CN108830451A - A kind of the convergence potential evaluation method and system of user side distributed energy storage - Google Patents
A kind of the convergence potential evaluation method and system of user side distributed energy storage Download PDFInfo
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Abstract
The invention discloses a kind of convergence potential evaluation method of user side distributed energy storage and systems, which is characterized in that the method includes：Receive the operation data of the corresponding distributed energy storage node of each distributed energy storage Node Controller acquisition；The convergence potentiality index value of each distributed energy storage node is calculated separately using the operation data of each distributed energy storage node according to distributed energy storage convergence computation model；The convergence potential index of each distributed energy storage node is calculated according to the convergence potentiality index index value of each distributed energy storage node.Technical solution of the present invention converges computation model by establishing the energy storage of meter and time scale, capacity scale, effective binding time and reliability index index, potential index is converged using analytic hierarchy process (AHP) analysis distribution formula energy storage, and the power output of the convergence potential index control energy-storage system according to each distributed energy storage node, to realize that the convergence application of multi-drop arrangement distributed energy storage system provides technical support.
Description
Technical field
The present invention relates to technical field of power systems, and more particularly, to a kind of remittance of user side distributed energy storage
Poly- potential evaluation method and system.
Background technique
A large amount of research is carried out with regard to power distribution network energy-storage system both at home and abroad, main point of three aspects：First, energy storage access is real
Existing scheme, such as circuit topological structure, waveform controlling method；Second, stored energy application its control and energy after power distribution network/micro-capacitance sensor
Problem of management is measured, mainly there is the research of energy storage modeling, control strategy；Third, the economic analysis after energy storage access, including energy storage
Selection, capacity configuration and power optimization of system etc..Currently, the application due to distributed energy storage not yet forms scale, about
The document of the convergence management of distributed energy storage is less, and more cohesively manageds around distributed generation resource are conducted a research and applied, i.e.,
" virtual plant ".In terms of " distributed energy+energy storage ", domestic mature business model is less, and the field has been opened up by foreign countries
Some explorations are opened.
The convergence of distributed energy storage resource has begun to show blank, but the storage converged towards power grid application, in the country such as the U.S., Germany
Energy source resource and application model are more single, and convergence scale is limited, electric power structure and power grid knot in particular for China
Structure, although being available for the technical know-how used for reference, its key problem is not yet touched, and additionally involves the technology of convergence concept also
There are virtual plant and Load aggregation quotient etc., the part for being directed to convergence frame and operation mode has reference value, but has
Body converges application technology to systematic distributed energy storage, and at present on the basis of existing, there is also several big crucial problems demands are prominent
It is broken, it is the convergence potential evaluation method of energy storage resource first, complementarity planning technology on the basis of existing resource, followed by point
The clustered control technology and critical equipment of cloth energy-storage system are researched and developed, to realize the convergence application of multi-drop arrangement distributed energy storage system
Technical support is provided.
Therefore, it is necessary to a kind of convergence potential evaluation methods of user side distributed energy storage, to solve how to realize to energy storage
The problem of convergence potentiality of resource are assessed.
Summary of the invention
The invention proposes a kind of convergence potential evaluation method of user side distributed energy storage and systems, how real to solve
The problem of now the convergence potentiality of energy storage resource are assessed.
To solve the above-mentioned problems, according to an aspect of the invention, there is provided a kind of remittance of user side distributed energy storage
Poly- potential evaluation method, which is characterized in that the method includes：
Receive the operation data of the corresponding distributed energy storage node of each distributed energy storage Node Controller acquisition；
Computation model is converged according to distributed energy storage to calculate separately often using the operation data of each distributed energy storage node
The convergence potentiality index value of a distributed energy storage node；
Each distributed energy storage node is calculated according to the convergence potentiality index index value of each distributed energy storage node
Convergence potential index.
Preferably, wherein the convergence potentiality index includes：Dynamic response capability, power enabling capabilities, capacity support energy
Power, effective binding time, system stability and system reliability,
g1=| StateBk+StateD|+1|/3(0≤g1≤ 1),
g2=Capk/Cap(0≤g2≤ 1),
g3=ρk*PBk/PD(0≤g3≤ 1),
g4=TBk/TD(0≤g4≤ 1),
g5=1-VBk/VR(0≤g5≤ 1),
g6=(1- ηk)(0≤g6≤ 1),
Wherein, g1For dynamic response capability；StateBkFor the current operation of the energy-storage system where distributed energy storage node k
State, StateBk=1 expression energy storage is just charged, StateBk=-1 expression energy storage is just discharged, StateBk=0 indicates that energy storage is in hot standby
Use state；StateDIndicate action state when current system scheduling distributed energy storage, StateD=1 indicates that energy storage is needed to charge,
StateD=-1 indicates that energy storage is needed to discharge；g2For capacity enabling capabilities；Cap is that demand when system calls distributed energy storage is held
Amount；Capk is the available scheduling total capacity of distributed energy storage of distributed energy storage node k, if Cap<Capk then g1=1；g3For
Power enabling capabilities；PDDemand power when being called for energy-storage system to distributed energy storage；PBkFor point of distributed energy storage node k
The available schedule power of cloth energy storage, ρkFor Node distribution formula energy storage power-conversion efficiencies, if PD<PBkThen g2=1；g4To have
Imitate binding time, TDDemand total time when being called for energy-storage system to distributed energy storage；TBkDistributed energy storage for node k can
The scheduling total time of offer, if TD<TBkThen g4=1；g5For system stability；VRFor the voltage rating of distributed energy storage node, VBk
Node voltage fluctuation amplitude caused by contribute because of the distributed energy storage at distributed energy storage node k；g6For system reliability；ηk
For the failure rate of the distributed energy storage node at scheduling periods interior nodes k.
