EP2864943A1 - Procédé d'exploitation d'un réseau d'alimentation et réseau d'alimentation - Google Patents

Procédé d'exploitation d'un réseau d'alimentation et réseau d'alimentation

Info

Publication number
EP2864943A1
EP2864943A1 EP13730229.5A EP13730229A EP2864943A1 EP 2864943 A1 EP2864943 A1 EP 2864943A1 EP 13730229 A EP13730229 A EP 13730229A EP 2864943 A1 EP2864943 A1 EP 2864943A1
Authority
EP
European Patent Office
Prior art keywords
network
resource
network units
units
supply network
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.)
Withdrawn
Application number
EP13730229.5A
Other languages
German (de)
English (en)
Inventor
Florian Steinke
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 EP2864943A1 publication Critical patent/EP2864943A1/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05F—SYSTEMS FOR REGULATING ELECTRIC OR MAGNETIC VARIABLES
    • G05F1/00—Automatic systems in which deviations of an electric quantity from one or more predetermined values are detected at the output of the system and fed back to a device within the system to restore the detected quantity to its predetermined value or values, i.e. retroactive systems
    • G05F1/66—Regulating electric power
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • 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
    • H02J4/00—Circuit arrangements for mains or distribution networks not specified as ac or dc
    • 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 Operator Op ben ⁇ a supply network, such as a Ener ⁇ giessensnetzwerks with generators and consumers.
  • a supply network such as a Ener ⁇ giessensnetzwerks with generators and consumers.
  • a cost-effective deployment planning of the existing power plants is desired.
  • a large number of individual energy producers have to be activated, ie driven up and down, and their entry into the network estimated.
  • the need for energy for consumers must be plausibly estimated.
  • an effi possible ⁇ ente utilization should take place in particular the use of energy and of on-the network distribution of the resource.
  • corresponding cost functions for the network nodes or network units in the supply network are created and linked to a target function.
  • a method for operating a supply network for a resource with a plurality of network units wel ⁇ che generate the resource or consume.
  • the network units are coupled to exchange the resource.
  • the method comprises:
  • the marginalization involves minimizing the total cost function over the resource flow parameters, the cost functions being considered as logarithms of local potentials of an undirected graphical model. Furthermore, marginalization may also mean forming a boundary value distribution for the cost functions interpreted as a probability distribution. Furthermore, a supply network for a resource comprising several network units is proposed. The network entities generate or consume the resource and are coupled together to exchange the resource. The supply network is set up to carry out a corresponding procedure for triggering and providing deployment planning for the network facilities.
  • the method or the supply network makes it possible, in particular, to operate a supply network efficiently with controllable network units.
  • the resulting objective function or total cost function of the utility network can be expressed, for example, as a sum of local terms describing the characteristics of the individual network units.
  • the cost functions in particular couple with one another through a next adjacent link, which is described by the respective resource flow parameter.
  • This allows the application of methods for undirected graphical models for use, for example, in the deployment planning of power plants in utility networks. It is thus proposed to model and thus to solve statistical costs and objective functions usually considered in the context of nonlinear optimization methods in the context of graphical models. As a result, the computational effort is minimized ⁇ Lich, so that a low-cost operation of supply networks can be done.
  • a state estimation for the supply network can be made cost-effectively
  • the resource can be energy, such as electrical energy, but also resources other than commodities. It may, for example, be an energy source such as gas or oil. It is also conceivable that the resource is computing time or computing power in computer networks. Intermediate products in a production network can also be considered as a resource.
