WO2010057516A1 - Procédé de détermination de flux de charge électrique dans un réseau d'alimentation en énergie électrique - Google Patents

Procédé de détermination de flux de charge électrique dans un réseau d'alimentation en énergie électrique Download PDF

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Publication number
WO2010057516A1
WO2010057516A1 PCT/EP2008/009980 EP2008009980W WO2010057516A1 WO 2010057516 A1 WO2010057516 A1 WO 2010057516A1 EP 2008009980 W EP2008009980 W EP 2008009980W WO 2010057516 A1 WO2010057516 A1 WO 2010057516A1
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WO
WIPO (PCT)
Prior art keywords
load
profiles
energy supply
supply network
modified
Prior art date
Application number
PCT/EP2008/009980
Other languages
German (de)
English (en)
Inventor
Thomas Werner
Original Assignee
Siemens Aktiengesellschaft
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 Aktiengesellschaft filed Critical Siemens Aktiengesellschaft
Priority to PCT/EP2008/009980 priority Critical patent/WO2010057516A1/fr
Publication of WO2010057516A1 publication Critical patent/WO2010057516A1/fr

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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
    • 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
    • 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 most accurate knowledge of the current and future occurring in the network at different sections load flows is necessary. Namely, knowing these load flows, production, transmission and distribution capacities required for providing and transporting the quantities of electrical energy required by different electrical consumers can be set and planned.
  • the load flows can be controlled in the electrical power supply network such that a gleichoniaige as possible utilization of the power supply network is present, so that no overloads occur at any point of the electrical power grid.
  • the load profile indicates the power output likely to be required at a particular time to meet the energy needs of the particular electrical load.
  • These load profiles may, for example, depend on the particular time of day (eg day, night, certain hours with high or low energy demand), a specific weekday (eg weekdays, weekends, public holidays, special days with high or low energy requirements) or the season be (eg summer, winter).
  • the load profiles used to determine the load flows are usually determined statistically from the energy consumption behavior of the respective electrical end users determined in the past, as well as from empirical values and determined statically in a control and control system of the electrical power supply network.
  • the load flows determined on the basis of the load profiles therefore never agree wholly with the actual load flows in the electrical energy supply network.
  • it can also feeds smaller decentralized power generation units occur in the electrical energy grid (for example, inputs of excess energy from electric photovoltaic systems), which cause a change in the load flows in the electrical energy supply network.
  • the invention is therefore based on the object of specifying a method for determining electrical load flows in an electrical energy supply network, in which load flows can be determined with comparatively little effort, which correspond as exactly as possible to the actual load flows in the electrical energy supply network, without the number of to increase meters installed in the energy supply network.
  • a method for determining electrical load fluxes in an electrical energy supply network in which load currents assumed in the energy supply network are calculated using individual sections of the energy supply network, the load profiles indicating time-dependent load flows in their respective part of the energy supply network.
  • an actual load flow at the at least one measuring point is recorded in the energy supply network and a dynamic adaptation of the load profiles is determined by comparing the actual load flow at the at least one measuring point with a load flow calculated on the basis of the assumed load flow for the at least one measuring point carried out by each of at least one of each load profile previously used derived modified load profile is formed and calculated on the basis of the modified load profiles for the at least one measuring point, a load flow is calculated.
  • Those modified load profiles are used instead of the previously used load profiles for future determinations of the load flows in the energy supply network, for which the smallest deviation of the load flow from the actual load flow at the at least one measuring point is determined.
  • a mathematical optimization method can be used to determine the modified load profiles.
  • the particular advantage of the method according to the invention is that no statically defined load profiles are used, and thus dynamic adaptation of the load profiles to changing consumer and / or feed behavior of the individual electrical consumers can take place. In addition, this can be done to adapt to changing network configurations. On the other hand, in order to carry out this method, it is not necessary to carry out a previous determination of concrete load profiles and scaling factors associated with great expense, since the load profiles themselves are changed dynamically.
  • a mathematical evolution strategy can be used to form the modified load profiles.
  • the advantage of such an evolutionary strategy is, among other things, that it is possible to dispense with a closed formulation of an optimization problem. On the contrary, it is sufficient to evaluate the quality of the modified load profiles and to select the best load profiles for the next computation processes by means of given algorithms for load flow calculation in electrical energy supply networks and the existing measured values.
  • a further advantageous development of the method according to the invention consists in that, in order to select the modified load profiles to be used for future determinations of the load flows in the energy supply network, it is also checked whether the modified load profiles lead to violations of limit values in the energy supply network, and only those modified load profiles which lead to the least violation of limit values. In this way it can be prevented that load profiles are accepted for the calculation of the load flows, which mathematically a solution of the optimization problem, but to a violation of physically prescribed limits of the electrical energy supply network - such as maximum allowable currents or voltage bands to be observed - drove.
  • Such modified load profiles have been evaluated with a low quality value, so to say, and are not used for future determinations of the load flows in the electrical energy supply network.