Preferably, wherein described calculate each distribution according to the convergence potentiality index value of each distributed energy storage node
The convergence potential index of formula energy storage node, including：
9 grades of scaling law Judgement Matricies G are utilized according to the preset value of each index in convergence potentiality indexA-C, and calculate
Judgment matrix GA-CCorresponding characteristic vector W 'A-C；
Each index pair is constructed respectively using 9 grades of scaling laws according to the convergence potentiality index value of each distributed energy storage node
The judgment matrix answered；
Calculate the characteristic vector W of the corresponding judgment matrix of each index；
The convergence potential index of each distributed energy storage node is calculated using convergence potential index calculation formula.
Preferably, wherein the convergence potential index calculation formula is：
λmax=W*G 'A-C,
Wherein, λmaxTo converge potential index；W is the corresponding judgment matrix of each index after normalized
Feature vector；GA-CFor the judgment matrix for utilizing 9 grades of scaling laws construction according to the preset value of each index in convergence potentiality index
GA-CCorresponding feature vector.
Preferably, wherein the method also includes：
According to it is described convergence potential index determine each distributed energy storage node power output sequence and corresponding power index,
Wherein convergence potential index is bigger, then the corresponding distributed storage node of priority scheduling carries out energy storage；
Power instruction is determined according to power output sequence and corresponding power index, and the power instruction is sent to pair
The distributed energy storage Node Controller answered, to control the power output of energy-storage system.
According to another aspect of the present invention, a kind of convergence Potential Evaluation system of user side distributed energy storage is provided,
It is characterized in that, the system comprises：
Operation data acquiring unit, for receiving the corresponding distributed storage of each distributed energy storage Node Controller acquisition
The operation data of energy node；
Potentiality index value determination unit is converged, is stored up for converging computation model according to distributed energy storage using each distribution
The operation data of energy node calculates separately the convergence potentiality index value of each distributed energy storage node；
Potential index determination unit is converged, based on the convergence potentiality index value according to each distributed energy storage node
Calculate the convergence potential index of each distributed energy storage node.
Preferably, wherein the convergence potentiality index value includes：Dynamic response capability, power enabling capabilities, capacity support
Ability, effective binding time, system stability and system reliability,
g1=| StateBk+StateD|+1|/3(0≤g1≤ 1),
g2=Capk/Cap(0≤g2≤ 1),
g3=ρk*PBk/PD(0≤g3≤ 1),
g4=TBk/TD(0≤g4≤ 1),
g5=1-VBk/VR(0≤g5≤ 1),
g6=(1- ηk)(0≤g6≤ 1),
Wherein, g1For dynamic response capability；StateBkFor the current operation of the energy-storage system where distributed energy storage node k
State, StateBk=1 expression energy storage is just charged, StateBk=-1 expression energy storage is just discharged, StateBk=0 indicates that energy storage is in hot standby
Use state；StateDIndicate action state when current system scheduling distributed energy storage, StateD=1 indicates that energy storage is needed to charge,
StateD=-1 indicates that energy storage is needed to discharge；g2For capacity enabling capabilities；Cap is that demand when system calls distributed energy storage is held
Amount；Capk is the available scheduling total capacity of distributed energy storage of distributed energy storage node k, if Cap<Capk then g1=1；g3For
Power enabling capabilities；PDDemand power when being called for energy-storage system to distributed energy storage；PBkFor point of distributed energy storage node k
The available schedule power of cloth energy storage, ρkFor Node distribution formula energy storage power-conversion efficiencies, if PD<PBkThen g2=1；g4To have
Imitate binding time, TDDemand total time when being called for energy-storage system to distributed energy storage；TBkDistributed energy storage for node k can
The scheduling total time of offer, if TD<TBkThen g4=1；g5For system stability；VRFor the voltage rating of distributed energy storage node, VBk
Node voltage fluctuation amplitude caused by contribute because of the distributed energy storage at distributed energy storage node k；g6For system reliability；ηk
For the failure rate of the distributed energy storage node at scheduling periods interior nodes k.
