EP3063713A1 - Optimizing the distribution of electrical energy - Google Patents
Optimizing the distribution of electrical energyInfo
- Publication number
- EP3063713A1 EP3063713A1 EP14801963.1A EP14801963A EP3063713A1 EP 3063713 A1 EP3063713 A1 EP 3063713A1 EP 14801963 A EP14801963 A EP 14801963A EP 3063713 A1 EP3063713 A1 EP 3063713A1
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- Prior art keywords
- dispatcher
- distribution
- electrical energy
- energy
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- 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
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0205—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system
- G05B13/021—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system in which a variable is automatically adjusted to optimise the performance
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02B90/20—Smart grids as enabling technology in buildings sector
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- 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
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
Definitions
- the invention relates to the technical field of distributing electrical energy.
- New energy networks are composed of autonomous regions, which are also referred to as islands or network areas, and which are balanced by mutual distribution of energy among themselves. This balancing can be accomplished by a node, which we also refer to as a dispatcher or optimizer, and which collects all necessary information via a generic interface and / or to which that information is sent, based on energy intervals requested by the autonomous regions.
- necessary information may include, for example, a predicted energy requirement, a predicted energy output and / or a flexibility with regard to the predicted energy requirement and / or energy output of the island.
- the dispatcher then tries to calculate the optimal distribution of the available energy over the autonomous regions and assigns these energy transfers.
- the energy flows can be centrally controlled by means of an external dispatcher / optimizer or by one of the
- Peers that is, one of the islands, is selected as the node for the dispatcher / optimizer which handles the transmissions of Energy calculated using a low-complexity algorithm.
- a single central management node configured as a dispatcher entails disadvantages such as communication bottlenecks, peak load on one of the peer nodes, or a single point of failure. It is also possible that the central management node will only provide suboptimal results due to limited time available, limited computational power, and limited disk space.
- the present invention is therefore based on the object of optimizing the distribution of electrical energy in an autonomous network areas comprehensive electric power grid. This object is achieved by the solutions described in the independent claims. Advantageous embodiments of the invention are specified in further claims.
- a method for optimizing the distribution of electrical energy in an electrical grid is presented.
- the power grid includes autonomous network areas. The method comprises the following method steps:
- input data is received by at least two dispatcher instances.
- the input data represent energy intervals requested by the autonomous network areas.
- at least one solution of the distribution of electrical energy to the network areas is calculated by each of the at least two dispatcher instances.
- one of the calculated solutions for the distribution of electrical energy in the power grid is selected.
- a system for optimizing the allocation of electrical energy in an electrical grid is presented.
- the electric power network includes autonomous network areas.
- the system comprises at least two dispatcher instances and one selection means.
- Each of the at least two dispatcher instances comprises an interface means and a calculation means.
- the interface means of the at least two dispatcher instances are each adapted to receive input data representing the energy intervals requested by the autonomous network domains.
- the calculation means of the at least two dispatcher instances are each adapted to calculate a solution of the distribution of electrical energy to the network areas.
- the selection means is adapted to select one of the calculated solutions for the distribution of electrical energy in the power grid.
- Figure 1 is a block diagram of a power network, by a
- Figure 2 is a block diagram of a system according to one embodiment of the invention for optimizing the sharing of electrical energy in the power network of Figure 1;
- FIG. 3 shows a flowchart of a method according to an embodiment of the invention.
- Figure 1 shows power grid 1, which is controlled by a data network 19, according to an embodiment of the invention.
- the power grid 1 is highlighted in Figure 1 by the drawn in bold lines elements and includes the autonomous network areas 5, 6, 7, 8, 9 and electrical connections that connect the autonomous network areas together.
- not all autonomous network areas need to be connected to all other autonomous network areas. Rather, there are many opportunities to connect the autonomous network areas with each other. In reality, not all network areas are connected to all network areas in a large power grid, usually for cost reasons and due to geographical conditions.
- the data network 19 is highlighted in Figure 1 by the elements drawn in solid bold lines and includes the instances 5a, 6a, 7a, 8a, 9a and data links connecting these instances 5a, 6a, 7a, 8a, 9a to the network 19.
- the data network 19 does not need to have the same topology as the power grid 1, but may have its own topology.
- Each of the autonomous network areas 5, 6, 7, 8, 9 of the power network comprises at least one instance 5a, 6a, 7a, 8a, 9a, which controls the respective autonomous network area.
- At least two of the instances 5a, 6a, 7a, 8a, 9a are configured as a dispatcher instance. In the in Figure 1 and FIG 2 illustrated embodiments, these are the instances 5a and 7a.
- FIG. 2 shows a system 2 for optimizing the allocation of electrical energy in an electric power grid 1 comprising autonomous network areas 5, 6, 7, 8, 9.
- the system 2 comprises at least two dispatcher instances 5 a, 7 a and a selection means 3
- Each of the at least two dispatcher instances 5a, 7a comprises an interface means 5b, 7b and a calculation means 5c, 7c.
- Each of the interface means 5b, 7b of the at least two dispatcher instances 5a, 7a is adapted to receive input data 11.
- the input data represent energy intervals 5i, 6i, 7i, 8i, 9i requested by the autonomous network areas 5, 6, 7, 8, 9.
- Each of the calculation means 5c, 7c of the at least two dispatcher instances 5a, 7a is adapted to calculate a solution 5s, 7s of the distribution of electrical energy to the network regions 5, 6, 7, 8, 9.
- the selection means 3 is adapted, one of the calculated solutions 5s, 7s for the distribution of electrical energy in the power grid 1 by means of a leader
- FIG. 3 shows a method for optimizing the distribution of electrical energy in the power grid 1 according to a preferred embodiment of the invention.
- method step 31 for each of the network regions 5, 6, 7, 8, 9, its expected energy requirement is determined in the form of an energy interval 5i, 6i, 7i, 8i, 9i, respectively. See also FIG. 1.
