wind balance report siemens 161208

8/2/2019 Wind Balance Report Siemens 161208

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Technical ReportV02

Siemens AS

Energy Sector

Power Distribution Division, T&D Service

Power Technologies International

Project reference A20125

Date 16. December 2008

Editors Mr. Nemanja Krajisnik Mr. Haakon Engen Mr. Steve Stapleton

Office address Bratsbergveien 57493 Trondheim, Norway

Phone +47 73 95 92 58

Fax +47 73 95 90 70

E-Mail [email protected] / [email protected]/[email protected]

Siemens PTI Network Consulting

Answers for energy.
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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Summary

The main task of this project is to investigate the consequences for the balance managementif the intended amount of wind power will be installed and operated in the Norwegiantransmission network. The information derived in this report will provide Statnett a basis ofvaluable information understanding some of the consequences larger amount of wind power,installed in the power system, will have on the long term planning and daily operation.

Due to the variability of wind, integration of wind power will give greater variations of energyproduction. This means that the operators need better prediction tools for wind powerproduction to be able to balance the system. The system operator has to increase the reservesto be prepared to compensate for unpredicted power balance variations, whether they comefrom wind power generation or load consumption variations.

Today the consumption in Norway constitutes the largest deviation with respect to thebalance. The consumption is the most significant source of uncertainty due to the strongcorrelation with the temperature. If larger amounts of wind power are introduced to thepower system, this will increase the deviation between the forecast prognosis and theoperation hour.

The quantification of necessary additional reserves for prognosis uncertainty can be done byan analytical approach with statistical measures using time series of wind production andother system variables. The wind power production is the variable which is most difficult todefine, since the forecast error is different depending on which time horizon we are lookingat.

Analyses have been conducted of load data provided by Statnett, and determined a standarddeviation of forecast error margin of 2,4% for day-ahead planning. Since the data of windpower production was missing and due to limited time frame, it was concluded to use theforecast error margin obtained in the DENA-study. The standard deviation of wind powerforecast error for day-ahead planning was determined in the DENA-study to be 7,2%.

Simulations for 5 different cases are carried out to determine the required additional reserve.

The frequency response diagrams for the different cases and reference cases can be found inChapter 5. The new regulating force has been calculated based on the power imbalance andfrequency deviation for each case.

Based on the calculated results from the simulations and the statistical assessment ofprognosis uncertainty, the required additional reserves for each scenario are:

Wind power production of 3500MW would require a total increase of reserve of 228 MW.

Wind power production of 7000MW would require a total increase of reserve of 878 MW.

The calculated reserves are considered to be worst case based on available information today.Development of new technology regarding control strategy, research and development ofbetter prediction methodology and prediction tool, will probably improve the situation. The

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forecast error margin of wind power production will decrease based on higher accuracy inprediction, and implementation of control logic to wind farms to simulate the droop andconventional frequency control, will increase the flexibility of the power system and ease thedaily operation for the transmission system operator.

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TableofContents

Summary .............................................................................................................................2

Table of Contents ..................................................................................................................4

1 Introduction ........................................................................................................6

1.1 Background ..................................................................................................... 6

1.2 Goal ................................................................................................................ 6

1.3 General description of work.............................................................................. 7

1.4 The Norwegian power system........................................................................... 7

1.4.1 General overview of the power system.............................................................. 7

1.4.2 Growth of wind power ..................................................................................... 8

1.4.3 Available reserve categories.............................................................................. 9

2 Consequences of wind power generation to the main grid............................11

2.1 System perspective .........................................................................................11

2.2 Power system control principles and requirements............................................13

3 The need of regulating reserves in Norway.....................................................15

3.1 Introduction....................................................................................................15

3.2 Prognosis Uncertainty......................................................................................15

3.3 Forecast error of wind power and load .............................................................15

3.4 Statistical approach .........................................................................................17

3.5 Results............................................................................................................18

3.6 Future trading market .....................................................................................18

4 Description of input data ..................................................................................20

4.1 Network data and exchange scenarios............................................................. 20

4.2 Ramping values.............................................................................................. 22

5 Study analyses ..................................................................................................25

5.1 Assumptions and considerations..................................................................... 25

5.2 Inertial & Governor response load flow (INLF).................................................. 25

5.3 Analyzed scenarios......................................................................................... 28

5.4 Base case scenario.......................................................................................... 29

5.5 Scenario of 3500 MW of wind generation........................................................ 32

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5.6 Scenario of 7000 MW of wind generation.........................................................37

6 Findings.............................................................................................................42

7 Conclusion.........................................................................................................46

7.1 Main conclusions ........................................................................................... 46

7.2 Proposal of future work .................................................................................. 48

Literature ...........................................................................................................................49

Appendix ...........................................................................................................................50

Appendix A Description of primary and secondary control...............................................50

Appendix B Nordel harmonised balance management....................................................51

Appendix C Dynamic data ............................................................................................ 52

Appendix D Description of network model .....................................................................53

Appendix E Wind turbine generator technologies...........................................................55

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1 Introduction

1.1 Background

Since the 1990s there has been a rapid growth of wind power in the world. Wind power is anunreliable energy resource which can vary widely for different areas, and also within thesame site. The wind conditions in Norway are well suited for production of wind power.Today, it is installed approximately 420 MW wind power in Norway with an annual productionof 0,9TWh [1]. There are plans for a considerable increase in the installed power along thewhole Norwegian coast.

A power system with large amounts of wind power will give Statnett, as system operator, bigchallenges. This is related both to the long term planning and as well the daily operation ofthe power system. Consequently, Statnett started in 2006 a research and developmentproject on integration of new energy sources in the power system. A part of this project is toinvestigate operational consequences with new energy sources integrated in the powersystem.

1.2 Goal

The main task of this project is to investigate the consequences for the balance managementif the intended amount of wind power will be installed and operated in the Norwegiantransmission network. The information derived in this report will provide Statnett a basis ofvaluable information understanding some of the impacts larger amount of installed windpower will have on the long term planning and daily operation of the power system.

The study includes:

Estimation of additional reserve requirements for Norway.

Prognosis uncertainty Frequency controlled normal operation reserve

Frequency controlled disturbance reserve

Forecast accuracy evaluation

The aspects of different time horizon for planning of daily operation

Consideration regarding secondary control-aspects

Utilization of secondary reserve

Automatic Generation Control (AGC)

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1.3 Generaldescriptionofwork

Due to the limited time frame, it was decided only to run simulations of peak load conditionsand not considering bottlenecks.

The following scenarios will be studied and investigated:

Base case, 0 MW wind power generation

3500 MW of wind power generation

7000 MW of wind power generation

The work will mainly be performed with the network tool PSSE, both power flow anddynamic studies. In addition research has been performed to acquire necessary information tobe able to decide suitable error margin values and ramping values used in this report.

To include the effect of wind power, various aspects have to be assessed; it is important tolook at geographical diversity of wind farm locations and the weather pattern and forecast inthe different areas to model the influence and expected wind variation and the possibleconsequences that can occur in the network based on changing conditions. Anotherimportant aspect is the different wind turbine technologies and theadvantages/disadvantages these may have on system performance.

1.4 TheNorwegianpowersystem

1.4.1 Generaloverviewofthepowersystem

Norway is one of the longest countries in the world, 1700km, and relatively few inhabitantscompared to the size. The major part of the population is mainly settled in south, south-eastof Norway, and the production is mainly along the western coast and northern part ofNorway. That means major amount of the energy has to be transferred by overhead lines tosouthern part of Norway.

The electricity power system in Norway is divided in three levels.

