EP3867990A1 - Procédé et parc éolien pour fournir une puissance électrique à un réseau de distribution électrique - Google Patents

Procédé et parc éolien pour fournir une puissance électrique à un réseau de distribution électrique

Info

Publication number
EP3867990A1
EP3867990A1 EP19789665.7A EP19789665A EP3867990A1 EP 3867990 A1 EP3867990 A1 EP 3867990A1 EP 19789665 A EP19789665 A EP 19789665A EP 3867990 A1 EP3867990 A1 EP 3867990A1
Authority
EP
European Patent Office
Prior art keywords
wind
power
expected
performance
wind farm
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP19789665.7A
Other languages
German (de)
English (en)
Inventor
Johannes BROMBACH
Marcus Letzel
Swantje Amelsberg
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wobben Properties GmbH
Original Assignee
Wobben Properties GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Wobben Properties GmbH filed Critical Wobben Properties GmbH
Publication of EP3867990A1 publication Critical patent/EP3867990A1/fr
Pending legal-status Critical Current

Links

Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/028—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor controlling wind motor output power
    • F03D7/0284—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor controlling wind motor output power in relation to the state of the electric grid
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/048—Automatic control; Regulation by means of an electrical or electronic controller controlling wind farms
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2260/00—Function
    • F05B2260/82—Forecasts
    • F05B2260/821—Parameter estimation or prediction
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2260/00—Function
    • F05B2260/82—Forecasts
    • F05B2260/821—Parameter estimation or prediction
    • F05B2260/8211—Parameter estimation or prediction of the weather
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/10—Purpose of the control system
    • F05B2270/103—Purpose of the control system to affect the output of the engine
    • F05B2270/1033—Power (if explicitly mentioned)
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/335—Output power or torque
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Definitions

  • the present invention relates to a method for feeding electrical power into an electrical supply network by means of at least one wind farm having a plurality of wind energy plants.
  • the invention also relates to a corresponding wind farm.
  • a method for feeding electrical power into an electrical supply network by means of a wind farm, which combines several wind turbines, is generally known.
  • wind farms are also increasingly having tasks in the area of network system services and / or including support tasks up to network restoration tasks or even preparing or carrying out a so-called black start.
  • Such tasks are sometimes difficult to accomplish for a wind farm, especially because its primary energy, namely the wind, can not only fluctuate greatly, but is also difficult to predict.
  • German Patent and Trademark Office researched the following prior art in the priority application for the present application: DE 10 2017 129 299 A1, US 9 690 884 B2, US 2014/0 195 159 A1, EP 3 432 091 A1 and CA 2 770 637 A1.
  • the present invention is therefore based on the object of addressing at least one of the problems mentioned above.
  • the ability to plan available power for a wind farm should be improved.
  • At least an alternative solution to previously known solutions is to be proposed.
  • a method according to claim 1 is proposed. Accordingly, a method for feeding electrical power into an electrical supply network by means of at least one wind farm is assumed.
  • Such a wind farm has a number of wind turbines, at least one that feeds into the electrical supply network via a common network connection point. The wind farm thus feeds electrical power into the electrical supply network at the network connection point.
  • an expected performance is determined for a predetermined feed-in period.
  • the expected power specifies a value of a power or a time course of a power that is to be expected to be available to the at least one wind farm as power from the wind in the predetermined feed-in period.
  • this is to be understood in such a general way that this wind power is available and, at least in theory, also taken up by the wind farm and can be implemented. Initially, this can ignore any technical limits.
  • the expected output can be understood as the sum of all rotor outputs of all wind energy plants in the wind farm. At every wind power plant, there is wind, which acts on the rotor of the wind power plant and is therefore available to the rotor as power, which is referred to as rotor power.
  • the expected output of the wind farm is then the sum of all of these rotor outputs of all the wind farms of the wind farm or of all the wind farms of the wind farm under consideration.
  • the expected performance can also be understood as the feed-in power of the wind farm.
  • Such a feed-in power of the wind farm can differ from the sum of all rotor powers in that the feed-in power, which can also be referred to as feed-in power, can also take into account power losses and power limitations, and is therefore regularly less than the sum of all rotor powers.
  • an expected accuracy is determined for the expected performance, which is a measure of how exactly the performance in the feed-in period achieves the expected performance. The accuracy of expectations is therefore a measure of the quality of the performance forecast.
  • At least one wind maintenance variable representative of the expected wind speed is predetermined with the aid of a weather forecast, and the win
  • PF wind maintenance quantity
  • the weather forecast which is created especially for a larger geographical area than the space that the wind farm occupies, may show deviations or errors on the wind farm. For this purpose, it was recognized that such deviations or errors can at least be reduced if local weather data and / or operating data of the wind farm are used for improvement.
  • the local weather data and operating data of the wind farm can also be referred to as local wind farm data. They can include weather data in the wind farm or its surroundings, such as wind speeds, wind directions, temperature, precipitation and air pressure, but also specific operating data, such as generated or output power.
  • the local weather data or some of them can also be recorded in the wind farm, e.g. Wind speed and direction.
  • the wind direction can also be derived from azimuth positions of the wind turbine.
  • This local wind farm data can then be used to correct the weather forecast in order to have a better basis for determining the wind maintenance size.
  • the wind maintenance size is thus determined as a forecast and can have a high accuracy due to the correction rule.