  • a supply network can be understood in particular as: a power supply network, gas supply network, but also building management systems or networks of automation technology.
  • gases such as noble gases or compressed air
  • Each is desirable to find ⁇ a global minimum of the total cost function.
  • the network units are, for example, power generators or consumers, such as different Kraftwer ⁇ ke, depending on their energy or power generation processes have different cost functions. It is possible to describe the flow of the resource, such as the stream, by resource flow parameters. For example, in a power network in the usual DC approximation of the flow equation, the current phase of a respective network unit can be used as a resource flow parameter.
  • minimizing the overall cost function further includes:
  • the optimization method is chosen in particular from one of the groups of optimization methods, which comprises: Belief propagation, Loopy Belief propagation and Junction Tree algorithm.
  • Loops of multiple network devices but the network has a tree structure.
  • actually present couplings or connections for the exchange of resources can be approximated or estimated. It is possible to approximate any real network topology, which also contains loops, into a tree structure.
  • an application of marginalization or optimization methods in the field of graphical models can, however, also take place directly on networks which do not have a tree structure.
  • the feed network is configured such or modeled, that a respective network unit preferential ⁇ example with less than is coupled to a predetermined maximum number of adjacent network devices.
  • each network unit has a maximum of three adjacent network units to which it is coupled.
  • the process steps to be ⁇ organize and minimizing for creating a Netzechs- scheduling a predetermined time for a plurality of Time points performed in the period. For example, ⁇ the determined resource consumption for at least a selection of network entities in the supply network for the predetermined time period or estimated. Forecasts for the consumption of resources, such as electricity, can be created and corresponding cost functions generated on a time-dependent basis. Overall, the optimization process may gradually, so several times, performed in the prog ⁇ to nostilingerden or controlled period advertising to. As a result of the process, values are then provided for the power generation of the power plants in the utility network.
  • the cost function (c ⁇ ) of a respective network unit is preferably successively minimized by taking into account the additional network units coupled directly to the network unit via the resource flow parameters.
  • the cost functions of the network units and locally calculable cost coupling terms are individually minimized via the resource flow parameters assigned to the respective network unit and the adjacent network units.
  • the cost coupling terms can be calculated locally and the cost functions are then locally minimized.
  • the cost functions include, in particular, non-linear components in the resource flow parameters.
  • non-linear components in the resource flow parameters.
  • the efficiency of a corresponding power plant as a network unit can depend heavily on the load.
  • the network entities are controlled in dependence on the resource flow parameters.
  • determining sets of resource flow parameters, tern which achieve the lowest possible total cost function, can be determined, for example, in electrical supply networks, energy production or consumption of the individual network facilities.
  • the Kostenfunktio ⁇ NEN local cost functions which depends solely on the resource record or resource consumption of the network unit and / or the resource flow parameters of the network entity and the network entity with the direct-coupled further network units.
  • the resource is in particular electrical ⁇ specific energy and resource record or resource consumption, an electric power of a network unit.
  • the exchange of a resource takes place, for example, via electrical current, wherein the resource flow parameter is a phase angle of a current in or out of the respective network unit from or into the supply network.
  • a computer program product such as a computer program means can be used, for example, as a storage medium, such as a memory card, USB stick, CD-ROM, DVD or even in the form of a