Main Grid

Regional Grid

Distribution GridStatnett in the capacity of transmission system operator is responsible to maintain, operateand develop the main grid and the belonging substation in Norway and the foreigninterconnections. The main grid exists of mainly 300 and 420 kV.

The generation in Norway today consists of 98% hydro and 2% thermal energy. The totalproduction of electricity in 2007 was 137,7 TWh, where the wind power production come to0,9 TWh.[1]

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Figure 1.1 Map of Nordel-system [20]

1.4.2 Growthofwindpower

Today the installed capacity of wind power is about 420 MW in Norway with an annualproduction in 2007 of 0,9 TWh. Norway has one of the best conditions for production of windpower in the world. Along the Norwegian coast, Kristiansand in south and further up north toFinnmark there are many locations with excellent wind conditions, well suited for wind farms.

There are considerable plans for increasing the wind power production. The expected windpower may increase to about 10000 MW based on the amount reported to NVE (NorwegianWater and Energy directorate) by the parties in the market [2]. If this expected growth inwind generations should occur, it is highly dependent of government support and incentivesdesigned to promote renewable technologies.

The focus in the world today is to utilize new energy sources which have a positiveenvironmental impact, and wind power is expected to have these effects.

Both the EU directive 2001/77/EC and Kyoto-protocol is promoting renewable energy sourceswhich will reduce the emission of carbon dioxide and oxides of sulphur from conventional,fossil-fuelled generating plants.

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A large amount of wind power will probably give other impacts on the power system thanbefore. This is mainly because of the differences in physical and technical characteristicsbetween wind power and conventional generation. The increasing share of wind powerproduction will influence more or less some of the following characteristics:

Lack of inertia

The lack of ability to provide operating reserve

The unpredictable electrical output due to variability of wind

On the other hand, an increasing wind power production will also require reinforcementsand extensions in the grid. New transmission lines have to be build to be able to the transferthe increased capacity to the load centers. The best suited wind conditions in Norway areoften on locations with already weak power grid and in need of reinforcement before

building the wind power farm. Wind power has a shorter construction time than buildingnew transmission system which might give Statnett challenges both handling the policyframework and ensure the system security of the network at all times.

1.4.3 Availablereservecategories

Electricity has the physical characteristic that it has to be used immediately as it is produced.Statnett as transmission system operator (TSO) in Norway is responsible to ensure balancebetween consumption and production all along. Statnett has to operate the power systemunder different conditions, including disturbances and variations in both production andconsumption. The production plans given to Nord Pool once a day is the basis for the

production the next day. These plans are made 12-36 hours before the operation hour, and itis therefore not unusual to have imbalances in the hour of operation because the powerconsumption is not as predicted the day before. Therefore Statnett and Svenska Kraftnt (TSOin Sweden) cooperate in compensating for the net unbalances that occur. To do this theyhave the common Nordic regulating power market, where the parties offer to regulateupward or downward a fixed MW to a given price. The frequency is a measure of balance andshould be 50Hz when the system is balanced. The frequency demand in Scandinavia has to bewithin 50Hz 0.1. With a balance regulation equivalent to 6000MW/Hz, means that thesystem has a margin of all together 600MW before it will be outside the demand.

This report will focus on the primary control, and the primary operating reserves in Norwaywill be described generally in the following:

The different reserves available in the Nordel system are defined in the System OperationAgreement [3].

Frequency controlled normal operation reserve (FNR) shall be regulated upwards /downwards within 2-3 minutes. The frequency controlled normal operation reserve shallbe at least 600 MW at 50.0 Hz for the synchronous system. It shall be completely activatedat f = 49.9/50.1 Hz (f = +- 0.1 Hz), and it is distributed between the subsystems of thesynchronous system in accordance with the annual consumption (total consumptionexclusive of power plants own consumption) during the previous year. Each subsystemshall have at least 2/3 of the frequency controlled normal operation reserves in its ownsystem in the event of splitting up and island operation. The Norwegian share of thefrequency controlled normal operation reserve in 2007 was 197 MW.

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There shall be a frequency controlled disturbance reserve (FDR) of such magnitude andcomposition that dimensioning faults will not cause a frequency of less than 49.5 Hz in thesynchronous system. The overall frequency controlled disturbance reserve must be able tobe used until the fast active disturbance reserve has been activated. The frequencycontrolled disturbance reserve shall be activated at 49.9 Hz and be completely activated at49.5 Hz. It must increase as good as linearly throughout the frequency range of 49.9-49.5Hz. The major part of both the frequency controlled disturbance reserve and the frequencycontrolled normal operation reserve will be achieved via automatic frequency regulationfor production facilities. To meet the above requirements, the objective for each respectivesystem operator must be to place demands on turbine regulator settings, e.g. in the formof demands regarding regulating time constants. Distribution of the requirement for thefrequency controlled disturbance reserve between the subsystems of the interconnectedNordic power system shall be carried out in proportion to the dimensioning fault within

the respective subsystem. Distribution of the requirement shall be updated once a week ormore often if necessary. The frequency controlled disturbance reserve is for the Nordelsystem 1160 MW, and the Norwegian share is 348 MW.

The fast active disturbance reserve shall exist in order to restore the frequency controllednormal operation reserve and the frequency controlled disturbance reserve when thesereserves have been used or lost, and in order to restore transmissions within applicablelimits following disturbances. The fast active disturbance reserve shall be available within15 minutes. The size of the fast active disturbance reserve is determined by individualsubsystems assessment of local requirements, and the Norwegian share is 1600 MW.

The slow active disturbance reserve is active power available after 15 minutes. There is norequirement attached to this reserve by the agreement. This one is also not in use in

Norway.

Figure 1.2Balancing mechanisms in the Nordel system [4]

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2 Consequencesofwindpower

generationtothemaingrid

2.1 Systemperspective

Introducing wind power to the power system might have both costly and operationalconsequences. Wind power can affect the system security (power quality and powerreliability), and the impacts can be varying over time. This can be especially expressed if windfarms do not participate in any way in two crucial loops for one power system (active power-

frequency and reactive power-voltage). This chapter contains some of the issues which canbe relevant for a wind power technology from a system perspective, and will be describedbriefly.

Uncertainty and needed regulation

An increasing amount of wind power will result in higher variability and uncertainty ofavailable generation. This will of course affect the use and allocation of reserves in the powersystem. The time horizon of planning is of high importance to define the needed reserves incombination with error margin of wind power, load and other production sources. In shortterm, the wind turbines within a wind farm will have a smoothing effect of short term output

fluctuations. By increasing the geographical area including more wind farms will alsoexperience a certain smoothing effect. The reason is that the wind will not have the samestrength at all wind farms simultaneously. The requirement of balance management willdepend on geographical areas, location and distribution of production and especially windpower, load variations and how the system is operated (i.e admissible ramp rates, start/stop,market mechanisms).

System adequacy

It is important with a sufficient static condition of the power system during peak loadconditions. The long-term planning has to consider enough available generation to meet the

system load demand and to allow the necessary maintenance of production units. Some areconsidering, or already implemented, local capacity storages of energy to ensure a reliableoperation of the power system, due to larger amount and variability of wind power integratedin the power system. Especially during low load conditions and during night with suddenwind increase, it is important to have local capacity. In these conditions there could beshortage of downward regulating reserves at the Elspot, e.g. maintenance of production unitsor full production for export. It is expected that wind power would contribute in terms ofcapacity credit; however, nowadays it is still relied on reserves obtained from conventionalunits to cover its stochastic character.

