CN114039351A - Energy storage capacity configuration method and device - Google Patents

Energy storage capacity configuration method and device Download PDF

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CN114039351A
CN114039351A CN202210019815.9A CN202210019815A CN114039351A CN 114039351 A CN114039351 A CN 114039351A CN 202210019815 A CN202210019815 A CN 202210019815A CN 114039351 A CN114039351 A CN 114039351A
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capacity
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CN114039351B (en
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熊俊杰
饶臻
徐青山
夏元兴
赵伟哲
曾伟
李佳
何伟
周仕豪
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State Grid Corp of China SGCC
Southeast University
Electric Power Research Institute of State Grid Jiangxi Electric Power Co Ltd
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Southeast University
Electric Power Research Institute of State Grid Jiangxi Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • G—PHYSICS
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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Abstract

The invention discloses an energy storage capacity configuration method and device, wherein the method comprises the steps of constructing a utility objective function of a user according to acquired user load data, two power generation prices of a local power grid, an upper limit of transmission capacity of the power grid and energy storage parameters; according to the maximization principle of the income after a user purchases the cloud energy storage service, the cloud energy storage total capacity is optimally divided into target functions, and the cloud energy storage total capacity is divided based on a preset cake cutting game model, so that a pareto optimal division scheme is achieved; and solving the pareto optimal division scheme based on the variational inequality of the hyperplane projection to obtain an optimal solution of cloud energy storage total capacity division. The preset cake cutting game model is used for describing the division of the cloud energy storage total amount, so that the economic benefits of users participating in the cloud energy storage market can be maximized under the condition that the cloud energy storage total amount is not exceeded.

Description

Energy storage capacity configuration method and device
Technical Field
The invention belongs to the technical field of cloud energy storage capacity sharing, and particularly relates to an energy storage capacity configuration method and device.
Background
In recent years, with the continuous development of distributed energy storage technologies, a cloud energy storage trading mode of 'wholesale market-energy storage aggregator-user' based on a large number of distributed energy storage aggregation technologies is gradually popular. The cloud energy storage transaction form allows the energy user at the tail end not to configure energy storage locally, but to purchase energy storage service from the energy storage aggregator, so that load is subjected to peak clipping and valley filling, the short-time electricity utilization peak value of the user is reduced, and the electricity utilization efficiency of the user is improved.
At present, end users in the same region have certain simultaneity when purchasing energy storage services, so that the energy storage capacity of a cloud energy storage aggregator is difficult to simultaneously support the energy storage of all users for use.
Disclosure of Invention
The present invention provides a method and an apparatus for configuring energy storage capacity, which are used to solve at least one of the above technical problems.
In a first aspect, the present invention provides a method for configuring energy storage capacity, including: constructing a utility objective function of the user according to the acquired user load data, the two power generation prices of the local power grid, the upper limit of the transmission capacity of the power grid and the energy storage parameters, wherein the expression of the utility objective function of the user is as follows:
Figure 794066DEST_PATH_IMAGE001
，
in the formula (I), the compound is shown in the specification,
Figure 830155DEST_PATH_IMAGE002
the maximized profit after the user purchases the cloud energy storage service,
Figure 862833DEST_PATH_IMAGE003
for the purpose of the overall power saving benefit,
Figure 735105DEST_PATH_IMAGE004
earnings for peak-valley arbitrage of local energy storage,
Figure 777011DEST_PATH_IMAGE005
earnings for peak-to-valley arbitrage of purchased cloud energy storage capacity,
Figure 402027DEST_PATH_IMAGE006
the cost corresponding to the peak load reduced after the cloud energy storage is purchased for the user,
Figure 120453DEST_PATH_IMAGE007
in order to save the gain in the main transformer capacity,
Figure 932551DEST_PATH_IMAGE008
the return revenue of stored energy configured locally for the user,
Figure 525950DEST_PATH_IMAGE009
in order to be the total investment of the user,
Figure 474315DEST_PATH_IMAGE010
the investment in energy storage is configured locally,
Figure 160380DEST_PATH_IMAGE011
the investment to purchase services for cloud energy storage,
Figure 194195DEST_PATH_IMAGE012
the cost of maintaining local energy storage for the user,
Figure 640220DEST_PATH_IMAGE013
in order to achieve the rate of cash-out,
Figure 928244DEST_PATH_IMAGE014
in order to increase the inflation rate of the cargo,
Figure 4784DEST_PATH_IMAGE015
for the market period of the cloud energy storage transaction,
Figure 775163DEST_PATH_IMAGE016
in order to purchase the revenue obtained from the cloud energy storage service,
Figure 962562DEST_PATH_IMAGE017
in order to delay the benefits of transformer investment by utilizing local energy storage,
Figure 633321DEST_PATH_IMAGE018
in order to delay the income of transformer investment by utilizing cloud energy storage,
Figure 880763DEST_PATH_IMAGE019
for the maintenance cost per unit capacity of local energy storage,
Figure 404017DEST_PATH_IMAGE020
the rated power for the local energy storage is,
Figure 395107DEST_PATH_IMAGE021
the run-time of the stored energy locally,
Figure 923303DEST_PATH_IMAGE022
earnings for local energy storage peak-valley arbitrage,
Figure 341646DEST_PATH_IMAGE023
the reduced demand cost after load peaks is cut for local energy storage,
Figure 24300DEST_PATH_IMAGE024
a recovery factor for local energy storage cost;
according to the maximization principle of the income after a user purchases cloud energy storage service, the optimal cloud energy storage total capacity division is taken as a target function, the cloud energy storage total capacity is divided based on a preset cake cutting game model, so that a pareto optimal division scheme is achieved, the optimal cloud energy storage total capacity division comprises the minimized variance between transaction prices, and the expression of the variance between the minimized transaction prices is as follows:
Figure 615818DEST_PATH_IMAGE025
，
in the formula (I), the compound is shown in the specification,
Figure 464432DEST_PATH_IMAGE026
for the trade price variance of the cloud energy storage unit capacity,
Figure 37365DEST_PATH_IMAGE027
a trade price variance for cloud energy storage unit power;
the expression of the pareto optimal division scheme is as follows:
Figure 817102DEST_PATH_IMAGE028
，
in the formula (I), the compound is shown in the specification,
Figure 884415DEST_PATH_IMAGE029
for the utility under the k1 approach,
Figure 918361DEST_PATH_IMAGE030
for the optimal partitioning strategy corresponding to the k1 scheme,
Figure 678507DEST_PATH_IMAGE031
for the partitioning strategy corresponding to the k1 scheme,
Figure 679961DEST_PATH_IMAGE032
for the utility under the k2 approach,
Figure 596970DEST_PATH_IMAGE033
for the optimal partitioning strategy corresponding to the k2 scheme,
Figure 672373DEST_PATH_IMAGE034
for the partitioning strategy corresponding to the k2 scheme,
Figure 400158DEST_PATH_IMAGE035
for the total number of the schemes,
Figure 636711DEST_PATH_IMAGE036
an optimal division scheme is adopted;
solving the pareto optimal division scheme based on a variational inequality of hyperplane projection to obtain an optimal solution of cloud energy storage total capacity division, wherein the expression of the optimal solution is as follows:
Figure 45827DEST_PATH_IMAGE037
，
in the formula (I), the compound is shown in the specification,
Figure 303633DEST_PATH_IMAGE038
for the set of optimal partitioning strategies for schemes other than the k-scheme,
Figure 451586DEST_PATH_IMAGE039
for the partitioning strategy corresponding to the k scheme,
Figure 99736DEST_PATH_IMAGE040
in order to be an index to the schema,
Figure 374860DEST_PATH_IMAGE035
for the total number of the schemes,
Figure 237905DEST_PATH_IMAGE041
for the set of policies of all of the schemes,
Figure 245175DEST_PATH_IMAGE042
for the proportion of each of the schemes,
Figure 708517DEST_PATH_IMAGE043
for the total capacity of the energy stored by the cloud,
Figure 708703DEST_PATH_IMAGE044
for the optimal partitioning strategy corresponding to the k scheme,
Figure 675522DEST_PATH_IMAGE045
is as follows
Figure 916011DEST_PATH_IMAGE046
Utility objective function of individual user.