- These energy intervals 5i , 6i, 7i, 8i, 9i are received as input data 11 in method step 32 by two dispatcher instances 5a, 7a.
- the input data 11 thus represent the energy intervals 5i, 6i, 7i, 8i, 9i requested by the autonomous network areas 5, 6, 7, 8, 9.
- the reception of the input data I by the dispatcher instance 5a is illustrated in FIG.
- each of the at least two patcher instances 5a, 7a show a solution 5s, 7s of the distribution of electrical energy to the mesh areas 5, 6, 7, 8, 9.
- the calculation of the solution 5s by the dispatcher instance 5a is shown in FIG. 3 by the partial method step 33a while computing the solution 7s by the dispatcher instance 7a is represented by the sub-process step 33b.
- one of the calculated solutions 5s, 7s for distributing electrical energy in the power grid 1 is selected by means of a leader election.
- the selection means 3 can be adapted to evaluate the calculated solutions 5s, 7s by a target value function 3z and to each other in the leader
- the goal function provides a scalar value for each of the solutions 5s, 7s, which represents the quality of the solution, and thus makes the comparison possible.
- the target value function 3z can also supply vectors that allow a comparison.
- all of the at least two dispatcher instances 5a, 7a receive the same input data 11.
- the at least two dispatcher units 5a, 7a are adapted to calculate different solutions 5s, 7s of the distribution of electrical energy to the network areas 5, 6, 7, 8, 9, respectively.
- This can preferably be achieved, for example, by adapting the at least two dispatcher instances 5a, 7a to select different starting populations within the energy intervals 5i, 6i, 7i, 8i, 9i for the calculation of the respective at least one solution.
- the at least two dispatcher instances 5a, 7a are adapted to use different algorithms for the calculation of the respective at least one solution 5s, 7s.
- the network areas 5, 6, 7, 8, 9 will logically be represented as a selection of energy producers, energy consumers and prosumers.
- Prosumers represent network areas that can either produce or consume energy. This is the case, for example, with pumped storage power plants.
- Another example of a prosumer can also be an electric vehicle or a group of electric vehicles whose battery can be recharged to stabilize the power grid depending on network requirements or can provide power to the power grid.
- the requested energy interval can overlap zero, eg battery can be charged and discharged.
- the system 2 may comprise only the dispatcher instances 5a, 7a and the selection means, or it may also comprise the power grid.
- the power grid 1 is or comprises a DC power grid or an AC power grid.
- Preferred embodiments define the target value function 3z for the dispatcher instance and represent how optimal the solution for distributing electrical energy calculated by the dispatcher instance is. This is a byproduct of the actual calculation of the optimal distribution of energy.
- the cost function is transmitted in the input data 11 with the energy intervals 5i, 6i, 7i, 8i, 9i and expresses a preference within the energy interval, namely to optimize the costs. The optimization should try to always reach the minimum of the cost function.
- the required information for the respective dispatcher instance 5a, 7a is preferably broadcast by each of the instances, so that the dispatcher instances have a possible complete data record for the calculation of the distribution of electrical energy available.
- Each instance formed as node 5a, 6a, 7a, 8a, 9a receives the information and sends it further as needed to provide a complete image to all further instances 5a, 6a, 7a, 8a, 9a.
- all instances 5a, 6a, 7a, 8a, 9a dispatcher instance function to calculate a solution designed as an energy distribution using the broadcasted information and randomly selected initial standby states. After the calculation, respectively, when the allowed time window for the calculation has expired, each instance 5a, 6a, 7a, 8a, 9a broadcasts the value of the target value function for its respective calculated solution.
- the instances 5a, 6a, 7a, 8a, 9a compare their values according to a bullying scheme. This means that a node broadcasts its resulting value of the goal value function.
- a dispatcher instance 5a, 6a, 7a, 8a, 9a receives a message from another dispatcher instance 5a, 6a, 7a, 8a, 9a having a lower, that is a less optimal value, it broadcasts a message with its own higher one Value. If no more messages are received within a given time after the last message, that solution wins with the last and thus highest value.
- the dispatcher instance 5a, 6a, 7a, 8a, 9a wins the best solution and sends its calculated solutions for the distribution of electrical energy to the other entities 5a, 6a, 7a, 8a, 9a, which then calculate the computed solutions. Divide the electric energy to the autonomous network areas 5, 6, 7, 8, 9 implement.
- Other methods other than the bullying algorithm may also be used, such as a ring algorithm, see
- the optimization of the distribution of electrical energy is distributed over two or more dispatcher instances 5a, 6a, 7a, 8a, 9a.
- the computed solutions can be improved by the distributed computation, as these solutions of several dispatcher instances are compared and the best solution is selected. It also allows individual nodes 5a, 6a, 7a, 8a, 9a to function as a dispatcher instance and to participate in the computation of the solution, or not to do so due to limited resources.
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Abstract
Description
Beschreibung description
Optimieren des Verteilens von elektrischer Energie Die Erfindung bezieht sich auf das technische Gebiet des Verteilens von elektrischer Energie. Optimizing the distribution of electrical energy The invention relates to the technical field of distributing electrical energy.
Bekannt sind Algorithmen zur Leader-Election : Algorithms for leader election are known:
http://en.wikipedia.org/wiki/Leader_election. Aus DE 10 2011 078 045 AI sind zudem Verfahren und Vorrichtungen zum Zuteilen von Energiemengen bekannt . http://en.wikipedia.org/wiki/Leader_election. From DE 10 2011 078 045 AI methods and devices for allocating amounts of energy are also known.
