of a wind farm must be around 3 to 4 times higher than theinstalled capacity of a coal fired power plant in order to have the same effect on generation adequacy.2 7: Capacity Reduction Factor in function of installed wind generation, which is the ratio of capacity credit and average wind generation during fullfor the winter seasons 2015 and 2020. The “Capacity Reduction Factor” CRally the LOLP without wind generation.This is confirmed by Figure 15, in which thein terms of “Equivalent Firm Capacity”. Thismeans that the contribution of wind generation to the reliability of the South African power system is comparedin South Africa have an availability between 80% and 90% (consideringplanned and unplanned outages). This means that when comparing wind generation in South Africa with actualhigher than the reportedEquivalent Firm Capacity. Considering this aspect the following capacity credit figures can be assigned to wind% < CC < 33,5%% < CC < 31,7%% < CC < 28,2%These figures mean that the installed capacity of a wind farm must be around 3 to 4 times higher than thegeneration adequacy.
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 2 83 Part 2: Impact of Wind Generation in South Africaon System Operation3.1 Approach and MethodologyThis second part of the presented studies assesses the operational impact of wind generation in South Africa.The approach of these studies is based on a time series analysis of load and wind generation for the threescenarios introduced in section 2.2, which are:• Scenario 1: Year 2015, 2000MW of installed wind generation capacity• Scenario 2: Year 2020, 4800MW of installed wind generation capacity• Scenario 3: Year 2020, 10 000MW of installed wind generation capacitySince the studies presented in this part 2 are only based on an analysis of the residual load, without looking indetail at dynamic performance characteristics of existing South African power plants, the results presented herecan only give an indication about potential issues and cannot make any definite statement with regard to theimpact of wind generation on operational reserve and dynamic performance requirements of thermal and hydropower plants in South Africa.Further studies will be required for simulating the operation of the South African power system under the newconditions.This report will present time series data of the following worst case situations:• Worst-case Wind ramp-up and ramp-down conditions.• Peak load situations.• Minimum residual load situations.These time series simulations are not based on actual measurements but on projected hourly load data for 2015and 2020 (provided by ESKOM) and wind speed time series of all considered wind farm sites (provided byWindlab Systems, see section 2.1.4.3.1 and [4]).Based on these time series data, duration curves (cumulative probability curves) are calculated for windgeneration and load as well as for hourly variations of wind generation and load.
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 2 93.2 Results of Time Series Simulation StudiesWorst case situations for each of the three scenarios are depicted in Annex 3. The figures in Annex 3 show thefollowing situations:• Typical Winter Situation (long term)• Typical Summer Situation (long term)• Week with maximum observed wind generation• Week with minimum observed wind generation• Week with maximum observed ramp-up situation• Week with maximum observed ramp-down situation• Week during which peak load occurs• Week during which minimum residual load occurs• Load/residual load and wind generation duration curves• Duration curves of hourly variations of load, residual load and wind generation3.2.1 Impact on Main System Performance CharacteristicsTable 1: Worst-case observations of time domain simulationsSc1 Sc2 Sc3Peak Net Demand [MW] 40582 48 316 48 316Installed Wind Capacity [MW] 2000 4800 10000Max. Wind Generation [MW] 1741 4227 8471Max. hourly load variation (ramp-up) [MW/h] 3897 4539 4539Max. hourly load variation (ramp-down) [MW/h] -3845 -4536 -4536Max. hourly variation of wind generation (ramp-up) [MW/h] 502,4 1369 2547Max. hourly variation of wind generation (ramp-down [MW/h] -356 -1102 -1670Max. hourly variation of residual load (ramp-up) [MW/h] 3862 4542 4788Max. hourly variation of residual load (ramp-down) [MW/h] -3847 -4527 -4797
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 3 0Figure 16: Cumulative probability of hourly variations of load, wind generation and residual load(Scenario 3)Worst-case observations of time series studies carried out for the three scenarios are summarized in Table 1. Thecumulative probability curves of hourly variations of load, wind generation and residual load of Scenario 3 aredepicted in Figure 16.Based on the cumulative probability curves, which are depicted in Figure 16, the following conclusions withregard to the impact of up to 10 000MW of installed wind capacity until 2020 can be taken:• Hourly variations of wind generation (ramp-up and ramp-down) are substantially smaller than hourlyload variations in all three investigated scenarios.• Consequently, hourly variations of the residual load are almost not affected by wind generationconsidered for scenario 1, 2 and 3 (see also Figure 16).Consequently, required ramp-up and ramp-down speeds of thermal and hydro power plants in South Africa don’thave to be increased because of the added wind generation.The main impact on the operation of the South African power system will result from the predictability of windgeneration, which will depend on the quality of wind prediction tools that will be installed for supporting theoperation of the system. Based on the studies presented in this report, it is not possible to assess the impact ofwind generation in South Africa on regulation reserve. For this purpose, more detailed models considering theactual operation of the South African power system and simulated wind prediction will be required.25,0020,0015,0010,005,000,009000,006000,003000,000,00-3000,00-6000,00x-Axis: TimeSeries: Proability (Duration Curves) in %TimeSeries: Hourly Load Variation (up) in MW/hTimeSeries: Hourly Load Variation (down) in MW/hTimeSeries: Hourly Wind Variation (up) in MW/hTimeSeries: Hourly Wind Variation (down) in MW/hTimeSeries: Hourly Residual Load Variation (up) in MW/hTimeSeries: Hourly Residual Load Variation (down) in MW/hDIgSILENT