In a second aspect, the present invention provides an energy storage capacity configuration apparatus, including: the construction module is configured to construct a utility objective function of the user according to the acquired user load data, the two power generation rates of the local power grid, the upper limit of the transmission capacity of the power grid and the energy storage parameters, wherein the expression of the utility objective function of the user is as follows:
Figure 552135DEST_PATH_IMAGE001
，
in the formula (I), the compound is shown in the specification,
Figure 903482DEST_PATH_IMAGE002
the maximized profit after the user purchases the cloud energy storage service,
Figure 990387DEST_PATH_IMAGE003
for the purpose of the overall power saving benefit,
Figure 854306DEST_PATH_IMAGE004
earnings for peak-valley arbitrage of local energy storage,
Figure 26662DEST_PATH_IMAGE005
earnings for peak-to-valley arbitrage of purchased cloud energy storage capacity,
Figure 870115DEST_PATH_IMAGE006
the cost corresponding to the peak load reduced after the cloud energy storage is purchased for the user,
Figure 77105DEST_PATH_IMAGE007
in order to save the gain in the main transformer capacity,
Figure 597080DEST_PATH_IMAGE008
the return revenue of stored energy configured locally for the user,
Figure 505999DEST_PATH_IMAGE009
in order to be the total investment of the user,
Figure 402411DEST_PATH_IMAGE010
the investment in energy storage is configured locally,
Figure 463907DEST_PATH_IMAGE011
the investment to purchase services for cloud energy storage,
Figure 168165DEST_PATH_IMAGE012
the cost of maintaining local energy storage for the user,
Figure 315112DEST_PATH_IMAGE013
in order to achieve the rate of cash-out,
Figure 998904DEST_PATH_IMAGE014
in order to increase the inflation rate of the cargo,
Figure 914907DEST_PATH_IMAGE015
for the market period of the cloud energy storage transaction,
Figure 42263DEST_PATH_IMAGE016
in order to purchase the revenue obtained from the cloud energy storage service,
Figure 161660DEST_PATH_IMAGE017
in order to delay the benefits of transformer investment by utilizing local energy storage,
Figure 462191DEST_PATH_IMAGE018
in order to delay the income of transformer investment by utilizing cloud energy storage,
Figure 170384DEST_PATH_IMAGE019
for the maintenance cost per unit capacity of local energy storage,
Figure 780226DEST_PATH_IMAGE020
the rated power for the local energy storage is,
Figure 839449DEST_PATH_IMAGE021
the run-time of the stored energy locally,
Figure 678092DEST_PATH_IMAGE022
earnings for local energy storage peak-valley arbitrage,
Figure 590111DEST_PATH_IMAGE023
the reduced demand cost after load peaks is cut for local energy storage,
Figure 121586DEST_PATH_IMAGE024
a recovery factor for local energy storage cost;
the dividing module is configured to divide the cloud energy storage total capacity based on a preset cake cutting game model by taking the optimal cloud energy storage total capacity division as a target function according to a maximization principle of the income of a user after the user purchases the cloud energy storage service, so that a pareto optimal dividing scheme is achieved, the optimal cloud energy storage total capacity division comprises the minimization of the variance between transaction prices, and the expression of the variance between the transaction prices is as follows:
Figure 651794DEST_PATH_IMAGE025
，
in the formula (I), the compound is shown in the specification,
Figure 559707DEST_PATH_IMAGE026
for the trade price variance of the cloud energy storage unit capacity,
Figure 242492DEST_PATH_IMAGE027
a trade price variance for cloud energy storage unit power;
the expression of the pareto optimal division scheme is as follows:
Figure 695601DEST_PATH_IMAGE028
，
in the formula (I), the compound is shown in the specification,
Figure 463837DEST_PATH_IMAGE029
for the utility under the k1 approach,
Figure 644283DEST_PATH_IMAGE030
for the optimal partitioning strategy corresponding to the k1 scheme,
Figure 962000DEST_PATH_IMAGE031
for the partitioning strategy corresponding to the k1 scheme,
Figure 772961DEST_PATH_IMAGE032
for the utility under the k2 approach,
Figure 838613DEST_PATH_IMAGE033
for the optimal partitioning strategy corresponding to the k2 scheme,
Figure 229274DEST_PATH_IMAGE034
for the partitioning strategy corresponding to the k2 scheme,
Figure 401498DEST_PATH_IMAGE035
for the total number of the schemes,
Figure 445678DEST_PATH_IMAGE036
an optimal division scheme is adopted;
the solving module is configured to solve the pareto optimal division scheme based on a variational inequality of a hyperplane projection, so that an optimal solution of cloud energy storage total capacity division is obtained, and an expression of the optimal solution is as follows:
Figure 939238DEST_PATH_IMAGE037
，
in the formula (I), the compound is shown in the specification,
Figure 727066DEST_PATH_IMAGE038
for the set of optimal partitioning strategies for schemes other than the k-scheme,
Figure 504529DEST_PATH_IMAGE039
for the partitioning strategy corresponding to the k scheme,
Figure 906560DEST_PATH_IMAGE040
in order to be an index to the schema,
Figure 933422DEST_PATH_IMAGE035
for the total number of the schemes,
Figure 476006DEST_PATH_IMAGE041
for the set of policies of all of the schemes,
Figure 842396DEST_PATH_IMAGE042
for the proportion of each of the schemes,
Figure 228378DEST_PATH_IMAGE043
for the total capacity of the energy stored by the cloud,
Figure 460645DEST_PATH_IMAGE044
for the optimal partitioning strategy corresponding to the k scheme,
Figure 793537DEST_PATH_IMAGE045
is as follows
Figure 765167DEST_PATH_IMAGE046
Utility objective function of individual user.
In a third aspect, an electronic device is provided, comprising: the energy storage capacity configuration system comprises at least one processor and a memory which is connected with the at least one processor in a communication mode, wherein the memory stores instructions which can be executed by the at least one processor, and the instructions are executed by the at least one processor so as to enable the at least one processor to execute the steps of the energy storage capacity configuration method of any embodiment of the invention.
In a fourth aspect, the present invention also provides a computer-readable storage medium having stored thereon a computer program comprising program instructions which, when executed by a computer, cause the computer to perform the steps of the energy storage capacity configuration method of any of the embodiments of the present invention.
According to the energy storage capacity configuration method and device, the preset cake cutting game model is used for describing the division of the total cloud energy storage amount, the economic benefits of users participating in the cloud energy storage market can be maximized under the condition that the total cloud energy storage amount is not exceeded, the problems that in a scene that the distributed energy storage cost is high, the users configure large-scale distributed energy storage cost is too high, the profit is difficult to be built through peaks and valleys, and the cost is recovered by means of load peak clipping are solved, the corresponding game balance points can be efficiently and accurately calculated based on the solution algorithm of the variation inequality, and the method and device have high use value.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of a method for configuring energy storage capacity according to an embodiment of the present invention;
fig. 2 is a block diagram of an energy storage capacity configuration apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. 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 invention.