Neue Energie-Netze, welche auch als Softgrids bezeichnet werden, setzen sich aus autonomen Regionen zusammen, welche auch als Inseln oder Netzbereiche bezeichnet werden, und welche mittels gegenseitiger Verteilung der Energie untereinander ausbalanciert werden. Diese Ausbalancierung kann durch einen Knoten bewerkstelligt werden, welchen wir auch als Dispatcher oder Optimierer bezeichnen, und welcher basierend auf durch die autonomen Regionen angeforderten Energie- Intervallen alle notwendige Information mittels einer generischen Schnittstelle sammelt und/oder zu welchem diese Informationen gesendet werden. Notwendige Informationen können je nach Insel beispielsweise einen prognostizierten Energiebedarf, einen prog- nostizierten Energie-Output und/oder eine Flexibilität hinsichtlich des prognostizierten Energiebedarfs und/oder Energie-Outputs der Insel umfassen. New energy networks, also referred to as softgrids, are composed of autonomous regions, which are also referred to as islands or network areas, and which are balanced by mutual distribution of energy among themselves. This balancing can be accomplished by a node, which we also refer to as a dispatcher or optimizer, and which collects all necessary information via a generic interface and / or to which that information is sent, based on energy intervals requested by the autonomous regions. Depending on the island, necessary information may include, for example, a predicted energy requirement, a predicted energy output and / or a flexibility with regard to the predicted energy requirement and / or energy output of the island.
Der Dispatcher versucht dann die optimale Verteilung der zur Verfügung stehenden Energie über die autonomen Regionen zu berechnen und ordnet diesen Energie-Transfers zu. The dispatcher then tries to calculate the optimal distribution of the available energy over the autonomous regions and assigns these energy transfers.
Es gibt verschiedene mögliche Ansätze für die Optimierung der Verteilung der zur Verfügung stehenden Energie. Die Energie- flüsse können zentral mittels eines externen Dispatchers/Optimierers gesteuert werden oder indem einer der There are several possible approaches for optimizing the distribution of available energy. The energy flows can be centrally controlled by means of an external dispatcher / optimizer or by one of the
Peers, also eine der Inseln, als Knoten für den Dispatcher/Optimierer gewählt wird, welcher die Übertragungen von Energie mittels eines Algorithmus geringer Komplexität berechnet . Peers, that is, one of the islands, is selected as the node for the dispatcher / optimizer which handles the transmissions of Energy calculated using a low-complexity algorithm.
Ein einzelner zentraler als Dispatcher ausgestalteter Manage- ment-Knoten zieht jedoch Nachteile wie Kommunikations- Flaschenhälse, Lastspitzen an einem der Peer-Knoten oder einen Single Point of Failure nach sich. Zudem ist es möglich, dass der zentrale Management-Knoten aufgrund begrenzter ihm zur Verfügung stehender Zeit, begrenzter Rechenleistung und begrenztem Speicherplatz nur suboptimale Ergebnisse liefert. However, a single central management node configured as a dispatcher entails disadvantages such as communication bottlenecks, peak load on one of the peer nodes, or a single point of failure. It is also possible that the central management node will only provide suboptimal results due to limited time available, limited computational power, and limited disk space.
Um diese Nachteile abzumildern, können folgende Ansätze angewendet werden: To mitigate these disadvantages, the following approaches can be used:
- Bevorratung des Dispatchers mit erhöhten Ressourcen, - Stocking the dispatcher with increased resources,
z.B. einer besseren CPU; e.g. a better CPU;
- Die Dispatcher-Rolle unter den verfügbaren Knoten weitergeben, z.B. immer dem Knoten mit der vollsten Batterie ; - relay the dispatcher role among the available nodes, e.g. always the node with the fullest battery;
- Ein zentraler Dispatcher mit redundanten oder überbevor- rateten Kommunikationsverbindungen; - A central dispatcher with redundant or over-pre- served communication links;
- Konzepte für die Ausfallssicherung, wie z.B. Hot-Backup des Dispatchers; - Fail-safe concepts, such as Hot backup of the dispatcher;
- Heartbeat Monitoring des Dispatchers und Auswahl - Prozedur für einen Ersatz im Falle einer Fehlfunktion. - Heartbeat monitoring of the dispatcher and selection - Procedure for replacement in case of malfunction.