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 3 13.2.2 Local versus Global Wind GenerationFigure 17: Total wind generation of Scenario 3 and wind generation of single wind farms in theWestern Region (blue) and Southern Region (green).Figure 17 shows the total wind generation for Scenario 3 (2020/high wind generation) in comparison to thegeneration of one arbitrary selected in farm in the Western Region (blue curve) and one arbitrary selected windfarm in the Southern Region (green curve).The corresponding analysis leads to the following conclusions:• It is unlikely that the total wind generation in South Africa exceeds a value of 90% of the installedcapacity whereas the output of individual wind farms can reach 100% of their maximum possible output.• Variations of the total wind generation are substantially smoother that variations of the output ofindividual wind farms.• There is only a weak correlation between wind farms in the Western and the Southern Region.The weak correlation between wind speeds in the Southern region and the Western region will lead to smootheroverall wind variations and therefore to an improved overall predictability2900,002800,002700,002600,002500,002400,00 [-]1,200,900,600,300,00-0,30TimeSeries: Total Wind Generation in p.u. (base: 10000,00 MW)TimeSeries_Wind: Total Wind Generation in p.u. (base: 250,00 MW)TimeSeries_Wind_South: Total Wind Generation in p.u. (base: 200,00 MW)DIgSILENT
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 3 23.2.3 Energy Production and CO2 EmissionsWith regard to wind energy production, the following results for the average power (Pav), the capacity factor(CF) and the yearly energy yield (E) can be estimated:• Scenario 1: Pav= 543MW CF = 27,2% E=4,76 TWh/year• Scenario 2: Pav=1468MW CF = 30,6% E=12,86 TWh/year• Scenario 3: Pav=3200MW CF = 32,0% E=28,03 TWh/yearAssuming that wind generation will mainly replace energy produced by coal fired power plants, the specificavoided CO2 emissions can be estimated to be in a range of around 1000 kg/MWh. For a more accurateassessment, one would have to look at the actual generator dispatch in South Africa in the presence of windgeneration and take the reduced efficiency of coal fired power stations into account when letting them operatebelow rated output.Based on 1000 kg/MWh CO2 and not considering any side effects on the efficiency of thermal power stations, theavoided CO2 emissions can be estimated as follows:• Scenario 1: 4,76 Mio t CO2• Scenario 2: 12,86 Mio t CO2• Scenario 3: 28,03 Mio t CO2Because of the large number of assumptions, which led to these results, their accuracy is very limited and theresults should only be used for estimating possible order of magnitudes.
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 3 34 Conclusions and RecommendationsRenewable energies, such as wind energy can represent an economic and ecologic alternative for the generationof electrical energy. But besides contributing to the electrical energy supply, renewable energies such as windenergy can also have a valuable contribution to the equivalent firm capacity of a system. This means in otherwords, that with the addition of wind farms, the reliability of supply of a system is improved and that it is indeedpossible to replace some conventional power plants by wind farms completely. The percentage of the installedwind capacity that can be considered to be “firm capacity” is named Capacity Credit and is subject to the analysisof the presented studies.The studies presented in this report are based on three scenarios:• Scenario 1: Year 2015, 2000MW installed wind generation capacity• Scenario 2: Year 2020, 4800MW installed wind generation capacity• Scenario 3: Year 2020, 10000MW installed wind generation capacityFor each of the three scenarios, the average loss of load probability (LOLP) at the daily peak load has beencalculated and has been used as the relevant reliability index for assessing the capacity credit of wind generationin South Africa.Capacity credit has been defined in terms of the Equivalent Firm Capacity (EFC), which is a generation capacitywith 100% availability that one would have to add to the system in order to achieve the same improvement interms of the available reserve at a given confidence level.The resulting capacity credit figures for the different scenarios are the following:• Scenario 1: CC=26,8%• Scenario 2: CC=25,4%• Scenario 3: CC=22,6%With increasing wind penetration levels, the capacity credit of wind generation will drop. Based on Scenario 3(year 2020, 10 000MW of installed wind capacity), an assessment has been carried out by scaling the 67modelled wind farms up to a totally installed wind capacity of 25 000MW. The resulting capacity credit is equal to17,6%.Comparing these figures with the availability indices of newly planned coal fired power stations in South Africahaving a combined planned/unplanned outage rate of around 11%, the contribution of an average wind farm inSouth Africa to the equivalent firm capacity of the system will be in the following range:• Scenario 1: CC=29,8%• Scenario 2: CC=26,1%• Scenario 3: CC=25,1%When comparing the capacity credit of wind generation to older coal fired power stations with a combinedplanned/unplanned outage rate of around 20%, the capacity credit of wind generation will be around33,5%/31,7%/28,2% (Sc1/Sc2/Sc3) of the capacity credit of such an older existing South African coal firedpower station.
