EP4143938A1 - Procédé de prédiction de puissance d'un système énergétique - Google Patents

Procédé de prédiction de puissance d'un système énergétique

Info

Publication number
EP4143938A1
EP4143938A1 EP21725428.3A EP21725428A EP4143938A1 EP 4143938 A1 EP4143938 A1 EP 4143938A1 EP 21725428 A EP21725428 A EP 21725428A EP 4143938 A1 EP4143938 A1 EP 4143938A1
Authority
EP
European Patent Office
Prior art keywords
classification
energy
classifications
performance
profile
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
EP21725428.3A
Other languages
German (de)
English (en)
Inventor
Oliver DÖLLE
Houssame Houmy
Sebastian Schreck
Sebastian THIEM
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.)
Siemens AG
Original Assignee
Siemens AG
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 Siemens AG filed Critical Siemens AG
Publication of EP4143938A1 publication Critical patent/EP4143938A1/fr
Pending legal-status Critical Current

Links

Classifications

    • 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
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • 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
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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
    • Y04S10/40—Display of information, e.g. of data or controls
    • 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 invention relates to a method according to the preamble of claim 1 and a method according to the preamble of claim 11.
  • An energy system typically comprises several energy subsystems, for example residential buildings, commercial buildings, industrial plants and / or decentralized power plants.
  • energy subsystems increasingly include power engineering systems that generate renewable energies, for example photovoltaic systems and / or wind power systems.
  • STFL short-term load forecast
  • a sufficiently precise forecast is advantageous for efficient operation.
  • the participating energy systems can exchange and trade locally generated energy, in particular electrical energy (electricity).
  • the local energy market enables the locally generated energy to be efficiently coordinated with the local energy consumption.
  • a local energy market is therefore particularly advantageous with regard to renewable energies, which are typically obtained locally.
  • renewable energies which are typically obtained locally.
  • a forecast that is as precise as possible is required.
  • a local energy market is known, for example, from the document EP 3518369 A1.
  • An attempt is made to increase the forecast accuracy by aggregating the generation profiles and / or load profiles (performance profiles) of the energy subsystems.
  • the aim of the aggregation is that the individual fluctuations of the energy subsystems weaken, balance and / or average.
  • the disadvantage of an aggregation is that details (information) relating to the respective energy subsystem are lost as a result.
  • the present invention is based on the object of improving a forecast based on aggregated performance profiles by means of an improved aggregation.
  • the energy subsystems are classified, and each of the m classes is one of the classifications of one of the aggregated performance profiles is characterized by at least the following steps: Generating a plurality of initial classifications, each classification having disjoint classes of the n performance profiles P 1 ..., P n ;
  • the method according to the invention and / or one or more functions, features and / or steps of the method according to the invention and / or its configurations can be computer-aided.
  • Energy systems and / or energy subsystems typically include several components that focus on the production (generation), conversion, delivery and / or use of energy, in particular electrical energy (electricity) and / or thermal energy (heat), relate.
  • An energy system or energy subsystem is, for example, a city, a city district, a municipality, a residential building, an office building and / or another commercial building, an industrial plant, a power plant and / or a campus.
  • energy systems or energy subsystems include several energy-technical systems, for example energy conversion systems, consumption systems and / or storage systems.
  • the energy system is a district or a Municipality which comprises several residential buildings and / or office buildings and / or commercially used buildings and / or decentralized power plants, in particular photovoltaic systems, combined heat and power systems and / or waste incineration systems, as energy subsystems.
  • the energy system can furthermore be a building with several units as energy subsystems.
  • a power profile of an energy subsystem characterizes the time dependency or a time profile of the power of the energy subsystem that is provided, in particular obtained, and / or consumed by the energy subsystem.
  • a load (consumption) and a generation within the time range can be present jointly (residual power).
  • a performance profile can be a load profile and / or a generation profile.
  • a performance profile can be present as a time signal, for example as a function, and / or a time-discrete time signal (time series). The performance profile can also be referred to as a signal.
  • the performance profile can be present as a time series or measurement series, in particular in the form of data, and / or analytically, for example as a fitted function.
  • the performance profile or the performance profiles are measurement-based.
  • power refers to energy consumed and / or provided, in particular gained, within a time range.