Diese Ansätze lösen mildern jedoch nur einzelne der Nachteile ab, und bedürfen zudem eines erhöhten Aufwands für deren Implementierung . Der vorliegenden Erfindung liegt daher die Aufgabe zugrunde, das Verteilen von elektrischer Energie in einem autonome Netzbereiche umfassenden elektrischen Stromnetz zu optimieren . Diese Aufgabe wird durch die in den unabhängigen Ansprüchen beschriebenen Lösungen gelöst. Vorteilhafte Ausgestaltungen der Erfindung sind in weiteren Ansprüchen angegeben. Gemäß einem Aspekt wird ein Verfahren zum Optimieren des Ver- teilens von elektrischer Energie in einem elektrischen Stromnetz vorgestellt. Das Stromnetz umfasst autonome Netzbereiche. Das Verfahren umfasst folgende Verfahrensschritte: Solving these approaches, however, mitigates only a few of the disadvantages, and moreover requires an increased effort for their implementation. The present invention is therefore based on the object of optimizing the distribution of electrical energy in an autonomous network areas comprehensive electric power grid. This object is achieved by the solutions described in the independent claims. Advantageous embodiments of the invention are specified in further claims. In one aspect, a method for optimizing the distribution of electrical energy in an electrical grid is presented. The power grid includes autonomous network areas. The method comprises the following method steps:
In einem Verfahrensschritt werden Inputdaten durch mindestens zwei Dispatcher- Instanzen empfangen. Die Inputdaten repräsentieren durch die autonomen Netzbereiche angeforderte Energie- Intervalle. In einem weiteren Verfahrensschritt wird mindestens eine Lösung des Verteilens von elektrischer Energie auf die Netzbereiche durch jede der mindestens zwei Dispatcher- Instanzen berechnet. In einem weiteren Verfahrensschritt wird eine der berechneten Lösungen für das Verteilen von elektrischer Energie in dem Stromnetz ausgewählt. Gemäß einem weiteren Aspekt wird ein System zum Optimieren des Zuteilens von elektrischer Energie in einem elektrischen Stromnetz vorgestellt. Das elektrische Stromnetz umfasst autonome Netzbereiche. Das System umfasst mindestens zwei Dispatcher-Instanzen und ein Auswahlmittel. Jede der mindestens zwei Dispatcher- Instanzen umfasst ein Schnittstellenmittel und ein Berechnungsmittel. Die Schnittstellenmittel der mindestens zwei Dispatcher-Instanzen sind jeweils adaptiert, Inputdaten zu empfangen, die durch die autonomen Netzbereiche angeforderte Energie-Intervalle repräsentieren. Die Berech- nungsmittel der mindestens zwei Dispatcher- Instanzen sind jeweils adaptiert, eine Lösung des Verteilens von elektrischer Energie auf die Netzbereiche zu berechnen. Das Auswahlmittel ist adaptiert, eine der berechneten Lösungen für das Verteilen von elektrischer Energie in dem Stromnetz auszuwählen. In one method step, input data is received by at least two dispatcher instances. The input data represent energy intervals requested by the autonomous network areas. In a further method step, at least one solution of the distribution of electrical energy to the network areas is calculated by each of the at least two dispatcher instances. In a further method step, one of the calculated solutions for the distribution of electrical energy in the power grid is selected. In another aspect, a system for optimizing the allocation of electrical energy in an electrical grid is presented. The electric power network includes autonomous network areas. The system comprises at least two dispatcher instances and one selection means. Each of the at least two dispatcher instances comprises an interface means and a calculation means. The interface means of the at least two dispatcher instances are each adapted to receive input data representing the energy intervals requested by the autonomous network domains. The calculation means of the at least two dispatcher instances are each adapted to calculate a solution of the distribution of electrical energy to the network areas. The selection means is adapted to select one of the calculated solutions for the distribution of electrical energy in the power grid.
Die Erfindung wird nachfolgend anhand der Figuren beispielsweise näher erläutert. Dabei zeigen: The invention will be explained in more detail with reference to the figures, for example. Showing:
Figur 1 ein Blockdiagramm ein Stromnetzes, das durch ein Figure 1 is a block diagram of a power network, by a
Datennetz gesteuert wird, gemäß einem Ausführungsbeispiel der Erfindung; Figur 2 ein Blockdiagramm eines Systems gemäß einem Ausführungsbeispiel der Erfindung zum Optimieren des Zu- teilens von elektrischer Energie in dem Stromnetz von Figur 1 ; Data network is controlled, according to an embodiment of the invention; Figure 2 is a block diagram of a system according to one embodiment of the invention for optimizing the sharing of electrical energy in the power network of Figure 1;
Figur 3 zeigt ein Flussdiagramm eines Verfahrens gemäß einem Ausführungsbeispiel der Erfindung. FIG. 3 shows a flowchart of a method according to an embodiment of the invention.
Elemente mit gleicher Funktion und Wirkung sind in den Figu- ren mit denselben Bezugszeichen versehen. Elements with the same function and effect are provided with the same reference numerals in the figures.
Figur 1 zeigt Stromnetz 1, das durch ein Datennetzwerk 19 gesteuert wird, gemäß einem Ausführungsbeispiel der Erfindung. Das Stromnetz 1 ist in Figur 1 durch die in fetten Linien gezeichneten Elemente hervorgehoben und umfasst die autonomen Netzbereiche 5, 6, 7, 8, 9 und elektrische Verbindungen, die die autonomen Netzbereiche miteinander verbinden. Wie in Figur 1 dargestellt, brauchen nicht alle autonomen Netzbereiche mit allen anderen autonomen Netzbereichen verbunden zu sein. Vielmehr bestehen vielfältige Möglichkeiten, die autonomen Netzbereiche untereinander zu verbinden. In der Realität sind in einem großen Stromnetz in der Regel aus Kostengründen und aufgrund geographischer Gegebenheiten nicht alle Netzbereiche mit allen Netzbereichen verbunden. Figure 1 shows power grid 1, which is controlled by a data network 19, according to an embodiment of the invention. The power grid 1 is highlighted in Figure 1 by the drawn in bold lines elements and includes the autonomous network areas 5, 6, 7, 8, 9 and electrical connections that connect the autonomous network areas together. As shown in Figure 1, not all autonomous network areas need to be connected to all other autonomous network areas. Rather, there are many opportunities to connect the autonomous network areas with each other. In reality, not all network areas are connected to all network areas in a large power grid, usually for cost reasons and due to geographical conditions.
Das Datennetzwerk 19 ist in Figur 1 durch die in normal fetten Linien gezeichneten Elemente hervorgehoben und umfasst die Instanzen 5a, 6a, 7a, 8a, 9a und Datenverbindungen, die diese Instanzen 5a, 6a, 7a, 8a, 9a zu dem Netzwerk 19 verbinden. Das Datennetzwerk 19 braucht nicht dieselbe Topologie wie das Stromnetzes 1 zu haben, sondern es kann seine eigene Topologie haben. Jeder der autonomen Netzbereiche 5, 6, 7, 8, 9 des Stromnetzes umfasst mindestens eine Instanz 5a, 6a, 7a, 8a, 9a, welche den jeweiligen autonomen Netzbereich steuert. Mindestens zwei der Instanzen 5a, 6a, 7a, 8a, 9a sind als Dispatcher- Instanz ausgestaltet. In dem in Figur 1 und Figur 2 dargestellten Ausführungsbeispiel sind dies die Instanzen 5a und 7a. The data network 19 is highlighted in Figure 1 by the elements drawn in solid bold lines and includes the instances 5a, 6a, 7a, 8a, 9a and data links connecting these instances 5a, 6a, 7a, 8a, 9a to the network 19. The data network 19 does not need to have the same topology as the power grid 1, but may have its own topology. Each of the autonomous network areas 5, 6, 7, 8, 9 of the power network comprises at least one instance 5a, 6a, 7a, 8a, 9a, which controls the respective autonomous network area. At least two of the instances 5a, 6a, 7a, 8a, 9a are configured as a dispatcher instance. In the in Figure 1 and FIG 2 illustrated embodiments, these are the instances 5a and 7a.