Preferably, wherein the convergence potential index determination unit, according to the convergence of each distributed energy storage node
Potentiality index value calculates the convergence potential index of each distributed energy storage node, including：
9 grades of scaling law Judgement Matricies G are utilized according to the preset value of each index in convergence potentiality indexA-C, and calculate
Judgment matrix GA-CCorresponding characteristic vector W 'A-C；
Each index pair is constructed respectively using 9 grades of scaling laws according to the convergence potentiality index value of each distributed energy storage node
The judgment matrix answered；
Calculate the characteristic vector W of the corresponding judgment matrix of each index；
The convergence potential index of each distributed energy storage node is calculated using convergence potential index calculation formula.
Preferably, wherein calculating convergence potential index using following formula：
λmax=W*G 'A-C,
Wherein, λmaxTo converge potential index；W is the corresponding judgment matrix of each index after normalized
Feature vector；GA-CFor the judgment matrix for utilizing 9 grades of scaling laws construction according to the preset value of each index in convergence potentiality index
GA-CCorresponding feature vector.
Preferably, wherein the system also includes：
Power output order determination unit, for determining the power output of each distributed energy storage node according to the convergence potential index
Sequence and corresponding power index, wherein convergence potential index is bigger, then the corresponding distributed storage node of priority scheduling is stored up
Energy；
Power instruction control unit, for determining power instruction according to the power output sequence and corresponding power index, and
The power instruction is sent to corresponding distributed energy storage Node Controller, to control the power output of energy-storage system.
The present invention provides a kind of convergence potential evaluation method of user side distributed energy storage and systems, receive each distribution
The operation data of the corresponding distributed energy storage node of formula energy storage Node Controller acquisition；It is converged according to distributed energy storage and calculates mould
Type calculates separately the convergence potentiality index value of each distributed energy storage node using the operation data of each distributed energy storage node；
Referred to according to the convergence potentiality that the convergence potentiality index value of each distributed energy storage node calculates each distributed energy storage node
Number.The present invention is converged by establishing the energy storage of meter and time scale, capacity scale, effective binding time and reliability index index
Computation model converges potential index using analytic hierarchy process (AHP) analysis distribution formula energy storage, and according to each distributed energy storage node
The power output of potential index control energy-storage system is converged, to realize that the convergence application of multi-drop arrangement distributed energy storage system provides technology
Support.
Detailed description of the invention
By reference to the following drawings, exemplary embodiments of the present invention can be more fully understood by：
Fig. 1 is the process according to the convergence potential evaluation method 100 of the user side distributed energy storage of embodiment of the present invention
Figure；
Fig. 2 is to converge potential index hierarchy Model figure according to the distributed energy storage of embodiment of the present invention；And
Fig. 3 is the structure according to the convergence Potential Evaluation system 300 of the user side distributed energy storage of embodiment of the present invention
Schematic diagram.
Specific embodiment
Exemplary embodiments of the present invention are introduced referring now to the drawings, however, the present invention can use many different shapes
Formula is implemented, and is not limited to the embodiment described herein, and to provide these embodiments be at large and fully disclose
The present invention, and the scope of the present invention is sufficiently conveyed to person of ordinary skill in the field.Show for what is be illustrated in the accompanying drawings
Term in example property embodiment is not limitation of the invention.In the accompanying drawings, identical cells/elements use identical attached
Icon note.
Unless otherwise indicated, term (including scientific and technical terminology) used herein has person of ordinary skill in the field
It is common to understand meaning.Further it will be understood that with the term that usually used dictionary limits, should be understood as and its
The context of related fields has consistent meaning, and is not construed as Utopian or too formal meaning.
Fig. 1 is the process according to the convergence potential evaluation method 100 of the user side distributed energy storage of embodiment of the present invention
Figure.As shown in Figure 1, the convergence potential evaluation method for the user side distributed energy storage that embodiments of the present invention provide, receives every
The operation data of the corresponding distributed energy storage node of a distributed energy storage Node Controller acquisition；It is converged according to distributed energy storage
Computation model calculates separately the convergence potentiality of each distributed energy storage node using the operation data of each distributed energy storage node
Index value；The convergence of each distributed energy storage node is calculated according to the convergence potentiality index value of each distributed energy storage node
Potential index.Embodiments of the present invention are referred to by establishing meter and time scale, capacity scale, effective binding time and reliability
Computation model is converged in the energy storage for marking index, converges potential index using analytic hierarchy process (AHP) analysis distribution formula energy storage, and according to each
The power output of the convergence potential index control energy-storage system of distributed energy storage node, to realize that multi-drop arrangement distributed energy storage system is converged
Poly- application provides technical support.The convergence potential evaluation method for the user side distributed energy storage that embodiments of the present invention provide
A kind of convergence potential evaluation method 100 of user side distributed energy storage receives each point in step 101 since step 101 place
The operation data of the corresponding distributed energy storage node of cloth energy storage Node Controller acquisition.