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 3 4The second part of the presented studies analyses the impact of wind generation in South Africa on the residualload, which is the remaining load that must be supplied by thermal and hydro power plants.The analysis was mainly looking at:• Worst case situations with regard to wind and load variations.• Impact of wind generation on dynamic performance requirements.The results of the corresponding studies lead to the conclusion that:• Hourly ramp-up and ramp-down rates of the residual load are comparable to the corresponding ramp-rates of the system load (without wind generation).• There are no increased dynamic performance requirements for the existing thermal power plants inSouth Africa.The main impact on system operation will result from the limited predictability of wind speeds and not fromabsolute wind speed variations. The limited predictability of wind generation will result in an increased forecasterror of the residual load compared to the present load forecast error. Several factors will have an influence onthe accuracy of wind prediction; some of them are related to the spatial distribution of wind generation, some ofthem to the actual wind prediction system that will be put in place for supporting the operation of the SouthAfrican power system. Therefore, it is required to carry out additional studies that simulate the behaviour of awind prediction system in order to obtain indicative values for the required increase of the load following reserve.As overall conclusion, it can be stated that the capacity credit of wind generation in South Africa will be between25% and 30% for installed wind generation of up to 10 000MW. In the case of higher wind penetration (25000MW), capacity credit of wind generation in South Africa will drop below 20%.Based on part 2 of the presented studies it can further be concluded that it is very likely that it will be possible tooperate the system safely, without increased dynamic performance requirements for the conventional powerplants of South Africa. However, the use of state of the art wind prediction tools for assessing the required load(and wind) following reserves will be important. This second aspects requires further, more detailed studies thatmodel the dynamic performance characteristics of the South African power plants in more detail for ensuring asafe operation of the South African power system under all credible operating conditions.
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 3 55 References[1] GTZ: Kleinteilige Energie- und/oder Klimarelevante Maßnahmen – Teilwerk: Capacity Credit Study – Pr.-Nr.:95.3550.1-034.00 – Terms of Reference, 01.06.2010[2] ESKOM: Wind Project Sites for Study (Wind Project Sites for Study - 2010 May - Locations - MTS Sub - MWsizes.xls), 2010[3] ESKOM: File Wind_DigSilent_GxData_11062010.xlsx, 2010[4] Windlab Systems: Capacity Credit Study in South Africa – Consulting Report, 14.07.2010[5] Windlab Systems: Second version of wind speed data based on site adjustments, 15.11.2010[6] ESKOM: Integrated Resource Plan for Electricity – Draft, Version 8, 08.10.2010 (IRP 2010)[7] Windlab Systems: WindScapeTM Methodology – Capacity Credit Study in South Africa, 03.11.2010
    • P 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 3 6ANNEXESAnnexes
    • A n n e x 1 : R e s u l t s o f S i m u l a t i o n S t u d i e sP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 3 7Annex 1: Results of Simulation StudiesAnnex 1.1: Scenario 1 – Year 2015 – 2000MW Installed WindGeneration
    • A n n e x 1 : R e s u l t s o f S i m u l a t i o n S t u d i e sP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 3 8100,0080,00060,00040,00020,0000,0000 [%]60000,0050000,0040000,0030000,0020000,0010000,00Summary Grid: Total Available Capacity in MWSummary Grid: Available Dispatchable Capacity in MW100,0080,00060,00040,00020,0000,0000 [%]2.00E+41.50E+41.00E+45.00E+30.00E+0-5.00E+3Summary Grid: Total Reserve Generation (Unconstrained) in MWSummary Grid: Reserve Dispatchable Generation (Unconstrained) in MW100,0080,0060,0040,0020,000,00 [%]60000,0050000,0040000,0030000,0020000,0010000,00Summary Grid: Total Demand in MWSummary Grid: Residual Demand (Unconstrained) in MWDIGSILENTCapacity Credit Studies for South Africa DistributionScenario 1 - 2015 - 2000MW installed Wind Capacity Winter seasonDate: 12/25/2010Annex: 1.1.1 /1DIgSILENT
    • A n n e x 1 : R e s u l t s o f S i m u l a t i o n S t u d i e sP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 3 9100,0099,8099,6099,4099,2099,00 [%]8000,006000,004000,002000,000,00-2000,00Summary Grid: Total Reserve Generation (Unconstrained) in MWSummary Grid: Reserve Dispatchable Generation (Unconstrained) in MWX = 99,900 %2251.571 MW2786.828 MW0.000 MWY=0,000MW99.998 %DIGSILENTCapacity Credit Studies for South Africa Generation vs DemandScenario 1 - 2015 - 2000MW installed Wind Capacity Winter seasonDate: 12/25/2010Annex: 1.1.1 /2DIgSILENT