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Adequacy of transmission grid

The best wind conditions in Norway are on locations with weak transmission/distribution gridor even no grid at all. Extensions and reinforcements are needed to be able to transfer thepossible amount of wind power to load centres or for export. In the same manner, it is alsoimportant to take account for the construction time, which is shorter for wind farms, than forbuilding transmission grid. The variability of wind might lead to changes in power flowdirection and bottlenecks on other places than before. To be able to still maintain a securenetwork operation, new FACTS1 equipment has to be installed and also new controlmechanisms.

System stability

The voltage and power control is depending on type of used wind turbine technology. It islikely to believe that there will be different kinds of technology used in the Norwegiansystem, which gives different possibilities to support the power system during normaloperation and during and after disturbances. It is recommended to assess the integration ofwind power from different views to be able to get enough information to determine how thenetwork should be operated and necessary action for development of new control strategies.

Congestion management

Wind power generation which is connected to transmission or distribution grid cansignificantly increase the number of bottlenecks because of its stochastic character as well asthe future trend of maximum exploitation of existing transmission capacities. Usually,problems with overloads of elements or voltages out of ranges are resolved by operationalmeans in the power system itself, while the wind generators simply follow the conditions inthe main grid (frequency, voltage). The transmission system can be loaded to the limit duringwinter time as well as spring time. During the spring, it is often flood and low load. Underthese conditions, it is need to utilize the hydro power plants to maximum. Then thetransmission system is load to the maximum from north to south. Although the controlsystem for wind farms is completely synthesized (programmable control logic), activeparticipation of wind generators in removal of bottlenecks is possible, but still in experimentalstage. Use of wind generators as conventional sources in terms of maintaining power systemstability is highly dependant on type of technology used for wind power generation.

If the wind power technology continues its development, you will get larger turbines and

larger wind farms of hundreds of megawatt installed capacity. This might give the systemoperator challenges since the wind power production will be concentrated. The smoothingeffect over larger geographical areas would probably disappear, and pose problems onbalance management in short-term due to wind change. On the other hand, the developmentof power electronics, control strategies for production, voltage and reactive power supportand improved accuracy in forecasting wind power would increase the utilisation of windpower and make the system more stable. New technology would mean new requirementswith respect to integration of wind power, like remain connected to grid also by nearby faultsand provide reactive power support during disturbances.

1

FACTS Flexible Alternating Current Transmission System

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2.2 Powersystemcontrolprinciplesandrequirements

The secondary control of balance is today only assessed manually in Norway by spinningreserves. These are reserves the parties guarantee will be operating with response time equal1 minute within the quarter before a planned increase of production. If larger amount of windpower is introduced in the Norwegian market, it could be necessary to improve the balanceand frequency control. One possibility is to introduce automatic control mechanism. Thefollowing presents some mechanism to control the set points of hydro power plants thatmight be possible to include in the Norwegian Power system. The cost, agreements and howthis would be possible to implement is not considered. It is just a description of themechanism and how it may work.

Automatic generation control (AGC)

This mechanism has two basic objectives:

1. The primary objective is matching generation to the existing system loads and thedesired interchange.

2. The secondary objective is to optimize the production among individual units withinsame stationary group in accordance to preselected schedules.

AGC can control both frequency and power beyond normal speed controlled by primarycontrol (statics). The main purpose is to regulate the power output of electric generators

within prescribed area in response to changes in system frequency, tie-line loading, or therelation of these to each other, to maintain the scheduled system frequency and/or theestablished interchange within predetermined limits. AGC is well-known and in operationaround the world, and it is also used by the producers in the Nordel-system, but only forautomatic optimization between different plants within the same stationary group.

If a sudden load change should occur in the power system, the primary control will providepower to stabilize the system with a steady-state frequency deviation, determined by thestatics of the generator and the frequency-dependence of the load. The AGC will bring backthe frequency of the system to nominal value and re-establish the decided exchange betweendifferent areas by adjusting the production of available generators.

This means that the AGC by using measures on system frequency and power flow from lines

of importance can give signals to available production units to change the production. Howthis works is shown in Figure 2.1.

.

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Figure 2.1 Coordination between primary and secondary control today (source:SINTEF)

Increased use of production and load shedding

This is not in general a part of the secondary control mechanisms, but it has to be mentionedas one of the possibilities carried out due to e.g. a sudden change in the wind speed.

It is mainly two conditions where this is likely to be initiated. If the wind power production isincreasing more than forecasted, there have to be carried out production shedding or reduce

the ramping/maintain output of the wind power production of a wind power park bychanging angle of blades.

The other aspect is when the wind power production has a sudden decrease, and to getavailable hydro power plant up and running, larger consumptions has to be reduced ordisconnected until necessary hydro power plant is producing the needed amount of power.However this is only activated if the change in wind generation has more severeconsequences (very low value of system frequency). Load shedding should be considered as alast mean of protecting a power system from total blackout.

Power system

Loads

Tranmission

Production

AGC

Energy system

Guide van

operating

mechanism

Set point control Turbine regulator Turbine Generator

Power Flow

Frequency

Secondary control

Primary control

Rotational speed


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3 Theneedofregulatingreservesin

Norway

3.1 Introduction

Integration of wind power will give greater variations of energy production, due to variabilityof wind. This means that the operators need better prediction tools for wind powerproduction to be able to balance the system. The system operator has to increase the reservesto be prepared to compensate for not predicted power variations. The risk will be reduced by

improved accuracy of the forecasting, as well as the unnecessary assignment of too largeregulating reserves. This will have an impact on both the balancing cost and the systemsecurity.

3.2 PrognosisUncertainty

The generation plans are determined the day-ahead, 12-36h before the operation hour, aftereach party have submitted their production plans. [4] Therefore it is important to do theplanning as exactly as possible, to decrease the use of needed reserves during the operationhour. However, there are some sources that are related to certain uncertainty. In Norway

today the consumption constitutes the largest deviation with respect to the balance. Theconsumption is the most significant source of uncertainty due to the strong correlation withthe temperature. If larger amounts of wind power are introduced to the power system, thiswill increase the deviation between the prognosis and the operation hour. The wind is arather unreliable energy source, the wind varies greatly during a year and also within a day,from zero wind up to 25 m/s and above, and the wind has to be used immediately, since itcan not be stored.

The prognosis uncertainty also depends on the time horizon, when they decide theproduction plans. A longer time horizon before operation hour increases the deviation andthe need of reserves. Therefore, a good forecasting tool has to be developed in order topredict the production as exact as possible to reduce the deviations. There is a lot of research

on this field assessing different approaches to improve the existing forecasting tools, and findnew approaches on how to do this by involving more detailed parameters and measurementsvalues.

3.3 Forecasterrorofwindpowerandload

The prognosis uncertainty in this project is assessed statistically, which is the most suitable,since the operation and planning of the grid is based on risks, i.e. probability of occurrences.A statistical approach will also include almost all possible variations of the systems, making itsuitable to determine the reserves.

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Statnett provided data for 2005 until 2008 containing average load per hour and the day-ahead load forecast. Figure 3.1 shows the load and the load forecast for the first 3 days of

January 2008.

Analysis of the provided data set for 2005-2008 has been assessed to determine a standarddeviation of the load forecast error in Norway. During the years 2005 to 2008, there are 280hours where the forecast error is above 1000 MW.

The standard deviation of the load forecast error is determined to be 2,4 % for day-aheadprediction.

It is important to point out that the forecast error has decreased every year since 2005, whichmight indicate that the forecasting has improved. If this is due to better tools or just acoincidence, is not clear. To give a definite conclusion on this issue, more data has to beassessed.