In embodiments of the present invention, distributed energy storage Node Controller is for collecting each of each distributed battery energy storage
Related operating index, and supreme layer dispatching control center is sent, the judgement of each index is established using distributed energy storage coalescence model
It puts to the proof, the convergence potential index and power output sequence of each distributed energy storage is obtained using analytic hierarchy process (AHP), and according to each distributed storage
Convergence potential index and the power output sequence of energy determine the power instruction of each distributed energy storage node, pass through distributed energy storage node control
Power instruction is sent to corresponding distributed energy storage node by device processed, controls the power output of battery energy storage system.
Preferably, the fortune that computation model utilizes each distributed energy storage node is converged according to distributed energy storage in step 102
Row data calculate separately the convergence potentiality index value of each distributed energy storage node.
Preferably, wherein the convergence potentiality index includes：Dynamic response capability, power enabling capabilities, capacity support energy
Power, effective binding time, system stability and system reliability,
g1=| StateBk+StateD|+1|/3(0≤g1≤ 1),
g2=Capk/Cap(0≤g2≤ 1),
g3=ρk*PBk/PD(0≤g3≤ 1),
g4=TBk/TD(0≤g4≤ 1),
g5=1-VBk/VR(0≤g5≤ 1),
g6=(1- ηk)(0≤g6≤ 1),
Wherein, g1For dynamic response capability；StateBkFor the current operation of the energy-storage system where distributed energy storage node k
State, StateBk=1 expression energy storage is just charged, StateBk=-1 expression energy storage is just discharged, StateBk=0 indicates that energy storage is in hot standby
Use state；StateDIndicate action state when current system scheduling distributed energy storage, StateD=1 indicates that energy storage is needed to charge,
StateD=-1 indicates that energy storage is needed to discharge；g2For capacity enabling capabilities；Cap is that demand when system calls distributed energy storage is held
Amount；Capk is the available scheduling total capacity of distributed energy storage of distributed energy storage node k, if Cap<Capk then g1=1；g3For
Power enabling capabilities；PDDemand power when being called for energy-storage system to distributed energy storage；PBkFor point of distributed energy storage node k
The available schedule power of cloth energy storage, ρkFor Node distribution formula energy storage power-conversion efficiencies, if PD<PBkThen g2=1；g4To have
Imitate binding time, TDDemand total time when being called for energy-storage system to distributed energy storage；TBkDistributed energy storage for node k can
The scheduling total time of offer, if TD<TBkThen g4=1；g5For system stability；VRFor the voltage rating of distributed energy storage node, VBk
Node voltage fluctuation amplitude caused by contribute because of the distributed energy storage at distributed energy storage node k；g6For system reliability；ηk
For the failure rate of the distributed energy storage node at scheduling periods interior nodes k.
Fig. 2 is to converge potential index hierarchy Model figure according to the distributed energy storage of embodiment of the present invention.Such as Fig. 2 institute
Show, the distributed energy storage convergence potential index hierarchy Model of embodiment of the present invention includes：Destination layer, indicator layer and scheme
Layer.Solution layer includes multiple distributed energy storage node Pk(K of k=1,2,3 ..., K are the distributed energy storage number of accessing user side
Amount).Indicator layer includes：Dynamic response capability g1, power enabling capabilities g2, capacity enabling capabilities g3, effective binding time g4, be
Unite stability g5With system reliability g6.Wherein,
g1=| StateBk+StateD|+1|/3(0≤g1≤1)
g2=Capk/Cap(0≤g2≤ 1),
g3=ρk*PBk/PD(0≤g3≤ 1),
g4=TBk/TD(0≤g4≤ 1),
g5=1-VBk/VR(0≤g5≤ 1),
g6=(1- ηk)(0≤g6≤ 1),
Rule layer index dynamic response capability g1Node distribution formula energy storage response user side power grid convergence scheduling is reflected to refer to
The dynamic capability of order, StateBkIndicate current operating conditions of k-th of node in distributed battery energy-storage system, StateBk=1
Indicate that energy storage is just charged, StateBk=-1 expression energy storage is just discharged, StateBk=0 expression energy storage is in hot stand-by duty；StateDTable
When showing that current system dispatches distributed energy storage, the action state S of distributed energy storage is neededtateD=1 indicates that energy storage is needed to charge,
StateD=-1 indicates that energy storage is needed to discharge.
Rule layer index capacity enabling capabilities g2Middle Cap is demand capacity when system calls distributed energy storage；Capk
For the available scheduling total capacity of distributed energy storage of node k；If Cap<Capk then g1=1.
Rule layer index power enabling capabilities g3Middle PDDemand power when being called for system to distributed energy storage；PBkFor section
The available schedule power of distributed energy storage of point k, ρkFor Node distribution formula energy storage power-conversion efficiencies；If PD<PBk, then g2=
1。
The effective binding time g of rule layer index4Middle TDDemand total time when being called for system to distributed energy storage；TBkFor
The distributed energy storage of node k available scheduling total time；If TD<TBk, then g4=1.