  • power and energy are equivalent and interchangeable.
  • the power profile is in particular an electrical power profile, that is to say a time dependency of an electrical power, or a thermal power profile, for example with regard to a heating power and / or a cooling power.
  • the prognostic capability value is a value of a prognostic capability function.
  • the forecast capability function has values in the range from 0 to 1.
  • the prognostic capability function can be understood as a measure of prognostic capability.
  • a forecasting capability value close to 1 means that the associated aggregated performance profile has a high forecasting capability. It is therefore easily predictable, for example through its regular behavior.
  • a predictability value close to the value 0 suggests that there is little or no predictability at all.
  • the associated aggregated performance profile shows a high degree of randomness and hardly any or no regular behavior.
  • the predictability is thus a measure of the predictability of the associated aggregated performance profile.
  • several measures i.e. several functional dependencies, are conceivable for quantifying the forecasting capability.
  • a normalization to the value range from 0 to 1 is expedient, although other normalizations can be provided.
  • the fitness value is a value of a fitness function.
  • the evolutionary algorithm typically has such a fitness function that, symbolically with respect to the search operators of the evolutionary algorithm, is a measure of the fitness of each classification.
  • the fitness value is formed by the sum of the forecasting capability values assigned to a classification, so that the fitness value forms an overall or cumulative forecasting capability value of the respective classification.
  • a classification can also be called a grouping, so that the classes form the groups of the grouping.
  • An assignment of an energy subsystem to a class is equivalent to an assignment of the power profile belonging to the energy subsystem or provided by it.
  • the classification is made up of the individuals in the population.
  • the population is formed by all the classifications or by the totality or set of the classifications, that is to say the totality of the individuals.
  • the n performance profiles, in particular load profiles, of the energy subsystems of the energy system are divided into m classes, each class being assigned an aggregated performance profile to one of the classification.
  • the empty class can also be provided, for example if no fixed number of classes is provided.
  • several such classifications with typically at least partially different aggregated performance profiles and thus at least partially different classes are provided, in particular generated.
  • the n performance profiles are grouped into m aggregated performance profiles.
  • An aggregated service profile thus corresponds to a summed up subset / group of the service profiles.
  • the aggregated performance profiles can each be calculated using a sum and / or weighted sum of the performance profiles associated or assigned to the respective class.
  • the set of performance profiles is divided into m disjoint classes for each of the classifications assigned .
  • the classes K of each class are where the classes K of each class
  • each of the classifications is by means of a fit assigned measured value, which is formed from the sum of all forecast capability values of the respective classification.
  • the evolutionary algorithm is used to determine a classification which has the highest fitness value.
  • This ascertained classification corresponds to an aggregation of the performance profiles into aggregated performance profiles with the highest possible advantage
  • the best aggregation in this sense is determined according to the invention by means of the evolutionary algorithm.
  • a first step of the method or of the evolutionary algorithm initialization
  • several initial classifications are generated, each classification having disjoint classes of the n performance profiles.
  • the evolutionary algorithm with search operators and a fitness function is initially provided. Subsequently, new classifications are generated by means of a loop, which is carried out until the termination criterion is met.
  • a fitness value is assigned to each classification.
  • a fitness value is assigned to each individual in the population.
  • the assignment or determination / calculation of the fitness values is based on the forecasting capability values of the classes associated with each classification.
  • the predictability value for each class of each classification is calculated using a sum of the performance profiles associated with the respective class.
  • the fitness score is then calculated using a sum of the predictability scores of each classification (as described above).
  • the selection takes place in a second sub-step.
  • the individuals who are fc-fit with regard to their fitness values (classifications) are ascertained, determined or selected.
  • new individuals that is to say new ones, are created on the set of k-fittest individuals Classifications, carried out by means of the search operators, for example a mutation of an individual or a recombination of two individuals.
  • Each newly generated classification is in turn assigned a fitness value (evaluation).
  • the loop is run through again. If the termination criterion is met, a selection is made again. In other words, the classification with the highest fitness value, in this sense the fittest individual, is determined. The best aggregation in terms of predictability is determined by the classification with the highest fitness value and thus determined.