Figur 2 zeigt ein System 2 zum Optimieren des Zuteilens von elektrischer Energie in einem, autonome Netzbereiche 5, 6, 7, 8, 9 umfassenden elektrischen Stromnetz 1. Das System 2 um- fasst mindestens zwei Dispatcher-Instanzen 5a, 7a und ein Auswahlmittel 3. Jede der mindestens zwei Dispatcher- Instanzen 5a, 7a umfasst ein Schnittstellenmittel 5b, 7b und ein Berechnungsmittel 5c, 7c. Jedes der Schnittstellenmittel 5b, 7b der mindestens zwei Dispatcher- Instanzen 5a, 7a ist adaptiert, Inputdaten 11 zu empfangen. Die Inputdaten repräsentieren durch die autonomen Netzbereiche 5, 6, 7, 8, 9 angeforderte Energie- Intervalle 5i, 6i, 7i, 8i, 9i. Jedes der Berechnungsmittel 5c, 7c der mindestens zwei Dispatcher- Instanzen 5a, 7a ist adaptiert, eine Lösung 5s, 7s des Verteilens von elektrischer Energie auf die Netzbereiche 5, 6, 7, 8, 9 zu berechnen. Das Auswahlmittel 3 ist adaptiert, eine der berechneten Lösungen 5s, 7s für das Verteilen von elekt- rischer Energie in dem Stromnetz 1 mittels einer Leader FIG. 2 shows a system 2 for optimizing the allocation of electrical energy in an electric power grid 1 comprising autonomous network areas 5, 6, 7, 8, 9. The system 2 comprises at least two dispatcher instances 5 a, 7 a and a selection means 3 Each of the at least two dispatcher instances 5a, 7a comprises an interface means 5b, 7b and a calculation means 5c, 7c. Each of the interface means 5b, 7b of the at least two dispatcher instances 5a, 7a is adapted to receive input data 11. The input data represent energy intervals 5i, 6i, 7i, 8i, 9i requested by the autonomous network areas 5, 6, 7, 8, 9. Each of the calculation means 5c, 7c of the at least two dispatcher instances 5a, 7a is adapted to calculate a solution 5s, 7s of the distribution of electrical energy to the network regions 5, 6, 7, 8, 9. The selection means 3 is adapted, one of the calculated solutions 5s, 7s for the distribution of electrical energy in the power grid 1 by means of a leader
Election auszuwählen. Select Election.
Figur 3 zeigt ein Verfahren zum Optimieren des Verteilens von elektrischer Energie in dem Stromnetz 1 gemäß einem bevorzug- ten Ausführungsbeispiel der Erfindung. In dem Verfahrensschritt 31 wird für jeden der Netzbereiche 5, 6, 7, 8, 9 sein zu erwartender Energiebedarf in Form jeweils eines Energie- Intervalls 5i, 6i, 7i, 8i, 9i bestimmt, siehe auch Figur 1. Diese Energie- Intervalle 5i, 6i, 7i, 8i, 9i werden als Input- daten 11 im Verfahrensschritt 32 durch zwei Dispatcher- Instanzen 5a, 7a empfangen. Die Inputdaten 11 repräsentieren somit die durch die autonomen Netzbereiche 5, 6, 7, 8, 9 angeforderten Energie- Intervalle 5i, 6i, 7i, 8i, 9i. Der Empfang der Inputdaten I durch Dispatcher- Instanz 5a ist dabei in Figur 3 durch den Teil -Verfahrensschritt 32a dargestellt, während der Empfang der Inputdaten I durch Dispatcher- Instanz 7a durch den Teil-Verfahrensschritt 32b dargestellt ist. Im Verfahrensschritt 33 berechnet jede der mindestens zwei Dis- patcher- Instanzen 5a, 7a eine Lösung 5s, 7s des Verteilens von elektrischer Energie auf die Netzbereiche 5, 6, 7, 8, 9. Das Berechnen der Lösung 5s durch die Dispatcher- Instanz 5a ist in Figur 3 durch den Teil -Verfahrensschritt 33a darge- stellt, während das Berechnen der Lösung 7s durch die Dispatcher-Instanz 7a durch den Teil-Verfahrensschritt 33b dargestellt ist. Im Verfahrensschritt 34 wird eine der berechneten Lösungen 5s, 7s für das Verteilen von elektrischer Energie in dem Stromnetz 1 mittels einer Leader Election ausgewählt. Da- zu kann beispielsweise das das Auswahlmittel 3 adaptiert sein, die berechneten Lösungen 5s, 7s durch eine Zielwertfunktion 3z auszuwerten und miteinander in der Leader FIG. 3 shows a method for optimizing the distribution of electrical energy in the power grid 1 according to a preferred embodiment of the invention. In method step 31, for each of the network regions 5, 6, 7, 8, 9, its expected energy requirement is determined in the form of an energy interval 5i, 6i, 7i, 8i, 9i, respectively. See also FIG. 1. These energy intervals 5i , 6i, 7i, 8i, 9i are received as input data 11 in method step 32 by two dispatcher instances 5a, 7a. The input data 11 thus represent the energy intervals 5i, 6i, 7i, 8i, 9i requested by the autonomous network areas 5, 6, 7, 8, 9. The reception of the input data I by the dispatcher instance 5a is illustrated in FIG. 3 by the partial method step 32a, while the reception of the input data I by the dispatcher instance 7a is illustrated by the partial method step 32b. In method step 33, each of the at least two patcher instances 5a, 7a show a solution 5s, 7s of the distribution of electrical energy to the mesh areas 5, 6, 7, 8, 9. The calculation of the solution 5s by the dispatcher instance 5a is shown in FIG. 3 by the partial method step 33a while computing the solution 7s by the dispatcher instance 7a is represented by the sub-process step 33b. In step 34, one of the calculated solutions 5s, 7s for distributing electrical energy in the power grid 1 is selected by means of a leader election. For this purpose, for example, the selection means 3 can be adapted to evaluate the calculated solutions 5s, 7s by a target value function 3z and to each other in the leader