  • the wind maintenance variable can itself be a wind speed or also a profile of a wind speed, which in fact represents the expected wind speed if possible. However, it can also be another representative variable, such as a standardized value, which can be standardized, for example, to the nominal wind speed. However, it is also possible to consider a service, e.g. a rotor power, which could be generated with the expected wind speed.
  • the wind maintenance size is verified particularly depending on the operating data of the wind farm. This will particularly suggest for the variant that the wind maintenance size is itself a performance and is compared with the actual performance.
  • the wind maintenance size is a performance curve that was created over a predetermined forecast period. The start of the forecast period can correspond to the current point in time and the remaining forecast period is therefore in the future. Then the wind maintenance size, namely its performance value, can be used at the beginning of the forecast period current performance value, the comparable performance of the wind farm can be compared. If these two performance values are separated by a deviation factor, for example, if the performance value of the forecast is 20% below the current performance value of the wind farm, the forecast can be corrected by 20%, ie the deviation factor. This means that the wind maintenance size is corrected by this deviation factor for the entire forecast period. This deviation factor would then be the correction rule.
  • the principle can also be applied analogously to the wind maintenance value if this is a forecast wind speed and is compared with a measured wind speed, to name a further example.
  • This is also applicable to other cases, such as a wind direction, wherein a wind direction can be taken into account particularly as a boundary condition for the wind speed. But the wind direction can be an important factor, especially when considering parking effects.
  • the wind maintenance size can comprise several sizes, namely the wind speed and the wind direction according to one embodiment.
  • the expected performance is determined based on the wind expectation size.
  • the wind maintenance size is still largely independent of specific data from the wind farm, such as technical availability or shading effects in the park, and is essentially a forecast for the wind, whereas the expected performance specifically relates to the power that the park can then feed in or generate .
  • the expected performance may correspond to the wind expected amount.
  • the weather forecast is created over a forecast period and the wind maintenance size and / or the expected performance is created as a short-term forecast over a forecast period, the forecast period being at least ten times as large as, preferably at least fifty times, in particular at least one hundred times as large Forecast period.
  • the weather forecast extends further into the future than the short-term forecast. It was particularly recognized here that a short forecast period is sufficient for a network restoration or black start, and therefore a short, accurate forecast is better than a long, inaccurate forecast.
  • the determination of the wind maintenance size, in particular the short-term forecast does not serve to calculate an electricity remuneration or to control the operation of the wind farm after the electricity remuneration, but the short-term forecast enables the wind farm to take on a predictable support task for the electrical supply network.
  • a forecast period of a short-term forecast is particularly in the range up to one hour, in particular in the range up to half an hour, whereas a forecast space of a weather forecast is in the range above several hours, especially above half a day, or even above several days.
  • a short-term forecast in the range from 10 minutes to 30 minutes is preferably proposed, in particular about 15 minutes.
  • a forecast period can particularly include 3 calendar days, i.e. the current, next and the day after next, and therefore, according to an example, not in the sense of 72 hours.
  • an update is carried out every 6 hours, for example by the weather service that delivers the weather forecast.
  • you can use the weather forecast which can also be a forecast stored in a memory, to predict 48 to 72 hours into the future depending on the time of day. This forms the basis for the improved short-term forecast.
  • the forecast period is therefore significantly shorter than the forecast period.
  • the expected size and expected performance usually have the same time horizon and thus the same forecast period, so that both are then preferably provided as short-term forecasts.
  • This comparison period can generally correspond to the feed-in period, but it is preferably a shorter period of its own, which is also recurrently redefined or, with continuous predetermination, can also be a moving comparison period.
  • a prediction comparison be carried out for each comparison period for which the wind maintenance size was predetermined.
  • a prediction size with a current wind size is used for the current wind speed of the respective comparison period is representative.
  • the prediction variable can be the wind maintenance variable, for example, but it can also be the expected output, for example. It is also possible that this is a wind speed or a course of the wind speed, particularly in the case of the wind maintenance variable. This is then compared to a current wind size that is representative of the current wind speed of the respective comparison period.
  • the current wind size can also be a wind speed or, for example, also an output which is representative of the current wind speed at that moment.
  • the parking performance actually generated can be used. Possibly. this could still be changed arithmetically if, for example, a wind power plant in the park fails, which may be known to a central control unit.
  • a larger available output may be calculated, for example, from other key data such as rotor blade positions.
  • a comparison is made between the predicted value and the actual value, which can be referred to as a prediction comparison.
  • At least one adjustment rule is determined from this prediction comparison in order to improve the expected performance by means of the at least one adjustment rule.
  • it is particularly possible to improve a calculation rule for calculating the expected performance from the wind expected variable.
  • the correction rule can be improved by means of the adjustment rule.
  • the adjustment rule could be a factor, for example 1, 2 or 0.8, by which the calculation rule, the correction rule and / or another rule is multiplied, because comparison was found during the prediction that the expected performance was 20% below or 20% above the actual performance.
  • This adjustment rule can in particular also be valid beyond the comparison period and ideally apply for every feed-in period.
  • factor adjustment which will also be explained later.
  • a weather forecast is quite precise, but is not quite precise with regard to the location. Similar to clouds, however, wind speeds also move on and so it was recognized that a local inaccuracy can regularly be a temporal inaccuracy, that is to say that a predicted wind speed or forecast wind direction arrives at the wind farm somewhat later than predicted or something rather. It is therefore possible, for example, that a wind forecast predicts a specific wind profile for a specific period of time and that this wind profile actually occurs in a similar manner at the wind farm, but somewhat later or somewhat earlier. This can also be recognized in the prediction comparison and the adaptation rule can be determined accordingly.
