CN114819424B - Energy storage residual capacity distribution method suitable for multi-scene application - Google Patents

Energy storage residual capacity distribution method suitable for multi-scene application Download PDF

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CN114819424B
CN114819424B CN202210758294.9A CN202210758294A CN114819424B CN 114819424 B CN114819424 B CN 114819424B CN 202210758294 A CN202210758294 A CN 202210758294A CN 114819424 B CN114819424 B CN 114819424B
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曾伟
李建林
马速良
陈拓新
熊俊杰
范瑞祥
赵伟哲
叶钟海
崔宜琳
武亦文
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State Grid Corp of China SGCC
North China University of Technology
Electric Power Research Institute of State Grid Jiangxi Electric Power Co Ltd
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North China University of Technology
Electric Power Research Institute of State Grid Jiangxi Electric Power Co Ltd
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Abstract

The invention relates to an energy storage residual capacity distribution method suitable for multi-scenario application, which investigates technical service scenario requirements and shares available residual capacity of energy storage to ensure that all final technical services must be met; calculating whether each energy storage can be completed on each technical service scene or not and estimating the cost of each energy storage completion task; analyzing a maximum matching scheme of the shared energy storage and technical service scene and calculating the total cost; and obtaining the scheme with the minimum total cost in the maximum matching scheme in the shared energy storage and technical service scenes through an optimization algorithm. The method realizes the maximum matching of the shared energy storage and the technical service scenes through the supply and demand matching idea, ensures that all the technical service scenes have corresponding different shared energy storages to be matched with the shared energy storage, selects the maximum matching with the lowest cost in all the maximum matching through an optimization algorithm, and finally selects the optimal scheme, thereby having important significance and application value for the research and popularization of the shared energy storage.

Description

Energy storage residual capacity distribution method suitable for multi-scenario application
Technical Field
The invention relates to an energy storage residual capacity distribution method suitable for multi-scenario application, and particularly relates to a distribution process for energy storage residual capacity according to application scenario requirements.
Background
In order to promote the consumption of renewable energy and reduce the influence of a large amount of renewable energy on a power grid in a grid connection mode, energy storage becomes an important and wide resource in a future power system, and in recent years, energy storage technology and related applications thereof are greatly developed. However, the energy storage system has large investment scale and long return on investment period, and most energy storage users do not have the capacity of building the energy storage system; and due to the lack of a mature business model, the investors of the energy storage system do not obtain ideal benefits, which limits the development of the energy storage system. In recent years, the sharing economic model breaks through the traditional economic model and is capable of being vigorously developed, the sharing economic utilizes the modern internet information communication technology, and the transfer of the use right of the goods is realized through the sharing modes of renting, borrowing and the like. The sharing economic mode has strong resource optimal allocation and utilization capacity and can realize mutual benefits and win-win of participants. The shared economy is introduced into the energy storage technology, and the shared energy storage has higher development potential in the aspects of reducing investment cost, giving play to energy storage benefits and values, facilitating service users and the like.
The method comprises the steps of gathering idle shared energy storage resources and different technical service scenes to be served, and in order to ensure that the requirements of the technical service scenes can be fully met, regulating the number of the shared energy storage resources to be greater than the number of the technical service scenes, wherein each idle shared energy storage resource corresponds to one technical service scene one to one, ensuring that the requirements of each final technical service scene can be met, and judging that the energy storage can be matched with the technical service scenes if the shared energy storage power and the capacity are not enough to support the scenes. Under the condition that the shared energy storage can meet the technical service scene, the cost paid by the shared energy storage is different, the closer the power and the capacity required by the scene to the available power and the capacity of the shared energy storage, the smaller the waste, the smaller the cost, the closer the power and the capacity required by the scene to the available power and the capacity of the shared energy storage, the larger the waste and the higher the cost, and the scheme with the maximum matching and the minimum cost of the shared energy storage resource and the technical service scene is obtained by virtue of the matching idea of the supply-demand relationship, the evaluation idea of the scheme index after matching and the optimization idea of the evaluation result.
Disclosure of Invention
The invention aims to: the energy storage residual capacity distribution method suitable for multi-scenario application is provided. Firstly, researching technical service scene requirements and available residual capacity of shared energy storage, and ensuring that all the final technical service scenes are required to be met; then calculating whether each shared energy storage can be completed for each technical service scene and the cost for completing the task; calculating the total cost of the maximum matching scheme of the shared energy storage and technical service scenes according to the supply and demand relationship between the energy storage and the technical service scenes; and finally, obtaining a scheme with the minimum total cost in the maximum matching schemes in the shared energy storage and technical service scenes through an optimization algorithm. The method combines the maximum matching idea, the evaluation idea and the optimization idea, finally selects the optimal scheme, and has important significance and application value for research and popularization of shared energy storage.
The technical scheme adopted by the invention is as follows: an energy storage residual capacity distribution method suitable for multi-scenario application comprises the following steps:
step 1: researching technical service scene requirements and sharing available residual capacity of energy storage, and ensuring that all final technical services must be met;
step 2: calculating whether each energy storage can be completed on each technical service scene or not, and solving the cost of completing the task by combining an evaluation algorithm;
and step 3: calculating a maximum matching scheme of the shared energy storage and technical service scenes through a maximum matching algorithm and calculating total cost based on the shared energy storage supply capacity and the technical service scene demand level;
and 4, step 4: and optimizing an optimal energy storage residual capacity distribution scheme through an optimization algorithm to obtain a scheme with the minimum total cost in the maximum matching scheme in the shared energy storage and technical service scenes.
Further preferably, the specific process of step 1 is as follows:
step 1.1 number of technical service scenariosnFirst, ofiFor individual technical service scenariosX i To show, clearly, the power and capacity of the stored energy required by each technical service scenario,P in is as followsiThe energy storage power required by the individual technical service scenarios,Q in is a firstiEnergy storage capacity required by each technical service scenario;
step 1.2 sharing the number of stored energy asmThe number of the main components is one,m>nthe service requirement of an energy storage technology can be completed by only one shared energy storage,P jr is a firstjThe actual available power of the individual shared energy storages,Q jr is as followsjIndividual share of the actual available capacity of the stored energyjFor individual sharing energy storageY j And the shared energy storage needs to ensure that all technical service scene requirements are met.