Referring to fig. 1, a flow chart of a method for configuring energy storage capacity according to the present application is shown.
As shown in fig. 1, the energy storage capacity configuration method specifically includes the following steps:
and S101, constructing a utility objective function of the user according to the acquired user load data, the two power generation prices of the local power grid, the upper limit of the transmission capacity of the power grid and the energy storage parameters.
In this embodiment, the user load data includes year-round load data of users in each community-level microgrid, the data acquisition interval is 15 minutes at the minimum, two electricity prices of the local power grid adopt electricity prices newly formulated in Jiangsu province, and the two electricity prices mainly include the electricity price and the peak electricity price, the upper limit of the transmission capacity of the power grid includes the upper limit of the transmission power of the tie line and the rated capacity of the main transformer on the side of the microgrid, and the energy storage parameters include the price of unit capacity of the storage battery energy, the price of unit power, the charge-discharge coefficient and the constraints of the state of charge.
Specifically, constructing the utility objective function of the user includes:
(1) in the two part of electricity prices established in Jiangsu province, the electricity price is the peak-valley time-of-use electricity price, so that a user can purchase cloud energy storage capacity to carry out peak clipping and valley filling, and the total electricity consumption expense is reduced under the condition of meeting the total electricity consumption requirement;
the expression of the income obtained by purchasing the cloud energy storage service is as follows:
Figure 322050DEST_PATH_IMAGE047
，
in the formula (I), the compound is shown in the specification,
Figure 323504DEST_PATH_IMAGE048
in order to purchase the revenue obtained from the cloud energy storage service,
Figure 178196DEST_PATH_IMAGE049
for all points in time in the time period,
Figure 581496DEST_PATH_IMAGE050
is a time of day
Figure 260346DEST_PATH_IMAGE051
The electricity price of the electric power,
Figure 483517DEST_PATH_IMAGE052
is a time of day
Figure 158211DEST_PATH_IMAGE051
The cloud stored energy discharge power of (a),
Figure 868547DEST_PATH_IMAGE053
is a time of day
Figure 767233DEST_PATH_IMAGE051
The cloud storage charging power is used for charging,
Figure 431695DEST_PATH_IMAGE054
the length of time of a single period.
(2) After the user purchases the cloud energy storage service, the expenditure of the peak electricity price can be reduced by reducing the load peak value, so that the expenditure of the total electricity cost is further reduced; because the peak electricity prices of the two electricity prices are charged according to the monthly fee, the peak load of each month needs to be counted to calculate the peak clipping profit;
the expression of the cost corresponding to the peak load reduced after the user purchases the cloud energy storage is as follows:
Figure 706819DEST_PATH_IMAGE055
，
in the formula (I), the compound is shown in the specification,
Figure 756814DEST_PATH_IMAGE056
the peak electricity price among the two electricity prices,
Figure 341248DEST_PATH_IMAGE057
the load peak clipping rate after the cloud energy storage with a certain capacity is purchased for the user,
Figure 273432DEST_PATH_IMAGE058
the monthly load peak.
(3) After a user purchases peak clipping of the cloud energy storage capacity, the capacity of the main transformer can be further reduced, and therefore the cost for configuring the main transformer is reduced;
the expression of the benefit of saving the capacity of the main transformer is
Figure 289930DEST_PATH_IMAGE059
，
Figure 270131DEST_PATH_IMAGE060
，
In the formula (I), the compound is shown in the specification,
Figure 448302DEST_PATH_IMAGE061
in order to reduce the life of the transformer,
Figure 398941DEST_PATH_IMAGE062
the proportion of the installation cost of the main transformer to the total equipment manufacturing cost,
Figure 202817DEST_PATH_IMAGE063
for the cost per unit capacity of the customer-side transformer,
Figure 492984DEST_PATH_IMAGE064
is the load factor of the transformer and is,
Figure 655107DEST_PATH_IMAGE065
in order to be the power factor of the transformer,
Figure 765145DEST_PATH_IMAGE066
to take advantage of the curtailment rate of the capacity of the main transformer of cloud energy storage,
Figure 185762DEST_PATH_IMAGE067
for the peak load of a certain year,
Figure 314124DEST_PATH_IMAGE068
the gain in transformer capacity saved for the time scale of the transformer. Wherein, because the service life of the transformer is longer and is inconsistent with the scale of cloud energy storage lease time, the inflation rate of the currency is considered
Figure 161994DEST_PATH_IMAGE069
And rate of cash on demand
Figure 507132DEST_PATH_IMAGE013
By converting the life of the transformer
Figure 465860DEST_PATH_IMAGE070
Market period with cloud energy storage trading
Figure 465040DEST_PATH_IMAGE071
Therefore, the service life of the transformer is longer, and the time constant of the cloud energy storage renting time is unified.
(4) The user can purchase the capacity of the cloud energy storage for load peak clipping and valley filling, and also can configure the local distributed energy storage as a standby when the capacity of the cloud energy storage is insufficient, and the cost of the cloud energy storage and the local energy storage can be directly expressed as the multiple of the energy storage capacity and the rated power;
the expressions of the investment of the local configuration energy storage and the investment of the cloud energy storage purchasing service are respectively as follows:
Figure 733080DEST_PATH_IMAGE072
，
in the formula (I), the compound is shown in the specification,
Figure 552131DEST_PATH_IMAGE073
respectively is the cost coefficient of the unit capacity of the cloud energy storage distributed type energy storage, the cost coefficient of the unit capacity of the local distributed type energy storage, the cost coefficient of the unit power of the cloud energy storage distributed type energy storage and the cost coefficient of the unit power of the local distributed type energy storage,
Figure 48971DEST_PATH_IMAGE074
there is an upper limit to the total investment of the user in energy storage,
Figure 715707DEST_PATH_IMAGE075
the capacity of the cloud energy storage is purchased for the user,
Figure 843063DEST_PATH_IMAGE076
the power of the cloud energy storage is purchased for the user,
Figure 477307DEST_PATH_IMAGE077
the maximum capacity for storing energy locally,
Figure 964789DEST_PATH_IMAGE078
rated power for local energy storage.
(5) The capacity of the user for purchasing the cloud energy storage is influenced by the cloud energy storage aggregator, the higher the pricing of the aggregator is, the less the capacity purchased by the user is, the lower the pricing is, and the larger the capacity purchased by the user is. The transaction model is as follows:
Figure 735299DEST_PATH_IMAGE079
，
in the formula (I), the compound is shown in the specification,
Figure 781359DEST_PATH_IMAGE080
a price sensitivity coefficient for a cloud energy storage purchase,
Figure 637319DEST_PATH_IMAGE081
the maximum cloud energy storage capacity and the maximum cloud energy storage power which can be purchased by the user.
(6) Because the cloud energy storage aggregator aggregates a large amount of distributed energy storage, the users installed with the distributed energy storage can participate in the cloud energy storage transaction when the cloud energy storage price is high so as to obtain a profit, and the model participating in the transaction can be expressed as follows in a unified manner with the above sensitivity transaction:
Figure 475962DEST_PATH_IMAGE082
，
in the formula (I), the compound is shown in the specification,
Figure 553509DEST_PATH_IMAGE083
the capacity of the cloud energy storage is purchased for the user, when the price of the cloud energy storage is too high,
Figure 84984DEST_PATH_IMAGE084
namely, the user sells the distributed energy storage for profit,
Figure 365924DEST_PATH_IMAGE085
the power of the cloud energy storage is purchased for the user, when the price of the cloud energy storage is too high,
Figure 758990DEST_PATH_IMAGE086
i.e., the user sells power for his distributed stored energy.