Rule layer index system stability g5Middle VRFor node voltage rating, VBkTo go out because of the distributed energy storage at node k
Node voltage fluctuation amplitude caused by power.
Rule layer index system reliability g6Middle ηkFor the distributed energy storage basic unit at scheduling periods interior nodes k
Failure rate.Energy storage basic unit capacity, energy storage number of nodes, the system power/capacity of each Node distribution formula energy-storage system need
It asks and has a certain impact to system operation troubles rate.
Preferably, it is calculated in step 103 according to the convergence potentiality index index value of each distributed energy storage node every
The convergence potential index of a distributed energy storage node.
Preferably, wherein described calculate each distribution according to the convergence potentiality index value of each distributed energy storage node
The convergence potential index of formula energy storage node, including：
Step 1031,9 grades of scaling law Judgement Matricies are utilized according to the preset value of each index in convergence potentiality index
GA-C, and calculate judgment matrix GA-CCorresponding characteristic vector W 'A-C；
Step 1032, it is constructed respectively according to the convergence potentiality index value of each distributed energy storage node using 9 grades of scaling laws
The corresponding judgment matrix of each index；
Step 1033, the characteristic vector W of the corresponding judgment matrix of each index is calculated；
Step 1034, referred to using the convergence potentiality that convergence potential index calculation formula calculates each distributed energy storage node
Number.
Preferably, wherein the convergence potential index calculation formula is：
λmax=W*G 'A-C,
Wherein, λmaxTo converge potential index；W is the corresponding judgment matrix of each index after normalized
Feature vector；GA-CFor the judgment matrix for utilizing 9 grades of scaling laws construction according to the preset value of each index in convergence potentiality index
GA-CCorresponding feature vector.
Preferably, wherein the method also includes：
Before calculating characteristic vector W, the corresponding judgment matrix of each index is normalized.
Preferably, wherein the method also includes：
According to it is described convergence potential index determine each distributed energy storage node power output sequence and corresponding power index,
Wherein convergence potential index is bigger, then the corresponding distributed storage node of priority scheduling carries out energy storage；
Power instruction is determined according to power output sequence and corresponding power index, and the power instruction is sent to pair
The distributed energy storage Node Controller answered, to control the power output of energy-storage system.
In embodiments of the present invention, the step of determining power instruction include：
Step1：According to the preset value of each index in convergence potentiality index using 9 grades of scaling laws to each finger of judgment matrix
Element assignment is marked, and two two indexes are compared, Judgement Matricies GA-C, and calculate judgment matrix GA-CCorresponding feature to
Measure W'A-C.Wherein, the transforming relationship of index value and scale is as shown in table 1 in 9 grades of scaling laws.
1 index value of table and scale transforming relationship table
Step2：Operation data calculating convergence based on the distributed energy storage system that distributed energy storage Node Controller uploads
Potentiality index value constructs each finger using 9 grades of scaling laws according to the convergence potentiality index value of each distributed energy storage node respectively
Corresponding judgment matrix Gci-p is marked,
Wherein, i is index number, and 1≤i≤6, p are the number of distributed storage node, and judgment matrix Gci-p is 1 row p
The matrix of column.
Step3：Judgment matrix Gci-p corresponding to each index is normalized respectively.
Step4：Calculate the characteristic vector W of the judgment matrix Gci-p Jing Guo normalized, wherein 1≤k≤p,
Step 5:It calculates distributed energy storage and converges potential index λmax：
λmax=W*G 'A-C,
Wherein, λmaxTo converge potential index；W is the spy of the corresponding judgment matrix of each index by normalized
Levy vector；GA-CFor the judgment matrix G for utilizing 9 grades of scaling laws construction according to the preset value of each index in convergence potentiality indexA-C
Corresponding feature vector.
Step6：Size of the upper layer dispatching control center based on distributed energy storage convergence potential index is to each distributed energy storage
The distributed energy storage power output of node is ranked up and corresponding power index；Wherein, distributed energy storage convergence potential index is bigger,
Then priority scheduling Node distribution formula energy storage.
Step7：Power instruction is determined according to the power output sequence and corresponding power index, and the power instruction is sent out
It send to corresponding distributed energy storage Node Controller, to control the power output of energy-storage system.
By following groups data (including：Each node energy storage rated power, capacity, scheduling eve energy storage charge and discharge electric work
Energy storage SOC value, system demand power/capability value before rate value, scheduling), the effect to embodiment of the present invention is verified, each parameter is such as
Shown in table 2.