0

5000

10000

15000

20000

25000

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70

hour

MW

Hourly load Day-ahead forecast

Figure 3.1 Actual load vs. load forecast for 3 days in January 2008 (source: STATNETT)

The following values shown in Table 3.1 are used as basis for forecast error with respect to

wind power production and load consumption. The wind forecast error and load forecasterror are obtained from the DENA-study [5] and Statnetts own data regarding imbalances,respectively.

Table 3.1 Calculated forecast errors for wind generation and consumption

1h 4h D+1

Load forecast error 1,4% 1,4% 2,4%

Wind generation forecast error 2,5% 4,7% 7,2%

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3.4 Statisticalapproach

The quantification of necessary additional reserves can be done by an analytical approachwith statistical measures using time series of wind power production, load consumption anderror margins. The wind power production is the variable which is most difficult to define,since the forecast error is different depending on which time horizon we are looking at.

It has to be stated that this is a probabilistic approach, which will in most cases give a lowerreserve requirement than a deterministic approach. Nevertheless, several studies show that aprobabilistic approach is the most realistic way of doing these kinds of studies.

One way of doing this is by statistical estimation of the effects of variations using standarddeviation, which is a measure of the dispersion of a collection of numbers. The system seesthe net load (load - wind power production). As long as the load and wind power production

is uncorrelated, the net load variation is the root mean square combination of load and windpower variation.

222

windloadtotal += (1)

total = standard deviation of the net load

load = standard deviation of the load

wind = standard deviation of the wind power production

To determine the additional reserves, we need to include almost all possible variations, and itis decided to use 4 variations, which include 99,99 % of all possible occurring variations.(Figure 3.2) [6,7]

Figure 3.2 Gaussian distribution

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3.5 Results

Based on the statistical approach described in Chapter 3.4, the additional reservesrequirement due to prognosis uncertainty can be expressed as shown in Table 3.2.

Table 3.2 Additional requirements for reserves due to prognosis uncertainty

D+1 4h 1h

Scenario Case 1 Case 2 Case 1 Case 2 Case 1 Case 2

Wind power (MW) 3500 7000 3500 7000 3500 7000

Wind power penetration in %of gross demand 15,9 31,8 15,9 31,8 15,9 31,8

Stdev wind power forecast 252 504 164,5 329 87,5 175

Stdev load forecast 528 528 308 308 308 308

Stdev netload forecast 585,1 729,9 349,2 450,7 320,2 354,2

Increase in forecast error, 4 228,2 807,7 164,7 570,7 48,8 185,0

The results given in Table 3.2 determine that the time horizon of planning is a crucialparameter to define the exact amount to allocate for reserves due to prognosis uncertainty.Day-ahead planning with 7000 MW wind power production requires a need of 808 MW

additional reserve due to prognosis uncertainty. This amount can be reduced by having goodprediction tool and method to determine the wind power production the day-ahead. There isa lot of research within the field prediction tools, and since there probably is some years untilNorway would have those amounts of wind power integrating in the system, and there willprobably be technology developed improving the forecasting error and reduce the necessaryavailable reserve.

Due to the small amounts of installed wind power in Norway today, there is not muchavailable forecast data of wind power production. The standard deviation values used forwind power forecast is based on data from the DENA-study [5]. This data is based on Germanconditions, and might not be suited for Norwegian condition. However, there could be statedthat the calculated values of reserves needed available for prognosis uncertainty in Norway is

worst case. This statement is based on the arguments, that the Norwegian government andthe TSO would probably prefer a geographical diversity of wind farm locations. This will give asmoothing effect, and will decrease the error margin of wind power production and inaccordance with improved accuracy of forecasting will decrease the forecast error margin ofwind power production.

3.6 Futuretradingmarket

There is no doubt that large amount of wind power in the power system will increase theprognosis uncertainty. The necessary actions needed by Statnett, is to increase the reservecapacity of the regulating power market, cross-section handling and counter trade to be able

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to handle the variations of wind power. This will probably lead to less activity on the Elspot-market, and increasing differences of Elspot area-prices.

Elbas will be introduced in 2009 in the Norwegian market, and should be used by the partiesin Norway for power trading that leads to physical delivery, the so called intra-day market. It isbased on real time trading around the clock every day during a year. Elbas covers individualhours up to one hour before delivery [8].

Due to a greater need of regulating power, this may lead the parties to use Elbas as their newmarket instead of Elspot. Elbas will probably not be used that much in Norway until we getlarge amount of wind power operating in the power system. But this market will probablyplay an important role when the system is experiencing larger imbalances due to thevariability of wind, and the need of regulating until the hour before operation.

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4 Descriptionofinputdata

4.1 Networkdataandexchangescenarios

The studies are based on the provided PSSE network model Norge_d07h, which is adistinctive, representative operating condition, annual peak load in Norway. This modelcontains the main grid of Norway (420kV and 300kV), most of the regional grid (132kV). Italso contains some simplifications on the lower network levels to include necessaryproduction and important loads. The network model is mainly based on the work done in [9],and more details can be found in appendix D.

Total installed capacity of wind power in the model is 9000 MW.

The wind farms are modelled to operate with cos = 0.95. Figure 4.1 is showing thelocations and installed capacity of wind farms included in the network model.

The data and location of wind power is based on information given by NVE by May 2007.More detailed information about the included wind farms can be found in the appendix of thereport Storskala integrasjon av vindkraft Konsekvenser for Sentralnettet [9].

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Figure 4.1 Locations of modeled wind farms and installed capacity [9]

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4.2 Rampingvalues

It is important to include the effect of wind power to the power system, which means variousaspects have to be assessed: Since the wind farm locations have a geographical diversity, theweather pattern and forecast in the different areas have to be model to include the influenceand expected wind variation. This is important to determine the possible consequences thatcan occur in the network based on changing conditions.

It is clear that wind power output of a wind farm varies with the wind speed. Every singlewind generator at a site will contribute different of each other due to the dependence oflocation and the wind direction. It is also clear that the behavior of a wind farm will behavedifferent compared to a conventional power plant.

Aggregating the wind power farm will not have any high influence on a short term basis, due

to the smoothing effect over larger geographical areas.Statnett has provided a data set of wind production data, obtained from a wind farm inNorway. These data contains only 34 days of measurements. The measurements contain one-minute data series of October and November 2008. The provided data set is too small tomake final conclusions about tends, but there could be drawn some indications compared toresearch and studies performed by others.

Figure 4.2 shows a comparison of average wind power output and wind speed at the samelocation on hourly basis. Hourly wind speed data is used since it was the only resolution thatwas possible to obtain free by MET2.

Figure 4.2 is based on measurements done for a day with high wind, and shows the typical

correlation between high wind and high wind power output.

hourly average power and wind speed

0

5

10

15

20

1 3 5 7 9 11 13 15 17 19 21 23

m/s

0

20

40

60

80

100

120

140

160

MW

Wind speed Power

Figure 4.2 Hourly average power and wind speed

2

The Norwegian Meterological Institute

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The provided data set contained some characteristic data, which can be assumed to be worstcase ramping scenarios due to increase/decrease of nearly 2/3 of installed capacity during one

minute. A summary of these findings will follow:

October 22th 2008:

During hour 4 and 5 this day, arise the highest average increase/ decrease during one minute;+80 MW/-103 MW. This gives an average ramping value per second in the range of 1,3 MW/sto 1,7 MW/s, corresponding to about 1 % of installed capacity.

October 25th 2008:

During hour 7 this day, the wind farm experiences a downward regulating of 149 MW over 9minutes, and then after another 8 minutes an upward regulating of 149 MW over 7 minutes.This is shown in Figure 4.3.

Time 7 - 25.okt

0

20

40

6080

100

120

140

160

180

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

Figure 4.3 Wind power production output from one entire wind farm in Norway at October 25th

Based on the high wind power production both before and after this occurrence, the mostplausible reason is shut down due to high wind (>25 m/s).