Election zu vergleichen. Die Zielwerfunktion liefert dabei beispielsweise für jede der Lösungen 5s, 7s einen skalaren Wert, der die Qualität der Lösung repräsentiert, und den Vergleich somit ermöglicht. Anstelle skalarer Werte kann die Zielwertfunktion 3z beispielsweise aber auch Vektoren liefern, die einen Vergleich ermöglichen. Gemäß bevorzugen Ausführungsbeispielen empfangen alle der mindestens zwei Dispatcher- Instanzen 5a, 7a dieselben Input- daten 11. Compare Election. For example, the goal function provides a scalar value for each of the solutions 5s, 7s, which represents the quality of the solution, and thus makes the comparison possible. For example, instead of scalar values, the target value function 3z can also supply vectors that allow a comparison. According to preferred embodiments, all of the at least two dispatcher instances 5a, 7a receive the same input data 11.
Gemäß weiteren bevorzugen Ausführungsbeispielen sind die min- destens zwei Dispatcher- Instanzen 5a, 7a adaptiert, jeweils unterschiedliche Lösungen 5s, 7s des Verteilens von elektrischer Energie auf die Netzbereiche 5, 6, 7, 8, 9 zu berechnen. Dies kann vorzugsweise beispielsweise dadurch erreicht werden, dass die mindestens zwei Dispatcher-Instanzen 5a, 7a adaptiert sind, für die Berechnung der jeweiligen mindestens einen Lösung unterschiedliche Startpopulationen innerhalb der Energie- Intervalle 5i, 6i, 7i, 8i, 9i auszuwählen. Eine weitere Möglichkeit ist jedoch beispielsweise, dass die mindestens zwei Dispatcher- Instanzen 5a, 7a adaptiert sind, für die Berechnung der jeweiligen mindestens einen Lösung 5s, 7s unterschiedliche Algorithmen zu verwenden. Gemäß weiteren bevorzugen Ausführungsbeispielen werden die Netzbereiche 5, 6, 7, 8, 9 logisch als eine Auswahl aus Energieproduzenten, Energiekonsumenten und Prosumern dargestellt werden. Prosumer stellen dabei Netzbereiche dar, welche Ener- gie sowohl produzieren oder konsumieren können. Dies ist beispielsweise bei Pumpspeicherkraftwerken der Fall. Ein weiteres Beispiel für einen Prosumer kann auch bei Elektrofahrzeug oder eine Gruppe von Elektrofahrzeugen darstellen, dessen Batterie zur Stabilisierung des Stromnetzes je nach Netzbe- darf aufgeladen werden kann oder Strom dem Stromnetz zur Verfügung stellen kann. Bei einem Prosumer kann das angeforderte Energie- Intervall null überlappen, z.B. Batterie kann aufgeladen und entladen werden. Das System 2 kann wie in Figur 2 dargestellt lediglich die Dispatcher- Instanzen 5a, 7a und das Auswahlmittel umfassen, oder es kann auch das Stromnetz umfassen. Das Stromnetz 1 ist oder umfasst ein Gleichspannungsnetz oder ein Wechselspannungsnetz . According to further preferred exemplary embodiments, the at least two dispatcher units 5a, 7a are adapted to calculate different solutions 5s, 7s of the distribution of electrical energy to the network areas 5, 6, 7, 8, 9, respectively. This can preferably be achieved, for example, by adapting the at least two dispatcher instances 5a, 7a to select different starting populations within the energy intervals 5i, 6i, 7i, 8i, 9i for the calculation of the respective at least one solution. However, another possibility is, for example, that the at least two dispatcher instances 5a, 7a are adapted to use different algorithms for the calculation of the respective at least one solution 5s, 7s. According to further preferred embodiments, the network areas 5, 6, 7, 8, 9 will logically be represented as a selection of energy producers, energy consumers and prosumers. Prosumers represent network areas that can either produce or consume energy. This is the case, for example, with pumped storage power plants. Another example of a prosumer can also be an electric vehicle or a group of electric vehicles whose battery can be recharged to stabilize the power grid depending on network requirements or can provide power to the power grid. For a prosumer, the requested energy interval can overlap zero, eg battery can be charged and discharged. As shown in FIG. 2, the system 2 may comprise only the dispatcher instances 5a, 7a and the selection means, or it may also comprise the power grid. The power grid 1 is or comprises a DC power grid or an AC power grid.
Bevorzugte Ausführungsformen wird die Zielwertfunktion 3z für die Dispatcher- Instanz definiert und repräsentiert wie optimal die durch die Dispatcher- Instanz berechnete Lösung für das Verteilen von elektrischer Energie ist. Dies ist ein Ne- benprodukt der eigentlichen Berechnung der optimalen Verteilung der Energie. Preferred embodiments define the target value function 3z for the dispatcher instance and represent how optimal the solution for distributing electrical energy calculated by the dispatcher instance is. This is a byproduct of the actual calculation of the optimal distribution of energy.