Further preferably, the specific process of step 2 is as follows:
step 2.1, considering the power and the capacity of energy storage required by each technical service scene obtained in the step 1, and establishing a cost model for completing the task;
step 2.2 ifjThe actual available power of the shared energy storage is less than that of the secondiPower required for individual technical service scenarios orjThe actual available capacity of the shared energy storage is less than that of the secondiThe capacity of energy storage required by the technical service scene isjA shared energy storage pairiThe technical service scenario can not be completed ifjThe actual available power and capacity of the shared energy storage are both more than or equal to the secondiThe power and capacity of the stored energy required by the individual technical service scenariojA shared energy storage pairiThe technical service scenario can be completed, and the completion cost is obtained by the cost model in the step 2.1, so that the relation between the shared energy storage and the technical service scenario is obtained.
Further preferably, the cost model is established as follows:
setting indexes by adopting all shared energy storage for technical service scenes, and obtaining index values through testing or simple calculation according to power and capacityiThe technical service scenario adoptsjCost of individual shared energy storageC ijc_ Comprises the following steps:
Figure 806606DEST_PATH_IMAGE001
C P in order to share the unit cost per unit power of the energy storage system,C Q in order to share the unit cost per unit capacity of the energy storage system,P ijsu_ is a firstiThe technical service scenario adoptsjThe power of the stored energy is shared by the two,Q ijsu_ is a firstiThe technical service scenario adoptsjThe capacity of the shared energy storage;
the number of indexes isZThe set indexes are divided into cost indexes, benefit indexes and intermediate indexes, the indexes are subjected to forward processing, and the indexes are converted into benefit index data by adopting a formula (2.2):
Figure 864692DEST_PATH_IMAGE002
the data are converted into benefit index data by the formula (2.3):
Figure 910009DEST_PATH_IMAGE003
Figure 570797DEST_PATH_IMAGE004
is in the indexfFirst toiThe technical service scenario adoptsjAs a result of the forward quantization of the individual shared stored energy,
Figure 791694DEST_PATH_IMAGE005
is in the indexfFirst toiThe individual technical service scenarios take the maximum value among the individual shared energy storages,
Figure 410894DEST_PATH_IMAGE006
is in the indexfFirst toiThe technical service scenario adoptsjAs a result of the shared stored energy, the energy storage,
Figure 317408DEST_PATH_IMAGE007
is in the indexfFirst toiThe optimal value of each technical service scenario;
all index values are normalized using equation (2.4):
Figure 47467DEST_PATH_IMAGE008
Figure 981925DEST_PATH_IMAGE009
is in the indexfFirst toiThe technical service scenario adoptsjThe result after the normalization of the shared energy storage;
calculating the difference between each evaluation index and the optimal and worst indexes by adopting a formula (2.5) and a formula (2.6):
Figure 116234DEST_PATH_IMAGE010
Figure 401722DEST_PATH_IMAGE011
Figure 404313DEST_PATH_IMAGE012
is as followsiThe technical service scenario adoptsjThe difference between the individual shared energy storage and the best case, ω f Is an indexfThe weight of (b) can be obtained by means of expert scoring,
Figure 865381DEST_PATH_IMAGE013
for the index after standardizationfFirst toiThe technical service scenario adoptsjThe maximum index value of the shared energy storage is obtained,
Figure 436171DEST_PATH_IMAGE014
for the index after standardizationfFirst toiThe technical service scenario adoptsjThe minimum index value of the shared energy storage is,
Figure 208955DEST_PATH_IMAGE015
is as followsiThe technical service scenario adoptsjThe difference between the shared energy storage and the worst case;
evaluation of the formula (2.7)iThe technical service scenario adoptsjThe closeness of each shared energy storage to the optimal solution:
Figure 749658DEST_PATH_IMAGE016
D i j , is as followsiThe technical service scenario adoptsjThe proximity of each shared energy storage to the optimal solution;
using equation (2.8) to calculateiThe technical service scenario adoptsjCost of each shared energy storage completion task:
Figure 23820DEST_PATH_IMAGE017
Figure 890145DEST_PATH_IMAGE018
is a firstiThe technical service scenario adoptsjThe cost of the shared stored energy to complete the task,D i_max is as followsiThe maximum value of the closeness degree of each shared energy storage and the optimal scheme is adopted in each technical service scene.
Further preferably, the specific process of step 3 is as follows:
step 3.1 Each technical service scenario isX i ，i=1,2,…,nEach sharing the stored energy asY j ，j=1,2,…,mInitially, a match between the shared energy storage and technical service scenarios is establishedMAInitial state ofMA= Φ, Φ represents an empty set, i.e. represents an empty match between the energy storage and the technical service scenario;
step 3.2 if technical service scenario setXEach point in (1) isMASaturation point of (2) then does not existMAStep 3.5 is carried out;
step 3.3 inXGet one not yet trying to expandMAPoint of saturationX p If such a point does not exist in step 3.5, useURepresents fromX i Starting fromMACan widen the pathpIn thatXThe vertex of (2) is selected,Wto representpIn a shared energy storage setYThe initial value of the vertex in (1) is:U={X p }, W=Φ；
step 3.4 finding fromX p Starting fromMAIf there is an extensive routeUThe medium corresponding element can be corresponding to the walking pathYIs equal toWA middle element, thenpNot expandable, i.e. not present fromX i Starting an extendable path, go to step 3.2 ifUThe medium corresponding element can be corresponding to the walking pathYIs not equal toWMiddle element, get correspondingYDoes not belong toWOf middle elementsY j If at allY j Is unsaturated, thenX i ToY j Of (2) apIs an extendable path, then is extendedMA=MA∪p- MA∩pGo to step 3.2, ifY j Saturation, then existenceX i Is disclosed inX i ，Y j )∈MANeed to continue to expandpLet us orderU=U∪{X i },W= W∪{ Y j Fourthly, turning to the step 3.4;
step 3.5, all the unsaturated points in the technical service scene are tried to be expanded to obtain the maximum matching;
step 3.6 sums each path cost in the maximum match, calculates the cost of the maximum match.