2 data parameters table of table
| Energy storage node number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| Rated power (MW) | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 10 | 15 |
| Capacity (MWh) | 20 | 20 | 20 | 20 | 20 | 40 | 10 | 20 | 20 |
| Charge-discharge electric power | 0 | 0 | 0 | -10 | 10 | 0 | 0 | 0 | 0 |
| SOC | 0.3 | 0.5 | 0.8 | 0.5 | 0.5 | 0.2 | 0.5 | 0.5 | 0.5 |
Tentative influence of the energy storage output power to system node voltage is identical, and each energy storage node power transfer efficiency is 0.9；
When system requirements are charging 12MW*1h altogether, the participation convergence priority and convergence potential index difference of each distributed energy storage
For：Node 3 (0.147) -->Node 9 (0.121) -->Node 2 (0.120) -->Node 4 (0.117) -->Node 5 (0.110) --
>Node 1 (0.103) -->Node 6 (0.103) -->Node 7 (0.098) -->Node 8 (0.080)；When system requirements are to put altogether
When electricity -12MW*1h, the participation convergence priority of each distributed energy storage is：Node 1 (0.138) -->Node 6 (0.135) -->
Node 9 (0.118) -->Node 2 (0.115) -->Node 5 (0.113) -->Node 4 (0.107) -->Node 3 (0.099) -->Section
7 (0.0969) of point -->Node 8 (0.077).
Fig. 3 is the structure according to the convergence Potential Evaluation system 300 of the user side distributed energy storage of embodiment of the present invention
Schematic diagram.As shown in figure 3, the convergence Potential Evaluation system 300 for the user side distributed energy storage that embodiments of the present invention provide,
Including：Operation data acquiring unit 301, convergence potentiality index value determination unit 302 and convergence potential index determination unit 303.
Preferably, in the operation data acquiring unit 301, the corresponding distribution of each distributed energy storage Node Controller acquisition is received
The operation data of formula energy storage node.
Preferably, in the convergence potentiality index value determination unit 302, computation model is converged according to distributed energy storage and is utilized
The operation data of each distributed energy storage node calculates separately the convergence potentiality index value of each distributed energy storage node.
Preferably, wherein the convergence potentiality index value, including：Dynamic response capability, power enabling capabilities, capacity support
Ability, effective binding time, system stability and system reliability,
g1=| StateBk+StateD|+1|/3(0≤g1≤ 1),
g2=Capk/Cap(0≤g2≤ 1),
g3=ρk*PBk/PD(0≤g3≤ 1),
g4=TBk/TD(0≤g4≤ 1),
g5=1-VBk/VR(0≤g5≤ 1),
g6=(1- ηk)(0≤g6≤ 1),
Wherein, g1For dynamic response capability；StateBkFor the current operation of the energy-storage system where distributed energy storage node k
State, StateBk=1 expression energy storage is just charged, StateBk=-1 expression energy storage is just discharged, StateBk=0 indicates that energy storage is in hot standby
Use state；StateDIndicate action state when current system scheduling distributed energy storage, StateD=1 indicates that energy storage is needed to charge,
StateD=-1 indicates that energy storage is needed to discharge；g2For capacity enabling capabilities；Cap is that demand when system calls distributed energy storage is held
Amount；Capk is the available scheduling total capacity of distributed energy storage of distributed energy storage node k, if Cap<Capk then g1=1；g3For
Power enabling capabilities；PDDemand power when being called for energy-storage system to distributed energy storage；PBkFor point of distributed energy storage node k
The available schedule power of cloth energy storage, ρkFor Node distribution formula energy storage power-conversion efficiencies, if PD<PBkThen g2=1；g4To have
Imitate binding time, TDDemand total time when being called for energy-storage system to distributed energy storage；TBkDistributed energy storage for node k can
The scheduling total time of offer, if TD<TBkThen g4=1；g5For system stability；VRFor the voltage rating of distributed energy storage node, VBk
Node voltage fluctuation amplitude caused by contribute because of the distributed energy storage at distributed energy storage node k；g6For system reliability；ηk
For the failure rate of the distributed energy storage node at scheduling periods interior nodes k.
Preferably, in the convergence potential index determination unit 303, according to the convergence of each distributed energy storage node
Potentiality index value calculates the convergence potential index of each distributed energy storage node.
Preferably, wherein the convergence potential index determination unit, according to the convergence of each distributed energy storage node
Potentiality index value calculates the convergence potential index of each distributed energy storage node, including：According to each finger in convergence potentiality index
Target preset value utilizes 9 grades of scaling law Judgement Matricies GA-C, and calculate judgment matrix GA-CCorresponding characteristic vector W 'A-C；Root
The corresponding judgement square of each index is constructed respectively using 9 grades of scaling laws according to the convergence potentiality index value of each distributed energy storage node
Battle array；Calculate the characteristic vector W of the corresponding judgment matrix of each index；Each distribution is calculated using convergence potential index calculation formula
The convergence potential index of formula energy storage node.
Preferably, wherein calculating convergence potential index using following formula：
λmax=W*G 'A-C,
Wherein, λmaxTo converge potential index；W is the corresponding judgment matrix of each index after normalized
Feature vector；GA-CFor the judgment matrix for utilizing 9 grades of scaling laws construction according to the preset value of each index in convergence potentiality index
GA-CCorresponding feature vector.