  • the expected performance is finally determined based on the wind maintenance size and the adjustment rule, the latter having a direct or indirect influence, as described above.
  • the expected performance is preferably determined using a performance estimator. It is particularly proposed here that the expected power is determined from an estimated wind speed or an estimated wind speed curve using a power estimator.
  • a power estimator can be designed as a condition observer, which simulates a wind turbine or a wind farm and how it receives a wind speed and wind direction as an input variable and then behaves as a model like the wind turbine or wind farm and accordingly also outputs a power as a system variable.
  • the expected performance can also be determined taking into account boundary conditions.
  • boundary conditions One or more of the boundary conditions explained below can be taken into account.
  • technical availability of the wind farm's wind turbines can be taken into account. In the simplest case, this means taking into account which of the wind turbines of the Wind farms are in operation or whether one or more wind turbines have failed because, for example, they have to be serviced temporarily.
  • Such a power limit can be a nominal power, but such a power limit can also be an artificially specified limit, such as one that is specified for reasons of sound insulation, to name just one example. Such a limitation may then only affect one or individual wind turbines in the park.
  • a further limit power can also be specified by a network operator of an electrical supply network into which the wind farm feeds.
  • Information about available controllable loads can also be a constraint. It is particularly important here that such a load, e.g. a cold store operated by the wind farm, if necessary, may lag behind in its power consumption at least for a short time in the event of a power shortage This means that a service can be activated virtually. This is particularly important if you want to avoid falling below a minimum benefit.
  • a load e.g. a cold store operated by the wind farm
  • the above-mentioned boundary conditions can also be used for determining the accuracy of expectations.
  • the problem of falling below a minimum power is often that this happens only briefly and possibly only rarely due to the rarely ideally steady wind.
  • a low output or low energy in terms of storage can sometimes be sufficient, since the rest of the time, such support can be dispensed with on a regular basis.
  • Another boundary condition is information about wake effects in the wind farm.
  • the expected performance is additionally determined taking into account the wind power plant of the wind farm.
  • the technical performance of the wind farm is stored in a table for each wind energy installation.
  • the table for each wind energy installation is stored depending on its azimuth orientation or depending on the wind direction and optionally depending on a time of day a predicted technical performance.
  • the performance can be stored as a function of the wind speed. A multidimensional table can be provided for this. Then, together with the predicted wind speed, the expected performance can be determined or improved. Different variants can preferably be provided for the expectation accuracy, that is to say that the expectation accuracy contains or forms the information explained below. The expectation accuracy can also include several or all of the information explained below.
  • the expectation accuracy specifies a first performance limit, which in turn specifies a performance or a performance curve, which is not fallen below with a predetermined realization probability within the predetermined feed-in period.
  • a constant power value can be specified, or a wind profile over time, namely over the feed-in period, which, for example, has a probability of 95% within the predetermined feed-in period, that is, it will not fall below once .
  • this 95% probability which is mentioned here by way of example and which therefore denotes the predetermined implementation probability, is acceptable, ie if it is acceptable that the remaining 5% may occur.
  • the predetermined implementation probability does not necessarily have to be 1, that is to say 100%.
  • the accuracy of expectations can additionally or alternatively specify a second performance limit, which in turn specifies a performance or a performance history which may not be undercut in a medium-term average.
  • the performance curve may therefore fall short in the short term, but not in the long term.
  • a medium-term average is particularly one that is determined over a period of 10 to 60 seconds. If the performance drops below the second performance limit for 5 seconds, but is significantly higher before and after that, the medium-term average is then not below the second performance limit.
  • the accuracy of the expectation indicates a third performance limit which on the one hand indicates a performance or a performance history that must not be undercut in a short-term average.
  • a third performance limit is usually lower than the second performance limit because a short-term average fluctuates more than a medium-term average and can therefore also assume lower values.
  • a short-term mean is considered to be a mean over a period of 5 to 10 seconds.
  • the total output is of particular importance for a network operator, which is fed in by these multiple wind farms with the same mode of operation and the same determination of an expected accuracy of several wind farms.
  • the weather forecast or weather data for generating a weather forecast is or are regularly transmitted by an external weather service.
  • the wind farm does not make its own measurements for the weather forecast, but also makes use of generally available weather forecasts. These can also be prepared regularly so that a process computer in a wind farm can process them.
  • the wind farm at least temporarily save the weather forecast transmitted in each case. This is wisely foreseen for any interruption to the external weather service.
  • the expected performance is then estimated based on at least one stored weather forecast. In other words, there is clearly an older weather forecast than would be available if the interruption to the external weather service were not.
  • the stored weather forecast is adapted taking into account local current meteorological measured values, in particular that it is improved by means of the correction rule depending on the local wind farm data.
  • the underlying idea here is that, for example, a weather forecast predicted an increasing wind speed from 4 m / sec at 4 p.m. to 8 m / sec at 5 p.m.
  • the local current meteorological measured values only recognize, for example, only at 4:20 p.m. that the wind speed of 4 m / sec begins to increase.
  • the stored weather forecast it can be assumed that the wind speed increases to 8 m / sec by 5:20 p.m.
  • the data connection to the weather service may be interrupted. Then there are still older weather forecasts that the wind farm saved earlier. These older weather forecasts can then be improved based on the local wind farm data.