Further preferably, the step 4 adopts a genetic algorithm, and the specific process is as follows:
step 4.1 initialize the crossover rateP c And the rate of variationP m And maximum number of iterationsGSimultaneously setting the sequence of technical service scenes and defining the technologyThe form and number of coding strings corresponding to the sequence of technical service scenes, the number of bits of the coding strings being equal to the number of technical service scenesnIn the first placegIn a second iteration, the code string is
Figure 884646DEST_PATH_IMAGE019
，
Figure 104406DEST_PATH_IMAGE020
Randomly taking 1 tonIs given to the number of the one or more,
Figure 133542DEST_PATH_IMAGE021
randomly taking 1 tonOne of the remaining numbers except the previous taken number,
Figure 685614DEST_PATH_IMAGE022
randomly taking 1 tonExcept for one of the remaining numbers of the first two taken numbers,
Figure 901832DEST_PATH_IMAGE023
taking the last remaining number, and generating randomlyMCoding string corresponding to sequence of individual technical service scenarios
Figure 315496DEST_PATH_IMAGE024
，w=1,2,…,MThe number of initialization iterations is 0, i.e.g=0；
Step 4.2 obtaining the objective function of the genetic algorithm through the step 3, and calculatingMThe objective function value corresponding to each code string;
step 4.3 according to the objective function value corresponding to the current coding string, the coding bit value of each coding string is subjected to selection operation, cross operation and variation operation, the iteration number is increased by 1, namelyg=g+1；
Step 4.4 judging whether the current iteration times reach the maximum iteration timesGIf yes, the calculation is finished, the sequence of the technical service scene corresponding to the code string with the lowest target function of the genetic algorithm corresponding to the code string of the last generation is taken as a final result, the maximum matching result is the actually selected scheme,otherwise go back to step 4.2.
Further preferably, the step 4 adopts a genetic algorithm, and the specific process of the step 4.3 is as follows:
step 4.3.1, according to the objective function value corresponding to the coding string, carrying out 1 to 1 on the coding string from low to high according to the objective functionMIn a sequence ofkCode string selection rate ofP s =(M-k+1)/ M；
Step 4.3.2 encoding strings generated by Selection operations
Figure 808925DEST_PATH_IMAGE025
，w=1,2,…,MPerforming a crossover operation to generate a new code string, in the second placegIn the sub-iteration, the process is repeated,
Figure 282632DEST_PATH_IMAGE026
，
Figure 127091DEST_PATH_IMAGE027
and is
Figure 813287DEST_PATH_IMAGE028
Of 1 attA code string
Figure 551436DEST_PATH_IMAGE029
And a first step ofaA code string
Figure 930465DEST_PATH_IMAGE030
If the coding bit value before the intersection is the same as the coding bit value after the intersection, the same values are randomly selected from 1 to 1 from the first same numbernExcept for the coded bit value behind the cross point and the coded bit value different from the coded bit value before the cross point and the coded bit value behind the cross point, and the same numbers are different in value finally to generate a new coded string;
step 4.3.3 Pair of encoded strings generated after the crossover operation
Figure 262220DEST_PATH_IMAGE031
，w=1,2,…,MPerforming mutation operation to generate new code stringgIn the sub-iteration, the process is repeated,
Figure 752108DEST_PATH_IMAGE032
，
Figure 721594DEST_PATH_IMAGE033
first, ofsA code string
Figure 241830DEST_PATH_IMAGE034
To (1) abGenerating a code string by performing a mutation operation on each code bit, converting the generated code string, abThe numerical variation of each coding bit is 1 tonBecomes the value of the encoded bit of the number originally changed tobEncoding the original value of the bit, increasing the number of iterations by 1, i.e.g=g+1。
Compared with the closest prior art, the excellent effects of the invention are as follows:
the method comprehensively considers the idle shared energy storage resources and the technical service scene, can be used for matching each idle shared energy storage resource with the technical service scene one by one, forms the maximum matching of the shared energy storage and the technical service scene by analyzing the supply-demand relationship and the service cost of the shared energy storage and the technical service scene, ensures that all the technical service scenes have corresponding different shared energy storage to be matched with the shared energy storage, obtains the cost of completing tasks through an evaluation algorithm, has different costs required by different shared energy storage adopted by different technical service scenes, and matches the condition of paying the cost.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a flowchart of an energy storage remaining capacity allocation method suitable for multi-scenario application.
Fig. 2 is a wiring diagram between a shared energy storage and technical service scenario.
FIG. 3 is a first solution of the maximum matching algorithm for shared energy storage and technical service scenarios;
FIG. 4 is a second solution of the maximum matching algorithm for shared energy storage and technical service scenarios;
FIG. 5 is a third solution for the maximum matching algorithm between the shared energy storage and technical service scenarios;
FIG. 6 is a fourth solving diagram of the maximum matching algorithm for the shared energy storage and technical service scenarios;
fig. 7 is a fifth solving diagram of the maximum matching algorithm for the shared energy storage and technical service scenarios.
Fig. 8 is a flow chart of a maximum matching algorithm implementation.
FIG. 9 is a flow chart of a genetic optimization algorithm solving.
Fig. 10 is a schematic diagram of an encoding string.
FIG. 11 is a schematic diagram of a genetic crossover operation process.
FIG. 12 is a schematic diagram of a genetic variation calculation process.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
The invention provides an energy storage residual capacity allocation method suitable for multi-scenario application, and fig. 1 shows an implementation process of the method in the embodiment, which includes the following steps:
step 1: and (4) researching the technical service scene requirements and sharing the available residual capacity of the energy storage, and ensuring that all the technical services must be met finally.
Step 1.1 number of technical service scenariosnFirst, ofiFor individual technical service scenariosX i To express, to clarify the power and capacity of the stored energy required by each technical service scenario,P in is a firstiThe energy storage power required for the individual technical service scenario,Q in is a firstiEnergy storage capacity required by the individual technical service scenarios.
Step 1.2 sharing the number of stored energy asmThe number of the main components is one,m>none energy storage technology service requirement can be completed by only one shared energy storage,P jr is as followsjThe actual available power of the individual shared energy storages,Q jr is as followsjIndividual share of the actual available capacity of the stored energyjFor individual sharing energy storageY j The energy storage sharing method is used for representing that all technical service scene requirements need to be met.