    • A n n e x 1 : R e s u l t s o f S i m u l a t i o n S t u d i e sP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 4 0Annex 1.2: Scenario 2 – Year 2020 – 4800MW Installed WindGeneration
    • A n n e x 1 : R e s u l t s o f S i m u l a t i o n S t u d i e sP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 4 1100,0080,00060,00040,00020,0000,0000 [%]80000,0070000,0060000,0050000,0040000,0030000,00Summary Grid: Total Available Capacity in MWSummary Grid: Available Dispatchable Capacity in MW100,0080,0060,0040,0020,000,00 [%]2.00E+41.50E+41.00E+45.00E+30.00E+0-5.00E+3Summary Grid: Total Reserve Generation (Unconstrained) in MWSummary Grid: Reserve Dispatchable Generation (Unconstrained) in MW100,0080,00060,00040,00020,0000,0000 [%]80000,0070000,0060000,0050000,0040000,0030000,00Summary Grid: Daily Peak Load in MWSummary Grid: Daily Peak of Residual Load in MWDIGSILENTCapacity Credit Studies for South Africa DistributionScenario 2 - 2020/Low - 48000MW installed Wind Capacity Winter seasonDate: 12/25/2010Annex: 1.2.1 /1DIgSILENT
    • A n n e x 1 : R e s u l t s o f S i m u l a t i o n S t u d i e sP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 4 2100,0099,8099,6099,4099,2099,00 [%]1.20E+49.00E+36.00E+33.00E+30.00E+0-3.00E+3Summary Grid: Total Reserve Generation (Unconstrained) in MWSummary Grid: Reserve Dispatchable Generation (Unconstrained) in MWX = 99,900 %1459.123 MW2677.937 MW0.000 MWY = 0,000 MW99.991 %99.999 %DIGSILENTCapacity Credit Studies for South Africa Generation vs. DemandScenario 2 - 2020/Low - 48000MW installed Wind Capacity Winter seasonDate: 12/25/2010Annex: 1.2.1 /2DIgSILENT
    • A n n e x 1 : R e s u l t s o f S i m u l a t i o n S t u d i e sP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 4 3Annex 1.3: Scenario 3 – Year 2020 – 10000MW Installed WindGeneration
    • A n n e x 1 : R e s u l t s o f S i m u l a t i o n S t u d i e sP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 4 4100,0080,00060,00040,00020,0000,0000 [%]9.00E+47.00E+45.00E+43.00E+41.00E+4-1.00E+4Summary Grid: Total Available Capacity in MWSummary Grid: Available Dispatchable Capacity in MW100,0080,00060,00040,00020,0000,0000 [%]9.00E+47.00E+45.00E+43.00E+41.00E+4-1.00E+4Summary Grid: Total Reserve Generation (Unconstrained) in MWSummary Grid: Reserve Dispatchable Generation (Unconstrained) in MW100,0080,0060,0040,0020,000,00 [%]53000,0048000,0043000,0038000,0033000,0028000,00Summary Grid: Total Demand in MWSummary Grid: Residual Demand (Unconstrained) in MW0.291 %48316.363 MWDIGSILENTCapacity Credit Studies for South Africa DistributionScenario 3 - 2020/High - 10000MW installed Wind Capacity Winter seasonDate: 12/26/2010Annex: 1.3.1 /1DIgSILENT
    • A n n e x 1 : R e s u l t s o f S i m u l a t i o n S t u d i e sP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 4 5100,0099,8099,6099,4099,2099,00 [%]1.20E+49.00E+36.00E+33.00E+30.00E+0-3.00E+3Summary Grid: Total Reserve Generation (Unconstrained) in MWSummary Grid: Reserve Dispatchable Generation (Unconstrained) in MWX = 99,900 %1455.504 MW3715.627 MW-0.000 MWY=-0,000MW99.993 %100.000 %DIGSILENTCapacity Credit Studies for South Africa Generation vs DemandScenario 3 - 2020/High - 10000MW installed Wind Capacity Winter seasonDate: 12/26/2010Annex: 1.3.1 /2DIgSILENT
    • A n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t h A f r i c aP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 4 6Annex 2: Seasonal and Hourly Variations of theWind Generation in South AfricaThere are the following dependencies of capacity credit on wind characteristics:• Capacity credit increases with increasing average wind speeds.• A high correlation between wind generation and load increases capacity credits.With regard to correlation between wind generation and load, the two following aspects are of major importance:• Correlation between seasonal wind variations and seasonal load variations.• Correlation between hourly wind variations and hourly load variations.For better understanding the results of the previous sections, the above correlation aspects have been analyzedfor the South African case.Figure 18, Figure 21 and Figure 24 show the seasonal variations of wind generation in South Africa for the threeanalyzed scenarios. These figures highlight the positive seasonal correlation between wind speeds and load inSouth Africa.Figure 19, Figure 22 and Figure 25 show the average wind generation per hour for the winter season and Figure20, Figure 23 and Figure 26 for the summer season. Comparing these figures with the defined full load hours –between 18:00h and 21:00h in winter and 08:00h and 21:00h in summer- it can be concluded that the correlationbetween wind speeds and daily load variations is reasonably good:• During winter, which is the most relevant case because this corresponds to the peak-load season, theaverage wind generation during full load hours is approximately equal to the seasonal average.• During summer, there is a very good correlation between wind generation and the evening load peak.On the other hand, there is very low wind generation during morning hours, and hence only a lowcontribution of wind generation to the morning high load coverage.