(7) Therefore, the total utility model of the user participating in the market can combine the distributed energy storage configuration cost, the cloud energy storage purchase cost and the benefit of using the energy storage;
the expression of the utility objective function of the user is as follows:
Figure 238513DEST_PATH_IMAGE001
，
in the formula (I), the compound is shown in the specification,
Figure 878573DEST_PATH_IMAGE002
the maximized profit after the user purchases the cloud energy storage service,
Figure 958393DEST_PATH_IMAGE003
for the purpose of the overall power saving benefit,
Figure 138839DEST_PATH_IMAGE004
earnings for peak-valley arbitrage of local energy storage,
Figure 941710DEST_PATH_IMAGE005
earnings for peak-to-valley arbitrage of purchased cloud energy storage capacity,
Figure 562791DEST_PATH_IMAGE006
the cost corresponding to the peak load reduced after the cloud energy storage is purchased for the user,
Figure 552743DEST_PATH_IMAGE007
in order to save the gain in the main transformer capacity,
Figure 802459DEST_PATH_IMAGE008
the return revenue of stored energy configured locally for the user,
Figure 709104DEST_PATH_IMAGE009
in order to be the total investment of the user,
Figure 753284DEST_PATH_IMAGE010
the investment in energy storage is configured locally,
Figure 981265DEST_PATH_IMAGE011
the investment to purchase services for cloud energy storage,
Figure 503513DEST_PATH_IMAGE012
the cost of maintaining local energy storage for the user,
Figure 77714DEST_PATH_IMAGE013
in order to achieve the rate of cash-out,
Figure 479745DEST_PATH_IMAGE014
in order to increase the inflation rate of the cargo,
Figure 709869DEST_PATH_IMAGE015
for the market period of the cloud energy storage transaction,
Figure 744732DEST_PATH_IMAGE016
in order to purchase the revenue obtained from the cloud energy storage service,
Figure 642281DEST_PATH_IMAGE017
in order to delay the benefits of transformer investment by utilizing local energy storage,
Figure 277530DEST_PATH_IMAGE018
in order to delay the income of transformer investment by utilizing cloud energy storage,
Figure 729371DEST_PATH_IMAGE019
for the maintenance cost per unit capacity of local energy storage,
Figure 593422DEST_PATH_IMAGE020
the rated power for the local energy storage is,
Figure 892948DEST_PATH_IMAGE021
the run-time of the stored energy locally,
Figure 387514DEST_PATH_IMAGE022
earnings for local energy storage peak-valley arbitrage,
Figure 388968DEST_PATH_IMAGE023
the reduced demand cost after load peaks is cut for local energy storage,
Figure 305977DEST_PATH_IMAGE024
coefficient of recovery for local energy storage cost.
The constraints are as follows:
Figure 646960DEST_PATH_IMAGE087
，
Figure 374745DEST_PATH_IMAGE088
，
Figure 548981DEST_PATH_IMAGE089
，
Figure 285992DEST_PATH_IMAGE090
，
Figure 278219DEST_PATH_IMAGE091
，
Figure 786692DEST_PATH_IMAGE092
，
Figure 700421DEST_PATH_IMAGE093
，
Figure 975545DEST_PATH_IMAGE094
，
in the formula (I), the compound is shown in the specification,
Figure 274808DEST_PATH_IMAGE095
for the purpose of the grid-side power,
Figure 609975DEST_PATH_IMAGE096
in order to store the adjusted load,
Figure 493223DEST_PATH_IMAGE097
the load is the original load,
Figure 572038DEST_PATH_IMAGE098
the power stored for the local energy is,
Figure 476540DEST_PATH_IMAGE099
the power that is stored for the cloud is,
Figure 435138DEST_PATH_IMAGE100
is as follows
Figure 323459DEST_PATH_IMAGE101
The original load on each time node is,
Figure 425539DEST_PATH_IMAGE102
is composed of
Figure 512443DEST_PATH_IMAGE101
The charging power of the local distributed energy storage at the moment,
Figure 127095DEST_PATH_IMAGE103
is composed of
Figure 548718DEST_PATH_IMAGE101
The discharge power of the local distributed energy storage is at the moment,
Figure 907018DEST_PATH_IMAGE104
is composed of
Figure 582850DEST_PATH_IMAGE101
The charging power of the local energy storage is at the moment,
Figure 178523DEST_PATH_IMAGE105
is composed of
Figure 775858DEST_PATH_IMAGE101
The charging power of the cloud energy storage is at the moment,
Figure 469007DEST_PATH_IMAGE106
the peak clipping rate of the load is taken as the peak clipping rate,
Figure 779772DEST_PATH_IMAGE107
is the load peak value of the m-th month,
Figure 470647DEST_PATH_IMAGE108
the rated power for the local energy storage is,
Figure 617595DEST_PATH_IMAGE076
the power of the cloud energy storage is purchased for the user,
Figure 865168DEST_PATH_IMAGE109
is composed of
Figure 718854DEST_PATH_IMAGE101
The discharge state of the local energy storage is at the moment,
Figure 908527DEST_PATH_IMAGE110
is composed of
Figure 464142DEST_PATH_IMAGE101
The state of charge of the local energy store at the moment,
Figure 499094DEST_PATH_IMAGE111
is composed of
Figure 535184DEST_PATH_IMAGE101
The discharge state of the cloud energy storage at the moment,
Figure 581244DEST_PATH_IMAGE112
is composed of
Figure 437204DEST_PATH_IMAGE101
The charging state of the cloud energy storage is at the moment,
Figure 728377DEST_PATH_IMAGE113
is composed of
Figure 353393DEST_PATH_IMAGE101
State of charge of the local energy storage at the moment or
Figure 884869DEST_PATH_IMAGE101
The state of charge of the cloud energy storage at the moment,
Figure 916541DEST_PATH_IMAGE114
is composed of
Figure 558875DEST_PATH_IMAGE115
Time of dayState of charge of local energy storage or
Figure 241660DEST_PATH_IMAGE115
The state of charge of the cloud energy storage at the moment,
Figure 927725DEST_PATH_IMAGE116
in order to make the charging efficient,
Figure 758278DEST_PATH_IMAGE117
is composed of
Figure 141986DEST_PATH_IMAGE101
The charging power is charged at the moment,
Figure 489397DEST_PATH_IMAGE118
is composed of
Figure 97096DEST_PATH_IMAGE101
The power of the discharge is discharged at the moment,
Figure 352628DEST_PATH_IMAGE119
in order to achieve an efficient discharge,
Figure 851612DEST_PATH_IMAGE120
in order to be a step of time,
Figure 40147DEST_PATH_IMAGE121
is the upper limit of the capacity of the stored energy,
Figure 22010DEST_PATH_IMAGE122
in order to be in an initial state of charge,
Figure 577887DEST_PATH_IMAGE123
is the state of charge at the end of cycle D,
Figure 100136DEST_PATH_IMAGE124
the state of charge is the lowest state of charge,
Figure 612019DEST_PATH_IMAGE125
the state of charge is the highest state of charge,
Figure 76368DEST_PATH_IMAGE049
at all points in the time period.