Step 2: and calculating whether each energy storage can be completed to each technical service scene, and solving the cost of completing the task by combining an evaluation algorithm such as TOPSIS, VOKIA and the like.
Step 2.1, considering the power and capacity of energy storage required by each technical service scene obtained in step 1, establishing a cost model for completing tasks:
setting cost, income and other indexes for the technical service scene by adopting each shared energy storage, wherein the index value can be obtained by testing or simply calculating according to power and capacity, for exampleiThe technical service scenario adoptsjCost of individual shared energy storageC ijc_ Comprises the following steps:
Figure 60882DEST_PATH_IMAGE035
C P in order to share the unit cost per unit power of the energy storage system,C Q in order to share the unit cost per unit capacity of the energy storage system,P ijsu_ is as followsiThe technical service scenario adoptsjThe power of the stored energy is shared by the individuals,Q ijsu_ is as followsiThe technical service scenario adoptsjThe capacity of the shared energy storage.
Table 1 is the followingiIndex table adopting each shared energy storage in each technical service scene
Figure 760985DEST_PATH_IMAGE037
The number of indexes isZThe set indexes are divided into cost type indexes, benefit type indexes and intermediate type indexes, and the indexes are subjected to forward processing. To a first orderiThe individual technical service scenario takes each shared energy storage as an example, the firstiThe technical service scenario adopts each index table of shared energy storage as shown in table 1, wherein:
for the cost index with smaller index value and better index value, the formula (2.2) is adopted to convert the cost index into benefit index data:
Figure 473726DEST_PATH_IMAGE038
for cost indicators that are closer to a certain value and better, the data is converted into benefit indicator data by using a formula (2.3):
Figure 928978DEST_PATH_IMAGE039
Figure 468281DEST_PATH_IMAGE040
is in the indexfFirst toiThe technical service scenario adoptsjAs a result of the forward quantization of the individual shared stored energy,
Figure 503233DEST_PATH_IMAGE041
is in the indexfFirst toiThe individual technical service scenarios take the maximum value among the individual shared energy stores,
Figure 477006DEST_PATH_IMAGE042
is in the indexfFirst toiThe technical service scenario adoptsjAs a result of the shared stored energy, the individual,
Figure 103159DEST_PATH_IMAGE043
is in the indexfFirst toiThe optimal value of each technical service scenario;
all index values were normalized using equation (2.4):
Figure 896803DEST_PATH_IMAGE044
Figure 532183DEST_PATH_IMAGE045
is in the indexfFirst toiThe technical service scenario adoptsjThe result after the normalization of the shared energy storage;
calculating the difference between each evaluation index and the optimal and worst indexes by adopting a formula (2.5) and a formula (2.6):
Figure 688358DEST_PATH_IMAGE046
Figure 750992DEST_PATH_IMAGE047
Figure 267818DEST_PATH_IMAGE048
is as followsiThe technical service scenario adoptsjThe difference between the individual shared energy storage and the best case, ω f Is an indexfThe weight of (b) can be obtained by means of expert scoring,
Figure 706889DEST_PATH_IMAGE049
for the index after standardizationfFirst toiThe technical service scenario adoptsjThe maximum index value of the shared energy storage is,
Figure 983150DEST_PATH_IMAGE050
for the index after standardizationfFirst toiThe technical service scenario adoptsjThe minimum index value of the shared energy storage is,
Figure 92051DEST_PATH_IMAGE051
is as followsiThe technical service scenario adoptsjEach share the energy storage and the mostThe difference of the poor conditions;
evaluation of the formula (2.7)iThe technical service scenario adoptsjThe closeness of each shared energy storage to the optimal scheme:
Figure 453762DEST_PATH_IMAGE052
D i j , is a firstiThe technical service scenario adoptsjThe proximity of the shared energy storage to the optimal solution;
using equation (2.8) to calculateiThe technical service scenario adoptsjCost of each shared energy storage completion task:
Figure 430946DEST_PATH_IMAGE053
Figure 702658DEST_PATH_IMAGE054
is as followsiThe technical service scenario adoptsjThe cost of the shared stored energy to complete the task,D i_max is as followsiThe maximum value of the closeness degree of each shared energy storage and the optimal scheme is adopted in each technical service scene.
The procedure in step 2.1 is performed for each technical service scenario in turn.
Step 2.2 ifjThe actual available power of the shared energy storage is less than that of the secondiPower required for energy storage of individual technical service scenarios orjThe actual available capacity of the shared energy storage is less than that of the secondiThe capacity of energy storage required by the technical service scene isjA shared energy storage pairiThe technical service scenario can not be completed ifjThe actual available power and capacity of the shared energy storage are both more than or equal to the secondiThe power and capacity of the stored energy required by the individual technical service scenariojA shared energy storage pairiThe technical service scene can be completed, the completion cost is obtained by the cost model of the step 2.1, and as shown in figure 2, shared energy storage and skill are obtainedThe relation between the operation service scenes, if can be completed, thenX i AndY j and if the connection cannot be completed, no connection is formed.
And step 3: based on the shared energy storage supply capacity and the requirement level of the technical service scene, calculating a maximum matching scheme of the shared energy storage and the technical service scene through a maximum matching algorithm and calculating the total cost, wherein a flow chart of the maximum matching algorithm is shown in fig. 8.
Step 3.1 takes fig. 2 as an example, and each technical service scenario isX i ，i=1,2,…,nEach sharing the stored energy asY j ，j=1,2,…,mInitially, a match between the shared energy storage and technical service scenarios is establishedMAInitial state ofMA= Φ, Φ denotes an empty set, i.e. an empty match between the energy storage and the technical service scenario.
