Impact of wind power on power system operation

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This presentation is based on a thesis which investigated on various levels the technical impact of wind power on the operation of the electrical power system. …

This presentation is based on a thesis which investigated on various levels the technical impact of wind power on the operation of the electrical power system.

First, the state of the art of actual wind turbines is briefly reviewed.

Then, the importance of specific ‘grid connection requirements’ is explained. These requirements are generally a set of technical demands that wind turbines have to comply with in order not to cause instability of the electrical power grid. This issue has gained importance since the fast increase of installed wind power in some European countries, e.g. Denmark, Germany and Spain. Whether a wind turbine complies with these technical requirements or not can be examined using detailed dynamic models of wind turbines. This is pointed out in this dissertation.

In a next part, all wind power production units in one control zone (i.e. a zone where one power system operator controls the transmission system) are hypothetically considered together as one power plant. The value of this aggregated wind power production is discussed, using three different value indicators: 1) the capacity factor, 2) the capacity credit, and 3) the abatement of carbon dioxide emissions by the other power plants in the regarded control zone. This final value indicator highly depends on the grid considered. The value of wind power is worked out for the specific case of the Belgium, for various scenarios of wind power that can be installed in the future.

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  • 1. Impact of Wind Energy on Power System Operation Joris Soens web-event Leonardo ENERGY 16 February 2006 Katholieke Universiteit Leuven Faculteit Ingenieurswetenschappen Departement Elektrotechniek (ESAT) Afdeling ELECTA
  • 2. Presentation Outline
    • Introduction: wind power in Belgium, state of the art
      • installed power, turbine types
      • interaction with power grid
    • Dynamic modelling of wind power generators
    • Aggregated wind power in the Belgian control area
      • hourly time series
      • value of wind power
    • Conclusions
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 3.
    • I. Wind power, state of the art
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 4. Levels of installed wind power in Europe Introduction Dynamic Modelling Aggregated Wind Power Conclusions Installed [MW] end 2003 New [MW] 2004 Installed [MW] end 2004 Germany 14.609 2.037 16.629 Spain 6.203 2.065 8.263 Denmark 3.115 9 3.117 ... Netherlands 910 197 1.078 ... Belgium 68 28 95 (> 160 in 2005) Europe (EU25) 28.568 5.703 34.205
  • 5. Control options for wind turbines
    • Speed control
      • fixed speed
      • variable speed limited range
      • variable speed wide range
    • Reactive power control
    • Blade angle & active power control
      • fixed blade
      • pitchable blade
    • Yaw control
    highly dependent on generator type Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 6. Generator types for wind turbines (I)
    • squirrel cage induction generator
      • nearly fixed speed
      • always inductive load
    Turbine Grid shaft & gearbox wind SCIG ~ Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 7. Turbine generator types (II)
    • doubly fed induction generator
      • variable speed – limited range
      • reactive power controllable
    shaft & gearbox DFIG Converter ~ Grid Crowbar Turbine Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 8. Turbine generator types (III)
    • synchronous generator, direct drive
      • variable speed – wide range -> no gearbox
      • reactive power controllable
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions SG Turbine Converter ~ Grid Permanent Magnet OR Field Winding
  • 9. Interaction with power grid
    • Until recently:
      • wind power = negative load
    • Now:
      • wind power = actively contributing to power system control
        • ride-through capability
        • voltage control
        • output power control
      • specific grid connection requirements
      • development requires dynamic models
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 10. Example: ride-through requirement
    • Wind turbine disconnects at light grid disturbance
    • Disconnection causes new grid disturbance
    • Cascade-effect may result in major sudden loss of wind power
    • Example:
      • Spain, February 26, 2004
      • 600 MW loss of wind power due to one grid fault
    • Therefore: definition of voltage profiles that must not lead to disconnection
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 11. Example: ride-through requirement by E.ON Netz (Germany)
    • Each voltage dip remaining above red line must not result in disconnection of the generator
    • Within the grey area, extra reactive power is demanded from the wind power generator to deliver voltage support
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 12.
    • II. Dynamic modelling of wind power generators
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 13. Dynamic modelling of wind turbines for use in power system simulation
    • Power system simulation software:
      • simulate dynamically short-circuits, load steps, switching event ....
      • interaction wind turbine model and grid model:
    grid controlled wind turbine grid dispatch & control wind speed injected current voltage at turbine node reference P and Q controlled grid parameters Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 14. Detailed turbine model with doubly fed induction generator v wind u turb q ref p ref i turb Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 15. Detailed turbine model: simulation examples
    • step-wise wind speed increase
    • voltage dip at turbine generator
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 16. Detailed turbine model: simulation example I (1) simulation input: step-wise increasing wind speed wind speed at hub height 400 600 800 1000 1200 1600 1800 2000 10 20 [m/s] time [s] Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 17. Detailed turbine model: simulation example I (2) 400 600 800 1000 1200 1600 1800 2000 time [s] 0,5 1 power [p.u.] variable speed & pitch control fixed speed & pitch control fixed speed & no pitch control turbine power for increasing wind speed Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 18. Detailed turbine model: simulation example I (3) 400 600 800 1000 1200 1600 1800 2000 time [s] 0,5 1 speed [p.u.] turbine speed for increasing wind speed variable speed turbine constant speed turbine Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 19. Detailed turbine model: simulation example I (4) zoom on turbine speed variable speed: propeller speed variable speed: generator speed fixed speed: propeller speed fixed speed: generator speed 995 1000 1005 1010 1015 1020 1025 0.95 1 1,05 time [s] speed [p.u.] Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 20. Detailed turbine model: simulation example II (1) 1000 1001 1002 voltage at turbine generator 0.4 0.6 1 [p.u.] 0.8 0.2 time [s] simulation input: voltage dip at turbine generator Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 21. Detailed turbine model: simulation example II (2) 1000 1005 1010 1015 time [s] 0.9 1 1.1 1.2 speed [p.u.] propeller speed generator speed propeller and generator speed during voltage dip, for fixed-speed turbine with induction generator Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 22. Detailed turbine model: simulation example II (3) propeller and generator speed during voltage dip, for variable-speed turbine with doubly fed induction generator 1000 1005 1010 1015 time [s] 0.9 1 1.1 1.2 speed [p.u.] propeller speed generator speed Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 23. Dynamic turbine model: conclusions
    • Detailed model allows
      • examination of interaction between turbine and grid
      • electrical & mechanical quantities
      • good understanding of turbine behaviour
      • thorough insight in mechanical and electrical behaviour of turbine/grid
      • simulation of ‘heavy’ transients
      • help to set up connection requirements
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 24. III. Aggregated wind power in the Belgian control area Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 25. Wind power in Belgium 95 MW wind power in total installed by end of 2004 (onshore) One offshore wind farm (216 - 300 MW) permitted and near construction phase (start construction soon) Legal supporting framework for offshore wind farms ‘established’ in January 2005 Best wind resources are offshore or in the west part (near shore) Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 26. High voltage grid in Belgium Introduction Dynamic Modelling Aggregated Wind Power Conclusions 150 kV 220 kV 400 kV
  • 27. Aggregated wind power in the Belgian control area
    • Time series of aggregated wind power
    • Value of aggregated wind power
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 28. Time series for aggregated wind power
    • Research project ELIA - ELECTA
    • Research goal
      • estimation of hourly fluctuation of aggregated wind power in Belgium
    • Use
      • estimation of need for regulating power
      • estimation of value of wind power
    • Available data
      • Wind speed measurements at three sites in Belgium
      • Scenarios for future installed wind power
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 29. Available wind speed data Wind speed data from meteo-stations Ostend, Brussels, Elsenborn Three-year period (2001 – 2003), hourly resolution Anemometer height: 10 m Complementary to data from European Wind Atlas (turbulence, landscape roughness…) Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 30. Available wind speed data Ostend 140 km Brussels 110 km Elsenborn 60 km 140 km prevailing wind direction Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 31. Scenarios for installed wind turbines
    • Turbine type parameters:
      • power curve
      • hub height
    • Developed algorithm allows arbitrary number of types
    • In following application: two turbine types
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 32. Scenario I Evenly distributed Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 33. Scenario II Concentrated Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 34. Scenario III One offshore farm Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 35. Scenario IV Scen. II + Scen. III Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 36. Algorithm output: aggregated wind power time series Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 37. Quantization of power fluctuations: power transition matrices
    • Number of occurrences that a power value in hour H is in given range
    • As a function of power value in hour H – 1, H – 4….
    • Example: H vs. H-1 matrix for Scenario 1
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 38. H vs. H-1 matrices for all scenarios Scenario I Scenario II Scenario III Scenario IV Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 39. Value of aggregated wind power