    • A n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t h A f r i c aP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0Annex 2-1: Scenario 1CapacityFigure 18: Monthly Average of Wind Generation in South AfricaFigure 19: Hourly Average of Wind Generation in South Africa during winterA n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t h A f r i c a0 3 4 . 0 01: Scenario 1 – Year 2015/2000MW of Installed Wind: Monthly Average of Wind Generation in South Africa - Scenario 1: Hourly Average of Wind Generation in South Africa during winter -A n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t h A f r i c a4 7Year 2015/2000MW of Installed Wind- Scenario 1
    • A n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t h A f r i c aP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0Figure 20: Hourly Average of Wind Generation in South Africa during summerA n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t h A f r i c a0 3 4 . 0 0: Hourly Average of Wind Generation in South Africa during summerA n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t h A f r i c a4 8: Hourly Average of Wind Generation in South Africa during summer - Scenario 1
    • A n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t hP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0Annex 2-2: Scenario 2Wind CapacityFigure 21: Monthly Average of Wind Generation in South AfricaA n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t h0 3 4 . 0 02: Scenario 2 – Year 2020-Low/4800MW of Installed: Monthly Average of Wind Generation in South Africa - Scenario 2A n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t h A f r i c a4 9w/4800MW of Installed
    • A n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t h A f r i c aP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0Figure 22: Hourly Average of Wind Generation in South Africa during winterFigure 23: Hourly Average of Wind Generation in South Africa during summerA n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t h A f r i c a0 3 4 . 0 0: Hourly Average of Wind Generation in South Africa during winter -: Hourly Average of Wind Generation in South Africa during summerA n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t h A f r i c a5 0- Scenario 2: Hourly Average of Wind Generation in South Africa during summer - Scenario 2
    • A n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t h A f r i c aP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0Annex 2-3: Scenario 3Wind CapacityFigure 24: Monthly Average of WindA n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t h A f r i c a0 3 4 . 0 03: Scenario 3 – Year 2020-High/10000MW of InstalledMonthly Average of Wind Generation in South Africa - Scenario 3A n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t h A f r i c a5 1High/10000MW of Installed
    • A n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t h A f r i c aP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0Figure 25: Hourly Average of Wind Generation in South Africa during winterFigure 26: Hourly Average of Wind Generation in South Africa during summeA n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t h A f r i c a0 3 4 . 0 0Hourly Average of Wind Generation in South Africa during winter -: Hourly Average of Wind Generation in South Africa during summeA n n e x 2 : S e a s o n a l a n d H o u r l y V a r i a t i o n s o f t h e W i n d G e n e r a t i o n i n S o u t h A f r i c a5 2- Scenario 3: Hourly Average of Wind Generation in South Africa during summer - Scenario 3
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 5 3Annex 3: Results of Time Series AssessmentAnnex 3-1: Worst Case Situations and Duration Curves,Scenario 1 – 2015