  • the data connection to the weather service, which can be interrupted, is not required for the local wind farm data, or at least part of it.
  • a black start and / or a network restoration be planned based on the expected performance.
  • a black start is a situation in which the feeder, here the wind farm, starts an electrical supply network or a section of it without any outside help. This network section is therefore de-energized and the wind farm must build up the corresponding voltage at a corresponding frequency and in the process deliver power to the first consumers connected to the network section.
  • This can be followed by a network restoration in which further network sections that were de-energized or that were at least separated from one another are reconnected and then have a network voltage with a common network frequency and can transmit power.
  • network restoration can also be planned without a previous black start, or at least without a black start by the wind farm.
  • the network connection point be connected to a network section of the electrical supply network and the expected power is transmitted to a network operator operating this network section, namely as information about the expected power.
  • the network operator is able to plan with the performance of the wind farm.
  • this is also helpful in the case of a network setup situation in which at least this network section has failed.
  • the network operator can then use this service, which is reported to him as available, and coordinate the network structure based on this.
  • the expected accuracy can also be transmitted.
  • This enables the network operator to plan even better because he knows even better what he can rely on and in what framework.
  • the effect can be particularly emphasized by the fact that several wind farms work in the same way, in particular in such a way that all this information can be automated, so that the network operator can combine it in a process computer.
  • the accuracy of expectations can also affect various variants here.
  • the accuracy of expectations can also be transferred, at least in part, in the weather forecast. It can therefore be, at least in part, information that the wind farm receives from the external weather service, in particular in computer-processable form.
  • an accuracy setpoint is taken into account, which specifies the expectation accuracy with which the expected performance is to be provided, the accuracy setpoint preferably being received externally, in particular by a network operator. It is therefore proposed, instead of quantifying the accuracy of the forecast, or in addition to orienting the forecast on a predetermined accuracy.
  • the weather forecast comprises at least a time course of an expected wind speed.
  • the weather forecast can output a value of an expected wind speed at predetermined repetition intervals.
  • a continuous or quasi-continuous time course of an expected wind speed can be used. If the weather forecast delivers a value of an expected wind speed at predetermined repetition intervals, this can be in particular in the range of 1-5 min. A value is therefore determined and given every 1 min or every 5 min or in a range in between, so that there is a quasi-continuous course.
  • These values are to be distinguished from an update rate with which an existing weather forecast or weather data is updated, which can be in the range of 6 hours.
  • the expected performance and then, or alternatively, a minimum value of the expected performance is then determined from the weather forecast as the performance over time. This can be done in particular with the help of the performance appraiser. From the continuous or quasi-continuous course of the wind speed over time, the expected performance is determined as a course, namely as a course over time. For this course of performance it is then proposed to move, stretch and / or compress it by means of the adjustment regulation. This particularly affects the amplitude of the power curve, so that it is quasi pushed up or down or compressed. It can preferably or additionally also relate to its temporal extension. The performance curve can thus be pushed forward or back in time or stretched or compressed.
  • the weather forecast is basically assumed to be correct and reliable.
  • the performance profile which is calculated from the weather forecast, is essentially correct in its basic profile.
  • the weather forecast is created more globally, i.e. for a larger area.
  • the predicted weather pattern especially the performance curve derived from it, may hit the wind farm a little earlier or later. This can be offset by appropriate shifts.
  • the course of the wind farm is somewhat delayed and thus stretched, which can be compensated for by a compression.
  • a compression can also be considered, which can be compensated for by stretching.
  • meteorological measurement values of the wind farm in the wind farm and / or in the vicinity of the wind farm be included.
  • the weather forecast can thus be improved accordingly, in particular it can be adapted to the current local values or can be adapted on the basis of these values.
  • meteorological measured values include a wind speed, additionally or alternatively a wind direction, additionally or alternatively a temperature, additionally or alternatively an air density and additionally or alternatively, solar radiation. These values could also be used to make a comparison with the weather forecast. derived whether the predicted weather pattern at the wind farm is behind the weather forecast or before. The correction rule can be determined accordingly. This can basically be done with every comparison of the mentioned meteorological measured values, but especially depending on how significant the respective size is.
  • the adjustment rule can also be determined as a function of this if at least one of these measured values, particularly the wind speed, is compared with the forecast.
  • the values, e.g. the wind direction can also be taken into account as boundary conditions.
  • measured values and / or operating values of wind energy installations of the wind farm be used to determine the correction specification and / or the adaptation specification.
  • an available power be estimated from the measured values and / or operating values.
  • the underlying idea here is that the wind turbine itself, at least through its operating behavior, at least allows statements to be made about the prevailing wind conditions. This includes in particular that the wind speed can be derived from the power, speed and blade position and azimuth position and of course also the parameters of the wind turbine. The wind direction can be derived accordingly from the azimuth position, which is usually also expressly known in the wind energy installation. All of this can also be used for comparison, especially for the prediction comparison to determine the adjustment rule.
  • the correction rule can also be determined with the aid of at least one of the values of the wind farm, for example by allowing the estimated available power of the wind farm to draw conclusions about weather data in order to correct the weather forecast.
  • the determination of the expected performance of the wind farm is carried out on a wind farm computing unit of the wind farm. The calculation is therefore carried out locally on site and has the particular advantage that any system values from the wind turbine or wind farm can be used in a simple manner, without the need for complex transmission.