  • the aggregation does not take place randomly or according to fixed criteria, for example by combining according to the type of energy subsystem, but dynamically with a view to the highest possible predictability / forecast content.
  • the inventive design of the fitness function or the fitness values on which the evolutionary algorithm is based by means of the prognostic capability values that are dependent on the respective information content, make the classifications / aggregations or the population a higher one Predictive ability driven. A synergetic compromise is thus determined between an aggregation and the associated loss of information. In particular, this improves the accuracy of a performance forecast based on the aggregation.
  • the termination criterion can even be used to predetermine a forecast accuracy, so that a minimal aggregation in this sense can be determined by means of the present invention which fulfills the required forecast capability.
  • a minimal aggregation in this sense can be determined by means of the present invention which fulfills the required forecast capability.
  • the present invention can provide an improved forecast and thus a more efficient integration of the energy system or its participating energy subsystems.
  • the present invention enables the network to be operated more reliably and efficiently. Furthermore, unnecessary congestion management, such as redispatch, can be avoided.
  • the present invention is advantageous for an energy system design. If, for example, a load on the energy system or the energy subsystem is difficult to predict, that is to say it has a comparatively low forecasting capability value, it could be advantageous to provide additional storage capacities.
  • the initial classifications are generated by randomly assigning the performance profiles P 1, ..., P n to the classes.
  • the initial classifications that is, the initial population of the evolutionary algorithm, are each formed from the set of performance profiles by randomly drawing without replacing. This advantageously avoids an initial biased assignment.
  • an advantageous selection can be provided for the initial population or at least a part of the initial population.
  • the prognostic capability value ⁇ is determined by means of ⁇ 1 H a , H a denoting the value of the normalized Shannon entropy for the base a of the aggregated performance profile assigned to the class.
  • the information content of the aggregated performance profile is advantageously determined by the Shannon entropy.
  • the Shannon entropy of the aggregated performance profile is a measure of the information content of the aggregated performance profile. The higher the information content, the more random the aggregated performance profile and the less predictable it is.
  • the normalized Shannon entropy is formed from the ratio of the Shannon entropy and the maximum value of the Shannon entropy.
  • the normalized Shannon entropy thus has values in the range from 0 to 1.
  • the normalized Shannon entropy is referred to as efficiency.
  • the aggregated performance profile has little or no predictive capability.
  • a classification that has several such classes with a low prognostic capability has a low fitness, that is to say a low fitness value.
  • the Shannon entropy is determined by means of the spectral density (English: Power Spectral Density; abbreviated PSD) of the respective aggregated power profile.
  • spectral density English: Power Spectral Density; abbreviated PSD
  • the Shannon entropy is preferred by means of formed or calculated or determined.
  • w denotes the angular frequency and log a the logarithm to the base a, so that the Shannon entropy to the base a is also formed.
  • the Shannon entropy is thus formed by the differential entropy, with discrete formations also being provided.
  • the specific design, whether discrete or continuous, depends on the design of the time signal (continuous or discrete).
  • the spectral density is determined by means of the autocorrelation of the respective aggregated performance profile.
  • the autocorrelation of one of the aggregated performance profiles is determined by, where E denotes Expected value and denotes the time average of the aggregated performance profile.
  • E denotes Expected value
  • time average of the aggregated performance profile denotes the time average of the aggregated performance profile.
  • the author correlation is a measure of correlations within the respective performance profile and thus of its regularity.
  • the spectral density can be calculated efficiently by means of the autocorrelation using the Wiener-Chinchin theorem.
  • a mutation and / or a recombination are used as search operators of the evolutionary algorithm.
  • the assignment or affiliation of a performance profile to a class of the classification is randomly changed.
  • at least one performance profile of the classification is reassigned to a class of the classification with a fixed probability.
  • the class assignment of a performance profile is randomly changed within the same classification. This creates a new random classification.
  • One or more mutations can be provided.
  • a recombination (English: Cross Over) generates a new classification based on two classifications (parents), that is, an offspring (English: Offspring).
  • two classifications are mixed, with one class of the two classifications being used as the class of the new classification (descendant) with a probability of 0.5 (50 percent).