Preferably, wherein the convergence potential index determination unit further includes：Before calculating characteristic vector W, to each
The corresponding judgment matrix of index is normalized.
Preferably, wherein the system also includes：Power output order determination unit and power instruction control unit.The power output
Order determination unit, for determining that the power output of each distributed energy storage node is sequentially and corresponding according to the convergence potential index
Power index, wherein convergence potential index is bigger, then the corresponding distributed storage node of priority scheduling carries out energy storage.The power refers to
Control unit is enabled, for determining power instruction according to power output sequence and corresponding power index, and by the power instruction
It is sent to corresponding distributed energy storage Node Controller, to control the power output of energy-storage system.
The convergence Potential Evaluation system 300 of the user side distributed energy storage of the embodiment of the present invention and of the invention another
The convergence potential evaluation method 100 of the user side distributed energy storage of embodiment is corresponding, and details are not described herein.
The present invention is described by reference to a small amount of embodiment.However, it is known in those skilled in the art, as
Defined by subsidiary Patent right requirement, in addition to the present invention other embodiments disclosed above equally fall in it is of the invention
In range.
Normally, all terms used in the claims are all solved according to them in the common meaning of technical field
It releases, unless in addition clearly being defined wherein.All references " one/described/be somebody's turn to do [device, component etc.] " are all opened ground
At least one example being construed in described device, component etc., unless otherwise expressly specified.Any method disclosed herein
Step need not all be run with disclosed accurate sequence, unless explicitly stated otherwise.
Claims (10)
1. a kind of convergence potential evaluation method of user side distributed energy storage, which is characterized in that the method includes：
Receive the operation data of the corresponding distributed energy storage node of each distributed energy storage Node Controller acquisition；
Computation model, which is converged, according to distributed energy storage calculates separately each point using the operation data of each distributed energy storage node
The convergence potentiality index value of cloth energy storage node；
The remittance of each distributed energy storage node is calculated according to the convergence potentiality index index value of each distributed energy storage node
Poly- potential index.
2. the method according to claim 1, wherein the convergence potentiality index value, including：Dynamic response energy
Power, power enabling capabilities, capacity enabling capabilities, effective binding time, system stability and system reliability,
g1=| StateBk+StateD|+1|/3 (0≤g1≤ 1),
g2=Capk/Cap (0≤g2≤ 1),
g3=ρk*PBk/PD (0≤g3≤ 1),
g4=TBk/TD (0≤g4≤ 1),
g5=1-VBk/VR (0≤g5≤ 1),
g6=(1- ηk) (0≤g6≤ 1),
Wherein, g1For dynamic response capability；StateBkFor the current operating conditions of the energy-storage system where distributed energy storage node k,
StateBk=1 expression energy storage is just charged, StateBk=-1 expression energy storage is just discharged, StateBk=0 expression energy storage is in stand-by heat shape
State；StateDIndicate action state when current system scheduling distributed energy storage, StateD=1 indicates that energy storage is needed to charge, StateD
=-1 indicates that energy storage is needed to discharge；g2For capacity enabling capabilities；Cap is demand capacity when system calls distributed energy storage；
Capk is the available scheduling total capacity of distributed energy storage of distributed energy storage node k, if Cap<Capk then g1=1；g3For power
Enabling capabilities；PDDemand power when being called for energy-storage system to distributed energy storage；PBkFor the distribution of distributed energy storage node k
The available schedule power of energy storage, ρkFor Node distribution formula energy storage power-conversion efficiencies, if PD<PBkThen g2=1；g4Effectively to converge
Poly- time, TDDemand total time when being called for energy-storage system to distributed energy storage；TBkDistributed energy storage for node k can provide
Scheduling total time, if TD<TBkThen g4=1；g5For system stability；VRFor the voltage rating of distributed energy storage node, VBkFor because
Node voltage fluctuation amplitude caused by distributed energy storage at distributed energy storage node k is contributed；g6For system reliability；ηkTo adjust
Spend the failure rate of the distributed energy storage node at time cycle interior nodes k.
3. according to the method described in claim 2, it is characterized in that, the convergence according to each distributed energy storage node
Potentiality index value calculates the convergence potential index of each distributed energy storage node, calculates step and includes：
9 grades of scaling law Judgement Matricies G are utilized according to the preset value of each index in convergence potentiality indexA-C, and calculate judgement
Matrix GA-CCorresponding characteristic vector W 'A-C；
9 grades of scaling laws are utilized to construct each index respectively according to the convergence potentiality index value of each distributed energy storage node corresponding
Judgment matrix, and the corresponding judgment matrix of each index is normalized；
Calculate the characteristic vector W of the corresponding judgment matrix of each index.
The convergence potential index of each distributed energy storage node is calculated using convergence potential index calculation formula.