  • downloadable file from a server on a network. This can be done, for example, in a wireless communication network by the transmission of a corresponding file with the computer program product or the computer program means.
  • program- Controlled device is in particular a Steuereinrich ⁇ tion, such as a host computer for use planning of network units in a supply network in question.
  • a data carrier is provided with a stored computer program comprising instructions which causes the execution of a process corresponding to a programmge ⁇ controlled device.
  • Other possible implementations of the invention include not explicitly mentioned combinations of above or below with respect to the embodiments described method steps, features or embodiments of the Ver ⁇ driving or of the supply network.
  • the person skilled in the art will also add or modify individual aspects as improvements or additions to the respective basic form of the invention.
  • Figure 1 is a schematic representation of anwhosbei ⁇ game for a supply network with network units.
  • FIG. 2 shows a representation of a possible cost function for a generator as a network unit
  • Fig. 3 shows an illustration of a possible cost function for a consumer as a network unit
  • Fig. 4 is a schematic representation of another embodiment of a supply network with network units.
  • Fig. 1 shows a schematic representation of an exporting ⁇ approximately example of a supply network with power units.
  • the supply network 100 has this network units 1 to 11, for example, energy sources and energy sinks entspre ⁇ chen. That is, in an electrical supply network in particular are current consumers, but also power generators, such as power plants.
  • the subscribers of the supply network 100 referred to as network units or also nodes 1 to 11, are coupled to one another, for example via lines which are illustrated with edges in FIG.
  • the network unit 1 may be a consumer, such as a factory, coupled via the network node 3 to the remaining network nodes 2-11 present in the network 100.
  • the edges represent that the resource to be distributed, such as electricity, can flow.
  • the supply network is an electrical power supply network.
  • the resource is electrical energy, which is transmitted via electrical power in the network via lines with which the participants, ie
  • FIG. 2 shows one possible form of a cost function c ⁇ for an energy-generating device.
  • a current generation yi is plotted in arbitrary units, and on the Y-axis in arbitrary units a corresponding cost function c ⁇ (y ⁇ ).
  • the cost function is not constant between a minimum with a minimum power generation Prain and a maximum power generation P max .
  • a nonlinear form of the cost function c ⁇ (y ⁇ ) results.
  • each power generator is allocated a corresponding cost function in the network 100th Fig.
  • FIG. 3 shows a cost function for a consumer in the utility network.
  • D is also called Demand.
  • the energy input or the energy consumption from the current phase present at the node can be determined on the basis of continuity equations at each network node, ie every producer or consumer in the network, using a known DC approximation of the load flow equations. From the sum of the cost functions for all network nodes or power consumers or power generators results in a target function or total cost function for the network at a given time. It is now desired to minimize this objective function in order to determine the most favorable operating parameters, that is to say current consumers and power generators, for example in the context of phase angles. This results in a particularly favorable utilization of the network infrastructure. structure and a minimum effort for all network participants.
  • FIG. 4 shows a supply network 101 which, for example, distributes electrical energy.
  • the node 1 is coupled to the node 2.
  • the node 2 is coupled to the node 1, the node 5 and the node 3.
  • the node 3 is coupled to the node 2 and the node 4, the node 4 is coupled only to the node 3.
  • the node 5 is coupled to the node 2 and the node 6, and the node 6 is coupled to the node 5 only.
  • the nodes can be power-feeding network units or power-consuming network units.
  • the desired optimization is to find a global minimum of the following expression:
  • Equation 1 Equation 1 where c ⁇ stands for the respective cost function of the i-th