  • the wind farm computing unit be provided with an uninterruptible power supply and that in the event of a power failure of the electrical supply network the wind farm computing unit will continue to determine the expected power using the uninterruptible power supply and transmit it to one or the network operator.
  • This is based in particular on the knowledge that the proposed method enables the wind farm to be able to estimate the current weather situation as accurately as possible even in the event of a grid failure and thus has information on how much power may be required to restart the wind farm and thus to Support or reconstruction of the electrical supply network or a part thereof is available. This information can be transmitted to the network operator and he can plan with it.
  • the uninterruptible power supply can also be used to control and / or carry out the recording of local weather data and / or operating data of the wind farm or to provide the supply current required for this.
  • the uninterruptible power supply can also supply a supply current for a computing unit with a data memory on which older weather forecasts are stored, in particular the wind farm computing unit.
  • the predetermined wind expected variable or the expected output is compared with the current wind variable.
  • the expected performance corresponds in particular to the output that the wind farm ultimately feeds into the electrical supply network and this can be compared with the current wind size, which is then the current output of the wind farm.
  • an intermediate variable can also be used for comparison.
  • the predetermined wind maintenance value corresponds to such an intermediate value, which can, for example, indicate a power that is even greater than the power actually fed in, because, for example, no effects in the park, such as wake effects, have been taken into account.
  • the Wind maintenance size can also be a wind speed that has been predicted and compared to a representative measured value in the wind farm or in the vicinity of the wind farm.
  • the wind maintenance variable be determined using a weather model from the weather forecast, in particular from weather data from the weather forecast.
  • the weather model can contain the correction instruction, or the correction instruction follows the weather model.
  • an idealized feed-in power can be determined from the wind forecast using a wind power model. If the idealized feed-in power is determined, the expected power is determined from the idealized feed-in power using an availability model.
  • the method thus comprises at least three steps, namely to determine the wind maintenance variable from the weather forecast, from this to determine the idealized feed-in power and from this to determine the expected power.
  • the expected performance is determined from the wind expectation size using a parking model. To this extent, the expected output is determined without the intermediate step via the idealized feed-in power from the wind expected variable.
  • the expected performance is determined using one or the performance estimator, the performance estimator comprising at least one of the following models or being one of these models, namely the weather model, the wind power model, the availability model and the parking model.
  • the weather model be adapted by means of a weather model adaptation.
  • the weather model adaptation can be viewed as an adaptation regulation or as one of the at least one adaptation regulation.
  • the wind power model can be adapted by means of a wind power model adaptation. Then the wind power model adaptation is one of the adjustment regulations.
  • the correction rule is preferably adapted by means of one of the at least one adjustment rule. According to one variant, this can also be included in the adaptation of the weather model if the correction rule is part of the weather model.
  • the availability model can be adapted by means of an availability model adaptation. The availability model adaptation can then be viewed here as one of the at least one adaptation rule.
  • the parking model adaptation can then be viewed as an adaptation regulation.
  • each of the four models mentioned is adapted.
  • This adaptation can in particular be carried out in such a way that the result of the respective model is compared with a corresponding measured value or at least with a current value determined from current values and the comparison is used to adapt the respective model.
  • the power estimator is preferably designed as a neural network, particularly if it is used to determine the idealized feed-in power or the expected power.
  • the neural network is trained using an offline method, in particular that it is trained using meteorological measured values from the wind farm.
  • the trained neural network is used to determine the idealized feed-in power or the expected power.
  • the use and training of such a neural network are one way of implementing an adaptation.
  • the use of the neural network has the particular advantage that the structural relationships, which should in each case be used as the basis for a performance estimator, when using a such neural network must be known less precisely, as long as the structure, including the levels, of the neural network is chosen to be sufficiently large.
  • the underlying idea here is that a particularly critical case, in which the performance should be predicted as accurately as possible, only occurs in a rare case of a network restoration or even a black start.
  • the neural network can normally be trained to this extent.
  • the result is not used to control the wind farm, it is also considered to be an offline method, because the values determined are not directly incorporated into the control, but are saved.
  • the neural network and, in particular, the performance estimator can be continuously improved during operation.
  • the result can be used particularly when such a network reconstruction or black start has to be carried out and the connection to the weather service that supplies the weather forecast is interrupted. Then, it is preferable to use older stored predictions, which may accordingly require a high adjustment for the wind farm.
  • a wind farm is also proposed.
  • This wind farm is intended for feeding electrical power into an electrical supply network and it is provided that the wind farm is connected to a network connection point in order to feed electrical power into the electrical supply network.
  • it has an estimation device in order to determine an expected performance for a predetermined feed-in period.
  • This estimation device can be implemented as a device but also as an implemented program in a control computer, in particular a central parking computer.
  • the expected power indicates a value of a power or a time profile of a power that is to be expected to be available to the at least one wind farm as power from the wind in the predetermined feed-in period, in particular as the sum of all rotor powers of all wind energy systems wind farm and / or as feed-in power of the wind farm.
  • the various possible meanings of the expected performance have already been explained in connection with the feed-in method and the explanations given there also apply here.
  • an evaluation device is provided in order to determine an expected accuracy for the expected performance, which is a measure of how exactly the performance achieves the expected performance in the feed-in period. This evaluation device can also be provided as a device or implemented as a program.
  • the estimation device for determining the expected performance is proposed.