The average upward and downward ramping is respectively, 0,35 MW/s and 0,27 MW/s,which correspond to about 0,2 % of the installed capacity.

November 10th and 12th:

During these days, there is a characteristic upward ramping of wind power. The reason to thisupward ramping is not defined, but it could be e.g. start up because of internal failure ormaintenance of wind farm or a start-up after an outage due to a disturbance in the powersystem.

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On November 10th there is an upward ramping of 135 MW over 1h and 17 minutes. Thiscorresponds to an average ramping value of 1,75 MW/min.

On November 12th there is an upward ramping of 131 MW over 1h and 1 minute. Thiscorresponds to an average ramping value of 2,1 MW/min.

Average values of the data set:

The average upward and downward ramping values, defined by the assessment of theprovided data set are respectively +1,54 MW/min and -1,52 MW/min. These values correspondwell to the data assessed from November 10th and 12th, which gives a certain indication ofnormal ramping values.

No final conclusion can be taken based on this data, since the provided data set is only for

one wind farm in Norway. It is recommended to gather more data, which can be assessed inthe future, to determine the exact trends. Another important aspects, is the missing onesecond data series, which might have given worse numbers at least for the maximum valuesof 1 second change of wind power production. This is an important value to determine to beprepared for certain changes due to balance management, and to be able to include therequired reserve to handle conditions due to sudden wind change, e.g. missing wind or toohigh wind coming on the defined hour of operation.

Assessment of provided data set determined that expected worst case ramping would beabout 2 MW/s, which correspond to 1 % of installed capacity. Since the measurementscontaing to few data, no final conclusion can be taken. Therefore, discussion was carried outwith Statnett, research and desired to stress the system as much as possible, it was

determined to use 5% (per second) of installed capacity as a worst case value of suddenchange of wind, both increase and decrease. In case of simulations of sudden wind changesfor entire geographical area of wind farms, the same value of wind speed change was used (5%/s), with a time frame of 5 s. It must be pointed out that these are very unusual occurrencesand at ramp rate limits of any known wind generator. Ramp rate in range of 4-7 % of capacityis considered to be extreme ramp rates [10,16].

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5 Studyanalyses

5.1 Assumptionsandconsiderations

Prerequisites and assumptions which were included in this study are:

No variation of the consumption during a year is considered (only peak load scenario isconsidered)

Bottlenecks are not considered (frequency response study is done for loop P/f (activepower vs. frequency) with negligence of transmission system issues)

The wind farms are working at actual wind speed at all times and they do not participate infrequency control (this is current practice in wind power technology)

The distinctive areas for sudden wind changes are close to locations of definedoccurrences (Chapter 5.3)

Secondary control mechanisms such as AGC and system operator action is not included instudies and all analyses are done for period of action of primary control (50 s)

The expected changes of wind is assumed to be 5% of installed capacity per second(Chapter 4.2)

5.2 Inertial&Governorresponseloadflow(INLF)

In order to investigate the impact of high power generation of wind farms to the powersystem of Norway and their influence on power system control, it was necessary to build abase case model which will provide a response similar to the actual one (Table 5.1).

Table 5.1 Power system control constants () for Nordel interconnection [3]

Power system control constants 600 MW imbalance in Norway

Primary control action

Country Area Country Total SE,FI,DK NorwayArea Country

MW/Hz MW/Hz MW MW MW MW

EasternDenmark

23023

Finland 1450 145

Norway 1970 197

Nordel

Sweden 2350

6000

235

600 403 197

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The main goal in tuning the base case model was to obtain a response similar to the actualone which means loss of 600 MW production in Norway should cause frequency drop of 100

mHz (according to the overall power system control constant). Frequency drop is followed byprimary control reaction of generator governors in Norway, Sweden, Finland and EasternDenmark with an aim to restore generation vs. demand balance. Reaction of generatorscauses additional primary control which flows through transmission grid of Nordel, and inparticular case of outage in Norway, it is assumed that 403 MW will inrush from neighboringSweden and the remaining (197 MW) will be generated by Norwegian generators.

Power-frequency characteristic of one power system is adjusted by changing droop constantsof the governors of turbine-generators. Droop characteristic of one turbine-generator unitdetermines its primary control reaction to sudden imbalance of power in power system(which is followed by change of frequency). Consequently, droop settings also affect thereaction of secondary control since secondary control takes over the restoration of power

balance and frequency after primary control reaction.

It is important to mention that the entire interconnection has to be taken into considerationbecause all units in synchronous operation, with frequency control governors, participate inprimary control. The base case model consist of a full transient stability model of Norway,including two equivalent generators connected to ends of two interconnection lines (OHL420 kV Nea- Jrpstrmmen and Hasle-Borgvik) over which the synchronous parallel operationis achieved with the rest of Nordel countries. Beside these interconnections, there are alsosubmarine HVDC cables from Norway towards Denmark and Netherlands, but in loop ActivePower-Frequency they do not participate at all.

Adjustment of the base case model was done by usage of INLF/IGLF tool which is a standardpart of PSSE software [12]. Three values were of interest in the process adjusting the basecase model:

Frequency deviation

Cross Border primary control flow

Droop constants for generators in Norway and rest of Nordel (equivalent generators)

The selected reference disturbance was the simultaneous outage of two hydro generators inHPP Kvilldal which yields a loss of 600 MW. This loss should cause a frequency drop of 100mHz which was taken as a reference frequency deviation. Cross border primary control flowwas selected according to power system control constants of the surrounding countries in

Nordel-area (around 400 MW of inflow). The range of values for droop constants is highlydependant of the type of power system, nominal rating of unit and its position in powersystem, but they are in range from 1 % up to 20 %.

The calculation IGLF (Inertial-Governor Load Flow) is intended to indicate system conditionsthat would exist at least several seconds after the initiation of an event during a steady-statesystem condition. In this time frame, it is assumed that voltage regulator and turbinegovernor effects are influential in bringing the system to a new steady-state condition andthose changes in generator powers are determined by governor droop and dampingcharacteristics. In this solution, generator scheduled voltages are unchanged except asdescribed below, and the default response to the VAR limit selection is to honor generatorreactive power limits. By default, tap adjustment by the stepping method and phase shift

angle adjustment are enabled, DC taps are unlocked, and switched shunts are active. These

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settings may be overridden by the user when initiating the solution. Area interchange controland the non-divergent solution option are always disabled.

Since the primary control has only proportional action, frequency deviation of each generatoris calculated according to equation 2:

where:

Pt is turbine power (equal to initial electrical power of generator)

Pe is active power of generators (after disturbance)

R is governor permanent droop

D is damping coefficient

Frequency deviation should be the same for each generator while changes of powergeneration are dependant on droop and active power reserve of each generator.

Application of IGLF enabled the adjustment of base case model thus that each equivalentgenerator (equivalents for the rest of Nordel) has permanent droop of 2.3 %, whilegenerators in Norway have droop of 16 % with the assumption that smaller hydro generatorsdo not participate in primary control. Change of power system losses due to the certain

outage is present but neglected in this analysis. Primary control import of power (inflow fromrest of Nordel) is less than 400 MW since the loss of two units in Kvilldal reduced the overallpower system losses, but, the primary control reaction of Norway is set to 200 MW.