  • the new classification includes, on average, 50 percent of each parent's classes. Multiple recombinations can be envisaged being. Mutations and recombinations are preferably mixed.
  • the classes of the classifications are arranged.
  • the class arrangements within a classification are reorganized / rearranged / quantity order (English: reordering).
  • the reordering or new numbering of the classes is preferably carried out with each new generation of new classifications, i.e. after initialization (first generation of the classifications) and / or after using a search operator, in particular after a mutation and / or recombination.
  • a preferred reordering is achieved in that the classes are reordered or numbered according to the order in which they appear in the classification, for example from right to left.
  • the termination condition is formed by means of the fitness values.
  • the fitness values as a whole determine when a sufficiently advantageous population (set of classifications) has been reached. This ensures that a classification with the highest possible fitness value for the aggregation can be determined.
  • the termination condition is particularly preferably formed by the mean value of the fitness values. If the mean value of the fitness values does not change over several previous generations, the loop is preferably stopped and the aggregation is determined by the classification with the highest fitness value of the current generation. Alternatively or in addition a threshold value for the fitness values and / or for the mean value of the fitness values can be defined. In other words, the termination condition is formed by the threshold value. If the mean value of the fitness values is above the threshold value mentioned, for example, then the termination condition is fulfilled and the loop of the evolutionary algorithm stops. In this way, a classification with the highest possible fitness value can advantageously be determined in an appropriate computing time. Further termination conditions can alternatively or additionally be provided.
  • the termination condition is basically a kind of measure for the overall fitness of the population.
  • a load profile and / or generation profile of the respective energy subsystem is used as the performance profile.
  • the performance profile can in principle be a load profile, a generation profile or a residual profile mixed from load and generation.
  • generation is also understood to mean provision.
  • the power profile of an energy store in particular an electricity store and / or thermal energy store, can be used.
  • the ascertained aggregation of the power profiles of the energy subsystems is used for the power prognosis, in particular for a load prognosis and / or generation prognosis, of the energy system.
  • the aggregated performance profiles are particularly preferred for a performance prognosis of the energy system, for example of some of its energy subsystems. This is the case because the performance profiles of the energy subsystems are not aggregated randomly or according to defined criteria, but according to their fitness, i.e. according to their fitness Predictive ability. As a result, the most advantageous compromise possible between aggregation and predictability can be determined.
  • FIG. 1 shows an aggregation according to an embodiment of the present invention
  • FIG. 2 shows a flow diagram of an evolutionary algorithm according to an embodiment of the present invention.
  • Figure 3 shows an order of a classification.
  • FIG. 1 shows an aggregation or a schematic sequence of an aggregation according to an embodiment of the present invention.
  • An energy system 1 for example a community, a city district, a building complex, an industrial plant and / or a campus, has several, in the present case n, energy subsystems 11.
  • n energy subsystems 11.
  • a district has several residential buildings or a building complex has several apartments.
  • the energy subsystems are each identified with the reference symbol 11.
  • a measurement 6 of the respective power / energy at each of the energy subsystems 11 is carried out at least within a time range, for example within a day with an hourly resolution or a 15-minute resolution accomplished.
  • n performance profiles are provided, three of which are shown here by way of example.
  • the performance profiles or their provision is identified with the reference symbol 2.
  • the measurements can be carried out by means of smart meters and / or by means of a respective energy management system.
  • the energy subsystems 11 can feed power out (load) and / or feed in (provision or generation) via a network 8, in particular a power network.
  • the power fed out and / or fed in is measured continuously or in fixed time steps, for example every 15 minutes or every hour, as a result of which the power profiles 2 are made available. For example, the performance profiles extend over a day with a resolution of 15 minutes or one hour. Equivalent to the power, the respective generated / provided and / or consumed energy can be measured or recorded within a time interval, for example within 15 minutes or an hour. The associated power then results from the recorded energy per time interval.
  • the measured or recorded performance profiles 2 are each plotted as curves within a P-t diagram (P performance, t time).
  • P performance, t time a P-t diagram