  • phase angle ⁇ ⁇ corresponds to a resource flow parameter which, in the case of a power supply network, determines the inflow and / or outflow of electrical current in the power supply network.
  • the edges or coupling lungs between the nodes can be understood as power lines.
  • Equation 2 Due to the locality of the interaction of the nodes with each other, equation 2 can be simplified.
  • bab Equation 2 can be written as follows: min ⁇ 1 ( ⁇ 1 , ⁇ 2 ) + min ⁇ 1 ( ⁇ 1 , ⁇ 2 , ⁇ 3 , ⁇ 5 ) + min [c 3 ( ⁇ 2 , ⁇ 3 , ⁇ 4 ) + c 4 ( ⁇ 3 , ⁇ 4 )] + ⁇ 1 , ⁇ 2 ⁇ 3 , ⁇ 5 ⁇ 4
  • Equation 3 The local cost coupling terms ⁇ j are determined as follows:
  • ⁇ * arg min 5i min c i (5 i , ⁇ ., ⁇ ⁇ ) + ⁇ ⁇ ( ⁇ ⁇ , ⁇ ⁇
  • a count of the possible combinations can be made at a respective node in order to determine the most favorable ⁇ ,,, ⁇ .
  • the optimization of a corresponding constructed because of local cost functions, the power supply network and the necessary computing power ⁇ increase only linearly with the number of present in the network node.
  • Each edge in the network is considered at most twice, for example, the edge or coupling between nodes 3 and 4 is taken into account only to compute m 34 and m 43 .
  • the net topology is preferably constructed in the manner of a tree, ie there are no closed loops. In principle, an exact optimization solution for a corresponding supply network can then be found.
  • Equation 2 the objective or total cost function for a corresponding utility network, as indicated in Equation 2, can be mapped to a graphical model.
  • stochastic methods for determining a maximum likelihood are known as an optimization task.
  • the algorithm shown is the most ⁇ th statistical methods "belief propagation".
  • a probability function for a non-directed graphical model is given, which in local potentials
  • p is a probability function
  • ⁇ are the local potentials
  • xi random variables.
  • MRF Markoff Random Field
  • a simple solution for minimizing the objective function ie the total cost function
  • the power stations or nodes can be activated accordingly, or deactivated so that IMP EXP ⁇ including an optimal operation of the Supply network.
  • the optimization task for the supply network of FIG. 4 can use an algorithm tree of the OWM MATLAB toolbox for the simulation program MATLAB.
  • a method of Belief Propagation is used.
  • the transfer into a tree structure of the supply network allows the use of known algorithms for optimization, such as Belief propagation.
  • the following algorithms of the OWM MATLAB Toolbox which can be accessed at http://www.di.ens.fr/ ⁇ mschmidt/Software/UGM.html, may be mentioned which can be used.
  • NEN Junction (an exact decoding graph with tree structure) LBP (an approximate decoding based on maximum-Product-loopy belief propagation), TRBP (a ge ⁇ approached decoding Max Product tree re-weighted Belief Propagation) Linprog (approximate decoding using linear programmed relaxation).
  • Possible cost functions c ⁇ for the network units 1 - 6 of the network 101 shown in FIG. 4 are, for example:
  • a simplified optimization task results from the mapping of the objective function or overall cost function of a supply network with the next neighbor coupling to an undirected graphic model.
  • the complexity increases only linearly with the number of nodes used in the network.
  • Conventional optimization methods usually become exponentially complex. If no loops are provided in the network, but one
  • Tree structure is present, there is a global optimal solution Lö ⁇ .
  • an assessment of the state of affairs can be carried out as an alternative to minimizing the cost function by means of optimization.
  • locally measured resource streams can be used to determine in the context ei ⁇ nes Marginalleitersvons for undirected graphical models the state of the supply network.
  • the boundary value distribution for each unknown resource flow parameter is determined in each case by averaging out of the remaining free variables of the probability model defined by the cost function.
  • An application of the state estimation is, for example, in the presence of power plants that do not provide real-time data for their power supply in power grids. For example, supply solar power plants depending on the radiation intensity different services, leading to variie ⁇ Governing tensions in the net.
  • a state estimate the probability for critical stress conditions provide in the supply network, for example, depending on the measured currents to be ⁇ knew nodes.