Table 5.2 Area/Owner/Zone Report for 0MW wind generation (referred as base case) before

reference outage in Norway

--------------------------------------------------------------------------------

PTI INTERACTIVE POWER SYSTEM SIMULATOR--PSS/E TUE, DEC 02 2008 2:04

STORSKALA INTEGRERING AV VINDKRAFT. AREA TOTALS

HOYLAST 22000 MW - 0 MW VINDKRAFT. IN MW/MVAR

FROM TO TO BUS TO LINE FROM TO DESIRED

X-- AREA --X GENERATION LOAD SHUNT SHUNT CHARGING NET INT LOSSES NET INT

1 350.3 1950.0 0.0 0.0 0.0 -1600.3 0.6 -1600.0

NORDEL 84.6 0.0 -100.0 0.0 68.1 242.9 9.8

2 22805.4 20390.4 1.0 -0.1 0.0 1600.3 813.7 1600.0

NORWAY 6891.7 3683.4 -1746.6 0.3 5954.9 -242.9 11152.4

TOTALS 23155.7 22340.4 1.0 -0.1 0.0 0.0 814.3 0.0

6976.3 3683.4 -1846.6 0.3 6023.0 0.0 11162.2

RD +

PPf et

1

= (2)

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Table 5.3 Area/Owner/Zone Report for 0MW wind generation (referred as base case) after

reference outage in Norway

--------------------------------------------------------------------------------

PTI INTERACTIVE POWER SYSTEM SIMULATOR--PSS/E TUE, DEC 02 2008 2:06

STORSKALA INTEGRERING AV VINDKRAFT. AREA TOTALS

HOYLAST 22000 MW - 0 MW VINDKRAFT. IN MW/MVAR

FROM TO TO BUS TO LINE FROM TO DESIRED

X-- AREA --X GENERATION LOAD SHUNT SHUNT CHARGING NET INT LOSSES NET INT

1 679.3 1950.0 0.0 0.0 0.0 -1271.0 0.4 -1600.0

NORDEL -118.2 0.0 -99.8 0.0 68.0 41.4 8.2

2 22415.2 20390.4 1.0 -0.1 0.0 1271.0 752.8 1600.0

NORWAY 6177.3 3683.4 -1753.6 0.5 6005.1 -41.4 10294.5

TOTALS 23094.5 22340.4 1.0 -0.1 0.0 0.0 753.2 0.0

6059.1 3683.4 -1853.4 0.5 6073.1 0.0 10302.7

Although, values of droops do not correspond to the actual values of permanent or transientdroops of governors in hydro power plants in Nordel (with Norway), at this level of accuracy(with respect to collected data), these adjustments will provide sufficiently good response ofsimulation.

From Table 5.2 and Table 5.3, it can be seen that the power generation of Norway is changedfrom 22805.4 Mw to 22415.2 MW (difference of 390.2 MW). This can be interpreted as adecrease of 600 MW (due to reference outage) and then the reaction of primary control ofNorway of 209.7 MW which is pretty close to the targeted value of primary control reaction.Expected primary control flows from rest of Nordel are below expected 390 MW (1600 MW 1271 MW = 329 MW) because outage in Norway caused the decrease of power system lossesfor 61MW.

In any case, the IGLF adjustment proved to be an efficient tool for setting up the base casefrequency response for further analyses.

5.3 Analyzedscenarios

The study contains three main scenarios:

0 MW wind power production, considered as base case

3500 MW wind power generation

7000 MW wind power generation

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Based on these three scenarios, the following cases will be assessed during the simulations:

Reference case 1: Loss of 600 MW production Reference case 2: Loss of 600 MW load

Reference case 3: Loss of 1200 MW production

Case 1: Loss of the largest production unit, HPP Kvilldal (1200 MW), and sudden decreaseof wind in one distinctive area

Case 2: Loss of a large load, ASU (aluminum smelting plant of 600 MW), and suddenincrease of wind in one distinctive area

Case 3: Sudden stop of large wind farms and sudden decrease of wind in one distinctivearea

Equivalent wind power park connected to Fosen (installed capacity of 1270 MW) Equivalent wind power park connected to Orkdal (installed capacity of 1110 MW)

Case 4: Sudden decrease of wind power

Case 5: Sudden increase of wind power

These cases will be the basis to decide the requirement of necessary additional reserve.

In addition the needed total operating reserves which have to be available due to prognosisuncertainty have been assessed statistically based on wind power production forecast errormargin and load forecast error margin. These margins are described detailed in Chapter 3.3and the results are described in Chapter 3.5.

5.4 Basecasescenario

According to the settings implemented during the process of governor load flow calculations(chapter 5.2), frequency response to the reference loss of production (2 units in HPP Kvilldal(2x300 MW)) corresponds to the adjusted value. In the new steady-state, obtained after 50 s,the frequency reaches the targeted value of 49.9 Hz (Figure 5.1), which is a confirmation ofIGLF calculation and a proper starting point for all future comparisons.

The reference loss of load in ASU (600 MW) is used as an opposite test to the usualgeneration outage tests in order to check the engagement of lower reserve due to the

opposite governor reactions of HPPs in Norway. Figure 5.2 shows that the response is nearlysymmetrical to the response to generation outage.

For the purpose of comparison with further simulation cases, additional simulation of HPPKvilldal was performed (1200 MW), while for the rest of cases, it is impossible to do this sincethere are no wind farms available in Base Case Scenario.

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Figure 5.1 Frequency response to ref.case 1 in base case scenario

Figure 5.2 Frequency response to ref.case 2 in base case scenario

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Figure 5.3 Frequency response to ref. case 3 in base case scenario

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5.5 Scenarioof3500MWofwindgeneration

Analyses of frequency responses have revealed differences compared to the same events inthe base case. The reference loss of 600 MW production which causes the frequency drop of100 mHz in the base case scenario, now causes the drop of 105.5 mHz. There is also adifference in shape of the curve, since the presence of wind farms brings some additionalinertial response at the first instant of disturbance.

The loss of load also brings changes and the difference is even higher in terms of shape of thecurve which means that engagement of lower reserve is not so fast as engagement of upperreserve. Frequency is however better restored (stationary rise of 103.5 mHz).

Particular cases of disturbances involving wind generation change have brought changes infrequency response and the most obvious changes are noticeable for Cases 3, 4 and 5 since

they include large scale imbalances and large scale wind generation deviation. Case 1 caused a frequency deviation of -228,5 mHz (Figure 5.7).

Case 2 caused a frequency rise of 124 mHz with maintaining the period of reaching thesteady state (Figure 5.8)

Case 3 caused a frequency deviation of -173,5 mHz (Figure 5.9)

Case 4 caused a frequency deviation of -150 mHz with notation that recovery of frequencywas delayed due to the gradual change of wind power generation in 5 s (Figure 5.10)

Case 5 caused a frequency rise of 168.5 mHz with significantly delayed settling on newstationary value. This is a consequence of gradual increase of wind power generation in 5 s

(Figure 5.11)

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Figure 5.4 Frequency responses for ref. case 1 in base case scenario (red) and 3500 wind scenario

(blue)

Figure 5.5 Frequency responses for ref.case 2 in base case scenario (red) and 3500 wind scenario

(blue)

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Figure 5.6 Frequency responses for ref.case 3 in base case scenario (red) and 3500 wind scenario

(blue)

Figure 5.7 Frequency responses for Case 1 (red) and ref.case 3 (blue) in 3500 wind scenario

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Figure 5.8 Frequency responses for Case 2 (red) and ref.case2 (blue) in 3500 wind scenario


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5.6 Scenarioof7000MWofwindgeneration

Frequency responses for this scenario have significant differences compared to the sameevents in the base case. The reference loss of 600 MW production which causes the frequencydrop of 100 mHz in the base case scenario, corresponds to a drop of 117 mHz. There is also adifference in shape of the curve, because small signal stability issues emerge with largepenetration of wind generation. Sub-synchronous oscillations which appear duringdisturbance (Figure 5.12) are a consequence of stiff synchronous operation of wind farms(maintaining of same injection of power independently of system frequency).

