EP3063713A1 - Optimieren des verteilens von elektrischer energie - Google Patents
Optimieren des verteilens von elektrischer energieInfo
- 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
- Authority
- EP
- European Patent Office
- Prior art keywords
- dispatcher
- distribution
- electrical energy
- energy
- instances
- 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.)
- Ceased
Links
Classifications
-
- 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
-
- 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
-
- 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
-
- 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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- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
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- Strategic Management (AREA)
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- General Physics & Mathematics (AREA)
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- Supply And Distribution Of Alternating Current (AREA)
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Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE102014201555.3A DE102014201555A1 (de) | 2014-01-29 | 2014-01-29 | Optimieren des Verteilens von elektrischer Energie |
PCT/EP2014/074177 WO2015113662A1 (de) | 2014-01-29 | 2014-11-10 | Optimieren des verteilens von elektrischer energie |
Publications (1)
Publication Number | Publication Date |
---|---|
EP3063713A1 true EP3063713A1 (de) | 2016-09-07 |
Family
ID=51945843
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP14801963.1A Ceased EP3063713A1 (de) | 2014-01-29 | 2014-11-10 | Optimieren des verteilens von elektrischer energie |
Country Status (5)
Country | Link |
---|---|
US (1) | US10461578B2 (de) |
EP (1) | EP3063713A1 (de) |
CN (1) | CN106415619A (de) |
DE (1) | DE102014201555A1 (de) |
WO (1) | WO2015113662A1 (de) |
Families Citing this family (9)
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IN2014CH01483A (de) * | 2014-03-20 | 2015-09-25 | Infosys Ltd | |
EP3226374B1 (de) * | 2016-04-01 | 2019-02-13 | Siemens Aktiengesellschaft | Verfahren und steuereinrichtung zum steuern eines stromnetzes |
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 |
US10088006B2 (en) * | 2016-05-19 | 2018-10-02 | The Boeing Company | Rotational inerter and method for damping an actuator |
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 (zh) * | 2018-07-25 | 2021-11-26 | 浙江工商大学 | 一种基于uwb定位的自动追踪加热系统及方法 |
CN109165822B (zh) * | 2018-08-06 | 2021-12-10 | 上海顺舟智能科技股份有限公司 | 一种能源补给管理系统及管理方法 |
CN110212533B (zh) * | 2019-07-10 | 2021-01-29 | 南方电网科学研究院有限责任公司 | 一种产消者功率的确定方法及系统 |
US11056912B1 (en) | 2021-01-25 | 2021-07-06 | PXiSE Energy Solutions, LLC | Power system optimization using hierarchical clusters |
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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 |
CA2749770C (en) | 2009-01-14 | 2021-07-20 | 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 (ko) * | 2009-12-03 | 2011-11-18 | 삼성에스디아이 주식회사 | 계통 연계형 전력 저장 시스템 및 전력 저장 시스템 제어 방법 |
US9335748B2 (en) * | 2010-07-09 | 2016-05-10 | Emerson Process Management Power & Water Solutions, Inc. | Energy management system |
US20120029720A1 (en) * | 2010-07-29 | 2012-02-02 | 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 (de) | 2011-06-24 | 2012-12-27 | Siemens Aktiengesellschaft | Verfahren und Vorrichtungen zum Zuteilen von Energiemengen |
WO2013070781A1 (en) * | 2011-11-07 | 2013-05-16 | Gridspeak Corporation | Systems and methods for automated electricity delivery management for out-of-control-area resources |
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2014
- 2014-01-29 DE DE102014201555.3A patent/DE102014201555A1/de not_active Withdrawn
- 2014-11-10 US US15/112,701 patent/US10461578B2/en not_active Expired - Fee Related
- 2014-11-10 EP EP14801963.1A patent/EP3063713A1/de not_active Ceased
- 2014-11-10 CN CN201480074526.2A patent/CN106415619A/zh active Pending
- 2014-11-10 WO PCT/EP2014/074177 patent/WO2015113662A1/de active Application Filing
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See also references of WO2015113662A1 * |
Also Published As
Publication number | Publication date |
---|---|
US10461578B2 (en) | 2019-10-29 |
US20160344234A1 (en) | 2016-11-24 |
DE102014201555A1 (de) | 2015-07-30 |
CN106415619A (zh) | 2017-02-15 |
WO2015113662A1 (de) | 2015-08-06 |
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