  • a further advantageous embodiment of the method according to the invention provides that the method is repeated until the deviations between the load flow at the at least one measuring point determined using the modified load profiles and the actual load flow at the measuring point determined on the basis of the measured values falls below a predetermined first threshold. In this way, a continuous dynamic adaptation of the load profiles used to calculate the load flow to the actual load flow determined by measurement can take place until the deviations are acceptably low.
  • the method is repeated until the change in the deviations between the load flow at the at least one measuring point determined using the modified load profiles and the actual load flow at the measuring point determined on the basis of the measured values falls below a predetermined second threshold value , In this case, the optimization process is aborted when only small improvements between individual generations of modified load profiles occur.
  • Figure 1 is a schematic representation of a portion of an electrical energy supply network and Figure 2 is a schematic representation of a process flow diagram for explaining a method for determining electrical load flow in an electrical energy supply network.
  • FIG. 1 shows a section 10 of a power supply network not further described below.
  • the section 10 of the electrical power supply network shown only by way of example comprises a first busbar 11, which is, for example, a busbar of a
  • the section 10 of the electrical energy supply network also has a second busbar 12, which may be, for example, a busbar of a 10 kV distribution network, for example for the distribution of electrical energy within a municipal area.
  • the second bus bar 12 is connected to the first bus bar 11 via a transformer station 13 in which a conversion of the electrical energy from the high voltage level (110 kV) to the lower voltage level (10 kV) is made.
  • branches 14, 15 and 16 are supplied via the electrical end consumers 17a, 17b, 17c and 17d with electrical energy.
  • the branch 15 is also divided into two branch branches 15a and 15b, via which the end consumers 17b and 17c are connected via the branch 15 to the second busbar 12.
  • the electrical end consumers 17a to 17d via these branches 14 to 16 excess electrical energy from self-generation (for example, electrical energy that is generated with a cogeneration plant or a photovoltaic system) in feed in the electrical energy supply network.
  • self-generation for example, electrical energy that is generated with a cogeneration plant or a photovoltaic system
  • the electrical end user 17a may be a housing estate
  • the end consumers 17b and 17c may be commercial customers
  • the end electrical consumer 17d may be an office building.
  • other and / or additional electrical energy consumers may be provided, some examples of other electrical energy consumers are sports facilities, public facilities, shopping centers and farms.
  • a network control center 18 For controlling and monitoring the electrical load flows in the electrical energy supply network, a network control center 18 is provided.
  • the network control center 18 receives measured values 19 a and 19 b provided at selected measuring points M 1 and M 2, which give an indication of the load flows at the respective measuring point M 1 or M 2.
  • the measuring devices 19a or 19b can, for example, determine measured quantities such as active and / or reactive powers, currents, voltages or voltage angles at their measuring point M1 or M2 and transmit corresponding measured values to the network control point 18.
  • the network control center 18 can not determine a complete overview of all load flows taking place in the electrical energy supply network on the basis of the existing measured values alone. Since a complete metrological coverage of the electrical energy supply network would only have to be accomplished with comparatively high installation costs and high costs, the energy supply companies usually fall back on so-called load profiles, in which a different usage or feed behavior for different types of electrical end consumers electrical energy for certain times of the day, weekdays and seasons.
  • load profile 20a for the end consumer 17a, which indicates the particular time-dependent use or feed behavior of a housing estate of a specific size.
  • load profiles 20b, 20c and 20d exist for the end users 17b, 17c and 17d, which indicate a typical usage behavior for commercial operations and office buildings.
  • the network control center 18 Utilizing the load profiles 20a to 2Od, the network control center 18, knowing the topology of section 10 of the electrical power grid and using well-known load flow calculation algorithms in electrical energy supply networks, can calculate the load flows in section 10 of the electrical grid that result were when the use or feed behavior of the end user 17a to 17d exactly correspond to the assumed load profiles.
  • the usage or feed-in behavior of the individual end users can not be specified exactly by statically predefined load profiles, so that deviations will result between the load flows calculated on the basis of the load profiles at the measuring points M1 and M2 and the actual load flows determined by measurement.
  • a method is to be described with which the load profiles can be adapted dynamically to the respective actual states in the section 10 of the electrical energy supply network.
  • 2Od changes made so that this results in modified load profiles.
  • This process can be performed as often as desired, resulting in a certain amount of modified load profiles for each of the load profiles 20a to 2Od.
  • the assumed load flows for the measuring points M1 and M2 can again be calculated in this way.
  • those load profiles are selected on the basis of which load flows result at the measuring points M1 and M2, which have the smallest deviations from the actual load flows determined by measurements.
  • M number of measuring points considered
  • m running index of the measuring points M
  • f measured value at the respective measuring point
  • s vector of the optimization variable of the load profile
  • F function that maps the vector of the optimization variable to the corresponding measured value f
  • w optional weighting factor
  • each load profile is considered to be one
  • Vector s considered the bottlenecks of the individual load profiles and scaling values, with which the respective nodes can be scaled accordingly includes.
  • the support points specify, so to speak, the basic load profile for a specific end user type.