Loss of load also brings changes and the difference is even higher in terms of shape of thecurve which means that engagement of lower reserve is not as fast as the engagement ofupper reserve. Frequency is restored several seconds later what can be seen in Figure 5.13(stationary rise of 107 mHz).

Particular cases of disturbances involving wind generation change have brought significantchanges in frequency response compared to the previous scenario of 3500 MW of windpower generation. Cases 3, 4 and 5 still bring largest excursions of frequency since theyinclude large scale outages and large scale wind generation deviation.

Case 1 caused a frequency deviation of -253 mHz (Figure 5.15).

Case 2 caused a frequency rise of 127 mHz with maintaining the period of reaching thesteady state (Figure 5.16)

Case 3 caused a frequency deviation of -377 mHz (Figure 5.17). In this scenario wind farmsat Fosen and Orkdal were generating 1853 MW before the sudden stop, which makes this

event the most critical one. Case 4 caused a frequency deviation of -160 mHz with notation that recovery of frequency

was delayed due to the gradual change of wind power generation in 5 s (Figure 5.18)

Case 5 caused a frequency rise of 191.5 mHz with significantly delayed settling time. Thisis a consequence of gradual increase of wind power generation in 5 s (Figure 5.19)

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Figure 5.12 Frequency responses for ref.case 1 in base case scenario (red) and 7000 wind

scenario (blue)


scenario (blue)

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scenario (blue)


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6 Findings

All values of frequency drops due to governor responses in Nordel are gathered in Table 6.1for analyses and comparison. Except for Case 3, all values are comparable for analyzedscenarios. In particular case 3 (loss of large wind farms Fosen and Orkdal with wind decreaseat the same time), wind power generation from these farms are different in correspondingscenarios, so the frequency deviation in 7000 MW wind scenario is more than twice as high.

On basis of power imbalance and frequency deviation, a value of power system controlconstant (a.k.a. frequency bias, regulating force), can be calculated by simple equation 3.

(3)=

Hz

MW

f

P

Figure 6.1 Comparison of frequency responses for case 1 for base case (red), 3500 wind (blue)

and 7000 wind (green) scenarios

Figure 6.1 shows that with increasing new wind power generation the power system ofNorway (as a part of Nordel interconnection) becomes inherently weaker in terms of powersystem stability. From the frequency diagrams in previous chapter, it is clear that Norway incooperation with the Nordel interconnection still has the ability to cope with largedisturbances, but noticeable differences in magnitude and time of response do appear asexplained in the previous chapters.

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Values of regulating forces were calculated based on frequency deviations in Table 6.1 andpresented on diagram in Figure 6.2.

Table 6.1 Frequency responses in different scenarios for all analyzed cases

Frequency Drops

Cases MW OutageMW winddecrease

Base case

mHz

3500 MW

mHz

7000 MW

mHz

Ref.case 1 600 NA -98,5 -105,5 -117

Ref.case 2 -6003 NA 100,54 103,5 107

Ref.case 3 1200 NA -197,5 -215 -234,5

Case 1 1200 104 N/A -228,5 -253

Case 2 -600 -104 N/A 124 127

Case 3 903/1853 71 N/A -173,5 -377

Case 4 - 822 N/A -150 -160

Case 5 - -822 N/A 168,5 191,5

The initial value for this study, which is nowadays the current value for regulating force, was6000 MW/Hz. The base case was adjusted to this value, and this value is used as a reference

for all comparisons.

The new calculated average regulating force of Nordel for scenario of 3500 MW wind powergeneration is 5650 MW/Hz. This means that Nordel will have less primary control reserve toachieve the new steady-state condition after imbalance occurs.

For the scenario of 7000 MW of wind power generation, average value of regulating force isreduced to a even lower value of 5315 MW/Hz. As a consequence, the same loss of powercauses deeper frequency drops and longer time for frequency recovery which is overlappingwith initiation of secondary control action.

3negative value of MW refers to loss of consumption

4positive value of frequency refers to frequency rise

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-values of Nordel for different scenarios and cases

4000

4500

5000

5500

6000

6500

-1000 -500 0 500 1000 1500

Power Imbalance [MW]

[MW/Hz]

base case

wind 3500

wind 7000

Figure 6.2 Diagram of regulating forces (frequency biases) for different scenarios and

corresponding cases

In both of the wind power generation scenarios, there is obviously a necessity to add more

power to the primary control reaction in order to reach the base case targeted regulatingforce of 6000 MW/Hz. There is no exact method to give the exact number or amount ofregulating power, since there are many different measures how to set the regulating force tosome defined value. The first measure is definitely increasing active power reserve of powerplants in Norway and Nordel (expansion of existing or construction of new units). Anotherplausible measure is to revise and change the droop settings for power plant governors. Oneof the latest solutions is to implement such control logic to wind farms to actually simulatethe droop and conventional frequency control.

Since it is obvious from the frequency response diagrams, and Figure 6.2 presents that acertain amount of primary control power is missing, an overall value of additional primarycontrol reserve can be given. This power can be calculated according to equation 4.

(4)( )= [ ]MWfP ettwindBASEres arg

where:

BASE is base case regulating force of Nordel

wind is regulating force of Nordel with wind power penetration

ftarget is targeted reference value of frequency drop defined by Nordel regulations (100 mHz)

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Table 6.2 Regulating forces and required additional reserves for analyzed scenarios

Regulating force Required additional reserveScenario

MW/Hz MW

Base case 6000 0

Scenario 3500 MW 5650 35

Scenario 7000 MW 5315 70

Since both values of reserve Table 6.2 correspond to approximately 1% of the actual windpower generation in particular scenario, the value of primary control reserve for 7000 MW

wind scenario is rounded from 68.5 MW to 70 MW. Actual means of increasing theregulating force can be a subject of a new study.

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7 Conclusion

7.1 Mainconclusions

Integration of wind power will give greater variations of energy production, due to variabilityof wind. This means that the transmission system operators need knowledge about howincreasing wind power production affects the balance management of the power system. Thesystem operator might need to increase the reserves to be prepared to compensate for notpredicted power balance variations. Load variations are easier to predict, so the variability ofwind may increase the need for additional reserve.

Simulations are performed to determine additional requirement of frequency-controllednormal operation reserve, the calculations show that the need of additional reserves inNorway would be:

The frequency-controlled normal operation reserve would require an increase of 35 MWwith 3500 MW wind power production.

The frequency-controlled normal operation reserve would require an increase of 70 MWwith 7000 MW wind power production.

The frequency-controlled disturbance reserve is considered not to be changed. The existingdefinition is that this reserve is based on loss of the dimensioning fault. This is currently thesystem protection scheme in southern Norway, which could be maximum 1200 MW. Sincenone of the planned wind farms will be larger than dimensioning fault, there is no need of

changing this reserve.

The calculated reserves for frequency-controlled normal operation reserve is rather small, butin range of what is expected. Increasing wind power penetration in the power system hasrelatively small impact in the time scale one second to a few minutes. When wind farms havea geographical diversity over a larger area, there will be obtained a smoothing effect for veryshort variations of wind power generation; since these variations are uncorrelated they willcancel each other. On the other hand, there is ongoing development which in the futuremight give larger wind turbines, and lead to fewer and more concentrated wind farms. Thiswill eliminate the smoothing effect, and give challenges for the balance management.