Dies kann beispielsweise dadurch erreicht werden, dass in eine konkreten Implementierung die Werte einer Kostenfunktion, wie in der deutschen Patentanmeldung DE102011078045 AI beschrieben, bei den zugeordneten Energie-Koordinaten aufsummiert werden. Die Kostenfunktion wird in den Inputdaten 11 mit den Energie- Intervallen 5i, 6i, 7i, 8i, 9i übertragen und drückt eine Präferenz innerhalb des Energie- Intervalls aus, nämlich die Kosten zu optimieren. Die Optimierung soll versuchen, immer das Minimum der Kostenfunktion zu erreichen. Die benötigte Information für die jeweilige Dispatcher- Instanz 5a, 7a wird vorzugsweise durch jede der Instanzen gebroadcastet , damit die Dispatcher-Instanzen einen mögliches vollständigen Datensatz für die Berechnung der Verteilung der elektrischen Energie zur Verfügung haben. Dabei empfängt jede als Knoten 5a, 6a, 7a, 8a, 9a ausgebildete Instanz die Information und sendet sie weiter wie benötigt um ein komplettes Abbild allen weiteren Instanzen 5a, 6a, 7a, 8a, 9a zur Verfügung zu stellen. This can be achieved, for example, by summing the values of a cost function, as described in German patent application DE102011078045 A1, at the assigned energy coordinates in a concrete implementation. The cost function is transmitted in the input data 11 with the energy intervals 5i, 6i, 7i, 8i, 9i and expresses a preference within the energy interval, namely to optimize the costs. The optimization should try to always reach the minimum of the cost function. The required information for the respective dispatcher instance 5a, 7a is preferably broadcast by each of the instances, so that the dispatcher instances have a possible complete data record for the calculation of the distribution of electrical energy available. Each instance formed as node 5a, 6a, 7a, 8a, 9a receives the information and sends it further as needed to provide a complete image to all further instances 5a, 6a, 7a, 8a, 9a.
Gemäß einer weiteren bevorzugten Ausführungsform funktionieren alle Instanzen 5a, 6a, 7a, 8a, 9a Dispatcher- Instanz um unter Verwendung der gebroadcasteten Informationen und zufällig gewählten initialen Starzuständen eine als Energie- Verteilung ausgestaltete Lösung zu berechnen. Nach der Berechnung, respektive wenn das zugestandene Zeitfenster für die Berechnung abgelaufen ist, broadcastet jede Instanz 5a, 6a, 7a, 8a, 9a den Wert der Zielwertfunktion für ihre jeweils berechnete Lösung. According to another preferred embodiment, all instances 5a, 6a, 7a, 8a, 9a dispatcher instance function to calculate a solution designed as an energy distribution using the broadcasted information and randomly selected initial standby states. After the calculation, respectively, when the allowed time window for the calculation has expired, each instance 5a, 6a, 7a, 8a, 9a broadcasts the value of the target value function for its respective calculated solution.
Die Instanzen 5a, 6a, 7a, 8a, 9a vergleichen ihre Werte gemäß einem Bullying Schema. Dies bedeutet dass ein Knoten seinen resultierenden Wert der Zielwertfunktion broadcastet. Wenn eine Dispatcher-Instanz 5a, 6a, 7a, 8a, 9a eine Nachricht von einer andern Dispatcher- Instanz 5a, 6a, 7a, 8a, 9a mit einem tieferen, also einem weniger optimalen Wert empfängt, broadcastet sie eine Nachricht mit ihrem eigenen höheren Wert. Wenn keine Nachrichten mehr erhalten werden innerhalb einer gegebenen Zeit nach der letzten Nachricht, gewinnt diejenige Lösung mit dem letzten und somit höchsten Wert. Beispielsweise gewinnt diejenige Dispatcher- Instanz 5a, 6a, 7a, 8a, 9a mit der besten Lösung und sendet ihre berechneten Lösungen für das Verteilen von elektrischer Energie an die anderen Instanzen 5a, 6a, 7a, 8a, 9a, welche dann die berechnete Ver- teilung der elektrischen Energie auf die autonomen Netzbereiche 5, 6, 7, 8, 9 umsetzen. Andere vom Bullying-Algorithmus unterschiedliche Methoden können auch verwendet werden, wie beispielsweise ein Ring- Algorithmus, siehe The instances 5a, 6a, 7a, 8a, 9a compare their values according to a bullying scheme. This means that a node broadcasts its resulting value of the goal value function. When a dispatcher instance 5a, 6a, 7a, 8a, 9a receives a message from another dispatcher instance 5a, 6a, 7a, 8a, 9a having a lower, that is a less optimal value, it broadcasts a message with its own higher one Value. If no more messages are received within a given time after the last message, that solution wins with the last and thus highest value. For example, the dispatcher instance 5a, 6a, 7a, 8a, 9a wins the best solution and sends its calculated solutions for the distribution of electrical energy to the other entities 5a, 6a, 7a, 8a, 9a, which then calculate the computed solutions. Divide the electric energy to the autonomous network areas 5, 6, 7, 8, 9 implement. Other methods other than the bullying algorithm may also be used, such as a ring algorithm, see
http : //en . wikipedia . org/wiki/Leader_election . http: // en. wikipedia. org / wiki / Leader_election.