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 5 45000,04700,04400,04100,03800,03500,02000,01600,01200,0800,00400,000,0000x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Wind Generation in MW5000,04700,04400,04100,03800,03500,05.00E+43.75E+42.50E+41.25E+40.00E+0-1.25E+4x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 LongerTerm_WinterTime Series Studies - Scenario 1 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.1 /1DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 5 58700,08360,08020,07680,07340,07000,02000,01600,01200,0800,00400,000,0000x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Wind Generation in MW8700,08360,08020,07680,07340,07000,05.00E+43.75E+42.50E+41.25E+40.00E+0-1.25E+4x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 LongerTerm_SummerTime Series Studies - Scenario 1 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.1 /2DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 5 64218,04184,44150,84117,24083,64050,02000,01600,01200,0800,00400,000,0000x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Wind Generation in MW4136.0004136.000 h1738.038 MW4218,04184,44150,84117,24083,64050,05.00E+43.75E+42.50E+41.25E+40.00E+0-1.25E+4x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 MaxWindTime Series Studies - Scenario 1 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.1 /3DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 5 77168,07134,47100,87067,27033,67000,02000,01600,01200,0800,00400,000,0000x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Wind Generation in MW7066.0007066.000 h10.286 MW7168,07134,47100,87067,27033,67000,05.00E+43.75E+42.50E+41.25E+40.00E+0-1.25E+4x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 MinWindTime Series Studies - Scenario 1 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.1 /4DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 5 88568,08534,48500,88467,28433,68400,02000,01600,01200,0800,00400,000,0000x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Wind Generation in MWY =502,404*MW/h*x-4276921,668 MW8568,08534,48500,88467,28433,68400,05.00E+43.75E+42.50E+41.25E+40.00E+0-1.25E+4x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 MaxWindUpTime Series Studies - Scenario 1 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.1 /5DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 5 9168,00134,40100,8067,20033,6000,0000 [-]2000,01600,01200,0800,00400,000,0000TimeSeries: Total Wind Generation in MWY=-355,999*MW/*x+1193,220MW168,00134,40100,8067,20033,6000,0000 [-]4.00E+43.00E+42.00E+41.00E+40.00E+0-1.00E+4TimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 MaxWindDownTime Series Studies - Scenario 1 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.1 /6DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 6 08618,08584,48550,88517,28483,68450,02000,01600,01200,0800,00400,000,0000x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Wind Generation in MW8618,08584,48550,88517,28483,68450,04.00E+43.00E+42.00E+41.00E+40.00E+0-1.00E+4x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MW8525.0008525.000 h19738.392 MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 MinResLoadTime Series Studies - Scenario 1 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.1 /7DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 6 14468,04434,44400,84367,24333,64300,02000,01600,01200,0800,00400,000,0000x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Wind Generation in MW4468,04434,44400,84367,24333,64300,05.00E+43.75E+42.50E+41.25E+40.00E+0-1.25E+4x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MW4388.0004388.000 h40581.754 MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 MaxLoadTime Series Studies - Scenario 1 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.1 /8DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 6 2100,0080,00060,00040,00020,0000,00002000,001500,001000,00500,000,00-500,00x-Axis: TimeSeries: Proability (Duration Curves) in %TimeSeries: Wind Generation Duration Curve in MW100,0080,00060,00040,00020,0000,000043000,0038000,0033000,0028000,0023000,0018000,00x-Axis: TimeSeries: Proability (Duration Curves) in %TimeSeries: Load Duration Curve in MWTimeSeries: Residual Load Duration Curve n MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 DurationTime Series Studies - Scenario 1 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.1 /9DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 6 325,0020,0015,0010,005,000,007500,005000,002500,000,00-2500,00-5000,00x-Axis: TimeSeries: Proability (Duration Curves) in %TimeSeries: Hourly Load Variation (up) in MW/hTimeSeries: Hourly Load Variation (down) in MW/hTimeSeries: Hourly Wind Variation (up) in MW/hTimeSeries: Hourly Wind Variation (down) in MW/hTimeSeries: Hourly Residual Load Variation (up) in MW/hTimeSeries: Hourly Residual Load Variation (down) in MW/hDIgSILENTCapacity Credit Studies for South Africa - Part 2 Duration_VariationsTime Series Studies - Scenario 1 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.1 /10DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 6 4Annex 3-2: Worst Case Situations and Duration Curves,Scenario 2 – 2020