For the time frame 15 minute to 1 hour, it is important from a system point of view, toallocate necessary reserves in case of imbalances according to forecast. In Norway and theNordel-area, the planning and forecast is performed 12-36 hours ahead of operation hour.This is due to the market requirement, and the trading of power is today performed at Elspot.The required additional reserve for prognosis uncertainty is assessed statistically by aprobabilistic method including 99,99 % of the variations of the forecast errors of net load. Thestatistical approach has given these required additional reserves due to prognosis uncertaintybased on day-ahead planning:

The prognosis uncertainty would require an increase of the reserve to 228 MW with 3500MW wind power production by day-ahead planning.

The prognosis uncertainty would require an increase of the reserve to 808 MW with 7000MW wind power production by day-ahead planning.

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The forecast error margin obtained from the DENA-study is considered to be the worst casevalues. Based on the ongoing research and development in prediction methodology and

prediction tools, this will improve the accuracy of forecasting and decrease the error margin.The time horizon of planning the operation hour is also a sensitive parameter. The parties inNorway can trade on the Elspot market until 12 hours before operation hour. It may benecessary to change this practice when larger amount of wind power is introduced in thepower system, since shorter time horizon of planning will decrease the need to allocate solarge reserves as indicated in this study.

Large amount of wind power in the power system can have an impact on the system alsoduring low load conditions. Especially during summertime with low load and during nightwith sudden wind increase, it is important to have local capacity. In these conditions therecould be shortage of downward regulating reserves at the regulating market, e.g. due tomaintenance of production units or full production for export. During the spring, it is also

often flood and low load. Under these conditions, the hydro power plants are utilized tomaximum. A sudden unexpected change of wind speed could now generate a problem. Thelatter alternative will mainly be of economical character, since the owner of hydro powerstations have no or small interest reducing its power generation.

Increasing wind power generation and due to the variability of wind, there might be need formechanisms to follow the quick changes better then todays solution with regulation forceand regulations in the regulating power market, and AGC could be an alternative. AGC cancontrol both frequency and power beyond normal speed controlled by primary control(statics). The main purpose is to regulate the power output of electric generators withinprescribed area in response to changes in system frequency, tie-line loading, or the relation ofthese to each other, to maintain the scheduled system frequency and/or the established

interchange within predetermined limits.Due to a larger need of regulating power, this may lead the parties to use Elbas as their newmarket instead of Elspot. Elbas will probably not be used that much in Norway until largeamount of wind power is installed in the power system, and experiencing larger imbalancesdue to the variability of wind, and the need of regulating power until the hour beforeoperation. Elbas will be introduced in 2009 in the Norwegian market.

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7.2 Proposaloffuturework

In light of achieved results in this study and thorough examination of initial obtained datafrom Statnett, several proposals are imposing, regarding improvement of existing model andfurther work on elaborated theme.

Improvement of existing Statnett model should include:

Modeling of wind farms with newer version of dynamic models

Include wind farms for the Nordic PSSE-model

Data quality checking of PSSE network model in order to make it more correct

Voltage band for transformer with tap-position

Inclusion of up-to-date wind turbine models to replace obsolete/old GE model Check electrical data for components on lower voltage levels

Go through all 3-windings transformers modeled as 3 x 2-winding

Future analysis on such built model could include

Revision of existing studies and more in-depth studies as described below.

More comprehensive forecast error margin study for both wind power production and loadconsumption

Transmission Reliability Margin Study

Implementation of automatic secondary control in power system and assessment ofimpacts on power system

Affects of increasing regulating force

Comprehensive analysis of low load conditions

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Literature

[1] SSB, Energiregnskap,www.ssb.no/energiregn

[2] NVE, Installed and planned wind power in Norwayhttp://www.nve.no/modules/module_111/netbasNVE.asp?script=8

[3] Nordel, System Operation Agreement, 2006

[4] Nordel, Description of Balance Regulation in the Nordic Countries, 2008

[5] Deutsche Energi-Agentur GmbH, Energiwirtschaftliche Planung fr dieNetzintegration von Windenergi in Deutschland an Land und Offshore bis zum Jahr2020, 2005

[6] Hannele Holtinen The impact of large scale wind power production on the Nordicelectricity system, Doctor Thesis, VTT 2004

[7] R.Hudson, B.Kirby, Y.H.Wan: Regulation requirements for wind generation facilities,2003

[8] Elbas homepage,http://www.elbas.net/

[9] Norconsult, Storskala integrasjon av vindkraft Konsekvenser for sentralnettet

[10] B.K.Parsons, Y.Wan, B.Kirby, Wind farm power fluctuations, ancillary services, andsystem operating analysis activities in the United States, 2001

[11] Siemens PTI, PSSE Wind Modeling User guide

[12] Siemens PTI, PSSE manuals

[13] Thomas Ackermann (ed), Wind power in power systems

[14] J.F.Manwell, J.G.McGowan, A.L.Rogers, Wind Energy Explained - Theory, Design andApplication

[15] Nordel, Improved balanced and frequency control of the Nordic power system, 2008

[16] Yih-Huei Wan, Wind power plant behaviors: Analyses of long-term wind power data,2004

[17] Statnett, Load and load forecast data for the period 2005-2008.

[18] Statnett, Wind power production data

[19] Nordels homepage, www.nordel.org

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Appendix

AppendixA Descriptionofprimaryandsecondarycontrol

Primary operating control

This is the most critical reserve for the system security, as this reacts during the first secondsafter an occurrence (e.g. change in load consumption, loss of generation etc.) to stop the fallin frequency.

Inertial response the inherent response of synchronized generators to do changes in thesystem frequency

Fast response the automated action to increase generation from scheduled plant

Secondary operating control

The role of secondary response is to return system frequency to 50Hz after the initialoccurrence. This response is normally provided by part-loaded generating plants, which canproduce full load within few minutes after a low frequency event, or reduced in a highfrequency event.

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AppendixB Nordelharmonisedbalancemanagement

In the beginning of January 2009, Nordel has agreed to introduce common principles for thebalance management. The harmonisation applies to:

Principles for cost allocation

Calculation and pricing of balance power and common fee structure

Introduction of intra-day trading in the Nordic exchange area

Defined cost elements will be included in the cost basis for balance management in allcountries and financed by the balance responsible parties (BRPs).

There will be two balances, one for generation, which will be settled in accordance to thetwo-price system, and one for consumption and trade, which will be settled in accordance

with the one-price system. The balances will be calculated per price area and be settled withthe area balance power prices.

This will lead to a need that all market players have the same possibility to plan their balanceand to adjust it when necessary. Therefore Elbas will be introduced in Norway in 2009. Aharmonised gate closure time of 45 minutes for regulating bids and production will also beintroduced in all countries. [4]

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AppendixC Dynamicdata

Statnett has provided the following prepared dynamic files (dyre-files) which have been usedin PSSE:

3500MWvind.dyr

Norge_d07h_a-modifisert-for-vindkr-prosj.dyr

Karsto.dyr

SVC_tunnsj_vikl.dyr

There was not provided a dynamic file suitable for the 7000 MW-scenario, so this file has

been prepared based on the same term as the dyre-file 3500MWvind.dyr. PSSEWind hasbeen used to prepare the files and do the setup. This is a graphical user interface, provided bySiemens PTI.

The modelling of the wind parks in the provided dynamic data set is based on the 3.6 MWdoubly-fed induction generator (DFIG) by GE, where all wind parks have been attached thesame dynamic models described in the following:

GAERA Aerodynamic model which calculates the aerodynamic torque applied to therotor taking in account wind speed, tip speed ratio Lambda, performancecoefficient Cp etc.

GECNA Active rotor control model (representation of rotor side converter control)

GEDFA Doubly-fed induction generator model including

wind balance report siemens 161208

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