Gemäß bevorzugten Ausführungsformen wird anstelle einer einzelnen Dispatcher- Instanz die Optimierung der Verteilung von elektrischer Energie verteilt über zwei oder mehr Dispatcher- Instanzen 5a, 6a, 7a, 8a, 9a vorgenommen. Dadurch wird die Problematik eines Single point of failure beseitigt und die Stabilität erhöht. Auch können die berechneten Lösungen verbessert werden durch die verteilte Berechnung, da diese Lösungen mehrerer Dispatcher- Instanzen verglichen werden und die beste Lösung ausgewählt wird. Es ermöglicht auch einzelnen Knoten 5a, 6a, 7a, 8a, 9a als Dispatcher- Instanz zu funktionieren und an der Berechnung der Lösung teilzunehmen, oder dies aufgrund beschränkter Ressourcen nicht zu tun. According to preferred embodiments, instead of a single dispatcher instance, the optimization of the distribution of electrical energy is distributed over two or more dispatcher instances 5a, 6a, 7a, 8a, 9a. This eliminates the problem of a single point of failure and increases stability. Also, the computed solutions can be improved by the distributed computation, as these solutions of several dispatcher instances are compared and the best solution is selected. It also allows individual nodes 5a, 6a, 7a, 8a, 9a to function as a dispatcher instance and to participate in the computation of the solution, or not to do so due to limited resources.
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Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
IN2014CH01483A (en) * | 2014-03-20 | 2015-09-25 | Infosys Ltd | |
EP3226374B1 (en) * | 2016-04-01 | 2019-02-13 | Siemens Aktiengesellschaft | Method and control device for controlling a power grid |
US10088006B2 (en) * | 2016-05-19 | 2018-10-02 | The Boeing Company | Rotational inerter and method for damping an actuator |
US10107347B2 (en) * | 2016-05-19 | 2018-10-23 | The Boeing Company | Dual rack and pinion rotational inerter system and method for damping movement of a flight control surface of an aircraft |
US10452032B1 (en) * | 2016-09-08 | 2019-10-22 | PXiSE Energy Solutions, LLC | Optimizing power contribution of distributed energy resources for real time power demand scheduling |
CN108563249B (en) * | 2018-07-25 | 2021-11-26 | 浙江工商大学 | Automatic tracking heating system and method based on uwb positioning |
CN109165822B (en) * | 2018-08-06 | 2021-12-10 | 上海顺舟智能科技股份有限公司 | Energy supply management system and management method |
CN110212533B (en) * | 2019-07-10 | 2021-01-29 | 南方电网科学研究院有限责任公司 | Method and system for determining power of either person from birth or death |
US11056912B1 (en) | 2021-01-25 | 2021-07-06 | PXiSE Energy Solutions, LLC | Power system optimization using hierarchical clusters |
Family Cites Families (15)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6681156B1 (en) * | 2000-09-28 | 2004-01-20 | Siemens Aktiengesellschaft | System and method for planning energy supply and interface to an energy management system for use in planning energy supply |
US7188260B1 (en) * | 2001-08-29 | 2007-03-06 | Cisco Technology, Inc. | Apparatus and method for centralized power management |
US7343361B2 (en) * | 2001-12-07 | 2008-03-11 | Siemens Power Transmission & Distribution, Inc. | Apparatus for market dispatch for resolving energy imbalance requirements in real-time |
US8232676B2 (en) * | 2008-05-02 | 2012-07-31 | Bloom Energy Corporation | Uninterruptible fuel cell system |
AU2010204729A1 (en) | 2009-01-14 | 2011-09-01 | Integral Analytics, Inc. | Optimization of microgrid energy use and distribution |
US20100332373A1 (en) * | 2009-02-26 | 2010-12-30 | Jason Crabtree | System and method for participation in energy-related markets |
KR101084214B1 (en) * | 2009-12-03 | 2011-11-18 | 삼성에스디아이 주식회사 | Grid-connected power storage system and control method of power storage system |
US9335748B2 (en) * | 2010-07-09 | 2016-05-10 | Emerson Process Management Power & Water Solutions, Inc. | Energy management system |
EP2599182A1 (en) * | 2010-07-29 | 2013-06-05 | Spirae Inc. | Dynamic distributed power grid control system |
US9245297B2 (en) * | 2011-04-28 | 2016-01-26 | Battelle Memorial Institute | Forward-looking transactive pricing schemes for use in a market-based resource allocation system |
DE102011078045A1 (en) | 2011-06-24 | 2012-12-27 | Siemens Aktiengesellschaft | Methods and apparatus for allocating energy |
WO2013070781A1 (en) * | 2011-11-07 | 2013-05-16 | Gridspeak Corporation | Systems and methods for automated electricity delivery management for out-of-control-area resources |
US9235847B2 (en) * | 2011-12-16 | 2016-01-12 | Palo Alto Research Center Incorporated | Energy-disutility modeling for agile demand response |
WO2013155598A1 (en) * | 2012-04-16 | 2013-10-24 | Temporal Power Ltd. | Method and system for regulating power of an electricity grid system |
US9846886B2 (en) * | 2013-11-07 | 2017-12-19 | Palo Alto Research Center Incorporated | Strategic modeling for economic optimization of grid-tied energy assets |
-
2014
- 2014-01-29 DE DE102014201555.3A patent/DE102014201555A1/en not_active Withdrawn
- 2014-11-10 EP EP14801963.1A patent/EP3063713A1/en not_active Ceased
- 2014-11-10 US US15/112,701 patent/US10461578B2/en not_active Expired - Fee Related
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- 2014-11-10 WO PCT/EP2014/074177 patent/WO2015113662A1/en active Application Filing
Non-Patent Citations (3)
Title |
---|
ANONYMOUS: "N-version programming - Wikipedia", 15 February 2013 (2013-02-15), XP055635706, Retrieved from the Internet <URL:https://en.wikipedia.org/w/index.php?title=N-version_programming&oldid=538408306> [retrieved on 20191024] * |
AVIZIENIS A: "THE N-VERSION APPROACH TO FAULT-TOLERANT SOFTWARE", IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, IEEE SERVICE CENTER, LOS ALAMITOS, CA, US, vol. SE-11, no. 12, 1 December 1985 (1985-12-01), pages 1491 - 1501, XP008063631, ISSN: 0098-5589 * |
See also references of WO2015113662A1 * |
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