And S102, dividing the total cloud energy storage capacity based on a preset cake cutting game model according to a maximization principle of earnings after a user purchases the cloud energy storage service and by taking the optimal division of the total cloud energy storage capacity as an objective function, so that a pareto optimal division scheme is achieved.
In this embodiment, the optimally dividing the cloud energy storage total capacity includes minimizing a variance between transaction prices, where an expression for minimizing the variance between transaction prices is:
Figure 306492DEST_PATH_IMAGE025
in the formula (I), wherein,
Figure 632431DEST_PATH_IMAGE026
for the trade price variance of the cloud energy storage unit capacity,
Figure 543362DEST_PATH_IMAGE027
a trade price variance for cloud energy storage unit power;
after the objective function of each user participating in cloud energy storage sharing and the cloud energy storage aggregator is determined, the cloud energy storage partitioning problem based on the cake cutting game model can be solved as follows:
(8) the cake cutting game model has the characteristics that:
Figure 132606DEST_PATH_IMAGE126
，
in the formula (I), the compound is shown in the specification,
Figure 646764DEST_PATH_IMAGE127
including both the cloud storage capacity and the power purchased by the user,
Figure 963345DEST_PATH_IMAGE128
for the proportion of each of the schemes,
Figure 512138DEST_PATH_IMAGE129
storing the total capacity for the cloud.
The partitioning scheme is complete when the sum of the purchase values of all users is exactly equal to the total capacity of the cloud energy storage aggregator. When the utility functions of all users meet the following conditions, the division scheme is socially optimal:
Figure 757437DEST_PATH_IMAGE130
，
in the formula (I), the compound is shown in the specification,
Figure 493311DEST_PATH_IMAGE131
for the utility under the k-scheme,
Figure 426632DEST_PATH_IMAGE132
purchasing capacity and power for the cloud energy storage service for the user,
Figure 16883DEST_PATH_IMAGE133
the strategy set of all the schemes is adopted, when the sum of the utilities of all the users is maximum, the cloud energy storage division scheme achieves social optimization,
Figure 479088DEST_PATH_IMAGE134
is the total number of schemes.
When no user improves the self benefit on the premise of not damaging the benefits of other users, the division scheme reaches the pareto optimal, and when no distribution scheme reaches the following conditions, the division scheme reaches the pareto optimal.
The expression of the pareto optimal division scheme is as follows:
Figure 967838DEST_PATH_IMAGE028
，
in the formula (I), the compound is shown in the specification,
Figure 124757DEST_PATH_IMAGE029
for the utility under the k1 approach,
Figure 382563DEST_PATH_IMAGE030
for the optimal partitioning strategy corresponding to the k1 scheme,
Figure 484511DEST_PATH_IMAGE031
for the partitioning strategy corresponding to the k1 scheme,
Figure 709825DEST_PATH_IMAGE032
for the utility under the k2 approach,
Figure 657052DEST_PATH_IMAGE033
for the optimal partitioning strategy corresponding to the k2 scheme,
Figure 34944DEST_PATH_IMAGE034
for the partitioning strategy corresponding to the k2 scheme,
Figure 792946DEST_PATH_IMAGE035
for the total number of the schemes,
Figure 256289DEST_PATH_IMAGE036
an optimal division scheme is adopted;
in the cake cutting game, when the dividing scheme of each user is complete and social optimal, the dividing scheme is pareto optimal.
(9) The cake-cutting game model can thus be expressed as follows:
Figure 803945DEST_PATH_IMAGE135
，
in the formula (I), the compound is shown in the specification,
Figure 223294DEST_PATH_IMAGE134
for the total number of the schemes,
Figure 932624DEST_PATH_IMAGE136
for the set of policies of all of the schemes,
Figure 123344DEST_PATH_IMAGE045
is the utility under the k scheme.
And S103, solving the pareto optimal division scheme based on the variational inequality of the hyperplane projection so as to obtain an optimal solution of cloud energy storage total capacity division.
In this embodiment, the existence of the cake-cutting game equilibrium point is first verified based on the variation inequality. Solving the cake cutting game model of the invention is to solve the corresponding variation inequality
Figure 740270DEST_PATH_IMAGE137
Wherein X is the negative gradient of the utility function, and the expression is as follows:
Figure 764858DEST_PATH_IMAGE138
，
in the formula (I), the compound is shown in the specification,
Figure 425515DEST_PATH_IMAGE139
is a function of
Figure 597871DEST_PATH_IMAGE140
To pair
Figure 690592DEST_PATH_IMAGE141
Of the gradient of (c).
As the principal formula of each order of the Jacobian matrix of the pseudo gradient of the objective function of the related users is positive, the Jacobian matrix is positive and X is a utility function which is monotonically increased, the game has unique equilibrium points. The solution of the inequality is the equilibrium point (optimal solution) of the cloud energy storage cake slicing game, and the expression of the optimal solution is as follows:
Figure 648314DEST_PATH_IMAGE142
，
in the formula (I), the compound is shown in the specification,
Figure 433868DEST_PATH_IMAGE038
for the set of optimal partitioning strategies for schemes other than the k-scheme,
Figure 93519DEST_PATH_IMAGE039
for the partitioning strategy corresponding to the k scheme,
Figure 35936DEST_PATH_IMAGE040
in order to be an index to the schema,
Figure 769537DEST_PATH_IMAGE035
for the total number of the schemes,
Figure 788309DEST_PATH_IMAGE041
for the set of policies of all of the schemes,
Figure 620742DEST_PATH_IMAGE042
for the proportion of each of the schemes,
Figure 117583DEST_PATH_IMAGE043
for the total capacity of the energy stored by the cloud,
Figure 33586DEST_PATH_IMAGE045
is as follows
Figure 144630DEST_PATH_IMAGE046
Utility objective function of individual user.
In summary, the method of the application describes the division of the cloud energy storage total amount by using the preset cake cutting game model, can maximize the economic benefit of each user participating in the cloud energy storage market under the condition of ensuring that the cloud energy storage total amount is not exceeded, and solves the problems that in a scene with high distributed energy storage cost, the user self-configures large-scale distributed energy storage cost is too high, benefit is difficult to be accumulated through peaks and valleys, and the cost is recovered by means of load peak clipping, and the corresponding game equilibrium point can be efficiently and accurately calculated by a solution algorithm based on a variation inequality, so that the method has high use value.
Referring to fig. 2, a block diagram of an energy storage capacity configuration device according to the present application is shown.
As shown in fig. 2, the energy storage capacity configuration apparatus 200 includes a building module 210, a dividing module 220, and a solving module 230.