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Abstract

L'invention concerne un procédé d'exploitation d'un réseau d'alimentation comprenant des unités de réseau qui fournissent ou consomment une ressource. Les fonctions de coût des unités de réseau sont représentées sur des potentiels locaux d'un modèle graphique non dirigé. Des procédés de marginalisation ou des procédés d'optimisation, comme la propagation de croyances pour interférence stochastique réduisent à un minimum une fonction de coût global pour commander les unités de réseau. L'invention concerne également un réseau d'alimentation comprenant des unités de réseau qui est exploité de manière correspondante. Le procédé proposé permet par exemple de déterminer simplement une planification d'utilisation de centrales comme unités de réseau dans un réseau d'alimentation en énergie. Cela permet également des estimations d'état pour des réseaux.
EP13730229.5A 2012-06-21 2013-06-18 Procédé d'exploitation d'un réseau d'alimentation et réseau d'alimentation Withdrawn EP2864943A1 (fr)

Applications Claiming Priority (2)

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DE102012210509 2012-06-21
PCT/EP2013/062587 WO2013189914A1 (fr) 2012-06-21 2013-06-18 Procédé d'exploitation d'un réseau d'alimentation et réseau d'alimentation

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EP2864943A1 true EP2864943A1 (fr) 2015-04-29

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US (1) US20150185749A1 (fr)
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108833183A (zh) * 2018-06-28 2018-11-16 电子科技大学 一种多时隙能量采集多归属异构网络的功率分配方法

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103391233B (zh) * 2013-07-31 2016-04-13 清华大学 跨域虚拟网映射方法
CN106549373A (zh) * 2015-09-23 2017-03-29 国网天津市电力公司 一种面向调度运行的电网拓扑分区方法
CN110837940B (zh) * 2018-08-15 2023-01-10 新智数字科技有限公司 能源站资源流动的计算方法及装置
CN109242286A (zh) * 2018-08-27 2019-01-18 华北电力大学 一种基于径向基神经网络的需求侧响应潜力挖掘方法

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1418477A1 (fr) * 2002-11-06 2004-05-12 Abb Research Ltd. Procéde pour contrôler un réseau de transmission d'énergie électrique
DE102010005955A1 (de) * 2010-01-27 2011-07-28 Abb Technology Ag Anordnung und Verfahren zur Optimierung der Arbeitsweise eines Versorgungsnetzes

Family Cites Families (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6745157B1 (en) * 2000-06-02 2004-06-01 Mitsubishi Electric Research Laboratories, Inc Super-node normalized belief propagation for probabilistic systems
GB2367970B (en) * 2000-10-09 2004-01-21 Ericsson Telefon Ab L M Network topologies
US7349887B2 (en) * 2002-03-11 2008-03-25 Siemens Power Transmission & Distribution, Inc. Security constrained optimal dispatch for electricity markets
CN1305196C (zh) * 2003-05-10 2007-03-14 清华大学 灾变事故下避免电力系统崩溃的解列方法
US7660649B1 (en) * 2004-07-02 2010-02-09 Optimal Innovations Inc. Resource management using calculated sensitivities
US7844370B2 (en) * 2006-08-10 2010-11-30 Gridpoint, Inc. Scheduling and control in a power aggregation system for distributed electric resources
CN101803138B (zh) * 2007-09-21 2013-08-28 西门子公司 分散式电网以及用于在分散式电网中配送电能的方式
US8352095B2 (en) * 2009-09-23 2013-01-08 Electronics And Telecommunications Research Institute Electric power generation device, electric power demand device, and electric power control system, and method of controlling electric power
EP2599182A1 (fr) * 2010-07-29 2013-06-05 Spirae Inc. Système de régulation de réseau électrique réparti dynamique
AU2011300518B2 (en) * 2010-09-07 2014-06-12 Bae Systems Plc Assigning resources to resource-utilising entities
US8626353B2 (en) * 2011-01-14 2014-01-07 International Business Machines Corporation Integration of demand response and renewable resources for power generation management
CN102354981B (zh) * 2011-09-30 2013-07-03 河海大学 一种基于分布式计算的互联电网中子网电压稳定评估方法
US20130178994A1 (en) * 2012-01-11 2013-07-11 Nagaraju Valluri Power metering and load control device
US8977524B2 (en) * 2012-03-06 2015-03-10 Siemens Aktiengesellschaft Interior point method for reformulated optimal power flow model

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1418477A1 (fr) * 2002-11-06 2004-05-12 Abb Research Ltd. Procéde pour contrôler un réseau de transmission d'énergie électrique
DE102010005955A1 (de) * 2010-01-27 2011-07-28 Abb Technology Ag Anordnung und Verfahren zur Optimierung der Arbeitsweise eines Versorgungsnetzes

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of WO2013189914A1 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108833183A (zh) * 2018-06-28 2018-11-16 电子科技大学 一种多时隙能量采集多归属异构网络的功率分配方法
CN108833183B (zh) * 2018-06-28 2020-12-18 电子科技大学 一种多时隙能量采集多归属异构网络的功率分配方法

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US20150185749A1 (en) 2015-07-02
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