  • a wind maintenance estimator that is prepared to determine or verify at least one wind maintenance variable that is representative of the expected wind speed with the aid of a weather forecast, and comprises a correction unit that is prepared to additionally determine the wind maintenance variable based on the weather forecast by means of a correction specification depending on local weather data and / or operating data of the wind farm, where
  • the estimation device is prepared to determine the expected performance based on the wind expected size.
  • the wind farm is thus prepared to determine the expected power, as explained above in connection with aspects of the method for feeding in electrical power.
  • the wind maintenance estimator, the correction unit and / or the estimation device can be provided individually or together as a device or as a solution implemented on a process computer.
  • the wind maintenance estimator is prepared to recurrently or continuously predetermine the wind maintenance variable using a weather forecast for a comparison period. The wind maintenance estimator therefore uses the weather forecast to build on the wind maintenance size based on that, in particular continuously or quasi-continuously.
  • the wind maintenance variable is representative of the expected wind speed and can therefore each include a wind speed value.
  • Power values are particularly representative of a wind speed if they are based on the assumption en lies in the fact that they each indicate a power that can be generated with a wind turbine or a wind farm depending on the wind speed.
  • a comparison unit is provided in order to carry out a prediction comparison for each comparison period for which the wind maintenance variable has been predetermined, in which a prediction variable is compared with a current wind variable that is representative of the current wind speed of the respective comparison period.
  • the comparison unit can also be designed as a device or as an implemented program.
  • an adjustment unit is proposed in order to determine at least one adjustment rule from the prediction comparison in order to improve the expected performance with the at least one adjustment rule.
  • the adjustment unit therefore determines the adjustment rule. This was also explained above in connection with the method for feeding electrical power and applies here analogously.
  • the adaptation unit which therefore determines the adaptation regulation, can in turn also be provided as a device or implementation of a program.
  • the estimation device is then prepared to determine the expected performance based on the wind expected size and the adjustment rule.
  • the wind farm is prepared to carry out a method according to at least one of the above-described embodiments.
  • the method can be implemented in a central parking computer, also using the described devices and units.
  • Figure 1 shows a wind turbine in a perspective view.
  • Figure 2 shows a wind farm in a schematic representation.
  • FIG. 3 shows a structure for the schematic description of the methodology according to at least one embodiment.
  • FIG. 4 illustratively shows a diagram with an uncertainty funnel.
  • Figure 5 shows illustratively a distribution curve.
  • FIG. 1 shows a wind energy installation 100 with a tower 102 and a nacelle 104.
  • a rotor 106 with three rotor blades 108 and a spinner 110 is arranged on the nacelle 104.
  • the rotor 106 is rotated in operation by the wind and thereby drives a generator in the nacelle 104.
  • FIG. 2 shows a wind farm 112 with, for example, three wind energy plants 100, which can be the same or different.
  • the three wind energy plants 100 are therefore representative of basically any number of wind energy plants of a wind farm 112.
  • the wind energy plants 100 provide their power, namely in particular the electricity generated, via an electrical parking network 114.
  • the currents or powers of the individual wind turbines 100 generated in each case are added up and a transformer 116 is usually provided, which transforms up the voltage in the park in order to then feed into the supply network 120 at the feed-in point 118, which is also generally referred to as PCC .
  • Fig. 2 is only a simplified presen- tation of a wind farm 112, which for example shows no control, although of course there is a control.
  • the parking network 114 can also be designed differently, for example by also having a transformer at the output of each wind energy installation 100, to name just another exemplary embodiment.
  • the wind farm 112 also has a central park computer 130, which can be referred to as a wind farm computing unit, not only for the embodiment in FIG. 2.
  • This central parking computer 130 is particularly intended to communicate with each wind energy installation 100, in particular to transmit these control commands and / or information, but also to receive information from the wind energy installation 100.
  • Such information can include operating values such as currently generated outputs and also measured values such as a recorded wind speed or measured temperature.
  • the central parking computer 130 can also be coupled to a weather service 132 in order to receive weather forecasts from there.
  • a weather service 132 provides information to the weather service, which is indicated by the double arrow.
  • the connection 134 between the central parking computer 130 and the weather service 132 is drawn partly in dashed lines to indicate that the weather service can be located far apart from the wind farm 112.
  • a network operator 136 is also indicated, with whom the wind farm 112 can likewise communicate by means of the central parking computer 130.
  • a network operator connection 138 is also provided here, which, indicated by the double arrow, enables mutual communication.
  • the network operator connection 138 is also shown partially in dashed lines in order to clarify the possible local distance.
  • FIG. 3 shows a structural diagram 350 which illustrates a methodology for achieving a wind forecast or performance forecast that is as accurate as possible in terms of location.
  • a weather service 532 provides weather data, such as various distributed values for air pressure, temperature, wind and precipitation, for just a few examples, for a region, and can also or alternatively provide weather data from one or more weather satellites .
  • a weather forecast can be created.
  • a correction rule is implemented in the prediction block 352 which, depending on local weather data and / or operating data of the wind farm, can improve the weather forecast, namely can specifically adapt it to the wind farm.
  • the correction regulation can correct or at least improve the weather forecast related to the wind farm, so it can refit the weather forecast for the wind farm.
  • the correction rule can also be implemented as part of the weather model.
  • the wind speed is particularly important for the present purposes and thus the prediction block 352 in particular outputs a wind maintenance variable PMO.