  • differences can be seen in terms of their regularity. These differences correspond to different predictabilities of the performance profiles 2. For example, an approximately periodic performance profile is easily predictable. A performance profile with several irregular fluctuations is more difficult to predict. Typically, the fluctuations at the level of the energy subsystems 11 are too high for a sufficiently precise forecast, so that an aggregation 4 takes place. This can reduce fluctuations and thus increase predictability.
  • the aggregation 4 classifies or groups the energy subsystems 11. In other words, within the Aggregation 4 each of the energy subsystems is assigned to exactly one class 42 or group.
  • the totality of the classes forms a classification 40.
  • the classification 40 of the energy subsystems 11 according to their performance profiles 2 or the classification 40 of the performance profiles 2 comprises only two classes 42, two of the four energy subsystems 11 shown being assigned to each class 42.
  • the performance profiles assigned to or associated with a class 42 are totaled for the aggregation 4, as a result of which averaged or aggregated performance profiles are formed. A weighted sum can be provided.
  • Each class 42 is therefore assigned or associated with an aggregated performance profile. It is a technical purpose of the aggregation 4 to reduce fluctuations in the individual energy subsystems 11. In other words, the aggregated power profiles should have less fluctuations, which improves their predictability. Thus, a forecast based on the aggregated performance profiles can be more reliable or more precise.
  • the aggregation 4 takes place according to the present invention, that is to say it takes place based on a prognostic capability measure or on a prognostic capability function and by means of an evolutionary algorithm.
  • an aggregation 4 that is as optimal as possible with regard to its predictability can be determined.
  • a classification 40 is determined with the greatest possible predictability.
  • This optimal aggregation which corresponds to the classification 40 in the figure, is the result of the aggregation 4.
  • the evolutionary algorithm is advantageous here because the classification 40 or grouping shows a strong non-linear behavior with regard to the prediction capability measure .
  • FIG. 2 illustrates a flow diagram of an aggregation, in particular of the evolutionary algorithm used here.
  • a start step S of the aggregation the power profiles of the energy subsystems, which were recorded, for example, by means of a respective measurement, are provided.
  • an initial or initial classification of the energy subsystems or the performance profiles takes place.
  • the energy subsystems or their performance profiles are divided into, or assigned to, several classes, in the present case in m classes.
  • Exactly one performance profile is assigned to each energy subsystem, so that a classification of the energy subsystems and a classification of the performance profiles are equivalent.
  • Each class of the initial classification is therefore assigned to none of the, one or more of the performance profiles.
  • the initial classification is preferably carried out by means of a random assignment / division of the performance profiles or the energy subsystems to the m classes.
  • This random division of the energy subsystems into m disjoint classes creates a possible classification that corresponds to a possible aggregation.
  • several such possible classifications in particular randomly, are also generated.
  • This totality of generated initial classifications forms the initial population in the sense of the evolutionary algorithm.
  • the classifications are thus the individuals of the population.
  • the initial population is thus generated by means of randomly generated individuals.
  • an assessment measure is required that quantifies how fit an individual, i.e. a classification, is with regard to the evolutionary algorithm. The fitter an individual, the more likely their characteristics or classes will be found in the next generation.
  • a prediction capability value is initially assigned to each class of each classification.
  • a classification or an individual is assigned a fitness value through the sum of the prognostic values of his classes.
  • each classification or individual is assigned an overall predictive ability.
  • the classifications are thus rated according to their predictability in the sense mentioned above.
  • An individual or a classification is the fitter, the higher its overall predictive ability, that is, the higher its predictability.
  • the evaluation measure or the selection criterion of the evolutionary algorithm is thus formed by predictability.
  • the assignment of the prognostic value to each class is decisive for this.
  • the prediction capability value of a class is calculated using the aggregated performance profile assigned to the class.
  • the aggregated performance profile is the sum of the performance profiles of the energy subsystems assigned to the class. A weighted sum can be provided here. If the total is weighted equally, the aggregated performance profile corresponds to the mean value of the performance profiles.
  • the temporal autocorrelation of the aggregated performance profile is calculated.
  • the spectral density of the aggregated performance profile calculated, where denotes the imaginary unit.
  • the spectral density normalized to the variance forms a probability density function by means of which the prediction capability value can be determined. In particular, 0 applies such as 1, as required for a probability density function.