  • the scaling values are needed because different numbers of these end-user types with different levels of energy consumption can be connected to the individual removal points in the energy supply network.
  • measured values which can be used for characterizing load flows can be used as measured values f, for example effective and / or reactive powers, currents, voltages or voltage angles.
  • the objective function can be determined according to
  • Equation (1) can be extended by one violation term, resulting in an extended objective function according to equation (2):
  • R number of injured constraints
  • r running index of injured constraints
  • g limit of a constraint
  • G Function that maps the vector of the optimization variable s to the corresponding constraint.
  • maximum currents in the branches 14, 15 and 16 or on the busbars 11 and 12 and voltage bands to be maintained on the busbars (for example 110 kV on the first busbar 11 and 10 kV on the second busbar 12) can be used.
  • voltage bands to be maintained on the busbars for example 110 kV on the first busbar 11 and 10 kV on the second busbar 12
  • Equation (2) allows such load profiles, which result in comparatively large violations of the specified limits of certain secondary conditions, to be excluded from the outset from the use of future load profiles, since they are given a comparatively poor rating by the added infringement term.
  • Those modified load profiles which best meet the extended objective function according to equation (2) are used as load profiles for the future determination of the load flows in the electrical energy supply network.
  • the described method can be repeated so often, that by constant modification of the previously used load profiles such load profiles result, which result in small deviations from the actual load profiles determined by measurement or only small improvements compared to the loader profiles. Due to the possibility of a continuous dynamic adaptation, the load profiles can also be adapted to changing topology conditions of the energy supply network or time-varying decentralized energy feeds from the end consumers into their respective branches.
  • optimization problems are solved with mathematical methods from the field of optimal planning, e.g. with the dynamic programming, the Lagrang 'see relaxation or the mixed-integer linear optimization.
  • these optimization procedures require a closed and adapted formulation of the optimization problem, which is often either very expensive or even impossible.
  • these methods require special features of the optimization problem, which are often not given, e.g. Convexity or Markov's property. Due to the changing nature of the topology of the power grid over time, the existing optimization problem had to be constantly redefined, which is a considerable effort.
  • the evolutionary process begins with an initial generation of load profiles that consists of a number of vectors called parents.
  • the first step, recombination combines the genetic information of two or more parents. This can e.g. by an averaging of the parental vectors involved or by a piecewise recombination of the parent genes.
  • the recombination is followed by the mutation, in which the individual genes of the recombinants are slightly modified.
  • each solution vector is evaluated using a quality function, e.g. equations (1) or (2).
  • the best children of the current generation in the sense of the quality function are designated as parents of the next generation.
  • the evolutionary process continues until a termination criterion is met. This can e.g. reaching a maximum number of generations or falling below a predetermined minimum improvement of the last generation.
  • FIG. 2 shows a mathematical process flow diagram according to which the following steps are carried out:
  • first load lot startup solvers are generated, which provide evolutionary strategy origins, which can be populated with random numbers if no further information is available. or already with existing load profiles that appear suitable for approximate description of the load flows in the power grid. These may, for example, be load profiles obtained from measurements of past load flows or those already calculated by the method at an earlier point in time. Thereafter, the starting loops of the load flow values for the scaling are determined. These starting solutions are filled either with random numbers, with the connected services of the end users or with archived data.
  • MOD modified load profiles are generated, for example, by mutation and recombination.
  • the results of the load flow determinations are determined on the basis of the modified load profiles and the deviations from the measured values and, if necessary, the secondary conditions, for example, according to equations (1) or (2) ertu ttelt These deviations are squared, weighted This results directly in the objective function value of the considered solution, ie the combination of the modified load profiles, so that in a following evaluation step 24 "EVAL" the quality of the respective solution can be determined in the sense of the evolution strategy.
  • step 25 All evaluated solutions are collected according to a further step 25 ("DEPOT"). According to step 26, it is checked whether there are a sufficient number of solutions (a sufficiently large "population") so that a meaningful selection can be made.
  • step 27 The modified load profiles with the lowest target function values-that is, the highest quality-are selected and, according to the evolution strategy, serve as parents of the next generation.
  • step 28 the method is terminated as soon as the value of the objective function falls below a predetermined limit or the changes from generation to generation are only very small. Otherwise, those described above
  • the found values for the best vectors s ie the best interpolation points of the load profiles and their scaling factors, thus indicate the best estimate of the load behavior at the respective times and are selected according to a final step 29 as future load profiles to be used.
  • the method can be carried out again so that continuous adaptation is possible.
  • a method can be provided which allows adjustment of the stan-ended load profiles used in an electrical power supply network and in this case back accesses comparatively easy to implement mathematical optimization processes to ⁇ .
  • the method is auto-adaptive; it adapts independently to changes in consumer behavior or producer behavior. Interventions by the operator are not necessary.
  • the method is independent of heuristics and can therefore be easily used for different network types and network configurations.