Step 3.2 if technical service scenario setXEach point in (1) isMASaturation point of (2) then does not existMAStep 3.5 is carried out;
step 3.3 inXGet one not yet trying to expandMASaturation pointX p If such a point does not exist in step 3.5, useURepresents fromX i Starting fromMACan extend the pathpIn thatXThe vertex of (2) is selected,Wto representpIn a shared energy storage setYThe initial value of the vertex in (1) is:U={X p }, W=Φ；
step 3.4 finding fromX p Starting fromMAIf there is an extensive routeUThe medium corresponding element can be corresponding to the walking pathYIs equal toWMiddle element, thenpNot expandable, i.e. not present fromX i Starting from an extendable path, step 3.2, ifUMiddle pairResponse element walkable path correspondenceYIs not equal toWMiddle element, taking correspondenceYDoes not belong toWOf middle elementsY j If, ifY j Is unsaturated, thenX i ToY j Of (2) apIs an extendable path, then is extendedMA=MA∪p- MA∩pGo to step 3.2, ifY j Saturation, then existenceX i Is disclosed inX i ，Y j )∈MANeed to continue to expandpLet us orderU=U∪{X i },W= W∪{ Y j And f, turning to the step 3.4.
Selecting the first edge (X 1 ，Y 1 ) Is added to the matchingMAIn, i.e.MA={(X 1 ，Y 1 ) As shown in fig. 3.
Get nonMASaturation pointX 2 ，U={X 2 }，W= Φ, looking forX 2 Starting fromMACan widen the pathpFromX 2 Starting fromX 2 Y 2 AndX 2 Y 5 ，Y 2 unsaturated, selectionY 2 Then, thenp=X 2 Y 2 For the purpose of broadening the pathMA，MA=MA∪p- MA∩pI.e. byMA={(X 1 ，Y 1 )，(X 2 ，Y 2 ) As shown in fig. 4.
Get nothingMAPoint of saturationX 3 ，U={X 3 }，W= Φ, look forX 3 Starting fromMACan extend the pathpFromX 3 Starting from there areX 3 Y 1 、X 3 Y 4 AndX 3 Y 7 get itY 1 Because of (X 1 ，Y 1 )∈MATherefore, it isY 1 Saturation, needs to continue to expandpLet us orderU={X 3 ，X 1 }，W={Y 1 FromX 1 Starting fromX 1 Y 1 ，X 1 Y 2 AndX 1 Y 4 ，Y 1 、Y 2 are all saturated and therefore selectedY 4 Then, thenp=X 3 Y 1 、X 1 Y 4 For the purpose of broadening the pathMA，MA=MA∪p- MA∩pI.e. byMA={(X 3 ，Y 1 )，(X 2 ，Y 2 ) ，(X 1 ，Y 4 ) As shown in fig. 5.
Get nothingMASaturation pointX 4 ，U={X 4 }，W= Φ, looking forX 4 Starting fromMACan widen the pathpFromX 4 Starting fromX 4 Y 3 、X 4 Y 4 AndX 4 Y 6 ，Y 3 unsaturated, selectionY 3 Then, thenp=X 4 Y 3 In order to be able to extend the path,MA=MA∪p- MA∩pi.e. byMA={(X 3 ，Y 1 )，(X 2 ，Y 2 ) ，(X 1 ，Y 4 ) ，(X 4 ，Y 3 ) As shown in fig. 6.
Get nothingMASaturation pointX 5 ，U={X 5 }，W= Φ, look forX 5 Starting fromMACan extend the pathpFromX 5 Starting from there areX 5 Y 4 Get itY 4 Because (A) isX 1 ，Y 4 )∈MATherefore, it is possible toY 4 Saturation, needs to continue to expandpLet us orderU={X 5 ，X 1 }，W={Y 4 FromX 1 Starting fromX 1 Y 1 ，X 1 Y 2 AndX 1 Y 4 ，Y 1 、Y 2 、Y 4 are all saturated, getY 1 ，(X 3 ，Y 1 )∈MANeed to continue to expandpLet us orderU={X 5 ，X 1 ，X 3 }，W={Y 4 ，Y 1 FromX 3 Starting fromX 3 Y 1 、X 3 Y 4 AndX 3 Y 7 ，Y 1 、Y 4 are all saturated and therefore selectedY 7 Then, thenp=X 5 Y 4 、X 1 Y 1 、X 3 Y 7 For the purpose of broadening the pathMA，MA=MA∪p- MA∩pI.e. byMA={(X 5 ，Y 4 )，(X 1 ，Y 1 ) ，(X 2 ，Y 2 ) ，(X 3 ，Y 7 ) ，(X 4 ，Y 3 ) As shown in fig. 7.
Step 3.5 all unsaturated points in the technical service scenario have attempted to be extended to obtain maximum matchMA max ={(X 5 ，Y 4 )，(X 1 ，Y 1 ) ，(X 2 ，Y 2 ) ，(X 3 ，Y 7 ) ，(X 4 ，Y 3 )}，
Step 3.6 summing the cost of each path in the maximum match, calculating the cost of the maximum match
Figure 372674DEST_PATH_IMAGE055
。
And 4, step 4: and optimizing an optimal energy storage residual capacity allocation scheme by an optimization algorithm, taking a genetic optimization algorithm as an example, and obtaining a scheme with the minimum total cost in the maximum matching scheme in the shared energy storage and technical service scenes.
Step 4.1 initialize the crossover rateP c And the rate of variationP m And maximum number of iterationsGSetting the sequence of technical service scenes, defining the form and number of coding strings corresponding to the sequence of the technical service scenes, wherein the bit number of the coding strings is equal to the number of the technical service scenesnIn the first placegIn the second iteration, the code string is
Figure 221681DEST_PATH_IMAGE056
，
Figure 612342DEST_PATH_IMAGE057
Randomly taking 1 ton(ii) a number of (a) to (b),
Figure 863195DEST_PATH_IMAGE058
randomly taking 1 tonExcept for one of the remaining numbers of the previous taken number,
Figure 438533DEST_PATH_IMAGE059
randomly taking 1 tonExcept for one of the remaining numbers of the first two already taken numbers,
Figure 414317DEST_PATH_IMAGE060
taking the last remaining number, and generating randomlyMCoding string corresponding to sequence of individual technical service scenarios
Figure 733303DEST_PATH_IMAGE061
，w=1,2,…,M，MAs shown in fig. 10, for example, 5 technical service scenarios are taken, a first coding bit may take one of 1 to 5, after 1 is randomly selected, a second coding bit may take one of 2 to 5, after 3 is randomly selected, a 3 rd coding bit may randomly take one of 2,4, and 5, after 5 is randomly selected, a fourth coding bit may take one of 2 and 4, after 2 is randomly selected, a 5 th coding bit is 4, and the number of initialization iterations is 0, that is, the coding string represents the order of the technical service scenariosg=0。
Step 4.2 obtaining the objective function of the genetic algorithm through the step 3, and calculatingMAnd the objective function values corresponding to the coding strings.