  • the constant normalized spectral density or probability density function / for a sine is obtained - or cosine-shaped signal or aggregated power profile with 0 ⁇ U / (- ⁇ , ⁇ ) and regardless of 0, i.e. with a random frequency according to the probability distribution, applies The uncertainty of a forecast results exclusively from the probability distribution .
  • the Shannon entropy is a measure of the information content of the aggregated performance profile. A high information content corresponds to a high randomness of the aggregated performance profile, so that its predictability is low. Information content and predictability are thus symbolically opposed. It is therefore of particular advantage to check the forecasting capabilities of the aggregated performance profile to be defined or calculated.
  • the maximum denotes the Shannon entropy over all possible aggregated performance profiles.
  • the maximum of the Shannon entropy is determined by an aggregated power profile in the form of white noise, so that it is present.
  • the measure thus quantifies the predictability of the aggregated performance profile in a particularly advantageous manner. That is true
  • the aggregated performance profile is particularly predictable when its predictive ability value is close to 1.
  • the aggregated performance profile is difficult to predict, i.e. it exhibits a high degree of randomness, if approx. is almost 0.
  • Each class of a classification is assigned a predictive ability value calculated as above.
  • the classification is assigned its fitness value by means of the sum of its forecasting capability values.
  • one of the classifications F where applies to the fitness value across all classes of classification is totaled.
  • the fitness value F is thus a measure of the overall predictability of the classification and thus of the aggregation. This is carried out in the evaluation step LI for all classifications. In other words, every classification has such a fitness value.
  • the fitness value 0 is assigned to an empty class.
  • a termination criterion is formed in particular by means of the totality of the fitness values, for example by means of the mean value of the fitness values.
  • the final aggregation is achieved through the classification with the highest fitness value and thus with the comparatively best predictability set. This ends or stops the evolutionary algorithm (end E).
  • the k fittest individuals that is to say the classifications with the k highest fitness values, are determined. This selection is identified by the reference symbol L2.
  • step L3 new classifications are generated from the previous ones from the k selected individuals.
  • a new population is generated from the previous population in step L3.
  • This new population forms the offspring of the k fittest individuals in the sense of the evolutionary algorithm. In other words, only the fittest individuals are allowed to procreate.
  • the offspring or the new classifications are generated from the k fittest classifications using search operators L3b and L3c.
  • the search operator to be carried out is selected in a step L3a.
  • a mutation L3b or a recombination L3c can be carried out.
  • the new classification is generated from the previous one by a random change in an affiliation of a performance profile to a class of the classification.
  • the mutation or a mutation event should be illustrated for eight energy subsystems 1, ..., 8 in three classes 1, ..., 3.
  • (1,2,2,3,1,2,3,1) be a possible classification. This notation means that energy subsystem 1 of class 1, energy subsystem 2 of class 2, energy subsystem 3 of class 2, energy subsystem 4 of class 3, energy subsystem 5 of class 1, energy subsystem 6 of class 2, the Energy subsystem 7 is assigned to class 3 and the energy subsystem 8 to class 1.
  • a mutation or a mutation event now randomly changes the affiliation of an energy subsystem or a performance profile to a class. For example, (1,2,2,3,1,2,3,1) ⁇ (1,2,1,3,1,2,3,1) is such a mutation event.
  • An energy subsystem can be chosen at random and randomly assigned to one of the classes.
  • Several mutations L3b can be provided one after the other and / or in parallel on the set of the k fittest classifications.
  • a new classification is determined from two previous classifications (parents).
  • the previous classifications are mixed with a probability of 50% to 50%.
  • the new classification has a 50% probability of the class of one parent or the other. If, for example, (1,2,2,3,1,2,3,1) and (1,1,1,2,3,1,2,2) are the previous classifications (parents), a recombination could occur generate the new classification (1,2,2,3,1,1,3,2) as descendants.
  • Multiple recombinations L3c can one behind the other and / or in parallel on the set of the k fittest classifications.
  • FIG. 3 shows an order of a classification 40 which is advantageously always used when the classification 40 has been newly generated, for example by the search operators.
  • the use of the search operators typically leads to a non-ordered classification, the order relating to the order, in the present case from right to left, of the numbering of the classes 42.
  • the numbering / designation of classes 42 is basically irrelevant.
  • a class 42 is referred to as the first or, for example, the second class.
  • any order can be chosen. However, it is advantageous to order or number the classes 42 according to their occurrence within the classification.
  • the classification 40 (2,1,1,3,2,1,3,2) of the energy subsystems 11 (1,..., 8) of the energy system 1 is therefore not ordered, since class 2 is on comes first.
  • class 1 should appear first, followed by class 2 and 3.
  • (1,2,2,3,1,2,3,1) is the ordered classification 40 'of the unordered classification 40.