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 6 55000,04700,04400,04100,03800,03500,0 [-]4800,03840,02880,01920,0960,000,0000TimeSeries: Total Wind Generation in MW5000,04700,04400,04100,03800,03500,0 [-]50000,40000,30000,20000,10000,0,0000TimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 LongerTerm_WinterTime Series Studies - Scenario 2 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.2 /1DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 6 68700,08360,08020,07680,07340,07000,0 [-]4800,03840,02880,01920,0960,000,0000TimeSeries: Total Wind Generation in MW8700,08360,08020,07680,07340,07000,0 [-]50000,40000,30000,20000,10000,0,0000TimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 LongerTerm_SummerTime Series Studies - Scenario 2 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.2 /2DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 6 74768,04734,44700,84667,24633,64600,0 [-]4800,03844,12888,21932,2976,3120,388TimeSeries: Total Wind Generation in MW4687.0004226.631 MW4768,04734,44700,84667,24633,64600,0 [-]5.04E+43.99E+42.94E+41.88E+48.33E+3-2.18E+3TimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 MaxWindTime Series Studies - Scenario 2 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.2 /3DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 6 88168,08134,48100,88067,28033,68000,0 [-]4800,03840,02880,01920,0960,000,0000TimeSeries: Total Wind Generation in MW8048.00026.425 MW8168,08134,48100,88067,28033,68000,0 [-]50000,40000,30000,20000,10000,0,0000TimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 MinWindTime Series Studies - Scenario 2 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.2 /4DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 6 9168,00134,40100,8067,20033,6000,0000 [-]4800,03840,02880,01920,0960,000,0000TimeSeries: Total Wind Generation in MWY=-1101,748*MW/*x+2787,225MW168,00134,40100,8067,20033,6000,0000 [-]50000,40000,30000,20000,10000,0,0000TimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 MaxWindDownTime Series Studies - Scenario 2 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.2 /5DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 7 0960,00928,00896,00864,00832,00800,00 [-]4800,03840,02880,01920,0960,000,0000TimeSeries: Total Wind Generation in MWY =1369,078*MW/ *x-1171796,607 MW960,00928,00896,00864,00832,00800,00 [-]50000,40000,30000,20000,10000,0,0000TimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 MaxWindUpTime Series Studies - Scenario 2 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.2 /6DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 7 18760,08726,48692,88659,28625,68592,0 [-]5000,004000,003000,002000,001000,000,00TimeSeries: Total Wind Generation in MW8760,08726,48692,88659,28625,68592,0 [-]50000,0040000,0030000,0020000,0010000,000,00TimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MW8717.00022534.721 MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 MinResLoadTime Series Studies - Scenario 2 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.2 /7DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 7 24468,04434,44400,84367,24333,64300,0 [-]5000,004000,003000,002000,001000,000,00TimeSeries: Total Wind Generation in MW4468,04434,44400,84367,24333,64300,0 [-]50000,40000,30000,20000,10000,0,0000TimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MW4388.00048316.363 MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 MaxLoadTime Series Studies - Scenario 2 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.2 /8DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 7 3100,0080,00060,00040,00020,0000,00005000,003750,002500,001250,000,00-1250,00x-Axis: TimeSeries: Proability (Duration Curves) in %TimeSeries: Wind Generation Duration Curve in MW100,0080,00060,00040,00020,0000,000070000,0060000,0050000,0040000,0030000,0020000,00x-Axis: TimeSeries: Proability (Duration Curves) in %TimeSeries: Load Duration Curve in MWTimeSeries: Residual Load Duration Curve n MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 DurationTime Series Studies - Scenario 2 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.2 /9DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 7 425,0020,0015,0010,005,000,009000,006000,003000,000,00-3000,00-6000,00x-Axis: TimeSeries: Proability (Duration Curves) in %TimeSeries: Hourly Load Variation (up) in MW/hTimeSeries: Hourly Load Variation (down) in MW/hTimeSeries: Hourly Wind Variation (up) in MW/hTimeSeries: Hourly Wind Variation (down) in MW/hTimeSeries: Hourly Residual Load Variation (up) in MW/hTimeSeries: Hourly Residual Load Variation (down) in MW/hDIgSILENTCapacity Credit Studies for South Africa - Part 2 Duration_VariationsTime Series Studies - Scenario 2 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.2 /10DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 7 5Annex 3-3: Worst Case Situations and Duration Curves,Scenario 3 – 2020