Abstract

L'invention concerne un procédé de détermination de flux de charge électrique dans un réseau d'alimentation en énergie électrique, procédé permettant de déterminer de manière relativement simple, des flux de charge qui correspondent, avec le maximum d'exactitude, avec les flux de charge effectifs dans le réseau d'alimentation électrique. A cet effet, l'invention est caractérisée en ce qu'en comparant le flux de charge effectif en au moins un point de mesure (M1, M2), avec un flux de charge calculé au moyen de flux de charge admis pour au moins le point de mesure (M1, M2), une adaptation dynamique des profils de charge (20a - 20d) est effectuée, en ce qu'au moins un profil de charge modifié, dérivé de chaque profil de charge (20a - 20d) utilisé jusqu'à présent est formé, et en ce qu'au moyen du profil de charge modifié ainsi formé pour au moins le point de mesure (M1, M2), un flux de charge est calculé. Ces profils de charge modifiés sont utilisés à la place des profils de charge (20a - 20d) utilisés jusqu'à présent, pour des déterminations ultérieures du flux de charge dans le réseau d'alimentation en énergie, pour lesquelles on observe, en au moins un point de mesure (M1, M2), le plus faible écart de flux de charge par rapport au flux de charge effectif.
PCT/EP2008/009980 2008-11-20 2008-11-20 Procédé de détermination de flux de charge électrique dans un réseau d'alimentation en énergie électrique WO2010057516A1 (fr)