The constructing module 210 is configured to construct a utility objective function of a user according to the acquired user load data, two power generation rates of a local power grid, an upper limit of transmission capacity of the power grid, and an energy storage parameter, where an expression of the utility objective function of the user is:
Figure 778874DEST_PATH_IMAGE001
，
in the formula (I), the compound is shown in the specification,
Figure 767821DEST_PATH_IMAGE002
the maximized profit after the user purchases the cloud energy storage service,
Figure 538331DEST_PATH_IMAGE003
for the purpose of the overall power saving benefit,
Figure 633326DEST_PATH_IMAGE004
earnings for peak-valley arbitrage of local energy storage,
Figure 941816DEST_PATH_IMAGE005
earnings for peak-to-valley arbitrage of purchased cloud energy storage capacity,
Figure 46038DEST_PATH_IMAGE006
the cost corresponding to the peak load reduced after the cloud energy storage is purchased for the user,
Figure 405476DEST_PATH_IMAGE007
in order to save the gain in the main transformer capacity,
Figure 622437DEST_PATH_IMAGE008
the return revenue of stored energy configured locally for the user,
Figure 965694DEST_PATH_IMAGE009
in order to be the total investment of the user,
Figure 608027DEST_PATH_IMAGE010
the investment in energy storage is configured locally,
Figure 540080DEST_PATH_IMAGE011
the investment to purchase services for cloud energy storage,
Figure 242457DEST_PATH_IMAGE012
the cost of maintaining local energy storage for the user,
Figure 10693DEST_PATH_IMAGE013
in order to achieve the rate of cash-out,
Figure 941871DEST_PATH_IMAGE014
in order to increase the inflation rate of the cargo,
Figure 541480DEST_PATH_IMAGE015
for the market period of the cloud energy storage transaction,
Figure 352441DEST_PATH_IMAGE016
in order to purchase the revenue obtained from the cloud energy storage service,
Figure 653978DEST_PATH_IMAGE017
in order to delay the benefits of transformer investment by utilizing local energy storage,
Figure 638114DEST_PATH_IMAGE018
in order to delay the income of transformer investment by utilizing cloud energy storage,
Figure 92230DEST_PATH_IMAGE019
for the maintenance cost per unit capacity of local energy storage,
Figure 87474DEST_PATH_IMAGE020
the rated power for the local energy storage is,
Figure 830302DEST_PATH_IMAGE021
the run-time of the stored energy locally,
Figure 618129DEST_PATH_IMAGE022
earnings for local energy storage peak-valley arbitrage,
Figure 176019DEST_PATH_IMAGE023
the reduced demand cost after load peaks is cut for local energy storage,
Figure 594362DEST_PATH_IMAGE024
a recovery factor for local energy storage cost;
the dividing module 220 is configured to divide the cloud energy storage total capacity based on a preset cake-cutting game model according to a maximization principle of earnings after a user purchases the cloud energy storage service and by taking the optimal cloud energy storage total capacity division as an objective function, so that a pareto optimal dividing scheme is achieved, the optimal cloud energy storage total capacity division comprises the minimization of a variance between transaction prices, wherein an expression of the variance between the transaction prices is as follows:
Figure 575218DEST_PATH_IMAGE025
，
in the formula (I), the compound is shown in the specification,
Figure 901157DEST_PATH_IMAGE026
for the trade price variance of the cloud energy storage unit capacity,
Figure 267548DEST_PATH_IMAGE027
a trade price variance for cloud energy storage unit power;
the expression of the pareto optimal division scheme is as follows:
Figure 106060DEST_PATH_IMAGE028
，
in the formula (I), the compound is shown in the specification,
Figure 620218DEST_PATH_IMAGE029
for the utility under the k1 approach,
Figure 749848DEST_PATH_IMAGE030
for the optimal partitioning strategy corresponding to the k1 scheme,
Figure 718547DEST_PATH_IMAGE031
for the partitioning strategy corresponding to the k1 scheme,
Figure 744272DEST_PATH_IMAGE032
for the utility under the k2 approach,
Figure 729415DEST_PATH_IMAGE033
for the optimal partitioning strategy corresponding to the k2 scheme,
Figure 662735DEST_PATH_IMAGE034
for the partitioning strategy corresponding to the k2 scheme,
Figure 738139DEST_PATH_IMAGE035
for the total number of the schemes,
Figure 482235DEST_PATH_IMAGE036
an optimal division scheme is adopted;
the solving module 230 is configured to solve the pareto optimal division scheme based on a variational inequality of a hyperplane projection, so that an optimal solution for cloud energy storage total capacity division is obtained, where an expression of the optimal solution is:
Figure 908668DEST_PATH_IMAGE142
，
in the formula (I), the compound is shown in the specification,
Figure 380101DEST_PATH_IMAGE038
for the set of optimal partitioning strategies for schemes other than the k-scheme,
Figure 824858DEST_PATH_IMAGE039
for the partitioning strategy corresponding to the k scheme,
Figure 989123DEST_PATH_IMAGE040
in order to be an index to the schema,
Figure 385076DEST_PATH_IMAGE035
for the total number of the schemes,
Figure 660199DEST_PATH_IMAGE041
for the set of policies of all of the schemes,
Figure 975774DEST_PATH_IMAGE042
for the proportion of each of the schemes,
Figure 294629DEST_PATH_IMAGE043
storing the total capacity for the cloud.
It should be understood that the modules depicted in fig. 2 correspond to various steps in the method described with reference to fig. 1. Thus, the operations and features described above for the method and the corresponding technical effects are also applicable to the modules in fig. 2, and are not described again here.
In other embodiments, the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores computer-executable instructions, where the computer-executable instructions may perform the energy storage capacity configuration method in any of the above method embodiments;
as one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions configured to:
constructing a utility objective function of the user according to the acquired user load data, the two power generation prices of the local power grid, the upper limit of the transmission capacity of the power grid and the energy storage parameters;
according to the maximization principle of the income after a user purchases the cloud energy storage service, the cloud energy storage total capacity is optimally divided into target functions, and the cloud energy storage total capacity is divided based on a preset cake cutting game model, so that a pareto optimal division scheme is achieved;
and solving the pareto optimal division scheme based on the variational inequality of the hyperplane projection to obtain an optimal solution of cloud energy storage total capacity division.
The computer-readable storage medium may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the energy storage capacity configuration device, and the like. Further, the computer-readable storage medium may include high speed random access memory, and may also include memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the computer readable storage medium optionally includes memory located remotely from the processor, and these remote memories may be connected to the energy storage capacity configuration device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330, and the output device 340 may be connected by a bus or other means, such as the bus connection in fig. 3. The memory 320 is the computer-readable storage medium described above. The processor 310 executes various functional applications and data processing of the server by executing the nonvolatile software programs, instructions and modules stored in the memory 320, that is, the energy storage capacity configuration method of the above method embodiment is realized. The input device 330 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the energy storage capacity configuration device. The output device 340 may include a display device such as a display screen.