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Human Computer Interaction (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Generation Of Surge Voltage And Current (AREA)

Abstract

L'invention concerne un procédé pour la détermination d'une agrégation de profils de puissance P1,...,P n (2) de n sous-systèmes énergétiques (11) d'un système énergétique (1) pour m < n profils de puissance agrégés de formule (I) (2), en particulier pour la prédiction de puissance, une classification (40) des sous-systèmes énergétiques (11) étant prévue à cet effet, et chacune des m classes (42) étant associée à l'une des classifications (40) de l'un des profils de puissance agrégés de formule (I) (2). Le procédé est caractérisé par les étapes suivantes consistant à : (I) générer une pluralité de classifications initiales (40), chaque classification (40) comprenant des classes disjointes (42) des n profils de puissance P1,...,P n (2) ; (L) fournir un algorithme évolutif comprenant des opérateurs de recherche, les étapes suivantes étant répétées jusqu'à la réalisation d'une condition de fin : (L1a) calculer une valeur de prévisibilité Ω pour chaque classe (42) de chaque classification (40) au moyen d'un total des profils de puissance (2) associés à la classe en question (42) ; (L1b) calculer une valeur de compatibilité de la classification au moyen d'un total des valeurs de prévisibilité Ω de la classification (40) pour chaque classification (40) ; (L2) sélectionner κ classifications (40) ayant les κ valeurs de compatibilité les plus grandes ; et (L3) générer de nouvelles classifications (40) au moyen des opérateurs de recherche sur la quantité des κ classifications sélectionnées (40) ; (T) définir les profils de puissance agrégés de formule (I) (2) par la classification (40) ayant la valeur de compatibilité la plus grande. L'invention concerne en outre un procédé de prédiction de puissance d'un système énergétique (1).
EP21725428.3A 2020-06-22 2021-05-03 Procédé de prédiction de puissance d'un système énergétique Pending EP4143938A1 (fr)

Applications Claiming Priority (2)

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DE102020207706.1A DE102020207706A1 (de) 2020-06-22 2020-06-22 Verfahren zur Leistungsprognose eines Energiesystems
PCT/EP2021/061548 WO2021259540A1 (fr) 2020-06-22 2021-05-03 Procédé de prédiction de puissance d'un système énergétique

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EP4143938A1 true EP4143938A1 (fr) 2023-03-08

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EP4311059A1 (fr) * 2022-07-18 2024-01-24 Siemens Aktiengesellschaft Dispositif et procédé d'agrégation, ainsi que de commande de puissances électriques d'un réseau électrique

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CA2889257C (fr) * 2012-12-07 2020-06-23 Battelle Memorial Institute Procede et systeme pour utiliser des ressources cote demande pour realiser une regulation de frequence a l'aide d'une attribution dynamique de ressources en energie
CN103617467A (zh) * 2013-12-13 2014-03-05 重庆大学 一种短期组合负荷预测方法
EP3518369A1 (fr) 2018-01-30 2019-07-31 Siemens Aktiengesellschaft Méthode et dispositif pour contrôler le transfert de puissance électrique et réseau électrique
CN110674993A (zh) * 2019-09-26 2020-01-10 广东电网有限责任公司 一种用户负荷短期预测方法和装置

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AU2021298025B2 (en) 2023-10-26

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