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 7 65000,04700,04400,04100,03800,03500,010000,008000,006000,004000,002000,000,00x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Wind Generation in MW5000,04700,04400,04100,03800,03500,050000,40000,30000,20000,10000,0,0000x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 LongerTerm_WinterTime Series Studies - Scenario 3 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.3 /1DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 7 78700,08360,08020,07680,07340,07000,010000,8000,06000,04000,02000,00,0000x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Wind Generation in MW8700,08360,08020,07680,07340,07000,050000,0040000,0030000,0020000,0010000,000,00x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 LongerTerm_SummerTime Series Studies - Scenario 3 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.3 /2DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 7 85168,05134,45100,85067,25033,65000,010000,008000,006000,004000,002000,000,00x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Wind Generation in MW5053.0005053.000 h8468.058 MW5168,05134,45100,85067,25033,65000,050000,40000,30000,20000,10000,0,0000x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 MaxWindTime Series Studies - Scenario 3 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.3 /3DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 7 98168,08134,48100,88067,28033,68000,010000,008000,006000,004000,002000,000,00x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Wind Generation in MW8048.0008048.000 h78.456 MW8168,08134,48100,88067,28033,68000,050000,0040000,0030000,0020000,0010000,000,00x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 MinWindTime Series Studies - Scenario 3 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.3 /4DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 8 0168,00134,40100,8067,20033,6000,000010000,008000,006000,004000,002000,000,00x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Wind Generation in MWY=-1670,481*MW/h*x+5818,205MW168,00134,40100,8067,20033,6000,000050000,40000,30000,20000,10000,0,0000x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 MaxWindDownTime Series Studies - Scenario 3 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.3 /5DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 8 1968,00934,40900,80867,20833,60800,0010000,008000,006000,004000,002000,000,00x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Wind Generation in MWY =2546,982*MW/h*x-2179586,034 MW968,00934,40900,80867,20833,60800,0050000,40000,30000,20000,10000,0,0000x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 MaxWindUpTime Series Studies - Scenario 3 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.3 /6DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 8 28760,08726,48692,88659,28625,68592,010000,8000,06000,04000,02000,00,0000x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Wind Generation in MW8760,08726,48692,88659,28625,68592,050000,40000,30000,20000,10000,0,0000x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MW8717.0008717.000 h21383.508 MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 MinResLoadTime Series Studies - Scenario 3 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.3 /7DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 8 34468,04434,44400,84367,24333,64300,010000,8000,06000,04000,02000,00,0000x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Wind Generation in MW4468,04434,44400,84367,24333,64300,050000,40000,30000,20000,10000,0,0000x-Axis: TimeSeries: Hour of year in hTimeSeries: Total Load in MWTimeSeries: Total Residual Load in MWTimeSeries: Total Wind Generation in MW4388.0004388.000 h48316.363 MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 MaxLoadTime Series Studies - Scenario 3 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.3 /8DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 8 4100,0080,00060,00040,00020,0000,00001.00E+47.50E+35.00E+32.50E+30.00E+0-2.50E+3x-Axis: TimeSeries: Proability (Duration Curves) in %TimeSeries: Wind Generation Duration Curve in MW100,0080,00060,00040,00020,0000,000060000,0050000,0040000,0030000,0020000,0010000,00x-Axis: TimeSeries: Proability (Duration Curves) in %TimeSeries: Load Duration Curve in MWTimeSeries: Residual Load Duration Curve n MWDIgSILENTCapacity Credit Studies for South Africa - Part 2 DurationTime Series Studies - Scenario 3 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.3 /9DIgSILENT
    • A n n e x 3 : R e s u l t s o f T i m e S e r i e s A s s e s s m e n tP 1 4 2 9 - P N : 9 5 . 3 5 5 0 . 1 - 0 3 4 . 0 0 8 525,0020,0015,0010,005,000,009000,006000,003000,000,00-3000,00-6000,00x-Axis: TimeSeries: Proability (Duration Curves) in %TimeSeries: Hourly Load Variation (up) in MW/hTimeSeries: Hourly Load Variation (down) in MW/hTimeSeries: Hourly Wind Variation (up) in MW/hTimeSeries: Hourly Wind Variation (down) in MW/hTimeSeries: Hourly Residual Load Variation (up) in MW/hTimeSeries: Hourly Residual Load Variation (down) in MW/hDIgSILENTCapacity Credit Studies for South Africa - Part 2 Duration_VariationsTime Series Studies - Scenario 3 Worst Case SituationsDate: 12/26/2010Annex: Annex 3.3 /10DIgSILENT