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PCT/EP2008/009980 WO2010057516A1 (fr) 2008-11-20 2008-11-20 Procédé de détermination de flux de charge électrique dans un réseau d'alimentation en énergie électrique

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PCT/EP2008/009980 WO2010057516A1 (fr) 2008-11-20 2008-11-20 Procédé de détermination de flux de charge électrique dans un réseau d'alimentation en énergie électrique

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012103904A2 (fr) 2011-02-05 2012-08-09 Maschinenfabrik Reinhausen Gmbh Procédé et dispositif de détermination du débit de puissance dans un réseau d'alimentation en énergie
WO2012037989A3 (fr) * 2010-09-24 2012-10-04 Siemens Aktiengesellschaft Procédé de commande assistée par ordinateur de la répartition d'énergie électrique dans un réseau décentralisé
EP3125397A1 (fr) 2015-07-29 2017-02-01 Siemens Aktiengesellschaft Procede, systeme de traitement de donnees et produit de programme informatique destine a la mise a niveau d'un reseau d'energie electrique et procede d'optimisation d'un reseau d'energie electrique existant
EP3594702A1 (fr) * 2018-07-12 2020-01-15 Siemens Aktiengesellschaft Procédé et dispositif de détermination de paramètres d'un composant primaire d'un réseau d'alimentation en énergie électrique
WO2020216667A1 (fr) 2019-04-26 2020-10-29 Wago Verwaltungsgesellschaft Mbh Système de dimensionnement d'un réseau de distribution basse tension sur une station réseau local
EP4075623A1 (fr) * 2021-04-16 2022-10-19 Siemens AG Österreich Procédé de fonctionnement d'une communauté de l'énergie

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WO2004109914A1 (fr) * 2003-06-05 2004-12-16 Enfo Broadcast As Procede et systeme de gestion automatique de demande de biens non durables
US20050090995A1 (en) * 2003-10-27 2005-04-28 Itron Inc. Distributed asset optimization (DAO) system and method
US20080058998A1 (en) * 2006-08-29 2008-03-06 The Boeing Company Method and system for adaptive power management

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2004109914A1 (fr) * 2003-06-05 2004-12-16 Enfo Broadcast As Procede et systeme de gestion automatique de demande de biens non durables
US20050090995A1 (en) * 2003-10-27 2005-04-28 Itron Inc. Distributed asset optimization (DAO) system and method
US20080058998A1 (en) * 2006-08-29 2008-03-06 The Boeing Company Method and system for adaptive power management

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012037989A3 (fr) * 2010-09-24 2012-10-04 Siemens Aktiengesellschaft Procédé de commande assistée par ordinateur de la répartition d'énergie électrique dans un réseau décentralisé
WO2012103904A2 (fr) 2011-02-05 2012-08-09 Maschinenfabrik Reinhausen Gmbh Procédé et dispositif de détermination du débit de puissance dans un réseau d'alimentation en énergie
WO2012103904A3 (fr) * 2011-02-05 2013-06-13 Maschinenfabrik Reinhausen Gmbh Procédé et dispositif de détermination du débit de puissance dans un réseau d'alimentation en énergie
EP3125397A1 (fr) 2015-07-29 2017-02-01 Siemens Aktiengesellschaft Procede, systeme de traitement de donnees et produit de programme informatique destine a la mise a niveau d'un reseau d'energie electrique et procede d'optimisation d'un reseau d'energie electrique existant
US10664630B2 (en) 2015-07-29 2020-05-26 Siemens Aktiengesellschaft Method, data processing arrangement and computer program product for retrofitting an electrical energy network and method for optimizing an existing electrical energy network
EP3594702A1 (fr) * 2018-07-12 2020-01-15 Siemens Aktiengesellschaft Procédé et dispositif de détermination de paramètres d'un composant primaire d'un réseau d'alimentation en énergie électrique
WO2020216667A1 (fr) 2019-04-26 2020-10-29 Wago Verwaltungsgesellschaft Mbh Système de dimensionnement d'un réseau de distribution basse tension sur une station réseau local
EP4075623A1 (fr) * 2021-04-16 2022-10-19 Siemens AG Österreich Procédé de fonctionnement d'une communauté de l'énergie

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