  • This wind maintenance variable PMO can be a wind speed or a course of a wind speed that is expected in a comparison period that lies in the future.
  • the wind maintenance value is provided as a short-term forecast.
  • the wind maintenance variable PMO is a power that is representative of a wind speed, and the same also applies to a wind speed curve.
  • a rotor power can be used in particular as a wind maintenance variable, which indicates a value above one at one Rotor value of a wind turbine. Such a value can also be extrapolated to the relevant wind farm examined here.
  • the wind maintenance variable PMO could therefore be the sum of all expected rotor powers in the considered comparison period.
  • the wind maintenance amount PMO is then input to a wind power block 354.
  • the wind power block 354 contains a wind power model with which an idealized feed-in power P is determined from the PMO.
  • the wind power block 354 can emulate the wind power model using a neural network.
  • the wind power block 354 can obtain properties of the neural network from a parameter block 356.
  • the parameter block 356 can also transfer the entire structure of the neural network including parameterization according to a corresponding learning process.
  • a neural network is only one example, however, and other control-related implementations for the wind power model are also possible, which can also receive corresponding parameterizations, if necessary structures and / or initial values, from parameter block 356. It is also possible to apply the correction rule only or additionally in the wind power block. This is particularly provided in the event that the wind maintenance variable forms a power forecast and this is verified in the wind power block 354 by means of the correction rule. This correction in the wind power block 354 can also be combined with further changes in the wind maintenance size.
  • the result of the wind power block 354 is an idealized feed-in power Pi, which the investigated wind farm could theoretically generate if the forecast, in particular the wind maintenance variable PMO, is correct and all wind turbines in the park are also fully available. Then the expected performance would correspond to the verified wind expectation size. The wind direction is also taken into account.
  • the idealized feed-in power P can be regarded as a verified wind maintenance variable and it also depends in particular on parking effects in the wind farm. This includes the general topology of the area in which the wind farm is located and that of the wind farm surrounds, but also the mutual influence of the wind turbines on each other.
  • this generally includes a weakening of the wind field by the wind farm, but on the other hand it can also affect concrete effects of a leading wind energy plant compared to a trailing, ie precisely lying, wind energy plant. All of these relationships are taken into account in the wind power model that the wind power block 354 uses.
  • Availability block 358 takes into account the technical availability of each individual plant in the wind farm. He receives the data for this from the data block 360. In the data block 360, therefore, all the availability data of the wind turbines of the wind farm are collected and continuously updated. It is particularly included if, for example, a wind turbine fails. However, it is also possible that a wind turbine may only be operated in a reduced manner, because this is dictated, for example, by noise protection regulations. All such information is stored for each wind turbine in the park in data block 360 and is passed to availability block 358. In this case, general data of the wind energy installation in question can also be transferred, such as, for example, its nominal output if such data is not already permanently stored in the availability block 358.
  • the availability block 358 can determine an expected power PF of the wind farm from the idealized feed-in power P.
  • This expected power PF is a predicted power, in particular as a short-term forecast, which in the ideal case corresponds to the power actually recorded in the wind farm, which can also be the power fed in by the wind farm.
  • a wind equivalent can also be used here, i.e. a wind speed that would lead to such performance.
  • a measured wind farm output PM is recorded by a symbolically represented wind farm 312.
  • Both the expected power PF and the measured wind farm power PM are then entered into a comparison block 362 and compared.
  • the respective values or trends of the identical comparison period are used. So if a forecast is made for a period of time that is about half an hour in the future, the expected performance PF determined therefrom is compared accordingly with the measured parking performance PM, which corresponds to the aforementioned is measured half an hour later.
  • the comparison block 362 can also be correspondingly complex and, in particular, also have a memory for a number of expectations.
  • comparison block 362 then also carries out an evaluation, wherein a separate block could also be used for this, namely an adaptation unit which carries out this evaluation and creates an adaptation regulation. In the embodiment of FIG. 3, this is also integrated in the comparison block 362.
  • the adjustment rules are referred to as AV1 or AV2.
  • the adjustment rule AV2 can also be used to adjust the correction rule implemented in the prediction block 352.
  • a forecast is mainly used in network restoration, where after a few hours of power failure, the forecasts already calculated can have a significant deviation from the actual available power.
  • the forecast should also be available at the wind farm level and may even at the wind farm level.
  • a short-term forecast, provided via the network operator interface, can also be used for active network management of distribution networks or for balancing group management.
  • a device for maintaining and providing a short-term forecast at the wind farm level which comprises the following functionality:
  • a forecast which in particular stands for a weather forecast, at regular intervals, e.g. by forecasting service providers, such as weather services,
  • an anemometer on wind turbines of the wind farm especially a gondola anemometer
  • - nearby wind farms especially their anemometers or corresponding information about a feed-in power
  • the proposed methodology includes the following, or parts thereof:
  • Rapid update cycle i.e. carrying out rapid updates of the recorded and / or forecast values using an internal weather forecast model, and thereby
  • Controllable local loads and usable storage namely at least one storage charge state and / or available power are taken into account in the forecast or to minimize the uncertainty.
  • the forecast model is divided into three individual models that can be validated separately, namely in particular the weather model, the wind power model and the availability model.
  • a correction loop or more be built into the prediction chain, namely in particular from the weather data from the prediction block 532 to certain expectations, or that make the model prediction statistically more smooth, particularly via the improvement or correction means smoothing, linear regression Average, dynamic weighting, bias correction, corrected with measured data.