Step 4.3 according to the objective function value corresponding to the current coding string, the coding bit value of each coding string is subjected to selection operation, cross operation and variation operation, the iteration number is increased by 1, namelyg=g+1。
Step 4.3.1, according to the objective function value corresponding to the coding string, carrying out 1 to 1 on the coding string from low to high according to the objective functionMIs ordered askCode string selection rate ofP s =(M-k+1)/ M。
Step 4.3.2 on the code string resulting from the selection operation
Figure 104241DEST_PATH_IMAGE062
，w=1,2,…,MPerforming a crossover operation to generate a new code string, ingIn the sub-iteration of the process,
Figure 725847DEST_PATH_IMAGE063
，
Figure 283867DEST_PATH_IMAGE064
and is
Figure 672123DEST_PATH_IMAGE065
Of 1 attA code string
Figure 507355DEST_PATH_IMAGE066
And a firstaA code string
Figure 690075DEST_PATH_IMAGE067
If the coded bit value before the intersection is the same as the coded bit value after the intersection, randomly taking 1 to 1 from the first same numbernExcept for the coded bit value after the cross point and the coded bit value before and after the cross point, and these same numbers are different in value finally, to generate a new coded string,as shown in FIG. 11, again taking 5 technical service scenarios as examples, [5,3,1,2,4]And [1,2,3,4,5]After the 2 nd coded bit, a cross operation takes place, the code string after the cross is [5,3, 4,5]And [1,2, 4]Then for the encoding string [5,3, 4,5]In other words, the values of 5 and 3 before the intersection are the same as the value after the intersection, 1 to 5 have 1 and 2 except 3,4 and 5 after the intersection, and 1 and 2 except the values of 3 and 4 before the intersection and 2 after the intersection, the first identical coded bit (i.e. the first coded bit) randomly takes one of 1 and 2, the next identical coded bit randomly takes the other numbers except the value obtained by the previous identical coded bit in 1 and 2, if the first identical coded bit takes 1, the second identical coded bit takes 2, if the first identical coded bit takes 2, the second identical coded bit takes 1, and the other code string after the intersection is converted in the same manner.
Step 4.3.3 pairs of encoded strings generated after the crossover operation
Figure 970DEST_PATH_IMAGE068
，w=1,2,…,MPerforming mutation operation to generate new code stringgIn the sub-iteration, the process is repeated,
Figure 537125DEST_PATH_IMAGE069
，
Figure 351497DEST_PATH_IMAGE070
of 1 atsA code string
Figure 705118DEST_PATH_IMAGE071
To (1)bGenerating a code string by performing a mutation operation on each code bit, converting the generated code string, andbthe numerical variation of each coding bit is 1 tonBecomes the value of the encoded bit of the number originally changed tobThe original value of the coded bit is increased by 1, i.e. the number of iterationsg=g+1, as shown in FIG. 12, still take 5 technical service scenarios as an example, let the coding string be [5,3,1,2,4]If the third coded bit is changed from 1 to 5, the original coded bit value of the first coded bit with 5 is changed to 1, and the most important coded bit value isConversion of the final code string into [1,3,5,2,4]。
Step 4.4 judging whether the current iteration number reaches the maximum iteration numberGIf so, the calculation is finished, the sequence of the technical service scene corresponding to the code string with the lowest target function of the genetic algorithm corresponding to the code string of the last generation is taken as a final result, the maximum matching result is the actually selected scheme, and otherwise, the step 4.2 is returned to.
Finally, it should be noted that: the embodiments described are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Claims (4)

1. An energy storage residual capacity distribution method suitable for multi-scenario application is characterized by comprising the following steps:
step 1: researching technical service scene requirements and sharing available residual capacity of energy storage, and ensuring that all final technical services must be met;
step 1.1 number of technical service scenariosnFirst, ofiFor technical service scenariosX i To show, clearly, the power and capacity of the stored energy required by each technical service scenario,P in is a firstiThe energy storage power required for the individual technical service scenario,Q in is as followsiEnergy storage capacity required by each technical service scenario;
step 1.2 sharing the number of stored energy asmThe number of the main components is one,m>none energy storage technology service requirement can be completed by only one shared energy storage,P jr is as followsjThe actual available power of the individual shared energy storages,Q jr is as followsjIndividual share of the actual available capacity of the stored energyjFor individual sharing energy storageY j The method comprises the following steps that (1) shared energy storage needs to ensure that all technical service scene requirements are met;
step 2: calculating whether each energy storage can be completed to each technical service scene or not, and solving the cost of completing the task by combining an evaluation algorithm;
step 2.1, considering the power and the capacity of energy storage required by each technical service scene obtained in the step 1, and establishing a cost model for completing the task; the cost model is established in the following way:
setting indexes by adopting all shared energy storage for technical service scenes, and obtaining index values through testing or simple calculation according to power and capacityiThe technical service scenario adoptsjCost of individual shared energy storageC ijc_ Comprises the following steps:
Figure 266308DEST_PATH_IMAGE001
C P in order to share the unit cost per unit power of the energy storage system,C Q in order to share the unit cost per unit capacity of the energy storage system,P ijsu_ is as followsiThe technical service scenario adoptsjThe power of the stored energy is shared by the two,Q ijsu_ is a firstiThe technical service scenario adoptsjThe capacity of the shared energy storage;