The electronic device can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
As an embodiment, the electronic device is applied to an energy storage capacity configuration apparatus, and is used for a client, and includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to:
constructing a utility objective function of the user according to the acquired user load data, the two power generation prices of the local power grid, the upper limit of the transmission capacity of the power grid and the energy storage parameters;
according to the maximization principle of the income after a user purchases the cloud energy storage service, the cloud energy storage total capacity is optimally divided into target functions, and the cloud energy storage total capacity is divided based on a preset cake cutting game model, so that a pareto optimal division scheme is achieved;
and solving the pareto optimal division scheme based on the variational inequality of the hyperplane projection to obtain an optimal solution of cloud energy storage total capacity division.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (7)

1. A method of energy storage capacity allocation, comprising:
constructing a utility objective function of the user according to the acquired user load data, the two power generation prices of the local power grid, the upper limit of the transmission capacity of the power grid and the energy storage parameters, wherein the expression of the utility objective function of the user is as follows:
Figure 239880DEST_PATH_IMAGE001
，
in the formula (I), the compound is shown in the specification,
Figure 228827DEST_PATH_IMAGE002
the maximized profit after the user purchases the cloud energy storage service,
Figure 999337DEST_PATH_IMAGE003
for the purpose of the overall power saving benefit,
Figure 546862DEST_PATH_IMAGE004
earnings for peak-valley arbitrage of local energy storage,
Figure 402822DEST_PATH_IMAGE005
earnings for peak-to-valley arbitrage of purchased cloud energy storage capacity,
Figure 710307DEST_PATH_IMAGE006
the cost corresponding to the peak load reduced after the cloud energy storage is purchased for the user,
Figure 286388DEST_PATH_IMAGE007
in order to save the gain in the main transformer capacity,
Figure 817864DEST_PATH_IMAGE008
the return revenue of stored energy configured locally for the user,
Figure 348071DEST_PATH_IMAGE009
in order to be the total investment of the user,
Figure 990405DEST_PATH_IMAGE010
the investment in energy storage is configured locally,
Figure 673190DEST_PATH_IMAGE011
the investment to purchase services for cloud energy storage,
Figure 126300DEST_PATH_IMAGE012
the cost of maintaining local energy storage for the user,
Figure 894535DEST_PATH_IMAGE013
in order to achieve the rate of cash-out,
Figure 340560DEST_PATH_IMAGE014
in order to increase the inflation rate of the cargo,
Figure 392699DEST_PATH_IMAGE015
for the market period of the cloud energy storage transaction,
Figure 265977DEST_PATH_IMAGE016
in order to purchase the revenue obtained from the cloud energy storage service,
Figure 52667DEST_PATH_IMAGE017
in order to delay the benefits of transformer investment by utilizing local energy storage,
Figure 253448DEST_PATH_IMAGE018
in order to delay the income of transformer investment by utilizing cloud energy storage,
Figure 910825DEST_PATH_IMAGE019
for the maintenance cost per unit capacity of local energy storage,
Figure 204272DEST_PATH_IMAGE020
the rated power for the local energy storage is,
Figure 681521DEST_PATH_IMAGE021
the run-time of the stored energy locally,
Figure 469349DEST_PATH_IMAGE022
earnings for local energy storage peak-valley arbitrage,
Figure 731965DEST_PATH_IMAGE023
the reduced demand cost after load peaks is cut for local energy storage,
Figure 681466DEST_PATH_IMAGE024
a recovery factor for local energy storage cost;
according to the maximization principle of the income after a user purchases cloud energy storage service, the optimal cloud energy storage total capacity division is taken as a target function, the cloud energy storage total capacity is divided based on a preset cake cutting game model, so that a pareto optimal division scheme is achieved, the optimal cloud energy storage total capacity division comprises the minimized variance between transaction prices, and the expression of the variance between the minimized transaction prices is as follows:
Figure 973908DEST_PATH_IMAGE025
，
in the formula (I), the compound is shown in the specification,
Figure 486797DEST_PATH_IMAGE026
for the trade price variance of the cloud energy storage unit capacity,
Figure 915505DEST_PATH_IMAGE027
a trade price variance for cloud energy storage unit power;
the expression of the pareto optimal division scheme is as follows:
Figure 301487DEST_PATH_IMAGE028
，
in the formula (I), the compound is shown in the specification,
Figure 501130DEST_PATH_IMAGE029
for the utility under the k1 approach,
Figure 630760DEST_PATH_IMAGE030
for the optimal partitioning strategy corresponding to the k1 scheme,
Figure 366504DEST_PATH_IMAGE031
for the partitioning strategy corresponding to the k1 scheme,
Figure 923387DEST_PATH_IMAGE032
for the utility under the k2 approach,
Figure 862525DEST_PATH_IMAGE033
for the optimal partitioning strategy corresponding to the k2 scheme,
Figure 546578DEST_PATH_IMAGE034
for the partitioning strategy corresponding to the k2 scheme,
Figure 684298DEST_PATH_IMAGE035
for the total number of the schemes,
Figure 615345DEST_PATH_IMAGE036
an optimal division scheme is adopted;
solving the pareto optimal division scheme based on a variational inequality of hyperplane projection to obtain an optimal solution of cloud energy storage total capacity division, wherein the expression of the optimal solution is as follows:
Figure 87784DEST_PATH_IMAGE037
，
in the formula (I), the compound is shown in the specification,
Figure 28058DEST_PATH_IMAGE038
for the set of optimal partitioning strategies for schemes other than the k-scheme,
Figure 20285DEST_PATH_IMAGE039
for the partitioning strategy corresponding to the k scheme,
Figure 870036DEST_PATH_IMAGE040
in order to be an index to the schema,
Figure 580503DEST_PATH_IMAGE035
for the total number of the schemes,
Figure 42577DEST_PATH_IMAGE041
for the set of policies of all of the schemes,
Figure 154889DEST_PATH_IMAGE042
for the proportion of each of the schemes,
Figure 427739DEST_PATH_IMAGE043
for the total capacity of the energy stored by the cloud,
Figure 110655DEST_PATH_IMAGE044
for the optimal partitioning strategy corresponding to the k scheme,
Figure 189470DEST_PATH_IMAGE045
is as follows
Figure 93972DEST_PATH_IMAGE046
Utility objective function of individual user.
2. The method according to claim 1, wherein the user load data comprises year-round load data of users in each community-level microgrid, the two-part power prices of the local power grid comprise local power prices and local peak power prices, the grid transmission capacity upper limit comprises a transmission power upper limit of a tie line and a rated capacity of a microgrid-side main transformer, and the energy storage parameters comprise a price per unit capacity of storage energy of the storage battery, a price per unit power, a charge-discharge coefficient and a constraint on a state of charge.
3. The method according to claim 1, wherein the expression of the profit obtained by purchasing the cloud energy storage service is as follows:
Figure 849307DEST_PATH_IMAGE047
，
in the formula (I), the compound is shown in the specification,
Figure 268787DEST_PATH_IMAGE048
for all points in time in the time period,
Figure 823396DEST_PATH_IMAGE049
is a time of day
Figure 658104DEST_PATH_IMAGE050
The electricity price of the electric power,
Figure 69493DEST_PATH_IMAGE051
is a time of day
Figure 179532DEST_PATH_IMAGE050
The cloud stored energy discharge power of (a),
Figure 849416DEST_PATH_IMAGE052
is a time of day
Figure 728511DEST_PATH_IMAGE050
The cloud storage charging power is used for charging,
Figure 576381DEST_PATH_IMAGE053
a length of time that is a single period of time;
the expression of the cost corresponding to the peak load reduced after the user purchases the cloud energy storage is as follows:
Figure 924448DEST_PATH_IMAGE054
，
in the formula (I), the compound is shown in the specification,
Figure 883177DEST_PATH_IMAGE055
the peak electricity price among the two electricity prices,
Figure 131624DEST_PATH_IMAGE056
the load peak clipping rate after the cloud energy storage with a certain capacity is purchased for the user,
Figure 884817DEST_PATH_IMAGE057
the monthly load peak value;
the expression for saving the yield of the main transformer capacity is as follows:
Figure 766185DEST_PATH_IMAGE058
，
Figure 214091DEST_PATH_IMAGE059
，
in the formula (I), the compound is shown in the specification,
Figure 130094DEST_PATH_IMAGE060
in order to reduce the life of the transformer,
Figure 54188DEST_PATH_IMAGE061
the proportion of the installation cost of the main transformer to the total equipment manufacturing cost,
Figure 609803DEST_PATH_IMAGE062
for the cost per unit capacity of the customer-side transformer,
Figure 910334DEST_PATH_IMAGE063
is the load factor of the transformer and is,
Figure 884106DEST_PATH_IMAGE064
in order to be the power factor of the transformer,
Figure 995413DEST_PATH_IMAGE065
to take advantage of the curtailment rate of the capacity of the main transformer of cloud energy storage,
Figure 789057DEST_PATH_IMAGE066
for the peak load of a certain year,
Figure 893279DEST_PATH_IMAGE067
a benefit of saved transformer capacity for the time scale of the transformer;
the expressions of the investment of the local configuration energy storage and the investment of the cloud energy storage purchasing service are respectively as follows:
Figure 970825DEST_PATH_IMAGE068
，
in the formula (I), the compound is shown in the specification,
Figure 502301DEST_PATH_IMAGE069
respectively is the cost coefficient of the unit capacity of the cloud energy storage distributed type energy storage, the cost coefficient of the unit capacity of the local distributed type energy storage, the cost coefficient of the unit power of the cloud energy storage distributed type energy storage and the cost coefficient of the unit power of the local distributed type energy storage,
Figure 554481DEST_PATH_IMAGE070
there is an upper limit to the total investment of the user in energy storage,
Figure 196815DEST_PATH_IMAGE071
the capacity of the cloud energy storage is purchased for the user,
Figure 879600DEST_PATH_IMAGE072
the power of the cloud energy storage is purchased for the user,
Figure 831244DEST_PATH_IMAGE073
the maximum capacity for storing energy locally,
Figure 599480DEST_PATH_IMAGE074
rated power for local energy storage.