  • SCADA performance data e.g. also from neighboring wind farms
  • gondola anemometer data e.g. also from neighboring wind farms
  • status codes or meteorological measuring stations are used.
  • the correction with regard to wind data is applied to the weather models and, in the case of performance data, to the wind farm model, which can in particular be a neuronal network.
  • the accuracy of a forecast is particularly taken into account. This is based on the following thoughts.
  • no current, i.e. newly calculated, forecast for regenerative power plants such as wind farms is available, because e.g. Data centers are offline or no longer have a data connection.
  • the predictions already have a clear deviation from the actual available power or a clear uncertainty.
  • the network operator needs a forecast as well as a quantification of the uncertainty in order to take this into account when regulating the wind farms.
  • the forecast and the uncertainty of the forecast must also be available at the wind farm level and may even at the wind farm level.
  • a short-term forecast including uncertainty provided via the network operator interface can also be used for active network management of distribution networks or for balancing group management.
  • the idea is to carry out a quantification of the forecast inaccuracy.
  • a forecast especially with probability distribution, at regular intervals, e.g. by forecasting service providers, - storing the forecast in a wind farm storage,
  • the network operator could use the network operator interface, for example, to specify the availability with which the minimum available power and the (short-term) forecast are required, or he can query the uncertainty directly and thus take this into account in the regulation.
  • the internal know-how for wind turbine technology reduces the uncertainty about the predicted technical turbine availability to 1%.
  • the greatest uncertainty lies in the weather forecast. This can be reduced by intelligent weighting of various numerical weather models as well as statistical corrections based on various measurement data in the wind farm environment. It is therefore advantageous to combine these aspects.
  • the uncertainty funnel of the forecast can be reduced on the one hand by short-term corrections with measurement data and additionally or alternatively with coupled storage solutions in the wind farm.
  • a combination of several wind farms that run on one network operator interface can also reduce uncertainty.
  • the area to the left of the P90 value accordingly has an area of 10% and such an area is shown as the remaining area 510 for the 3-hour forecast.
  • this improves, which can be seen from the high value of the 1-hour forecast at 50.
  • this also leads to the curve becoming slimmer and the 10% remaining area ending further to the right, so that the P90 value is further to the right and therefore closer to the predicted performance.
  • the uncertainty of the weather forecast by the weather service 532 can be around 6 to 8%.
  • the uncertainty of the model that describes the wind farm which can be illustrated with parameter block 356, or with wind power block 354, in which the calculation then takes place, can be approximately 2.5%.
  • the technical availability which can be illustrated by the data block 36, or the availability block, is approximately 1%.
  • FIG. 4 shows an uncertainty diagram 400, in which prediction values are shown as their prediction curve over time with their uncertainty.
  • the time axis is divided into three areas.
  • the first time range 401 is not yet in the future, so that the forecast values correspond to the measured values, so that there is no uncertainty.
  • the second time range 402 is in the future and therefore there is an uncertainty that increases with time. The actual value can therefore lie in the range of the uncertainty range shown and accordingly deviate from the prediction curve 404.
  • the third time range 403 is still further in the future and a difference can now be clearly distinguished between a parking uncertainty curve 405 and a weather uncertainty curve 406.
  • the parking uncertainty curve 405 describes the unsecured uncertainty due to parking inaccuracy and measurement uncertainties, whereas the weather uncertainty curve 406 is an uncertainty due to the weather forecast.
  • the parking uncertainty curve 405 forms a much smaller funnel than the weather uncertainty curve 406.

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Abstract

La présente invention concerne un procédé de fourniture d'une puissance électrique dans un réseau de distribution électrique au moyen d'au moins un parc éolien comprenant plusieurs installations d'énergie éolienne, le parc éolien fournissant en un point de branchement de réseau une puissance électrique dans le réseau de distribution électrique. Une puissance attendue (PF) est déterminée pour une période temps de fourniture prédéfinie, la puissance attendue indiquant une valeur ou une courbe temporelle d'une puissance d'après laquelle on attend que la puissance soit disponible au ou aux parcs éoliens pendant la période de temps de fourniture prédéfinie en tant que puissance éolienne, en particulier en tant que somme de toutes les puissances de rotor de toutes les installations d'énergie éolienne du parc éolien et/ou en tant que puissance pouvant être fournie par le parc éolien. Il est déterminé, pour la puissance attendue, une précision attendue qui est une cote indiquant la précision avec laquelle la puissance atteindra la puissance attendue pendant la période de temps de fourniture, et/ou il est déterminé, pour la détermination de la puissance attendue (PF), au moins une grandeur attendue du vent (PMO) représentative de la vitesse attendue du vent à l'aide d'une prévision météorologique, et la grandeur attendue du vent est déterminée ou vérifiée, à partir de la prévision météorologique, en plus au moyen de données météorologiques locales dépendantes d'une directive de correction et/ou de données d'exploitation du parc éolien, la puissance attendue (PF) étant déterminée sur la base de la grandeur attendue du vent (PMO).
EP19789665.7A 2018-10-15 2019-10-15 Procédé et parc éolien pour fournir une puissance électrique à un réseau de distribution électrique Pending EP3867990A1 (fr)

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PCT/EP2019/077974 WO2020079000A1 (fr) 2018-10-15 2019-10-15 Procédé et parc éolien pour fournir une puissance électrique à un réseau de distribution électrique

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