    • Possible indicators for value of wind power
      • Capacity factor
      • Capacity credit
      • Potential reduction of CO 2 -emission by total power generation park in Belgium
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 40. Capacity factor
    • Calculated for separate turbine or for aggregated park
    • Most important parameter for turbine exploiters, when money income ~ produced energy
    capacity factor = annual energy production [MWh] installed power [MW] x 8760 [h] Introduction Dynamic Modelling Aggregated Wind Power Conclusions Scenario capacity factor [%] equivalent full-load hours I 20 1752 II 26 2278 III 31 2715 IV 29 2540
  • 41. Capacity credit: definition
    • reliable capacity
      • amount of installed capacity in a power system, available with given reliability to cover the total power demand
    • loss of load probability (LOLP)
      • probability that total power demand exceeds the reliable capacity
    • capacity credit of wind power
      • Amount of conventional power generation plants that can be replaced by a given level of wind power, without increase of the LOLP
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 42. Capacity credit: calculation H( 0 ) = LOLP = 4 h/year Assumption: probability that Total power demand > (reliable capacity + D MW ) Impact of additional power generator (park), with production probability p( P plant ) Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 43. LOLP graphical Introduction Dynamic Modelling Aggregated Wind Power Conclusions 0 500 4 3 2 1 0 D (Demand not served) [MW] [hour/year]  = 30 Q peak = 13.5 GW H(0) = 4 h/year LOLP H (D )
  • 44. Capacity credit graphical 0 500 4 3 2 1 0 D (Demand not served) [MW] H (D ) & H 2 (D) Introduction Dynamic Modelling Aggregated Wind Power Conclusions [hour/year]
  • 45. Absolute capacity credit for wind power in Belgium 1000 2000 3000 4000 0 100 200 300 400 5000 Installed wind power [MW] Capacity credit [MW] Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 46. Shortcomings of capacity factor/credit as value indicator
    • Moment of energy production?
      • Instantaneous demand for electrical energy?
      • Energy production in next time sample?
    • True value indicator must reflect difference of a chosen paramater, between case with and without wind power
    • This requires
      • Knowledge of entire power system
      • Dynamic simulation of entire power system
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 47. Dynamic simulation of entire power system (1)
    • Simulation tool PROMIX (‘Production Mix’)
    • Input data:
      • Parameters for all power plants in control area
        • Power range
        • Costs of start-up and continuous operation
        • Time for start-up and power regulation
        • Fuel consumption, gas emissions... for various operating regimes
      • Time series of aggregated load in control area (resolution: 1 hour)
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 48. Dynamic simulation of entire power system (2)
    • Output:
      • Optimal power generation pattern for every hour
      • Fuel consumption, emissions, costs... for every plant & hour
    • Integrating wind power time series in input data
      • As equivalent reduction of aggregated load
      • For large values: ‘reliable’ wind power required
    • Results: CO 2 -emission abatement for various levels of installed wind power
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 49. Relative annual abatement of CO 2 -emission Scenario I 5 10 15 20 0 2 4 6 8 Installed wind power [% of peak demand] CO 2 emission abatement [% of reference case] Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 50. Relative annual abatement of CO 2 -emission 5 10 15 20 0 2 4 6 8 Installed wind power [% of peak demand] Introduction Dynamic Modelling Aggregated Wind Power Conclusions Scenario III CO 2 emission abatement [% of reference case]
  • 51. Conclusions Value of wind power
    • Capacity factor: 20 - 31 % (spreading)
    • Capacity credit: 30 -10 % (installed power)
    • CO 2 emission abatement:
      • Optimum: 4% reduction for installed wind power equal to 5% of peak demand ( = 700 MW)
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 52.
    • IV. Conclusions
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 53. Conclusions (1)
    • Technical challenges for wind power integration are identified
    • Dynamic models are developed
      • responding to needs of quantifying higher electrical & mechanical demands towards wind turbines
      • detailed dynamic models, assessing all mechanical/electrical quantities
      • simplified dynamic models, allowing rough estimates of wind power absorption potential at busbar
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 54.
    • Hourly fluctuations of aggregated wind power in Belgium are quantified
    • Value of wind power in Belgium assessed with three indicators
      • Capacity factor
      • Capacity credit
      • Abatement of CO 2 -emission by total power generation park
    • > 700 MW installed power: wind power ≠ negative load
    Conclusions (2) Introduction Dynamic Modelling Aggregated Wind Power Conclusions
  • 55. Recommendations for further research
    • Accurate wind speed forecasting
    • Integrating forecast updates in implementation of electricity market
    • Electricity storage
    • Demand side management
    • Impact of wind power on European border-crossing power flows
    Introduction Dynamic Modelling Aggregated Wind Power Conclusions Impact of wind energy in a future power grid Ph.D Joris Soens – 15 december 2005, K.U.Leuven http://hdl.handle.net/1979/161