4. The method for configuring energy storage capacity according to claim 1, wherein the preset cake-cutting game model has an expression:
Figure 45505DEST_PATH_IMAGE075
，
in the formula (I), the compound is shown in the specification,
Figure 333529DEST_PATH_IMAGE076
for the total number of the schemes,
Figure 941228DEST_PATH_IMAGE077
for the set of policies of all of the schemes,
Figure 259077DEST_PATH_IMAGE045
is as follows
Figure 695743DEST_PATH_IMAGE046
Utility objective function of individual user.
5. An energy storage capacity configuration device, comprising:
the construction module is configured to construct a utility objective function of the user according to the acquired user load data, the two power generation rates of the local power grid, the upper limit of the transmission capacity of the power grid and the energy storage parameters, wherein the expression of the utility objective function of the user is as follows:
Figure 149858DEST_PATH_IMAGE001
，
in the formula (I), the compound is shown in the specification,
Figure 879523DEST_PATH_IMAGE002
after the cloud energy storage service is purchased for the userThe benefit of the process is increased greatly,
Figure 684668DEST_PATH_IMAGE003
for the purpose of the overall power saving benefit,
Figure 410179DEST_PATH_IMAGE004
earnings for peak-valley arbitrage of local energy storage,
Figure 233647DEST_PATH_IMAGE005
earnings for peak-to-valley arbitrage of purchased cloud energy storage capacity,
Figure 386411DEST_PATH_IMAGE006
the cost corresponding to the peak load reduced after the cloud energy storage is purchased for the user,
Figure 678852DEST_PATH_IMAGE007
in order to save the gain in the main transformer capacity,
Figure 693207DEST_PATH_IMAGE008
the return revenue of stored energy configured locally for the user,
Figure 856335DEST_PATH_IMAGE009
in order to be the total investment of the user,
Figure 960426DEST_PATH_IMAGE010
the investment in energy storage is configured locally,
Figure 209005DEST_PATH_IMAGE011
the investment to purchase services for cloud energy storage,
Figure 338635DEST_PATH_IMAGE012
the cost of maintaining local energy storage for the user,
Figure 572914DEST_PATH_IMAGE013
in order to achieve the rate of cash-out,
Figure 129797DEST_PATH_IMAGE014
in order to increase the inflation rate of the cargo,
Figure 68934DEST_PATH_IMAGE015
for the market period of the cloud energy storage transaction,
Figure 454785DEST_PATH_IMAGE016
in order to purchase the revenue obtained from the cloud energy storage service,
Figure 592505DEST_PATH_IMAGE017
in order to delay the benefits of transformer investment by utilizing local energy storage,
Figure 320290DEST_PATH_IMAGE018
in order to delay the income of transformer investment by utilizing cloud energy storage,
Figure 497455DEST_PATH_IMAGE019
for the maintenance cost per unit capacity of local energy storage,
Figure 703309DEST_PATH_IMAGE020
the rated power for the local energy storage is,
Figure 961115DEST_PATH_IMAGE021
the run-time of the stored energy locally,
Figure 312331DEST_PATH_IMAGE022
earnings for local energy storage peak-valley arbitrage,
Figure 288377DEST_PATH_IMAGE023
the reduced demand cost after load peaks is cut for local energy storage,
Figure 32342DEST_PATH_IMAGE024
a recovery factor for local energy storage cost;
the dividing module is configured to divide the cloud energy storage total capacity based on a preset cake cutting game model by taking the optimal cloud energy storage total capacity division as a target function according to a maximization principle of the income of a user after the user purchases the cloud energy storage service, so that a pareto optimal dividing scheme is achieved, the optimal cloud energy storage total capacity division comprises the minimization of the variance between transaction prices, and the expression of the variance between the transaction prices is as follows:
Figure 95720DEST_PATH_IMAGE025
，
in the formula (I), the compound is shown in the specification,
Figure 555520DEST_PATH_IMAGE026
for the trade price variance of the cloud energy storage unit capacity,
Figure 18862DEST_PATH_IMAGE027
a trade price variance for cloud energy storage unit power;
the expression of the pareto optimal division scheme is as follows:
Figure 520513DEST_PATH_IMAGE028
，
in the formula (I), the compound is shown in the specification,
Figure 752911DEST_PATH_IMAGE029
for the utility under the k1 approach,
Figure 993400DEST_PATH_IMAGE030
for the optimal partitioning strategy corresponding to the k1 scheme,
Figure 865409DEST_PATH_IMAGE031
for the partitioning strategy corresponding to the k1 scheme,
Figure 482336DEST_PATH_IMAGE032
for the utility under the k2 approach,
Figure 506923DEST_PATH_IMAGE033
for the optimal partitioning strategy corresponding to the k2 scheme,
Figure 931695DEST_PATH_IMAGE034
for the partitioning strategy corresponding to the k2 scheme,
Figure 838471DEST_PATH_IMAGE035
for the total number of the schemes,
Figure 931192DEST_PATH_IMAGE036
an optimal division scheme is adopted;
the solving module is configured to solve the pareto optimal division scheme based on a variational inequality of a hyperplane projection, so that an optimal solution of cloud energy storage total capacity division is obtained, and an expression of the optimal solution is as follows:
Figure 387450DEST_PATH_IMAGE078
，
in the formula (I), the compound is shown in the specification,
Figure 969741DEST_PATH_IMAGE038
for the set of optimal partitioning strategies for schemes other than the k-scheme,
Figure 567076DEST_PATH_IMAGE039
for the partitioning strategy corresponding to the k scheme,
Figure 276537DEST_PATH_IMAGE040
in order to be an index to the schema,
Figure 10138DEST_PATH_IMAGE035
for the total number of the schemes,
Figure 28909DEST_PATH_IMAGE041
for all partiesThe strategy set of the case is set up,
Figure 159545DEST_PATH_IMAGE042
for the proportion of each of the schemes,
Figure 859648DEST_PATH_IMAGE043
for the total capacity of the energy stored by the cloud,
Figure 775651DEST_PATH_IMAGE044
for the optimal partitioning strategy corresponding to the k scheme,
Figure 385231DEST_PATH_IMAGE045
is as follows
Figure 19475DEST_PATH_IMAGE046
Utility objective function of individual user.
6. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any of claims 1 to 4.
7. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1 to 4.
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