4. according to the method described in claim 3, it is characterized in that, the convergence potential index calculation formula is：
λmax=W*G 'A-C,
Wherein, λmaxTo converge potential index；W is the feature of the corresponding judgment matrix of each index after normalized
Vector；GA-CFor the judgment matrix G for utilizing 9 grades of scaling laws construction according to the preset value of each index in convergence potentiality indexA-CIt is right
The feature vector answered.
5. the method according to claim 1, wherein the method also includes：
According to it is described convergence potential index determine each distributed energy storage node power output sequence and corresponding power index, wherein
Convergence potential index is bigger, then the distributed energy storage power output of priority scheduling corresponding node；
Power instruction is determined according to power output sequence and corresponding power index, and the power instruction is sent to corresponding
Distributed energy storage Node Controller, to control the power output of energy-storage system.
6. a kind of convergence Potential Evaluation system of user side distributed energy storage, which is characterized in that the system comprises：
Operation data acquiring unit, for receiving the corresponding distributed energy storage section of each distributed energy storage Node Controller acquisition
The operation data of point；
Potentiality index value determination unit is converged, utilizes each distributed energy storage section for converging computation model according to distributed energy storage
The operation data of point calculates separately the convergence potentiality index value of each distributed energy storage node；
Potential index determination unit is converged, it is every for being calculated according to the convergence potentiality index value of each distributed energy storage node
The convergence potential index of a distributed energy storage node.
7. system according to claim 6, which is characterized in that the convergence potentiality index index includes：Dynamic response energy
Power, power enabling capabilities, capacity enabling capabilities, effective binding time, system stability and system reliability.
g1=| StateBk+StateD|+1|/3 (0≤g1≤ 1),
g2=Capk/Cap (0≤g2≤ 1),
g3=ρk*PBk/PD (0≤g3≤ 1),
g4=TBk/TD (0≤g4≤ 1),
g5=1-VBk/VR (0≤g5≤ 1),
g6=(1- ηk) (0≤g6≤1),
Wherein, g1For dynamic response capability；StateBkFor the current operating conditions of the energy-storage system where distributed energy storage node k,
StateBk=1 expression energy storage is just charged, StateBk=-1 expression energy storage is just discharged, StateBk=0 expression energy storage is in stand-by heat shape
State；StateDIndicate action state when current system scheduling distributed energy storage, StateD=1 indicates that energy storage is needed to charge, StateD
=-1 indicates that energy storage is needed to discharge；g2For capacity enabling capabilities；Cap is demand capacity when system calls distributed energy storage；
Capk is the available scheduling total capacity of distributed energy storage of distributed energy storage node k, if Cap<Capk then g1=1；g3For power
Enabling capabilities；PDDemand power when being called for energy-storage system to distributed energy storage；PBkFor the distribution of distributed energy storage node k
The available schedule power of energy storage, ρkFor Node distribution formula energy storage power-conversion efficiencies, if PD<PBkThen g2=1；g4Effectively to converge
Poly- time, TDDemand total time when being called for energy-storage system to distributed energy storage；TBkDistributed energy storage for node k can provide
Scheduling total time, if TD<TBkThen g4=1；g5For system stability；VRFor the voltage rating of distributed energy storage node, VBkFor because
Node voltage fluctuation amplitude caused by distributed energy storage at distributed energy storage node k is contributed；g6For system reliability；ηkTo adjust
Spend the failure rate of the distributed energy storage node at time cycle interior nodes k.
8. system according to claim 6, which is characterized in that the convergence potential index determination unit, according to described every
The convergence potentiality index value of a distributed energy storage node calculates the convergence potential index of each distributed energy storage node, including：
9 grades of scaling law Judgement Matricies G are utilized according to the preset value of each index in convergence potentiality indexA-C, and calculate judgement
Matrix GA-CCorresponding characteristic vector W 'A-C；
9 grades of scaling laws are utilized to construct each index respectively according to the convergence potentiality index value of each distributed energy storage node corresponding
Judgment matrix, and the corresponding judgment matrix of each index is normalized；
Calculate the characteristic vector W of the corresponding judgment matrix of each index；
The convergence potential index of each distributed energy storage node is calculated using convergence potential index calculation formula.
9. system according to claim 6, which is characterized in that calculate convergence potential index using following formula：
λmax=W*G 'A-C,
Wherein, λmaxTo converge potential index；W is the feature of the corresponding judgment matrix of each index after normalized
Vector；GA-CFor the judgment matrix G for utilizing 9 grades of scaling laws construction according to the preset value of each index in convergence potentiality indexA-CIt is right
The feature vector answered.
10. system according to claim 6, which is characterized in that the system also includes：
Power output order determination unit, for determining the power output sequence of each distributed energy storage node according to the convergence potential index
With corresponding power index, wherein convergence potential index is bigger, then the corresponding distributed storage node of priority scheduling carries out energy storage；
Power instruction control unit, for determining power instruction according to power output sequence and corresponding power index, and by institute
It states power instruction and is sent to corresponding distributed energy storage Node Controller, to control the power output of energy-storage system.
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