the number of indexes isZThe set indexes are divided into cost type indexes, benefit type indexes and intermediate type indexes, the indexes are processed in a forward mode, and cost type indexes which are better when the index values are smaller are converted into benefit type index data by adopting a formula (2.2):
Figure 652290DEST_PATH_IMAGE002
for cost type indexes which are closer to a certain value and better, the formula (2.3) is adopted to convert the cost type indexes into benefit type index data:
Figure 166448DEST_PATH_IMAGE003
Figure 30499DEST_PATH_IMAGE004
is in the indexfFirst toiThe technical service scenario adoptsjAs a result of the forward quantization of the individual shared stored energy,
Figure 310783DEST_PATH_IMAGE005
is in the indexfFirst toiThe individual technical service scenarios take the maximum value among the individual shared energy storages,
Figure 602087DEST_PATH_IMAGE006
is in the indexfFirst toiThe technical service scenario adoptsjAs a result of the shared stored energy, the individual,
Figure 337962DEST_PATH_IMAGE007
is in the indexfFirst toiThe optimal value of each technical service scenario;
all index values were normalized using equation (2.4):
Figure 271283DEST_PATH_IMAGE008
Figure 409003DEST_PATH_IMAGE009
is in the indexfFirst toiThe technical service scenario adoptsjThe result after the normalization of the shared energy storage;
calculating the difference between each evaluation index and the optimal and worst indexes by adopting a formula (2.5) and a formula (2.6):
Figure 871208DEST_PATH_IMAGE010
Figure 359958DEST_PATH_IMAGE011
Figure 565812DEST_PATH_IMAGE012
is a firstiThe technical service scenario adoptsjThe difference between the individual shared energy storage and the best case, ω f Is an indexfThe weight of (b) can be obtained by means of expert scoring,
Figure 558039DEST_PATH_IMAGE013
for the index after standardizationfFirst toiThe technical service scenario adoptsjThe maximum index value of the shared energy storage is obtained,
Figure 456725DEST_PATH_IMAGE014
for the index after standardizationfFirst toiThe technical service scenario adoptsjThe minimum index value of the shared energy storage is,
Figure 432771DEST_PATH_IMAGE015
is as followsiThe technical service scenario adoptsjThe difference between the shared energy storage and the worst case;
evaluation of the second place using the formula (2.7)iThe technical service scenario adoptsjThe closeness of each shared energy storage to the optimal solution:
Figure 176736DEST_PATH_IMAGE016
D i j , is a firstiThe technical service scenario adoptsjThe proximity of the shared energy storage to the optimal solution;
using equation (2.8) to calculateiThe technical service scenario adoptsjCost of each shared energy storage completion task:
Figure 289048DEST_PATH_IMAGE017
Figure 358636DEST_PATH_IMAGE018
is as followsiThe technical service scenario adoptsjThe cost of the shared stored energy to complete the task,D i_max is as followsiThe maximum value of the closeness degree of each shared energy storage and the optimal scheme is adopted in each technical service scene;
step 2.2 ifjThe actual available power of the shared energy storage is less than that of the secondiPower required for energy storage of individual technical service scenarios orjThe actual available capacity of the shared energy storage is less than that of the secondiThe capacity of energy storage required by the technical service scene isjA shared energy storage pairiThe technical service scenario can not be completed ifjThe actual available power and capacity of the shared energy storage are more than or equal toiThe power and capacity of the stored energy required by the individual technical service scenariojA shared energy storage pairiThe technical service scene can be completed, and the completion cost is obtained by the cost model in the step 2.1, so that the relation between the shared energy storage and the technical service scene is obtained;
and 3, step 3: calculating a maximum matching scheme of the shared energy storage and technical service scenes through a maximum matching algorithm and calculating total cost based on the shared energy storage supply capacity and the technical service scene demand level;
and 4, step 4: and optimizing an optimal energy storage residual capacity distribution scheme through an optimization algorithm to obtain a scheme with the minimum total cost in the maximum matching schemes in the shared energy storage and technical service scenes.
2. The method for allocating the remaining energy storage capacity suitable for the multi-scenario application as claimed in claim 1, wherein the specific process in step 3 is as follows:
step 3.1 Each technical service scenario isX i ，i=1,2,…,nEach sharing the stored energy asY j ，j=1,2,…,mInitially, a match between the shared energy storage and technical service scenarios is establishedMAInitial state ofMA= Φ, Φ being expressed as empty set, i.e. as energy storage and technical service yardMatching scenes with each other in a space mode;
step 3.2 if technical service scenario setXEach point in (1) isMAThe saturation point of (B) is not presentMAStep 3.5 is carried out;
step 3.3 inXGet one not yet trying to expandMASaturation pointX p If such a point does not exist in step 3.5, useURepresents fromX i Starting fromMACan extend the pathpIn thatXThe vertex in (2) is selected,WrepresentpIn a shared energy storage setYThe initial value of the vertex in (1) is:U={X p }, W=Φ；
step 3.4 finding the slaveX p Starting fromMAIf there is an extensive routeUThe medium corresponding element can be corresponding to the walking pathYIs equal toWA middle element, thenpNot expandable, i.e. not present fromX i Starting an extendable path, go to step 3.2 ifUThe medium corresponding element can be corresponding to the walking pathYIs not equal toWMiddle element, get correspondingYDoes not belong toWOf medium elementsY j If, ifY j Is unsaturated, thenX i ToY j Of (2) apIs an extendable path, then is extendedMA=MA∪p- MA∩pGo to step 3.2, ifY j Saturation, then existenceX i Is given byX i ，Y j )∈MANeed to continue to expandpLet us orderU=U∪{X i },W= W∪{ Y j Fourthly, turning to the step 3.4;
step 3.5, all the unsaturated points in the technical service scene are tried to be expanded to obtain the maximum matching;
step 3.6 sums each path cost in the maximum match, calculates the cost of the maximum match.
3. The method for allocating the energy storage residual capacity suitable for the multi-scenario application as claimed in claim 2, wherein the step 4 adopts a genetic algorithm, and the specific process is as follows:
step 4.1 initialize the crossover rateP c And the rate of variationP m And maximum number of iterationsGSetting the sequence of technical service scenes, defining the form and number of coding strings corresponding to the sequence of the technical service scenes, wherein the bit number of the coding strings is equal to the number of the technical service scenesnIn the first placegIn the second iteration, the code string is
Figure 556399DEST_PATH_IMAGE019
，
Figure 104055DEST_PATH_IMAGE020
Randomly taking 1 tonIs given to the number of the one or more,
Figure 70874DEST_PATH_IMAGE021
randomly taking 1 tonExcept for one of the remaining numbers of the previous taken number,
Figure 311362DEST_PATH_IMAGE022
randomly taking 1 tonExcept for one of the remaining numbers of the first two taken numbers,
Figure 996421DEST_PATH_IMAGE023
taking the last remaining number, and generating randomlyMCoding string corresponding to sequence of individual technical service scenarios
Figure 79259DEST_PATH_IMAGE024
，w=1,2,…,MThe number of initialization iterations is 0, i.e.g=0；
Step 4.2, the total cost of the maximum matching of the shared energy storage and the technical service scene obtained in the step 3 is used as a target function of the genetic algorithm to calculateMThe objective function value corresponding to each code string;
step 4.3 according to the objective function value corresponding to the current coding string, the coding bit value of each coding string is subjected to selection operation, cross operation and variation operation, and the iteration times are increased1, i.e. thatg=g+1；
Step 4.4 judging whether the current iteration number reaches the maximum iteration numberGIf so, the calculation is finished, the sequence of the technical service scene corresponding to the code string with the lowest target function of the genetic algorithm corresponding to the code string of the last generation is taken as a final result, the maximum matching result is the actually selected scheme, and otherwise, the step 4.2 is returned to.
4. The method for allocating the remaining energy storage capacity suitable for the multi-scenario application as claimed in claim 3, wherein a genetic algorithm is adopted in step 4, and the specific process in step 4.3 is as follows:
step 4.3.1, according to the objective function value corresponding to the coding string, carrying out 1 to the coding string from low to high according to the objective functionMIs ordered askCode string selection rate ofP s =(M-k+1)/ M；
Step 4.3.2 on the code string resulting from the selection operation
Figure 166164DEST_PATH_IMAGE025
，w=1,2,…,MPerforming a crossover operation to generate a new code string, in the second placegIn the sub-iteration, the process is repeated,
Figure 577554DEST_PATH_IMAGE026
，
Figure 749909DEST_PATH_IMAGE027
and is provided with
Figure 639368DEST_PATH_IMAGE028
First, oftA code string
Figure 580779DEST_PATH_IMAGE029
And a first step ofaA code string
Figure 428649DEST_PATH_IMAGE030
The cross operation and intersection occur at any random pointIf the coded bit value before the cross point is the same as the coded bit value after the cross point, randomly taking 1 to 1 from the first same numbernExcept for the coded bit value after the cross point and the coded bit value before and after the cross point, and the same number has different final values to generate a new coded string;
step 4.3.3 pairs of encoded strings generated after the crossover operation
Figure 822722DEST_PATH_IMAGE031
，w=1,2,…,MPerforming mutation operation to generate new code stringgIn the sub-iteration of the process,
Figure 515871DEST_PATH_IMAGE032
，
Figure 311789DEST_PATH_IMAGE033
of 1 atsA code string
Figure 64981DEST_PATH_IMAGE034
To (1) abGenerating a code string by performing a mutation operation on each code bit, converting the generated code string, andbthe numerical variation of each coding bit is 1 tonBecomes the value of the encoded bit of the number originally changed tobEncoding the original value of the bit, increasing the number of iterations by 1, i.e.g=g+1。
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111680816A (en) * 2020-04-21 2020-09-18 中国电力科学研究院有限公司 Energy storage system operation method and system for providing multiple services
CN113471990A (en) * 2020-01-03 2021-10-01 浙江大学台州研究院 Energy storage multi-scene application cooperative control method
CN113657739A (en) * 2021-08-06 2021-11-16 国网浙江嘉善县供电有限公司 Method for evaluating quantitative energy storage under multiple scenes
WO2022094746A1 (en) * 2020-11-03 2022-05-12 北京洛必德科技有限公司 Multi-robot multi-task collaborative working method, and server

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10903652B2 (en) * 2019-02-13 2021-01-26 Abb Power Grids Switzerland Ag Control architectures for power distribution networks with distributed energy resource
CN110138612B (en) * 2019-05-15 2020-09-01 福州大学 Cloud software service resource allocation method based on QoS model self-correction
CN113098040B (en) * 2021-04-09 2023-07-11 国网新疆电力有限公司经济技术研究院 Power grid side energy storage capacity optimal configuration method for obtaining multi-scene benefits
CN114256836B (en) * 2021-12-13 2023-07-04 国网青海省电力公司清洁能源发展研究院 Capacity optimization configuration method for shared energy storage of new energy power station
CN114529220A (en) * 2022-03-07 2022-05-24 华北电力大学 Multi-station fusion economic dispatching method considering energy storage dynamic dispatching capacity

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113471990A (en) * 2020-01-03 2021-10-01 浙江大学台州研究院 Energy storage multi-scene application cooperative control method
CN111680816A (en) * 2020-04-21 2020-09-18 中国电力科学研究院有限公司 Energy storage system operation method and system for providing multiple services
WO2022094746A1 (en) * 2020-11-03 2022-05-12 北京洛必德科技有限公司 Multi-robot multi-task collaborative working method, and server
CN113657739A (en) * 2021-08-06 2021-11-16 国网浙江嘉善县供电有限公司 Method for evaluating quantitative energy storage under multiple scenes

Non-Patent Citations (8)

* Cited by examiner, † Cited by third party
Title
《碳中和目标下移动式储能系统关键技术》;李建林，张则栋等;《储能科学与技术》;20211027;全文 *
application of improved particle swarm optimization algorithm in the location and capacity determination of distributed generation;chunxue wen，qi xiong等;《2020 23rd international conference on electrical machines and system》;20201222;全文 *
Energy management and optimal storage sizing for a shared community ：a multi-stage stochastic programming approach;Faeza Hafiz等;《Applied Energy》;20190215;全文 *
具有多种形式信息的指派问题的求解方法;刘洋等;《系统工程》;20080528(第05期);全文 *
基于共享储能电站的工业用户日前优化经济调度;李淋等;《电力建设》;20200501(第05期);全文 *
联合作战目标协同模型构建与求解方法;张宪等;《指挥控制与仿真》;20161215(第06期);全文 *
配网及光储微网储能系统配置优化策略;李建林，谭宇良等;《高压电技术》;20210220;全文 *
面向多应用场景的储能系统优化配置方法;章姝俊，钱啸等;《浙江电力》;20220531;全文 *

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